drilling risk management reservoir model validation real-time

SCHLUMBERGER OILFIELD REVIEW

SUMM

ER 1999VOLUM

E 11 NUM

BER 2

Summer 1999

Drilling Risk Management

Reservoir Model Validation

Real-Time, Wellsite Log Corrections

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Any context-setting discussion of the oil and gas industrymust surely begin with commodity prices. Capricious in nature, oil and gas prices are highly variable and determined by forces beyond our control. In these circumstances, attempts to predict future prices arefutile. Our energies are better spent ensuring that bothoperator and service sector businesses remain profitable,even at the bottom of a market cycle.

That’s a tough challenge, particularly when the pursuit of additional reserves takes the industry into uncharted territory. Whether in new geography or new geology, weconstantly encounter conditions that test the limits of existing know-how and technology; therefore, continuing toexplore while preserving precious profit margins demandsongoing improvement in cost and capital efficiency.

A well-known case in point is the deepwater Gulf ofMexico, which I characterize as drilling in 6000 feet ofwater, through 6000 feet of salt and unknown stratigraphyto a depth of 25,000 feet. This must be accomplished withrigs that are either new and untested or else aging andstretched to the limits of their capability. The stakes hereare huge. Conservatively, there are 5 billion barrels ofundeveloped oil-equivalent reserves in Gulf of Mexicodeep water alone, which implies development spending ofaround $15 billion. Of that amount, about 40% is expectedto be spent on drilling. When multiplied by the number of other provinces that are opening up, the potential fordrilling performance improvement swells.

Individual wells in the Gulf of Mexico can easily cost $30 million or more, a number we must reduce. Indeed,realizing a reduction is directly related to improveddrilling operations, which are as much about managingrisk as about achieving “breakthrough” performance. The most damage and highest losses result from bigevents—chance occurrences that are difficult to recoverfrom—precipitated by unexpected hazards encounteredwhile drilling.

Managing risk, like the approach to oil and gas prices, ismostly about recognizing and then managing uncertainty,rather than pretending that we can predict future outcomes. Currently, several factors that govern the management of drilling risk are coming together to createnew standards for delivering efficient, cost-effective wells.

Pore-pressure prediction and management—Today,the approaches to predicting formation pressure are morevaried than in the past. Integrating these new methods withfuture technologies, such as expandable casing and dual-density mud lift, will allow larger holes to be drilled acrossreservoirs and deeper, more efficient setting of casing.

Risk Management Improves Drilling Performance

Data acquisition at and ahead of bits—The technologies of measurements-while-drilling (MWD) are now mature; logging ahead of drilling bits and seismic-while-drilling are being applied and developed further. Acquiring pore pressures while drilling is still tocome, but the strength of constant data updates from just behind the bit and a rapid forecast ahead of the bitare the ways of the future. Even then, having real-timedata is one thing; being able to synthesize the informationquickly enough to do something with it is another.

Drilling simulation—In high-cost wells, mistakes can be debilitating and expensive. It is better to addresspotential hazards beforehand in the office, rather thanwaiting until problems occur on a rig. Although primarilyfor training, drilling simulators can be used for testingalternatives and contingencies by “crashing” a well tomodel catastrophic events for planning purposes.

Subsurface visualization—The simple capacity tohave all the disciplines responsible for well constructionshare a common image of what’s going on in the subsurface cannot be understated. This aspect is moreabout facilitating human interactions than aboutadvanced information technology.

Knowledge capture—Information technology (IT) is still in its infancy. Collecting, analyzing and archivingdata are key IT elements, but understanding what thisinformation tells us is even more important. A big prize—order-of-magnitude performance improvement—rests on integrating real-time data with knowledge systems that suggest what the data mean. For example,“the last time this trend was seen under these circumstances, it meant...”

Of course, the real power lies in integrating all of the above in a holistic confluence of technology andhuman interaction for both the planning and executionphases. We may not be there yet, but it’s not far off. The advances to date are building blocks of an excitingfuture (see “Managing Drilling Risk,” page 2).

Steve PeacockVice President, Gulf of Mexico Deepwater Exploration BP Amoco Houston, Texas, USA

Based in Houston, Texas, USA, Steve Peacock is currently the BP Amoco Vice President of Exploration for the Gulf of Mexico. In his 21 years with BP Amoco, he has had varied assignments around the world. These include serving as Commercial Analyst for the Middle East, Africa, Far East andEurope, and Exploration Manager for West of Shetlands (UK) and also for the Southern North Sea gas basin. Steve received a BA degree in natural sciences from the University of Cambridge in England.

Oilfield Review is published quarterly by Schlumberger to communicatetechnical advances in finding and producing hydrocarbons to oilfieldprofessionals. Oilfield Review is distributed by Schlumberger to itsemployees and clients.

Contributors listed with only geographic location are employees ofSchlumberger or its affiliates.

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Executive EditorDenny O’BrienSenior Production EditorMark E. TeelSenior EditorLisa StewartEditorsRussel C. HertzogGretchen M. GillisDavid E. Bergt

Contributing EditorsRana Rottenberg IllustrationTom McNeffMike MessingerGeorge StewartDesignHerring DesignPrintingWetmore Printing Company, USA

Advisory PanelTerry AdamsAzerbaijan International Operating Co., Baku

Syed A. AliChevron Production Co.New Orleans, Louisiana, USA

Antongiulio AlborghettiAgip S.p.AMilan, Italy

Svend Aage AndersenMaersk Oil Qatar ASDoha, State of Qatar

Michael FetkovichPhillips Petroleum Co.Bartlesville, Oklahoma, USA

George KingAmocoTulsa, Oklahoma

David Patrick MurphyShell E&P CompanyHouston, Texas, USA

Richard WoodhouseIndependent consultantSurrey, England

An asterisk (*) is used to denote a mark of Schlumberger.

Summer 1999Volume 11Number 2

Schlumberger

20 Validating Reservoir Models to Improve Recovery

Operating companies report increases in predicted ultimate recoveryon the order of 10% through data integration, reservoir simulation and proper development strategies. Reservoir models that draw on all available information must honor data with different scales from many sources to validate interpretations, decrease uncertainty andreduce risk. The best models use independent data sets and validationadvances to confirm and constrain multidisciplinary interpretations as much as possible, improving simulation reliability and extractingmore value from hard-won formation evaluation and production data.

2 Managing Drilling Risk

A new initiative to enhance drilling performance significantly reduces operational costs and nonproductive time. Through close cooperationbetween operator and service company, this approach helps provide planning and real-time drilling solutions that avoid stuck pipe, reduce drillstring failures, optimize drilling efficiency, and monitor as well ascontrol borehole stability. This article shows how a risk-management and loss-control framework is used to combine operator knowledge andexperience with the technical expertise and drilling measurements provided by Schlumberger to develop surprise-free drilling procedures.

58 Contributors

60 New Books

62 Coming in Oilfield Review

Oilfield Review Services and MORA Order Form (inside back cover)

Oilfield Review

1

36 Real-Time Openhole Evaluation

A new generation of wireline technology is revolutionizing fundamental formation evaluation. In this article, we unravel the mysteries of using real-time environmental corrections and inversions based on forward models to provide high-quality log measurements and interpretations at the wellsite. Field examples illustrate recent innovations such as the HRLA* High-Resolution Laterolog Array tool, which is designed to provide more accurate resistivity measurements in saline borehole environments, and post-logging processing techniques to interpret logsobtained under the most extreme subsurface environmental conditions.

2 Oilfield Review

Walt AldredDick PlumbSugar Land, Texas, USA

Ian BradfordJohn CookVidhya GholkarCambridge, England

Liam CousinsReginald MintonBP Amoco plcAberdeen, Scotland

John FullerGatwick, England

Shuja GorayaCabinda, Angola

Dean TuckerAberdeen, Scotland

For help in preparation of this article, thanks to LaurenceCahuzac and Chin Yuin Hui, Sedco Forex, Montrouge,France; Richard Carossino and Dave Ede, Anadrill, Aberdeen,Scotland; Charles Cosad, Camco, Houston, Texas, USA;Edward Habgood, GeoQuest, Gatwick, England; and WilliamStandifird, Anadrill, Youngsville, Louisiana, USA.APWD (Annular Pressure While Drilling), ARC5 (ArrayResistivity Compensated), DrilCast, DrilMap, DrilTrack,IDEAL (Integrated Drilling Evaluation and Logging), MDT(Modular Formation Dynamics Tester), PowerPak, RFT(Repeat Formation Tester), Schlumberger PERFORM andSPIN Doctor are marks of Schlumberger.

Oil and gas companies spend about $20 billionannually on drilling. Unfortunately, not all of thatmoney is well spent. A significant portion, around15%, is attributed to losses. These include loss ofmaterial, such as drilling equipment and fluids,and loss of drilling process continuity, called non-productive time (NPT). These losses are incurredwhile searching for and implementing remediesto drilling problems. Avoiding drilling problemscuts finding and development costs and allowsbillions of dollars now spent on losses to be betterspent—building and replacing reserves.

No well is drilled without problems. Managingdrilling risk means not letting small problemsbecome big ones. Knowing what the risks are andwhen they are likely to occur keeps surprises to aminimum. Most of the time spent drilling, andmost of the cost, is encountered not in the reser-voir, but in getting to it.

Numerous problems taunt the driller, andsolutions may be expensive if not impossible insome cases (above and next page). Drillpipe canbecome stuck against the borehole wall by dif-ferential pressures or lodged in borehole irregu-larities, requiring skill and force to free it.1 Whenthis fails, sometimes the only solution is to aban-don the stuck portion and drill a sidetrack aroundit, changing the drilling program completely and

Everyone loves a surprise. Everyone, that is, except a driller. Avoiding drilling

surprises means more than being prepared for problems when they occur; it means

averting them in the first place. New risk management tools help foretell well

behavior with enough advance notice to allow drilling teams to calmly make

technically sound operational decisions that lead to optimal drilling performance.

Managing Drilling Risk

Unconsolidated ZoneGeopressure Undergauge Hole Key SeatingDifferential Sticking Fractured or Faulted Zone

1. Bailey L, Jones T, Belaskie J, Houwen O, Jardine S,McCann D, Orban J and Sheppard M: “Causes, Detec-tion and Prevention,” Oilfield Review 3, no. 4 (October1991): 13-26.Adelung D, Askew W, Bernardini J, Campbell AT Jr.,Chaffin M, Congras G, Hensley R, Kirton B, Reese R andSparling D: “Techniques for Breaking Free,” OilfieldReview 3, no. 4 (October 1991): 27-35.Cline M, Granger G, Hache J-M and Lands J: “BackoffBasics,” Oilfield Review 3, no. 4 (October 1991): 48-51.

2. Addis T, Last N, Boulter D, Roca-Ramisa L and Plumb D: “The Quest for Borehole Stability in theCusiana Field, Colombia,” Oilfield Review 5, no. 2/3(April/July 1993): 33-43.

Summer 1999 3

potentially adding millions of dollars to the wellcost. Drilling at a high rate of penetration cansave time and money, but when accompanied bytoo low a drillstring rotation rate or mud flow ratethat fails to lift rock cuttings to surface, the resultis stuck pipe. Faults and fractures that the well-bore encounters open conduits for loss of drillingfluid to the formation.2 Excessively high mudpressure can fracture the formation and causelost circulation. Too low, and the mud pressurefails to keep high-pressure formations under con-trol, resulting in gas kicks or worse, blowouts.Drillstring vibrations can weaken and destroypipe and equipment as well as seriously damagethe wellbore. And some of these problems, evenif they don’t completely suspend the drilling pro-cess, jeopardize subsequent logging, completionand production.

Making drilling decisions to correct theseproblems is a complex process because manyfactors have to be considered. For example,increasing mud weight to control wellbore sta-bility in one interval in the well may causefracturing elsewhere. Solutions are often well- orfield-specific.

Successful drilling hinges on developing asound plan, continually updating it in light of newinformation and keeping the involved personnelinformed on a timely basis. The plan must includeprocedures to follow under normal circumstancesand methods for dealing with the most likely andmost severe problems that may be encountered.With the proper training, a well-defined drillingprocess, sufficient data and tools for interpre-tation, successfully drilling a well should be aroutine process.

>Common drilling problems.

Reactive Formation Drillstring VibrationCollapsed Casing Junk Cement-RelatedMobile Formation

Wellbore Geometry Poor Hole Cleaning

BackgroundDuring the last twenty years, the industry hascelebrated innovations in drilling practices fromthe introduction of measurements-while-drilling(MWD) and steerable motors to computerizedrigsite displays and high-resolution while-drillinglogs (above). In the early 1990s, different operatorand service companies applied the power ofmaturing while-drilling measurements to adoptnew methods of stuck-pipe avoidance and otherdrilling training programs.3 Why, ten years later,

do operating companies acknowledge that thedrilling process still needs to improve? The phys-ical forces acting on the borehole haven’tchanged. What has happened?

Two things have changed. First, explorationand production (E&P) companies have alteredtheir internal structures and reduced their workforces. Many senior, experienced hands have leftthe industry. Companies are operating with abare minimum of personnel. Experienced peoplewho remain may be specialized, and hence notsuited for the integrative role required.

Second, wells are becoming more complex.Extended-reach and horizontal wells react differ-ently to earth stresses than do vertical or low-angle wells. Drilling multilateral wells requiresextraordinary accuracy and control. Deepwaterand high-pressure, high-temperature wells offeradditional challenges. Wells are being drilled intectonically active and remote areas where theinfrastructure may be less well developed andcommunication problematic.

4 Oilfield Review

Advances in Drilling Technology

Telecointroduced

simple MWD

Baker Hughesintroduced

integrated servicesbit + motor + MWD

MultisensorMWD 1

introduced

SchlumbergerintroducedARC5 tool

First sonic toolintroduced

Schlumberger introducedIDEAL system withinstrumented motor

Sperry-Sunintroduced

LWD 2 MHz

PowerPakmotors introduced

Eastman & Smithintroduced

steerable motors

Spinning chainreplaced bypipe spinner

Offshore high-pressurehigh-temperature

(HPHT) drilling Topdrive

Partially mechanizedpipe handling

Step changein QHSE

Completely mechanized

pipe-handlingDual/Tri-Act

derrick

78 7977767574737271 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99High-rate

mud telemetry Rotarysystems

> Time line from 1971 to 1999 showing recent advances in drilling technology.

Earth Model Well Plan

Revise Interpret Detect

Asset OfficeOnshore Drilling Team

Rigsite Schlumberger PERFORM Engineer

Drill

> Integrated drilling process. The phases of a drilling project require joint effort by the asset office and the rig, and encompassconstruction of the earth model and well plan, the actual drilling, detection and interpretation of information obtained whiledrilling, and ultimately, revision of the model.

Summer 1999 5

engineer and geologist balance the requirementsof target location, cost and drillability. Manymore factors must be incorporated into a com-plete well plan. These include casing design,completion requirements, life-of-field issues, rigsize and selection, personnel considerations,costs, cement design, liners, drillstring and BHAdesign, and availability of equipment.

The best drilling plan optimizes well locationand trajectory, but also minimizes the risk ofwellbore instability and stuck pipe, improves wellproductivity and accelerates the drilling learningprocess. The plan should flag intervals in whichgeologic risks such as pore pressure, fracturepressure and other wellbore instabilities canthreaten wellbore integrity. To achieve this, theplan must be evaluated to identify all risks beforeany action takes place.

On the rig, the well is drilled according to thedrilling plan. During drilling, information is col-lected, interpreted and fed back to the drilling pro-cess, to the well plan, or to the earth model itself.Through modification and updating, the well planbecomes a living document rather than a staticone. Drilling risks are also continually reevaluated.The process is valid for wells drilled throughout

the life of a field, but at its core remain the threeprincipal phases that govern the very existence ofa well: developing the proper plan, executing it,and learning from the ongoing process.

The earth model can be simple or complex,depending on the information available and therequirements of the well. Creating a complexearth model can require dozens of input and dataintegration steps. In short, every pertinent datasource is used, from drilling reports, logs andtests in offset wells to seismic sections, velocitycubes and structural interpretations (above).

Mechanical Earth Model

Well Plan and Performance Prognosis

FaultsFormation tops

Elasticparameters

Rock strengthprofile

Pore pressureprofile

Stressdirection

Stress profilesSv, Sh, SH

2D cross sections3D velocity cubes

Structural interpretationSeismic attribute maps

Time and depth relations

Seismic Data Drilling Data

Exploration well reportsDirectional surveys

Bit recordsTime-depth curves

Mud weightsMud logs, drilling fluids

NPT—kicks, losses, stuck pipeCorrelation with geology

Log Data

Deep resistivityGamma ray

Oriented multi-arm calipersSonic P,S

Bulk densityBorehole images

Calibration Data

Microfrac, XLOTKicks and losses

Gas and flow checksCavingsCores

RFT and MDT pressure

> A partial list of the types of data that contribute to a complex mechanical earth model.

3. Bradley WB, Jarman D, Auflick RA, Plott RS, Wood RD,Schofield TR and Cocking D: “Task Force ReducedStuck-Pipe Costs,” Oil & Gas Journal 89, no. 21 (May 27,1991): 84, 86, 88-89.Nordt DP and Stone MS: “Professional Development ofNew Rig Supervisors a Must,” Oil & Gas Journal 90, no. 43 (October 26, 1992): 77-80, 83-84.

A New ApproachTo drill successfully amid these changes andchallenges requires a new approach to thedrilling process. In recent years, oil companiesand service companies have developed morecooperative relationships that make it easier forboth to achieve their objectives. The way ofdoing business together has evolved from one ofmanaged opposition to one of aligned objectives,with oil and service companies cooperating toface the uncertainty and risks of the subsurface.

The approach taken by the Schlumberger com-panies to provide technical and decision supportto operators has reduced drilling costs by as muchas 50% in a wide variety of drilling environments.The complete process integrates the efforts of oilcompany and service company personnel at theoffice and on the rig, during all stages of wellplanning and drilling and through every phase ofa drilling project (previous page, bottom).

Simply put, the process begins in the officewith construction of an earth model. The model isthen used as part of the well planning process tocreate the best drilling plan. This is a multidisci-plinary optimization process in which the drilling

6

0.9 0.95 1.0Drilling difficulty

0 1020

3040

50

60

70

80

90

100

110

120

130

140150

160170180

N

E

S

W

Stress0 MPa 200 W N

Stress direction Sh

Sh SH SVPp

Fault?

Regionaltrend

Grainsupport

facies

Claysupport

facies

Elastic

Stratigraphy

Strength Earth Stress and Pore Pressure

Poisson’sratio

Young’smodulus

kPa

Friction angle

E

1.0

10 20 400

0 100 0 70

Structure and Stratigraphy0

Unconfinedcompressive strength

> Earth model example. The earth model houses all information on rock properties and behavior and is usedduring all phases of the life of the well, including trajectory and wellbore stability planning, bit and rate ofpenetration (ROP) selection, pore-pressure prediction, casing design, sand control and reservoir stimulation.

Oilfield Review

> Which way to drill in a South Americanfield. With rock mechanics data such asexpected stress state, pore pressure androck failure parameters from a variety ofsources, a drilling risk profile can be plotted.Red signifies risky, difficult drilling and blue is less risky and easier. The numbersaround the arc represent azimuth; travelingalong a radius is the same as taking a pathof constant azimuth. Distance from the center depicts inclination from vertical. Thecenter of the circle represents a verticalwellbore, and the outer edge represents allpossible horizontal wellbores. This plotindicates that it is easier to drill a horizontalwell than a vertical well given the particularstress state.

7

Diagnose

Develop plan

DrilMap

DrilCast

DrilTrak

Schlumberger PERFORM Workflow

No Yes

Yes

Loss No loss

Summary and detailedrisk report

24-hr activity forecastRoles and responsibilities

To drilling team

Prepare risk assessmentfor each hole section

Develop forward planand contingencies for each

hole section with drilling team

Prepare daily risk assessmentfor next 24 hr

Develop forward planand contingencies for next

24 hr with drilling team

Drilling and geologicalprogram roles and

responsibilities

ObservationsData collectionInterpretation

Analysis

Compliancewith plan?

Contingencies?

Current rigearth state

Report tocompany

representative

Eventreport

Near-missreport

No

Diagnose

Develop plan

> The Schlumberger PERFORM workflow.Responsibilities extend from risk assessment and contingency planning to data collection andanalysis, then to reporting, well plan updatingand activity forecasting. The colors in the upperleft key refer to display, reporting or analysistools described in subsequent figures.

(A full treatment of the rock mechanics involvedis beyond the scope of this article.4) The result-ing mechanical earth model consists of forma-tion tops, faults, elastic parameters, stressdirections and variations with depth, and rockstrength and pore-pressure profiles (previouspage, top).

Once a target has been selected, it can bereached from many directions. Selecting the pathwith the least risk requires an understanding ofthe stress state and the rock parameters, andhow the drilling process will interact with them.An example of the information that can beextracted from an accurate mechanical earthmodel comes from a South American field. Forthis field, a risk profile was created that color-coded the difficulty with which particular trajec-

tories could be drilled (previous page, bottom).Drilling a horizontal well at a 90° azimuth waspredicted to be the least risky: wells at otherinclinations and azimuths would be prone toborehole collapse.

The best plan according to any earth modelmust be reconciled with trajectory goals of thatwell to optimize the process as a whole. Forexample, in one well, the preferred trajectory mayhave a 62°-inclination in one section, but hydrau-lics analysis may indicate that hole-cleaningproblems at this inclination could endanger wellintegrity. Two or more sections drilled at saferangles, though seemingly more time-consuming,could optimize the overall drilling process.

Once the best plan has been formulated, fol-lowing it through at the rig can be a surprisinglychallenging feat. To accomplish this, thePerformance through Risk Management effort, orSchlumberger PERFORM initiative for short, hasbeen launched within Anadrill. SchlumbergerPERFORM efforts have already reduced NPT byas much as 40%, saving as much as $300,000 perwell. The concept is simple and most of the stepsare almost intuitive, but a structured approach isrequired for success. The approach comprises aworkflow, software tools and engineer to ensurethat the technical solutions derived in the plan-ning stage become operationally effective solu-tions to aid decisions that help avoid drillingproblems (above).

Summer 1999

4. For more: Fjaer E, Holt R, Horsrud P, Raaen A and RisnesR: Petroleum Related Rock Mechanics. New York, NewYork, USA: Elsevier Science Publishing Company, 1992. Alsen J, Charlez P, Harkness R, Last N, McLean M andPlumb R: “An Integrated Approach to Evaluating andManaging Wellbore Instability in the Cusiana FieldColombia, S. America,” paper SPE 30464, presented atthe SPE Annual Technical Conference and Exhibition,Dallas, Texas, USA, October 22-25, 1995.

The goal of the Schlumberger PERFORM engi-neer is to work with operators to significantlyreduce cost and nonproductive time through inte-gration of planning and real-time drilling solutions.A risk-management and loss-control frameworkcombines Schlumberger technical expertise andmeasurements with operator knowledge andexperience to develop operational solutions.Communications and teamwork are essential inimplementing these solutions.

The process concentrates on the follow-ing areas:• wellbore stability and fluid loss• pore-pressure analysis• stuck pipe and pipe lost in hole• drillstring failure prevention• drilling efficiency, rate of penetration

and bit optimization.

Because each well can host a distinct set ofthese problems, a specially trained engineer isassigned to each job. The quality of the person-nel can make or break the process. As generalqualifications, the engineer must have good prob-lem-solving, data-integration and communicationskills, a solid technical background in petroleumor drilling engineering, ample seniority and expe-rience with operator organizations. Technicaltraining includes Schlumberger courses ondrilling mechanics, wellbore stability, pore-pres-sure analysis, bit performance and drilling fluids.Operational problem-solving techniques andcommunication skills are sharpened throughproblem-simulation exercises. Additional trainingincludes industry-standard courses in stuck-pipeprevention and well control.

In the planning stage of a drilling project, theSchlumberger PERFORM engineer works with theoperator staff to identify potential hazards,develops methods for detecting them, and finallywith the drilling team, formulates contingenciesto complete the drilling plan. The engineer deliversa DrilMap display that links well geometry, geo-logical and hazard information with contingencyplans to form a complete process map for thewell (above).

During drilling, the engineer evaluates thewell condition to identify any new hazards thatmay have developed and at every tour providesan updated risk assessment and 24-hr forecast(next page). The DrilCast report enumerates theconditions and potential hazards ahead andexplains how to detect and manage them.Detailed planning before a potential hazard isencountered and accurate identification of thehazards reduce the risks of losses and signifi-cantly improve performance.

8 Oilfield Review

500PP

DrilBase

1000

1500

2000

2500

3000

3500

4000

4500

5000

Dept

h, ft

Time

Stuck pipe at 1100 ft

BHA packoff at 1700 ft

50-bbl kick at 3200 ft

DrilMap

N

8 lbm/gal 14 lbm/gal

Hole collapseat 3700 ft

Pipe stuckat 4700 ft

> Mapping out the drilling plan. The DrilMap screen displays the planned well trajectory, expectedpore pressures (PP), and two drilling time-versus-depth curves—one optimal (blue) and the othertaking into account potential hazards (red). Hazards are identified with specific depths and tied tothe DrilBase database containing previous drilling and near-miss reports and contingency plans.

(continued on page 11)

Summer 1999 9

DrilCast

DrilCast Summary Report

13 lbm/gal 14 lbm/gal

6:00 am 2/3/99

11,000 ft

11,500 ft

6:00 pm 2/3/99

PP

DrilBase

> Forecasting drilling activity. The DrilCast display is a graphicaldaily report of what should be observed and what might beencountered in the next 24 hours. Each hazard is linked with amethod for its detection and a contingency plan for mitigatingactions. A summary report is distributed to the drilling team atthe morning meeting. Detailed reports, including roles andresponsibilities, are given to each drilling team member.

10 Oilfield Review

ClientWellSection

Date

IPM

Deepwater location.

14 3/4 X 17 1/2 Drilling Assembly

2/24/99 6:00

Client Representative

Perform Engineer

Randall Anderson

William B. Standifird

For Time Period... 2/23/99 6:00 2/24/99 6:00To24-Hour Summary

Start Time2/23/99 6:002/23/99 7:302/23/99 15:30

Rig Operation

Time Period24 hr

24 hr

When? What?

2/24/99 6:00 Drilled sand lobe at 7228 ft

Under-reamingShort trip

Drilling ahead

Variable Noteworthy Behavior

Comments

MWD SHOCKS

WOB,TQA,ECD,SPP,TFLOW,TRPM

Transverse shocks increase while reaming sands.

ECD and TQA spiking when annulus loads above under-reamer.

Under-reamed to 7038 ftHole stable, 500u B/U gas

Drilling new formation

How? Why?

24-Hour Forecast For Time Period... To2/24/99 6:00 2/25/99 6:00

This is a tough section. Depleted zone at 7375 ft is next major hazard.

1

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RIG OPERATIONS

TRENDS

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PROPOSED HAZARD PREVENTION ACTION

Operation When? Possible HazardsDrilling

Drilling

DrillingBack-reaming, U/RShort TripBack-reaming

Cutting sands

Cutting sands

Pumping

Pulling up

First depleted sand at 7375 ft.Stuck pipe, lost circulation.MWD shock high when U/R hits sands.MWD shock > 22 can damage BHA quickly.ECD will spike as U/R packs off.Stuck-pipe situation.Swab formation into wellbore.Gas or fluid entering wellbore.

High

High

High

Med

High

High

High

High

Severity Probability

Identify sand locations and verify stability. 7215, 7375, 7565 and 7745. Use offset e-logs/mud logs

Monitor MWD shocks on Anadrill display.

Monitor ECD closely. Spikes are rapid and must be addressed quickly.

Monitor trip speeds (swab-surge), gain/loss and gas.

Please contact the Schlumberger PERFORM Engineer if there are any questions or transmission errors: Call Ext. 158 (rig) 3460 (town)

PERFORM Daily Report

3 Consider picking up and back-reaming until ECD stabilized. First move is in opposite direction of resistance.

4 Back-ream or pump out of hole. Circulate gas out of hole if encountered. Work tight spots and keeppulling speeds minimal.

2 Rotate during connections.Notify PERFORM Engineer. May need to adjust RPM/WOB to control vibrations and avoid BHA damage.

1 Prepare LCM and other LC systems. Keep the pipe moving. Survey at 7200, 7350 and 7800. DO NOT surveyif formation is unstable. Stuck pipe is more expensive than a GYRO in casing.Torque and Slump differential sticking or coming off slips.is first action to

> Daily report of past and future drilling activity.

Summer 1999 11

In this example from a deepwater well coordi-nated by the Schlumberger Integrated ProjectManagement (IPM) group, the daily reportincludes a summary of rig operations, trends andevents of the past 24 hours along with the fore-cast for the next day (previous page). The look-ahead portion lists four possible hazards that maybe encountered in the upcoming hole section. Thesection is ranked as a tough one, with a depletedzone ahead posing the next major hazard. Thehazards are identified according to several fac-tors: the operation (drilling, back-reaming ortripping), and the specific procedure (cutting

sands, under-reaming or pumping), underwaywhen the hazard is met; the type of hazard and its consequences; the severity; and the prob-ability. Methods for detecting each hazard arelisted, as are actions to prevent an event fromcausing loss.

A member of the drilling team monitors wellconditions continuously to determine if the wellis behaving as planned (above). If the well is notproceeding as expected, the appropriate contin-gency is identified. The driller can then follow theplan for that contingency. If none of the plannedcontingencies is appropriate, the problem is ana-lyzed, and a new action plan is developed withthe drilling team.

Suites of data evaluation and problem diagno-sis tools have been developed to support thesedrilling displays. Diagnostic tools, such as theSPIN Doctor stuck drillstring prevention software,zero in on the most probable cause for each prob-lem by asking the user a series of questions. TheSPIN Doctor application also contains links toelectronic documents such as the SchlumbergerStuck Pipe Handbook for more in-depth investi-gation into unforeseen problems, and can becustom-hyperlinked to any desired electronicresource, including proprietary drilling processmanuals and help files (next page).

N

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h, ft

GO

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Hole collapseat 3700 ft

GO

GO

lbm/gal

> Tracking drilling progress. The DrilTrak plot updates the drilling map while drilling. Changes in the trajectory are recorded along with the response of the well and the effectiveness of the drilling plan. Hazards that were avoided with no material or process loss are reported as near misses (green arrows).Losses are reported as events (red arrows).

In addition to connecting to analysis anddiagnostic programs, the DrilTrak system incor-porates drilling alarms that analyze while-drillingmeasurements in real time to alert drillers tosevere problems. These alarms warn of highfriction factors, bearing failures, low drilling effi-ciency and bit performance, washouts and kicks.

Understanding RiskThe Schlumberger PERFORM approach is built on a foundation of risk management and loss-control methodologies. Controlling loss requiresan understanding of event causation, or the act or process of causing events, problems oraccidents that lead to loss. A model of eventcausation catalogs the stages in the evolution of an event from its original controlled state(next page). In the earliest stage preceding anevent, inadequacies in the system, or in stan-dards or compliance generate the potential foran event. In the case of drilling, the system is thebasis of design for the well; the standard is thedrilling program; and compliance is making surethe well is behaving as anticipated. Underlyingproblems that can be traced back to this firststage might be inappropriate casing or drillingfluids design or a drilling rig unsuitable for theparticular drilling program. In and of themselves,these do not cause a drilling mishap, but trying toadjust daily drilling activities to work aroundthese fundamental flaws requires human energythat could be better spent following the drillingroutine. This first stage is the one in which thelongest decision time—months in most cases—is available to avert a problem, and the mostbrain-power, in terms of numbers of highlytrained personnel, can focus effort on a solution.

In the second stage, basic causes of an eventcan be attributed to personal factors and job orsystem factors. Examples in drilling could beinferior or insufficient training, delaying a bitchange in anticipation of the end of the workshift or not putting a cover on the hole when thedrillstring is pulled out. Taken individually oreven together, these factors do not cause a prob-lem, but may allow problems to develop. Actionsat this stage typically are based on decisionsmade days to hours before an event, by one per-son or a few on the rig.

The third stage describes immediate causesof an event, such as substandard conditions,practices or acts—letting equipment fall intodisrepair, accidentally dropping a small hand tooldown the hole, or improperly interpreting a mea-surement. Decisions—not to report the faultyhardware or lost screwdriver or not to mentionwhat the shale shaker is accumulating—aremade days to minutes before the event, by some-one on the rig, often acting under stress.

12 Oilfield Review

Definitely no

Definitely yes

Probably yes

Possibly yes

Indeterminate

Possibly no

Probably no

SPIN DOCTOR

Restart Back Next Notes Produce log Help


Differential StickingPoor Hole Cleaning

Unconsolidated FormationsFractured/Faulted Formations

Junk in HoleCement Blocks

Reactive FormationsGeopressured Formations

Formation LedgesKey Seating

Wellbore GeometryUndergauge Hole

Mobile FormationsCollapsed Casing

String Component FailureBit Failure

















DIAGNOSIS

DIAGNOSIS

DIAGNOSIS

Welcome to

Stuck drillstring prevention software.

Version 3.4

Click on a diagnosis name for a handbook date.

To proceed, is this:

a real stuck pipe incident? just a training exercise?

Overpull in New Hole

Is the overpull in the new hole section?

Is circulation restricted?

Restricted Circulation

Schlumberger Drillers Stuck Pipe Handbook

SPIN DOCTOR


SPIN DOCTOR

Definitely yes

Probably yes

Possibly yes

Indeterminate

Possibly no

Probably no

Definitely no



> Three panels from the SPIN Doctor stuck drillstring prevention software. As the user answers ques-tions about a drilling problem and the accompanying well conditions and drilling activity, the systemrules out some mechanisms and highlights increased probability for others. In this case, the final diag-nosis is poor hole cleaning.

Summer 1999 13

In the fourth stage, the event, or incident,occurs. Drillpipe gets stuck or the well takes akick. There may be only minutes to make the rightdecision. The person making the decision thatmight free the pipe or prevent disaster is actingunder tremendous stress, and so with reducedability. Experts in the management of crises, suchas wars and natural disasters, report that undercomparable levels of stress, decision-makers uti-lize only one-fourth of the information available.

The final stage, the actual loss, results inunintended loss or damage to property and thedrilling process. The bottomhole assembly (BHA)and a section of drillpipe are lost in the hole, or akick advances to a situation that can be con-trolled only by killing the well. Afterwards, theincident is finally reported.

These risk management and cause analysisconcepts have their origins in health, safety andenvironment (HSE) awareness initiatives. Mostcompanies in the E&P industry have comprehen-sive, effective HSE awareness and training pro-grams. Maintaining an active training program isrecognized as being as important as any otheraspect of doing business. HSE training programsare based on the understanding that most inci-dents that lead to loss are caused by human error,error that could be prevented with proper care.

In the E&P industry, operators have examinedoccurrences of drilling problems and report thatmost unscheduled events can be attributed tohuman error. In one published report, 65% ofstuck-pipe incidents could be directly related toinadequate planning; 68% of incidents occurredwithin two hours of a tour change.5

Most of the techniques used in HSE trainingcourses are designed to combat human nature—to slow down speedy driving, do away with lazywaste-disposal habits or avoid distraction duringmachine operation. Managers understand theneed for constant vigilance and annual retrain-ing, and employees are required to keep theirtraining records up to date. Near-miss reportinghelps employees become more aware of situa-tions and conditions that could lead to accidents.

The same elements make an effectiveapproach to dealing with drilling incidents, andseveral of these have been incorporated into thisnew strategy for drilling. Better communication inthe form of near-miss reporting, documentation ofprocess compliance, increased awareness of teamgoals and understanding of the technical reason-ing behind contingency actions is the most impor-tant factor in applying these risk management andcause analysis methods to drilling operations.

Near-miss reporting is considered standardHSE practice for successfully reducing the fre-quency of workplace errors and accidents, butbefore the introduction of the SchlumbergerPERFORM methodology, it had not been appliedto drilling. In the past, when a well was com-pleted on schedule without major problems,everyone involved congratulated each other on ajob well done, but little thought was given toanalyzing the process that produced the success-ful result. The well may have been drilled withoutmajor problems, but it almost certainly was notdrilled without any problems at all. That itappeared to be so was because each of the smalldifficulties encountered along the way had beendealt with successfully. The story behind each ofthe forgotten small problems and its solution isthe secret of the well’s success.

Identifying drilling predicaments and report-ing them as soon as possible increases the likeli-hood that a small problem will be recognized andsolved at an early causation stage, before itbecomes unmanageable. Documenting the stepstaken to solve the problem produces two addi-tional benefits: the first is a report of the drillinghistory, complete with a record of how personnelresponded to problems. This record shows howsuccessfully workers comply with procedures.The second is an archive of problems and solu-tions that can be tapped in the future, whether indeeper sections of the same hole, or in otherwells or other fields.

Making known to all rig personnel the techni-cal reasons behind contingency actions isanother area in which good communication playsan important role. As in most situations influ-enced by habit, the easiest thing for a driller todo is what’s been done before. But if, when thetime comes, it’s important to do something dif-ferent, the driller is much more likely to react cor-rectly if the reason is understood. The case studyin the next section demonstrates how communi-cation, risk analysis, proper measurements and ateam approach help drill wells where successpreviously had been elusive.

Controlling InstabilityExperts estimate that wellbore instability coststhe industry more than $1 billion per year. Theindustry average cost of nonproductive time—often due to wellbore instability—works out toabout $1.5 million per well, and in extreme casescan reach $16 million for a single well.

Wellbore instability occurs when earthforces or interactions between the formationand the drilling fluid act to squeeze, stretch,constrict or otherwise deform the borehole.Consequences of wellbore instability are stuckpipe and BHAs, excessive trip and reaming time,mud losses, fishing or loss of equipment, side-tracks, inability to land casing, and poor loggingand cementing conditions.

Drilling plans include stability studies basedon information from neighboring wells so thatoptimal drilling trajectories, mud programs anddrilling practices can be established in advance.However, the earth doesn’t always behave aspredicted and sometimes the forces act contraryto expectations.

Wellbore instability often can be managed ifit can be detected in time. Control mechanismsinclude changing mud chemistry, mud weight andflow rate to exert more or less pressure on theformation or changing rate of penetration (ROP)or drillstring revolutions per minute (rpm) to facil-itate hole cleaning.

In an effort to develop a capability for real-time detection and control of wellborestability—while the well is being drilled—apartnership was formed in 1996 between Amoco,The Netherlands Institute of Applied Geoscience,GeoQuest and Schlumberger Cambridge Research,England. Partial funding was supplied by theEuropean Union THERMIE program.

5. Watson B and Smith R: “Training Reduces Stuck PipeCosts and Incidents,” Oil & Gas Journal, 92, no. 38(September 19, 1994): 44-47.

Causation Model

Thresholdlimit

Control

Inadequate

Basiccauses

Personalfactors

Job or systemfactors

Immediatecauses

Substandardacts or

practices

Substandardconditions

Incident

Event

Loss

Unintendedharm ordamage

•System•Standard•Compliance

>Evolution of an event. Inadequacies in the basic system, personal factors andsubstandard practice all contribute in more or less identifiable ways to a drillingproblem. Most events are not even reported until past the critical state of loss.

The methodology was tested in the Valhallfield, a major chalk reservoir discovered in 1975and operated currently by BP Amoco Norge, withpartners Elf, Amerada Hess and Enterprise. Thefield contains 600 million bbl [95 million m3] oilreserves, with a centralized production complexin 70 m [230 ft] of water. Reservoir depth is 2500 m [8200 ft]. Overall development objectivesare to increase the value of Valhall assets to 1 billion barrels [160 million m3], partly throughextended-reach drilling into downflank reserves.

Earlier drilling problems on Valhall werenumerous and typically included packoffs andstuck pipe, tools lost in hole, mud losses, side-tracking and inability to land casing or drill out ofcasing. As a consequence, there is a high riskthat wells will be suspended or abandonedbefore reaching the target.

The field test of the methodology, which wasdeveloped at Schlumberger Cambridge Research,called for an integrated approach to wellboreinstability control. The design stage compriseddata gathering, mechanical earth modelconstruction, well stability strategizing and for-mulation of a drilling plan. Execution includeddrilling monitoring, data acquisition and instabil-ity detection. Evaluation consisted of interpreta-tion of observations, updating the model andrecommending future actions.

In the planning phase for Well 2/8-A3C, amechanical earth model was generated thatdescribed the state of stress, rock properties andfailure mechanisms active in this region of theValhall structure. A mud-weight window was cal-culated taking into account traditional wellboreinstabilities, and other problems such as fracturezones—existing natural fractures—were identi-fied. A problem interval at 4000 m [13,100 ft]measured depth in the 121⁄4-in. hole section wasflagged as a zone where fracture zones couldbecome destabilized (above left).6

Depending on depth, the calculated mud-weight window is either extremely narrow ornonexistent (left). Instability was inevitable. Attoo high an effective mud weight, the fracturezone would be driven beyond its precariousbalance and cause irreparable borehole collapse.But for any mud weight below the fracturepressure, breakouts would occur. The solution,therefore, was based on recognition of theinevitability of formation failure. The only way to drill the well was to let the instability occur,then manage it. Breakout problems would becontrolled by good hole cleaning. Fracture zones, however, are uncontrollable, and must bekept stable.

14 Oilfield Review

> Valhall location (right) and mud-weight window (left).The pore pressure and minimum horizontal stress curvesare taken from the mechanical earth model. The breakoutcurve (red) is calculated as the mud weight needed toensure that none of the rock around the hole will bestressed beyond failure. Mud weight needs to lie betweenthe breakout curve and the horizontal stress curve (blue).In some sections of the hole, this is not possible.

> Problem section predicted in Valhall trajectory. Borehole inclination, earth stressesand formation characteristics combine to make this inclined section of the boreholeprone to cavings that could lead to stuck pipe if not properly managed.

0

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04000 3000 2000 1000 0 -1000 -2000

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Breakouts

10

Eldfisk

Valhall

Hod

Norwegian sector

Danish sector

UK sector

NORWAY

DENMARK

Stavanger

Bergen

km0

miles0

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124

miles

0 km

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Summer 1999 15

Typically, drilling in the Valhall Tertiary stratastarted with a mud weight of 14.3 lbm/gal [1.71 g/cm3]. As drilling proceeded and cavings,caused by shear failure of the wellbore wall,were observed, the mud weight would beincreased steadily, often exceeding 16 lbm/gal[1.92 g/cm3]. This caused problems in the lowersection, as it produced wellbore pressures abovethe fracture gradient. Mud was lost, and largeamounts of blocky cavings were produced fromthe naturally fractured zones, resulting in pack-offs. The new strategy proposed that drillingshould begin with mud at 14.2 lbm/gal [1.7 g/cm3], barely lower than usual, but that thisvalue should not be increased unless absolutelynecessary in response to gas, positive flowchecks or other signs of overpressure. If cavingswere produced by shear failure, they would beremoved by good hole-cleaning practices ratherthan be suppressed by higher mud weight.

The equivalent circulating density (ECD)would be kept lower than the minimum hori-zontal stress—15.3 lbm/gal [1.83 g/cm3] in theproblem zones, except in extreme circumstances.ECD is the effective mud weight that generatesthe downhole pressure observed while pumping,and is generally greater than the mud weightmeasured at surface because of frictional pres-sure drop in the annulus and cuttings loading inthe mud. In earlier Valhall wells, ECD wasallowed to exceed 15.3 lbm/gal, with consequentloss of mud to the fractures in the formation—an expensive problem, but not one previouslyregarded as threatening to wellbore integrity.

This new drilling strategy made the explicitassumption that cavings produced by shear fail-ure stemming from low mud weight would occurin quantities controllable by hole cleaning, butthat cavings produced by mud invasion and stim-ulation of fracture zones would be uncontrol-lable. It was clearly important to know whethermud invasion was occurring, and so a further partof the strategy was to monitor mud volume forlosses, and also monitor cavings at surface toidentify their source. This would be done by clas-sifying their shapes; shear-induced cavings frombreakouts are angular, those from fracture zonesare tabular and parallel-sided (above right).

If, in spite of the low mud weight, blockycavings were seen at surface, it would mean that the fracture zones were being invaded. Thiswould require addition of lost-circulation mate-rial to the mud in order to seal the fractures.

A Schlumberger PERFORM engineer wasstationed on the rig to monitor surface and down-hole measurements and advise on stuck-pipeissues: in particular to monitor and analyze cav-ings and act as liaison between the drilling staffon the rig and the wellbore-stability team onshore.

Three aspects of cavings information weretallied. First, the rate of cavings production at theshale shakers—the coarse solids separators on any rig—was recorded every 30 minutes bymeasuring the time required to fill a bucket. Thismethod may seem crude, but is reliable andversatile in terms of the number of different rigsto which it can be applied. More sophisticatedsolids-measuring devices have been tried, butfew have been satisfactory.

Second, the dominant shape of the cavingswas noted. Initially, the intention was to classifycavings into three types: angular ones originatingfrom breakouts, blocky from naturally fracturedzones, and elongate or splintery cavings fromzones of elevated pore pressure. Unfortunately,most cavings were just nondescript pieces ofbroken rock. However, some did indicate theywere from breakouts, and some from overpres-sured zones. Only two cavings were seen duringthe entire drilling program that came unam-biguously from fracture zones, attesting to thecorrectness of the selected drilling strategy.

Third, the geological age of the cavings givesan idea of where they are coming from in theinterval. This required micropaleontologicalanalysis that was not available immediately.When the results did arrive, they indicated thatall cavings were coming from the upper openholesection that had been exposed to drilling fluidsthe longest.

Onshore at the BP Amoco drilling team office,real-time data were displayed. The real-timedrilling parameters display proved popular, andgave the onshore drilling and wellbore-stabilitystaff close contact with drilling operations. Thewellbore-stability team attended morning drillingmeetings, advised on stability issues and gave aclass on wellbore stability to this group and onefrom another platform in the Valhall. The classesfocused the attention of the crew on the avoid-ance of instability problems, rather than thetraditional reactive approach, and allowed thestaff to meet and question the scientists and engineers who would be influencing theirdrilling procedures.

One of the tasks was to carefully monitor therate of penetration and the ECD. If the lattercrept up to 15.3 lbm/gal, there would be the riskof mud invading fracture zones and causing per-manent formation damage. If the ECD got toolow, cuttings and cavings could be accumulatingaround the bottomhole assembly, eventually pre-venting fluid flow and sticking the drillstring inthe hole. Rate of penetration is important in con-trolling ECD. If too much rock is drilled tooquickly, the suspended cuttings increase the muddensity and hence the ECD. While it is clear thismight lead to problems, one of the traditionalaims of the drilling crew on a rig is to drill as fastas possible. Crews assume that high ROP willhelp reach target depth more quickly, and some-times pay bonuses are tied to beating drillingschedules. In most areas, however, including theNorth Sea, a longer term view must be taken;high instantaneous drilling rates can lead toproblems that cost more to solve than is saved indrilling time.

Angular Caving

Tabular Caving

> Tabular caving (bottom) from natural fracturesand angular caving (top) caused by breakouts.Scale is in mm.

6. Kristiansen TG, Mandziuch K, Heavey P and Kol H:“Minimizing Drilling Risk in Extended Reach Wells atValhall Using Geomechanics, Geoscience and 3DVisualization Technology,” paper SPE/IADC 52863, pre-sented at the SPE/IADC Drilling Conference, Amsterdam,The Netherlands, March 9-11, 1999.

An example of the Schlumberger PERFORMprocess in action can be seen in the crew’s reac-tion to an anticipated problem. During drilling,gas levels and fluid volumes require continuousmonitoring to ensure that any gas is detected andthere is no risk of a kick developing. When back-ground gas levels were high in the interval from2100 to 2200 m [6890 to 7220 ft], the standardresponse would have been to increase mudweight substantially to suppress gas influx intothe borehole. This would have led to the destabi-lization of the critical fractured zone lower,between 4100 and 4300 m [13,450 to 14,100 ft].The driller was advised that mud weight had to be kept low, and that another way to controlgas leakage was to slow down. The mud-weight increase was restricted to 14.6 lbm/gal[1.75 g/cm3] and the rate of penetration wasreduced to below 30 m/hr [98 ft/hr]. The lowerROP decreased the rate at which crushed rockreleased gas into the annulus, and these actionsreduced background gas levels from the 20 to35% range down to less than 5%, while avoidingproblems deeper in the well.

The reservoir was penetrated ahead of sched-ule, with much lower mud loss to the formationthan usual and negligible activation of fracturezones. The asset team acknowledges that theimplementation of real-time wellbore-stabilitycontrol significantly reduced the risk and drillingcosts to the top of the reservoir, and achievedoptimal well construction technique earlier in thefield development cycle.

16 Oilfield Review

> Structure of the salt dome responsible for the Mungo field accumulation. White curvesare well trajectories and the yellow lines on the dome are interpreted faults.

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> Wellbore trajectory on a vertical slice throughthe stress field modeled around the Mungo field.In an unperturbed earth, the maximum principalstress is vertical, and the intermediate and minimum ones, horizontal. With the intrusion of salt, the stress field has been perturbed andtilted. This earth model can be interrogated atany point and the stresses visualized as axesthrough a sphere. In this example, the model hasbeen interrogated at 1500 m: the red crosshairsand circle indicate that the maximum and interme-diate principal stresses are tilted 27.6 degrees.

Summer 1999 17

Another field in the North Sea experiencedsimilar gains in drilling efficiency through opti-mized planning and monitoring of wellbore stabil-ity and hole-cleaning practices. Developmentwells in the Mungo field in the Eastern TroughArea Project (ETAP) encountered extreme instabil-ity problems as they neared the flanks of the over-hanging salt diapir (previous page, top). Tectonicactivity associated with the salt emplacementhad fractured and weakened formations throughwhich wells were to pass on their way to thereservoir. The highly disturbed sediments aroundthe salt intensified the hole-cleaning problem.Cavings, whether bounded by fractures or byweakened bedding planes, clogged the wellbore.Stuck-pipe problems were especially severe inthe long, inclined 60° sections of the S-shapedtrajectories that were necessitated by the cen-trally located platform. Overpressured formationsand high-pressure chalk rafts added further risk tothe drilling program.

The four wells in the first phase of Mungodevelopment drilling had experienced large costoverruns in the 121⁄4-in. sections. For the subse-quent phase of development, a mechanical earthmodel was constructed for the Mungo structureand used to plan the second phase of develop-ment wells. Some of these wells pierced the saltfor a 133⁄8-in. casing point then followed the saltdownflank to the reservoir sand (previous page,bottom). As in some sections of the Valhall wells,stress profiles indicated cavings would be abun-dant, so good hole-cleaning practices would becrucial to successful drilling. Downhole monitor-ing of ECD with the APWD Annular PressureWhile Drilling tool would help the engineerdetect hole-cleaning problems before they couldcause stuck pipe.7

In Well P2, the first well of the second phaseof Mungo development, wellbore instability didcause large amounts of cavings to enter theborehole (right). However, the combination ofsurface detection of cavings and cuttings, down-hole measurements for hydraulics monitoringand attentive drilling overcame this problem. TheNPT was significantly reduced, with substantialcost savings.

Currently, the Mungo wells team has aSchlumberger PERFORM engineer offshore and ageomechanical expert onshore as part of thedrilling team. This engineer and members of theonshore team, consisting of the geomechanicalexpert, drilling engineer, directional planner andgeologist, hold a morning conference call to dis-cuss what has occurred over the last 24 hours andwhat can be expected for the upcoming day. Theresults of this meeting are presented at the regu-

lar morning call where everyone is briefed andmade fully aware of any potential problems for thenext 24 hours. This process worked well on therecently drilled P3 well. A situation involving pos-sible losses was avoided by keeping the ECD lowwhile drilling through a fracture. A small volume ofmud was lost, but drilling continued unabated.

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> Cavings shapes predicted and found along the trajectory. The volume around the topof the salt dome was predicted to be highly fractured and prone to fracture-boundedcavings. Deeper along the inclined section, cavings were found to separate alongweaknesses in bedding planes.

7. For more on the application of the APWD tool in theMungo field and others: Aldred W, Cook J, Bern P,Carpenter B, Hutchinson M, Lovell J, Rezmer-Cooper Iand Leder PC: “Using Downhole Annular PressureMeasurements to Improve Drilling Performance,” OilfieldReview 10, no. 4 (Winter 1998): 40-55.

Optimizing Bits and Drilling PracticeThe Schlumberger PERFORM techniques for opti-mizing drilling performance can be applied toother drilling challenges. In addition to managingwellbore instability and promoting good hole-cleaning practice, the methodology has beenused to improve drilling efficiency by supportingbit selection and appropriate drilling practice toreduce damage to drillstring components.

Chevron is drilling and operating offshore inthe Cabinda enclave of Angola (above). Their cur-rent efforts concentrate on the South Sanhafields where the main reservoir, the Pinda forma-tion, is the deepest and hardest to drill. Theinterbedding of hard and soft layers in the Pindaformation plays havoc with drilling equipment,and it is a challenge to prolong the lives of bitsand other BHA components. In one instance,after drilling just two wells, Sedco Forex had toscrap about 80 joints of heavy-weight and stan-dard drillpipe due to eccentric wear.

The main goals for the Schlumberger PERFORMengineer were to improve ROP and eliminate drill-string failures. In essence, this meant finding waysto ensure that all the rig energy imparted throughthe rotary table or topdrive to the drillstring and bitbe used constructively to cut rock rather than todestroy the bit and drillstring. The differencebetween the two situations sometimes can besmall, and the best way to avoid the latter is bycareful planning, understanding the process andmonitoring both surface and downhole measure-ments in real time.

Standard practice for increasing ROP was toincrease weight on bit (WOB). But increasingWOB can cause other problems, includingincreased stick-slip and torsional vibration,which in turn damage the drillstring and

ultimately lead to higher per-foot costs. Stick-slipoccurs when high friction between the bit andthe formation actually stops the bit from rotat-ing—the stick phase—even though the drillpipeis still being turned at a constant rate on surface.After a short delay, slip takes over when torquebuilt up in the twisted drillpipe overcomes thefriction and the bit turns, but several times fasterthan the speed transferred from the rotary tableor topdrive. Torsional vibration, or oscillation ofthe drillstring around its rotational axis, is one ofthe three modes of drillstring vibration, the othertwo being axial—along the long axis of pipe, andlateral—from side to side across the pipe.8

Introduction of the Schlumberger PERFORMtechnique produced immediate results. In the firstwell to use such an engineer, monitoring surface

and downhole measurements of weight on bittorque, shocks and vibrations provided a clearguide to controlling stick-slip, shocks and vibra-tions by modifying WOB (below). Surface (SWOB)and downhole weight on bit (DWOB) were seen tocorrelate closely with the occurrence of torsionalvibrations at XX325 ft brought on by stick-slip, so astick-slip threshold weight was determined, underwhich the WOB would allow smooth drilling. Forthresholding purposes, the downhole weight on bitmeasurement was more reliable than that mea-sured on surface. For example, at XX360 ft, wheretorsional vibrations are low, the DWOB lies belowthe threshold, but the SWOB is above it. This is incontrast to the next lower section in which DWOB(and SWOB) are above the threshold, and vibra-tions are set in motion.

18 Oilfield Review

XX320

XX340

XX360

XX380

Dept

h, ft

0 50 0

0 40

40

ROP

SWOB

DWOBft/hr klbf

klbfSTOR

DTOR10

20

klbf

klbf 0 10

100 300Downhole

Axial Vibration

Lateral Vibration0 40

0 3000rpm

0 200

0 3000SHK Width

SHK Peak

Torsional Vib0

0 G

G µsec

G

radian/sec

Stick-slipthreshold

weight

> Surface and downhole measurements for optimizing drilling in a Chevron Cabinda well.Increases in surface (SWOB) and downhole (DWOB) weight on bit (track 2) correlate with theonset of dangerous torsional vibrations (track 5) induced by stick-slip, first seen in the zone fromXX325 to XX330 ft. To avoid torsional vibrations, a stick-slip threshold weight was determined andtied to measured DWOB, which is more reliable than SWOB. This can be seen in the interval fromXX360 to XX369 ft: there are no torsional vibrations when DWOB is below the threshold, butSWOB is above the threshold and would have given a false alarm.

NIGERIA

CAMEROON

CONGOGABON

ZAIRE

ANGOLA

GHANA

Cabinda

> Cabinda, a northern enclave of Angola on the west coast of Africa, having crude oil asits dominant export.

Summer 1999 19

The well took 11 days and one bit run to drillthe 10,000 ft [3050 m] to the top of the Pinda, then23 days with 6 bit runs to drill through the 3000-ft[900-m] Pinda. The sporadic success of any par-ticular bit and BHA combination in this field wasunexplainable. Sometimes one combinationwould achieve excellent ROP and footage, and atother times it would fail after the initial few feet.

The engineer combined data from surface anddownhole measurements and rock strength anal-ysis and related these to previous bit and BHAperformance. This allowed estimation of optimalranges for the while-drilling measurements andhelped in subsequent bit and BHA selection.Then specific drilling performance measurementswere monitored in real time on the rig and keptwithin the optimized range so as to achieve opti-mal cost per foot.

The experience gained while drilling this wellwas applied to subsequent wells. In all laterwells, the number of shocks measured with athreshold shock sensor that detects shocksgreater than 100 G decreased from a range of 6 to 8 million in the Pinda formation to almostzero. The problems of eccentric drillpipe weardisappeared completely and the learning curvefor selecting the right bit and BHA sped up,resulting in improved drilling performance.

Tools for SuccessThe successes delivered by the SchlumbergerPERFORM process stem from the combination ofSchlumberger technical strengths in measure-ment and interpretation with the operator’sdrilling expertise. High-quality while-drilling dataand accompanying analyses are vital for success-ful drilling, but they are most valuable when usedin a consistent way to support decisions madeduring the drilling process.

This process is a series of decisions and asso-ciated actions taken during the planning andexecution of a project that result in a completedwell. The degree of success or failure and effi-ciency of the well is determined by the quality ofthose decisions. Effective decision-makingdepends on having an accurate view of current

well conditions, understanding the consequencesof a decision and being prepared for the futurewith contingency plans. The SchlumbergerPERFORM initiative impacts this process most sig-nificantly by helping to provide an accurate view ofthe current conditions and a look ahead at poten-tial hazards. The result is that better decisions canbe made by transferring the decision-makingperiod from the stressful moments surrounding anincident to some earlier time when judgment isnot impaired by anxiety and pressure.

Researchers are investigating ways toimprove the decision-making process by makingmore data available faster and using knowledgegained in other areas. For example, new tech-niques are being devised for estimating the riskof a drilling incident such as stuck pipe. Usingstandpipe pressure and torque data from the

Valhall wellbore stability case study discussedearlier, scientists at Schlumberger CambridgeResearch have produced a stuck-pipe risk profilethat begins to foretell hole-cleaning problems(above). With further testing and experience,these advances will eventually change alarmsfrom signaling a surprising event when it occursto advising drilling teams long before the prob-lem becomes dangerous.

The oil industry, like all others, strives forcost-effectiveness and productivity. Eliminationof waste and losses, whether in process or mate-rials, is a key goal for all successful companies,regardless of prevailing economic conditions.Increasing drilling efficiency by managing drillingrisk is a sure way for E&P companies to achievethat objective. —LS

Risk

0

0.5

1

X450 X475 X500 X525 X550 X575 X600 X625 X650

Depth, m

Sta

ndpi

pe p

ress

ure,

psi

3100

3200

3300

3400

3500

Torq

ue, k

lbf

10

15

20Valhall Stuck-Pipe Risk

> Predicting the possibility of stuck pipe in the Valhall field. Torque (top) and standpipe pressure(middle) measured while drilling are two elements, along with signal processing techniques,that help identify well sections where the risk of stuck pipe is high (bottom). The shaded barsindicate where the drilling team did experience drilling difficulties, mostly related to hole-cleaning issues.

8. Jardine S, Malone D and Sheppard M: “Putting aDamper on Drilling’s Bad Vibrations,” Oilfield Review 6,no. 1 (January 1994): 15-20.

Propagate model through

field wellsGeological mapping

Special core analysis

Fluidanalysis

Biostratigraphy

Single-well

petrophysical model

Seismic horizon

maps

Borehole geophysics

Core dataNormalization

OutcropsEnvironmental

correctionSequence stratigraphy

Gravity andmagnetic dataProduction tests

Attribute analysis

Depth conversion

Depth

correction

Geologic model

Synthetic seismograms

Predictive Simulations and Decisions

Reservoir Model(Shared Earth Model)Simulation Model

Seismic processing

Property distribution

Log correlations

Reservoir volume

Fluid contacts

20 Oilfield Review

Validating Reservoir Models to Improve Recovery

Jack BouskaBP Amoco plcSunbury, England

Mike CooperAndy O’DonovanBP Amoco plcAberdeen, Scotland

Chip CorbettHouston, Texas, USA

Alberto MalinvernoMichael PrangeRidgefield,Connecticut, USA

Sarah RyanCambridge, England

For help in preparation of this article, thanks to Ian Bryant,Schlumberger-Doll Research, Ridgefield, Connecticut, USA;Henry Edmundson, Schlumberger Oilfield Services, Paris,France; Omer Gurpinar, Holditch-Reservoir Technologies,Denver, Colorado, USA; Steve McHugo, Geco-Prakla,Gatwick, England; Claude Signer and Lars Sønneland,Geco-Prakla, Stavanger, Norway; and James Wang,GeoQuest, Houston, Texas. We thank the Bureau ofEconomic Geology, The University of Texas at Austin, forpermission to use the Stratton field 3D Seismic and WellLog Data Set, and BP Amoco and Shell UK E&P for permission to use the Foinaven area data.ARI (Azimuthal Resistivity Imager), FMI (Fullbore Formation MicroImager), MDT (Modular FormationDynamics Tester), OFA (Optical Fluid Analyzer), RST(Reservoir Saturation Tool) and TDT (Thermal Decay Time)are marks of Schlumberger.

Valid and worthwhile reservoir simulation hinges on careful preparation of a

reservoir model and clear understanding of model uncertainty. Creative integration

and comparison of seemingly disparate data and interpretations elevate reservoir

models to new heights of reliability. Through this process, all data incorporated

into a model and ultimately fed into a simulator become more valuable, resulting

in increases on the order of 10% in predicted ultimate recovery.

Summer 1999 21

Making and testing predictions are part of oureveryday existence and basic to most industries.Safety equipment, medical treatments, weatherforecasts and even interior designs are evaluatedby simulating situations and predicting theresults. Similarly, the oil and gas industry makespredictions about hydrocarbon reservoirs todecide how to improve operations.

Reservoir optimization requires carefullyconstructing a reservoir model and performingsimulations. Interpreting and integrating quality-controlled data from a variety of sources and vin-tages, and at different scales, are prerequisitesfor preparing a comprehensive reservoir model.Most computer simulators take the reservoirmodel and represent it as three-dimensionalblocks through which fluids flow (previous page).The data, models and simulations provide a morecomplete understanding of reservoir behavior.

Working together, skilled interpreters use asimulator to predict reservoir behavior over timeand optimize field development strategies accord-ingly. For instance, the effectiveness of infilldrilling locations and trajectories can be deter-mined through simulations of multiple scenariosor assessment of the impact of the uncertainty ofspecific parameters. Reservoir simulation is alsouseful in evaluating different completion tech-niques as well as deciding whether to maximizeproduction rate or ultimate recovery. In this arti-cle, we consider how the integration of all avail-able data to validate and constrain reservoirmodels leads to more realistic reservoirsimulation (next page).

Reservoir simulation is a tool for reservoirmanagement and risk reduction.1 Although thefirst simulations were performed during the 1950s,for a long time limited computer availability andslow speed confined their use to only the mostsignificant projects.2

At present, reservoir simulation is performedmost commonly in high-risk, high-profile situa-tions, but could improve virtually any project. Thelist of typical applications is varied and extensive(next page):• New discoveries, to determine the number of

wells and the type and specification of facilitiesneeded for production. Particular attention ispaid to the reservoir’s drive mechanism and thedevelopment of potential oil, gas and water pro-files. All assessments are subject to the risk oflimited data, sometimes from only a single well.

• Deepwater exploration and other areas whereinitial test wells are expensive. Estimates drawon restricted data, such as seismic data andresults from a single well.

• Fields in which production surprises occur anddevelopment expenditures have already beenincurred. New measurements or productionstrategies might be advisable.

• Secondary recovery implementation. Appro-priate decisions are essential because of theexpense of enhanced production startup.

• Divestment and abandonment decisions.Simulation can help determine whether a fieldhas reached the end of its economic life orhow remaining reserves might be exploited.

These applications of simulation are madepossible by new programs and computers thatare faster and easier to use. (A full review of theadvances in simulation software that haveoccurred in the last few years is beyond thescope of this article, but will be covered in afuture article in Oilfield Review.) The new simu-lators run on less expensive computers and allowrapid studies to rank opportunities. Along withthese capabilities, however, arises the possibilitythat simulations might be performed indiscrimi-nately or before a validated reservoir model hasbeen built, potentially prompting misleading orerroneous results and poor decision-making.There is also the risk of performing simulationsbased on limited data.

Developing a first-rate reservoir model fromlimited data at a variety of scales is difficult. Inits most basic form, model validation is achievedthrough integrating different types of data.Researchers are investigating the best way tointegrate some new types of data, such as multi-component seismic data, into reservoir models. A more sophisticated approach involves uncer-tainty analysis (see “Validating Models UsingDiverse Data,“ page 24 ).

In some cases, it is best to begin with the sim-plest model that fits the data and the objectives ofthe project and reproduces reservoir behavior. Inall cases, the starting point should be an evalua-tion of what answers are required from reservoirsimulation, the accuracy needed and the level ofconfidence or the acceptable range of quantita-tive predictions. The model complexity might beincreased as more data become available. Thereward for increasing model complexity can beevaluated after each simulation run to decidewhether more complex simulation is justified.

Estimates of well flow rates and predictionsof reservoir performance from simulations affectdesign of production facilities and should bebelieved, even if they seem unlikely. For example,a deepwater Gulf of Mexico field required expan-sion and de-bottlenecking of facilities soon afterinitial production because the initial reservoirmodel was compromised by a pessimistic view ofthe interpreted reservoir continuity and flowrates. Better predictions allow operators to sizefacilities correctly the first time rather than havingto re-engineer them.

The quality of predevelopment reserve esti-mates, field appraisals and development strate-gies relates closely to reservoir architecture andstructural complexity; reserve estimates tend tobe underestimated in large, less complex fields,whereas reserves in smaller, more complex fieldsare commonly overstated. Poor reservoir modelsand resultant incorrect calculations of reserves,whether too high or too low, have negative eco-nomic consequences. In the North Sea, deficientreservoir models have led to improper facilitiessizing and suboptimal well placement, even infields where simulation studies were carried out.3

Better validation of models, particularly using 3Dseismic data, might have averted over- or under-sizing production facilities or drilling unnecessarywells in some cases. In other cases, reservoirsimulation has allowed identification of the keydrivers of reservoir performance so that data-gathering efforts can be targeted to reduceuncertainty in those areas. Alternatively, facili-ties can be designed to be flexible within a givenreservoir uncertainty.

22 Oilfield Review

Distributed disciplines

Use of data in isolation, obscuring relationshipsbetween data (for example, seismic and core data)

Inconsistent or poorly documented interpretation techniques

Overdependence on simple reservoir maps

Simulation dependent on computer availabilityand capability

Unlimited modification of simulation input valuesto achieve match with production history

Limited use of simulation to guide data acquisition

Multidisciplinary teamwork

Integration of data and interpretations to confirmreservoir models

Archiving of interpretations and consistent methods

Seismic-guided reservoir property mapping

Simulations run on personal computersor using massively parallel processing

Reservoir models constrained by integrated data andinterpretations and prudent adjustment of inputs

Modeling and simulation to determine optimaltiming for data gathering, such as 4D seismic surveys

Traditional Approach Leading-Edge Approach

> Simulation approaches. In the past, single-discipline interpretation and lack of computing capabilitylimited the use of reservoir simulation. Now, a more sophisticated approach to simulation makes themost of multidisciplinary teams and nearly ubiquitous computers.

Summer 1999 23

Increasing the Value of DataOperating companies spend considerable timeand money acquiring data—from multimillion-dollar seismic surveys and cores from costlyexploratory wells, to sophisticated well logs andproduction tests during and after drilling. Dataacquisition presents potential risks—to bothproject economics and the well itself—such aslogging or testing tools becoming stuck, a corebarrel malfunctioning or having to shut in or kill aproducing well. One would expect, then, thatdata would be analyzed and incorporated intomodels as fully as possible or not collected in thefirst place. Most reservoir simulations rely heav-ily on production data from wells and only fourtypes of geological or geophysical reservoirmaps: structure of the top of the reservoir, reser-voir thickness, porosity and the ratio of net pay togross pay. These maps are often constructedfrom seismic and well log data alone.Incorporating all available data, such as coreanalyses, seismic-guided reservoir property dis-tributions and fluid analyses, is a cost-effectiveway to strengthen and validate reservoir modelsacross disciplines.

A reservoir model usually combines produc-tion rates and volumes with geological and geo-physical maps of subsurface strata derived fromwell logs and seismic data. Aquifers are oftenincluded in the model and sealing rocks are typi-cally treated as zero-permeability layers. Thesubsurface maps take into account well locations

and trajectories. The reservoir model is strength-ened if a geological map of permeability values iscreated by applying a porosity-to-permeabilitytransform to the porosity map according to per-meability values interpreted from well tests, welllogs or cores. Even more rigorous results areobtained when, in addition to inclusion of wellrates and produced or producible hydrocarbonvolumes, all available production data are inputinto the model. These include pressure, gas/oilratios, and fluid densities, saturations, viscositiesand compressibilities.

In many instances, though, reservoir modelsfail to encompass the full diversity of reservoirdata because only a few basic geological andgeophysical maps, constructed from a subset ofthe data available, are used to describe varia-tions in the data. Additional data and interpre-tations are needed to make reservoir modelsmore robust. For example, core data can serve ascalibrators for geological, petrophysical andengineering data and interpretations, but areoften used only as guides to permeability. Coreanalysis refines model values of porosity, perme-ability, capillary pressure and fluid saturation.Whole cores, while not necessarily represen-tative of the entire reservoir, offer tangibleevidence of grain size and composition, sorting,depositional environment and postdepositionalreservoir history, such as bioturbation, cementa-tion or diagenesis.

Seismic and well test data enable mapping ofpermeability barriers, but are rarely used in tan-dem. For example, horizon dip, azimuth, coherencyor other seismic attributes might indicate faultpatterns.4 Such information is especially usefulwhen contemplating the addition of directionallydrilled or multilateral wells. These types of inter-pretations are just the beginning; all other datatypes should be similarly scrutinized.

As mentioned earlier, the reliance of simula-tors on four simple subsurface maps hasimpaired simulation effectiveness. Simulationbecomes more realistic as additional data areincorporated into the reservoir model—reconcil-ing all available data tends to rule out someinterpretations. For example, permeability valuescan be inferred from well logs and confirmed bycore and well test data, and possibly related toseismic attributes, rather than merely computedfrom an empirical transform of a porosity mapand well test data. Reconciling conflicting datarequires acceptance of a hierarchy of data confi-dence. This hierarchy might be developed on thebasis of probable measurement errors.

New discoveries

Deepwater exploration

Mature fields

Implementation of secondaryrecovery

Divestment or abandonment

Determine optimal number ofinfill wells

Size and type of productionfacilities

Decide whether to maximizeproduction rate orultimate recovery

Prospect evaluation

Scenario planning

Answers to suddenproduction problems

Determine appropriaterecovery method

Determine futureproduction volumes

Limited data, sometimes fromonly a single well

Drive mechanism

Terms of operating licenseor lease

Limited data, no wells available

Relatively inexpensive wayto extract maximum valuefrom development costs

Unanticipated future produc-tion problems might reduceproperty value

Situation Desired Results Pitfalls or Other Considerations

> Simulation uses. Reservoir simulation is useful during all phases of the life of a reservoir and in both high- and low-risk projects.

1. For a general introduction to reservoir simulation:Adamson G, Crick M, Gane B, Gurpinar O, Hardiman Jand Ponting D: “Simulation Throughout the Life of aReservoir,” Oilfield Review 8, no. 2 (Summer 1996): 16-27.

2. Watts JW: “Reservoir Simulation: Past, Present, andFuture,” paper SPE 38441, presented at the SPEReservoir Simulation Symposium, Dallas, Texas, USA,June 8-11, 1997.

3. Dromgoole P and Speers R: “Geoscore: A Method forQuantifying Uncertainty in Field Reserve Estimates,”Petroleum Geoscience 3, no. 1 (February 1997): 1-12.

4. Key SC, Nielsen HH, Signer C, Sønneland L, Waagbø Kand Veire HH: “Fault and Fracture Classification UsingArtificial Neural Networks – Case Study from the EkofiskField,” Expanded Abstracts, 67th SEG AnnualInternational Meeting and Exposition, Dallas, Texas, USA,November 2-7, 1997: 623-626.

(continued on page 26)

A shared earth model is a model of the geome-try and properties of the reservoir constrainedby a variety of measurements. To be predictive,the model should approximate the key featuresof the actual reservoir as closely as possible(right). In a valid reservoir model, predictionsfrom the model agree with the measured data. Agood fit between predictions and measurementsis not sufficient, though. Several models mightagree equally well with the data. The best modelis the one that agrees best with the data andwith prior information on the model parame-ters. The uncertainty of the model is defined asthe range of model parameters consistent withthe measurements.

Consider a thin bed imaged by seismic data(below right). The uncertainty of the sharedearth model in this case is described by therange of thickness and impedance values thatsatisfy the data. This range defines a probabilitydensity function (PDF).

24 Oilfield Review

Reservoir Model

Shared Earth Model

Measured Data

Predicted Data

Seismic dataWell log dataDynamic data

>Model inputs. A shared earth model begins with seismic, well log and dynamic data from theactual reservoir. In this example, the reservoir is represented by a model (top left). Measuredseismic data (top right) are compared with data predicted by the model (bottom right),which can be adjusted to improve the fit. The final three-dimensional shared earth model(bottom left) incorporates all available data.

Poor fit

Good fit

Best fit

Uncertainty ellipse

h

h

ρVP

ρVP

>Quantifying uncertainty. The thin bed shown as a red layer (left) has thickness hand acoustic impedance ρVP. The plot to the right displays the posterior probabilitydensity function. Thickness and impedance values within the red uncertainty ellipsesatisfy the data, and within that ellipse are red circles denoting a good fit and thebest fit. The red circle outside the uncertainty ellipse does not satisfy the model.

Validating Models Using Diverse Data

Summer 1999 25

Researchers at Schlumberger-Doll Research,Ridgefield, Connecticut, USA, are using aBayesian approach to quantify the uncertaintyof reservoir models (right). A prior PDFrepresents initial information on model para-meters—the vector m. This prior PDF can be combined with a likelihood PDF, whichquantifies information provided by additionaldata, to obtain a posterior PDF. When new databecome available, the posterior PDF is used asthe initial or prior PDF, and the model is againrefined. As additional measurements are incor-porated in the model, the uncertainty decreasesand a better reservoir description follows.

Effective model validation using a Bayesianapproach requires three modes of operation:interactive, optimization and uncertainty. In theinteractive mode of the prototype application in development, the user modifies the reservoirmodel and observes the consequences of inter-pretation decisions on the data (below right).In the example shown, predicted seismic dataare compared with measured data. In the opti-mization mode, the user selects the modelparameters to optimize. The software finds thebest local fit of the model to the data. Finally,in the uncertainty mode, the uncertainty ellipseof selected reservoir properties is computed anddisplayed. The uncertainty ellipse representsthe range of acceptable models.

The prototype application has been used totest a Bayesian validation approach againstdiverse data types, including seismic data, welllogs, while-drilling data and production infor-mation. By validating a reservoir model againstall available data before beginning the history-matching phase, the range of admissible modelscan be reduced substantially. The result is amore predictive reservoir model.

Prior PDFp (m)

LikelihoodL (m d[1])

Posterior PDFp (m d[1])

Prior PDFp (m d[1])

LikelihoodL (m d[2])

Posterior PDFp (m d[1],d[2])

m2

m1

m2

m1

m2

m1

m2

m1

m2

m1

m2

m1

Bestmodel

Uncertaintyellipse

>Reducing uncertainty. In a Bayesian approach, a prior PDF quantifies the initial information on model parameters, expressed as vector m. The prior PDF (top left) isrefined by the inclusion of new data (top center) to create the posterior PDF (top right).The uncertainty of the model is shown in the red uncertainty ellipse. The blue circle represents the best model. The posterior PDF then becomes the prior PDF (bottom left)when more data become available (bottom center). The next posterior PDF (bottom right) has a smaller uncertainty ellipse and a slightly different optimal model.

>Validation modes. Prototype software developed by Schlumberger includes an interactive mode in which theuser assesses the effects of interpretation decisions on reservoir models. In this case, the center of the upperpanel shows predicted seismic data as dotted lines and measured data as solid lines after the upper horizon,shown in green to the left, has been moved. The lower panel shows a better fit between the predicted andmeasured data (center) and the model uncertainty in the ellipse to the right.

Limitations of Reservoir ModelsGenerating and fine-tuning the model entail closecollaboration by the reservoir team. As in otherphases of exploration and production, such asgeological and geophysical interpretation ordrilling preparations, handing off results fromone team member to the next along a chain isless effective than working together from theoutset.5 Reservoir teams analyze data and per-form simulations more rapidly as their experienceincreases. Working as a team also ensures thatno one gets bogged down in endless tinkeringwith input parameters to try to obtain a matchwith production history.

In addition to working interactively, the teammust employ consistent methods to ensure thatnormalization and interpretation are performedproperly. If data are not normalized and inter-preted consistently, relationships between datamight be obscured, such as that between porosityand seismic attributes (see “Model Validation,”next page).6

Any uncertainty in the data limits confidencein reservoir models and reservoir simulations.Permeability barriers, pinchouts, faults and othergeological features are not always apparent fromwell, seismic and production data. Their exactlocations might be off by tens or hundreds ofmeters and their effectiveness as flow barriers or

conduits might be miscalculated. Formationthicknesses are usually defined by integratingseismic and averaged well log data, although the resolution of seismic data is on the order oftens to hundreds of feet, whereas well logs showvariations at the scale of inches. Under- or over-estimating pay thicknesses directly impactssimulation reliability.

Averaging techniques also affect simulationresults, especially when reservoir properties arehighly variable. Also, problems may occur whenaveraging fine detail, such as interpretationsfrom well logs, to integrate with data of lowerresolution, such as seismic data. For example, areservoir that consists of several distinct layerswith different properties might not behave like asingle layer of the same overall thickness andaverage properties. The uncertainty of manymeasurements increases dramatically with dis-tance from the wellbore. Even though there is adifferent level of uncertainty with each data type,proper model validation forces comparisons ofindependent data and interpretations.

Upscaling, or representing the data at a com-mon scale, coarsens the fine-scale reservoirdescription in the shared earth model to the degreethat a computer can cope with it (below). This stepusually reduces the number of cells, or subdivisions

of the reservoir model. Horizontal upscaling in theabsence of horizontal wellbores is typically simplerbecause there is generally less fine detail in seis-mic data, whereas vertical upscaling is compli-cated by the greater amount of detail available atthe wellbores. Thickness and porosity, whose vari-ations typically follow simple, linear averaginglaws, are less prone to upscaling problems thanpermeability. The ”average“ permeability of a two-layer system in which one layer has zero perme-ability is not one layer with the averagepermeability of the two layers. The reservoir modelmust be built around such impermeable layers.

No matter how carefully a model is preparedand simulation performed, the dynamics of pro-duction might affect the reservoir in ways thatreservoir simulation might not predict. Historymatching, or comparing actual production vol-umes and measured pressures with predictionsfrom simulations, is the most common methodfor judging the quality of the reservoir model. Theassumption is made that if the model yields asimulation that matches past production, thenthe model is more likely to be a useful tool forforecasting.7 Certainly, a model that does notmatch past production history or reservoirresponse to past production is unlikely to cor-rectly predict future production.

26 Oilfield Review

Classification system Reservoir simulationsDrilling dataGeological modeling 3D and 4D seismic data Seismic modeling

Well logsPetrophysical modeling

Simulation model

Upscaling

> Shared earth model. A numerical representation of the subsurface, housed in a database shared by multidisciplinary team members, allows constantaccess to and updating of the reservoir model used for simulation. As databases and software improve, the simulation model and the shared earth model,which now must be upscaled before being used as a simulation model, will be the same.

Summer 1999 27

Obtaining a good match between the produc-tion history and predictions from simulations isinexpensive in some cases, but can become timeconsuming when the model is continuouslyrefined and simulated. In certain situations, suchas waterfloods, tracers in the form of chlorides,isotopes or brines are introduced into injectedwater to reveal patterns in the reservoir.Comparisons of these patterns with expectedpatterns can be used to reevaluate input values,for example, porosity, permeability and transmis-sibility—the ease with which fluid flows fromone model cell to another, to improve the historymatch. Whenever a new well is drilled, it offersan opportunity to check the quality of a reservoirsimulation, principally by comparison of observedpressure with the pressure predicted by themodel at the drilling location.

The difficulty of simulating a reservoirunderscores the need to constrain the reservoirmodel with all available data. A reservoir modelconstrained and validated by geological, geo-physical and reservoir data before initiatingsimulation extracts as much information as pos-sible from the data and provides a better result.Also, understanding the range and impact ofreservoir uncertainty allows a quantitative andqualitative judgment of the accuracy or range ofmodel predictions.

Model ValidationA data set from the Stratton field of south Texas(USA) demonstrates the value of cross-disciplinaryinterpretation and model validation in calculatingin-situ gas reserves in the Frio formation (above).8

The data include 3D seismic data, logs from ninewells, correlations of geological markers and avertical seismic profile (VSP). Resistivity, neutronporosity, bulk density, and spontaneous potential,gamma ray or both curves were available for thenine wells. Preliminary examination of the welllogs and VSP data guided selection of horizons inthe Frio formation for seismic horizon tracking.The VSP provided a good tie between the welland the seismic data along with good under-standing of the phase of the seismic data.9

A thin, clean Frio sand that is easy to correlateand ties to a mappable seismic event wasselected for both well-by-well analysis and multi-well petrophysical interpretation that ensuredconsistent analysis of all the logs. The interpretersobserved that porous zones seemed to correspond

5. Galas C: “The Future of Reservoir Simulation,” Journal of Canadian Petroleum Technology 36, no. 1(January 1997): 5, 23.

6. Corbett C: “Improved Reservoir Characterization ThroughCross-Discipline Multiwell Petrophysical Interpretation,”presented at the SPWLA Houston Chapter Symposium,Houston, Texas, USA, May 18, 1999.

7. This assumption does not always hold. For example, a reservoir model might match the production historyeven when there is bypassed oil. Additional seismic data might reveal undrained reservoir compartments in this case.

8. Corbett C, Plato JS, Chalupsky GF and Finley RJ:“Improved Reservoir Characterization Through Cross-Discipline Multiwell Petrophysical Interpretation,”Transactions of the SPWLA 37th Annual LoggingSymposium, New Orleans, Louisiana, USA, June 16-19,1996, paper WW.

9. Phase refers to the motion of, or means of comparisonof, periodic waves such as seismic waves. Waves thathave the same shape, symmetry and frequency and thatreach maximum and minimum values simultaneouslyare in phase. Waves that are not in phase are typicallydescribed by the angular difference between them, suchas “180 degrees out of phase.” Zero-phase wavelets are symmetrical in shape about zero time whereas non-zero-phase wavelets are asymmetrical. Non-zero-phasewavelets are converted to zero-phase wavelets toachieve the best resolution of the seismic data. Known(zero) phase well synthetics and vertical seismic profiles(VSPs) can be compared with local surface seismic datato determine the relative phase of the surface seismicwavelets. Such knowledge allows the surface seismicdata to be corrected to zero phase.For more on combining vertical seismic profiles withother geophysical data: Hope R, Ireson D, Leaney S,Meyer J, Tittle W and Willis M: “Seismic Integration to Reduce Risk,” Oilfield Review 10, no. 3 (Autumn 1998): 2-15.

Data loading

Time horizoninterpretation

Depth grids

Attribute extraction

Model constructionReservoir propertydistribution

Petrophysicalinterpretation

Geologiccorrelation

Weighted average

VSP well tie

> Model construction workflow. Once data from the Stratton field were loaded, the team worked together from the outset to correlate well logs, vertical seismic profile (VSP) data and seismic data. Interpreted seismic horizons, depth conversion results and extracted attributes were compared withnormalized well log porosities and geologic log correlations. The consistent relationship between the weighted average porosity and seismic amplitudeprompted generation of a reservoir property distribution map, a seismic-guided map of porosity distribution in this case, to complete the reservoir model.

28 Oilfield Review

30 35 40

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to high seismic amplitude. To confirm this obser-vation, crossplots of effective porosity and ampli-tude were prepared. The crossplots of thewell-by-well petrophysical analysis showed sig-nificant scatter, whereas the multiwell analysisdemonstrated a clear relationship between seis-mic amplitude and effective porosity (left).

Next, an equation that related effectiveporosity to amplitude was used to generate amap of effective porosity. The mathematical rela-tionship between the weighted average porosityvalues at the wellheads and the seismic ampli-tudes at those locations guided the mapping.Combining carefully integrated core porosity, log-derived porosity and seismic attributes—in thiscase, amplitude—produced a single, validatedporosity map constrained by several independentsources of porosity information (next page).Using each type of data in isolation in theStratton field example obscured relationshipsbetween data and probably would have resultedin a set of incompatible subsurface maps thatwere not physically realistic.

The difference between the single-well ana-lytical approach and the consistent, normalizedpetrophysical analysis in the Stratton fieldaffects the economic evaluation of the reservoir.The single-well approach precluded integratingthe well logs with the seismic data to generate aseismic-guided porosity map because the cross-plot of effective porosity and amplitude indicatedno consistent relationship between the well logsand seismic data. The in-situ gas volume calcu-lated by single-well petrophysical analysis is12% greater than that calculated from the vali-dated, seismic-guided porosity distribution. Anoverstated gas volume might lead to unnecessaryinfill drilling.

In another case offshore Malaysia, 3D seis-mic data, well logs, wellbore image logs andcore data enabled generation of time-depthrelationships and synthetic seismograms to tielogs to seismic data.10 The relationship betweeneffective porosity, seismic amplitude and acous-tic impedance, expressed as a calibration func-tion, allowed prediction of effective porositythroughout the 3D seismic data, similar to theprevious Stratton field example. Additionaldata, such as pressure measurements fromwireline tools or well tests, make the reservoirmodel more robust and improve confidence inthe predictions from simulation.11

> Single well versus multiwell interpretation. Well-by-well petrophysical analysis(top) obscures the relationship between porosity and seismic amplitude. In thisexample from the Stratton field, the plot of effective porosity versus seismicamplitude shows considerable scatter around the line of best fit because the welllogs were not analyzed consistently. The relationship between seismic amplitudeand porosity is clear when the logs are normalized and consistent analyticalmethods are used (bottom). The observed relationship between seismic amplitude and effective porosity allowed interpreters to use the seismic data to generate a map of effective porosity.

Summer 1999 29

Model ManipulationBecause simulation inputs are subject to revisionby the project team to improve the match betweenthe simulation and production history, it is impor-tant to restrict the input model as much as thedata permit and avoid unnecessary adjustments ofinput values. Simulation software typically allowsinterpreters to change not only the geological andgeophysical maps used to build a reservoir model,but also variables such as pressure, temperature,fluid composition and saturation, permeability,transmissibility, skin, productivity index and rockcompressibility. Seasoned interpreters have differ-ent opinions about what changes to simulationinputs are acceptable, but prudently adjustingsimulation input parameters often improves thehistory match.

Simulation experts use a three-stageapproach to fine-tune a reservoir model, begin-ning with the energy balance, then an adjust-ment for multiple fluid phases, and finally thewell productivity. The energy balance stageaccounts for the reservoir pressure. The relativepermeabilities of different fluid phases areadjusted in the second stage. The final step usesrecent productivity test data, such as bottomholeflowing pressure, tubing surface pressure andtotal fluid production rate, to further improve thehistory match.

Reservoir thickness values typically are con-strained by seismic data and well logs, but arewrong if the interpreter tracks seismic horizonsincorrectly, if logs and seismic data are not tiedproperly, or if well logs are off-depth or miscor-

related. Poor-quality seismic data, a commonproblem in structurally complex areas, canhamper horizon tracking. Reprocessing canimprove seismic data quality.

The depth to the reservoir should also be wellconstrained if log and seismic data are inter-preted diligently. Comparing well logs or syn-thetic seismograms generated from logs withseismic data improves depth conversion.Additional data, such as VSPs, also tend toimprove depth conversion. In structurally com-plex areas, however, depth-based processingfrom the outset is preferable to depth conversion.The enhanced integrity that validation brings to adepth-converted structure map—that is, a mapdisplayed in units of depth rather than the seis-mic unit of time—is demonstrated by integratingdipmeter data with the structure map or by com-paring depth-converted seismic sections to dip

10. Corbett C, Solomon GJ, Sonrexa K, Ujang S and Ariffin T:“Application of Seismic-Guided Reservoir PropertyMapping to the Dulang West Field, Offshore PeninsularMalaysia,” paper SPE 30568, presented at the SPEAnnual Technical Conference and Exhibition, Dallas,Texas, USA, October 22-25, 1995.

11. For another example of seismic-guided property mapping:Hart BS: “Predicting Reservoir Properties from 3-DSeismic Attributes With Little Well Control—JurassicSmackover Formation,” AAPG Explorer 20, no. 4 (April 1999): 50-51.

Well 12Well 18

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0.0400.0600.0800.1000.1200.1400.1600.1800.2000.220

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20 40 180 20060 80 100 120 140 160

> Seismic-guided porosity distribution. In the Stratton field of south Texas, USA, a clear relationship between effectiveporosity and seismic amplitude permitted seismic-guided mapping of effective porosity. This map could not have beencreated without consistent, multiwell petrophysical analysis. Yellow and orange represent areas of high seismicamplitude; blue represents low amplitude.

interpretations from wellbore images, such asFMI Fullbore Formation MicroImager or ARIAzimuthal Resistivity Imager logs (left). By inter-preting seismic data, well logs and wellboreimages together rather than independently, theinterpreter ensures that the final structure maphas been rigorously checked.

Though typically not introduced in the large-scale model-construction phase, informationabout reservoir fluids can offer important insight.Formation tester data, such as MDT ModularFormation Dynamics Tester results, indicate thelocation of a fluid contact. This information, com-bined with well log and seismic data, yields amore constrained starting model for simulation.

Other fluid information may be used to con-strain the fine-scale model in the vicinity of thewellbore. Perforation locations are consideredknown, but the effectiveness of the perforationsmay be evaluated with production logs, andchanges in fluid saturations monitored with RSTReservoir Saturation Tool or TDT Thermal DecayTime data.

The ratio of net pay to gross pay can varywidely across a reservoir, but like other simula-tion input values, should not be altered duringthe history-matching stage without good reason.The net-to-gross ratio might be adjusted if sup-ported by drilling results, such as well logs andcores, or production logs.

Permeability values are obtained in severalways, including core and log analysis and welltests, so comparisons of the values from each ofthese approaches can limit the range of input val-ues, at least at well locations. Effective assimila-tion of wellbore image logs, probe permeabilitydata and core data allows characterization ofhorizontal permeability near the wellbore andprediction of vertical permeability.12 Saturationvalues, established through well log analysis, areverified by capillary pressure data from specialcore analysis, wireline formation tester results orRST measurements. All of these input parame-ters, within reason, are considered adjustable bysimulation experts.

30 Oilfield Review

-800

-1000

-1200

-1800-1400

-1600

-800

-1200 Structure contours in meters

Well location

Strike and dip from dipmeter

>Confirming depth conversion. Dipmeter data reduce interpretive contouring options for structure mapsif the mapper honors the data (top). Dipmeter data from the depth of interest, plotted at each well, showreasonable conformity with structure contours in the upper right and lower sections of the map, butrefute the contouring of the upper left area in this fictitious example. Dip interpretation from an image log,tied to an actual depth-converted seismic section, confirms dip direction and magnitude at horizons ofinterest (bottom). The color variation in the seismic section represents acoustic impedance.

T67V f/s*g/cm3

11,050

11,150

11,250

11,350

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12. Thomas S, Corbett P and Jensen J: “Permeability andPermeability Anisotropy Characterisation in the NearWellbore: A Numerical Model Using the ProbePermeameter and Micro-Resistivity Image Data,”Transactions of the SPWLA 37th Annual LoggingSymposium, New Orleans, Louisiana, USA, June 16-19, 1996, paper JJJ.

13. Crombie A, Halford F, Hashem M, McNeil R, Thomas EC,Melbourne G and Mullins OC: “Innovations in Wireline Fluid Sampling,” Oilfield Review 10, no. 3 (Autumn 1998): 26-41.

14. For more on skin: Hegeman P and Pelissier-Combescure J:“Production Logging for Reservoir Testing,” Oilfield Review 9, no. 2 (Summer 1997): 16-20.

Summer 1999 31

Multidisciplinary validation of reservoir mod-els increases the value of data beyond the cost ofdata-gathering activities alone. In 1998, forexample, Geco-Prakla acquired 3D multicompo-nent seismic data for Chevron in the Alba field inthe North Sea. The objectives of the survey wereto better image the sandstone reservoir, identifyintrareservoir shales that affect movement ofinjected water and map waterflood progress.After integration of the new shear-wave data toimprove the reservoir model, two additionalwells were drilled in the field. The first well isproducing up to 20,000 B/D [3200 m3/d]; the sec-ond well is being completed and has resulted inthe discovery of Alba’s highest net sand. Bothwells have confirmed some of the featuresobserved on the converted-wave data. Becausethe first well was drilled less than a year afterseismic acquisition started, Chevron felt the newdata arrived in time to make a significant com-mercial impact on the field’s development.17

Some reservoir engineers minimize adjust-ments to PVT samples, which indicate reservoirfluid composition and behavior at the pressure,volume and temperature conditions of the reser-voir. In cases of surface recombination of thesample or sample collection long after initial pro-duction, however, the engineer might decide toadjust PVT values. At the other extreme, produc-tion rates and volumes, and pressure data fromwells are considered inalterable by someexperts, although exceptions are made at times,such as when production measurement equip-ment fails. Many experts choose to honor themost accurate representation of production data.

Placing restrictions on the alteration of inputvalues makes a good history match from simula-tion more elusive, but many input values may beadjusted during simulation. Transmissibility iscomputed by the simulator using the input poros-ity and permeability. A high computed transmis-sibility value can be overridden if well tests,formation tester data or seismic data provide evi-dence of separate sand bodies, stratigraphicchanges, faults or other types of reservoir com-partmentalization. Differences in fluid chemistryor pressure from one well to another also sug-gest reservoir compartmentalization. In-situ fluidsamples obtained from the OFA Optical FluidAnalyzer component of the MDT tool are uncon-taminated and can be brought to surface withoutchanging phase for chemical analysis.13

Production logs, well tests and pressure tran-sient analyses indicate skin, which is a dimen-sionless measure of the formation damagefrequently caused by invasion of drilling fluids orperforation residue.14 When the location, pene-tration and effectiveness of perforations are ofconcern, production logs provide information thatmay affect the model input for skin. If a field islocated in a geological trend of similar accumu-lations, skin values in the trend might be a usefulstarting assumption if data within the field areinitially scarce.

15. For more on the shared earth model and integrated interpretation: Beardsell M, Vernay P, Buscher H, Denver L, Gras R and Tushingham K: “StreamliningInterpretation Workflow,” Oilfield Review 10, no. 1(Spring 1998): 22-39.

16. Major MJ: “3-D Gets Heavy (Oil) Duty Workout,” AAPGExplorer 20, no. 6 (June 1999): 26-27.O’Rourke ST and Ikwumonu A: “The Benefits ofEnhanced Integration Capabilities in 3-D ReservoirModeling and Simulation,” paper SPE 36539, presentedat the SPE Annual Technical Conference and Exhibition,Denver, Colorado, USA, October 6-9, 1996.Sibley MJ, Bent JV and Davis DW: “Reservoir Modelingand Simulation of a Middle Eastern CarbonateReservoir,” SPE Reservoir Engineering 12, no. 2 (May 1997): 75-81.

17. For more on the Alba field survey and shear wave seismic data: MacLeod MK, Hanson RA, Bell CR andMcHugo S: “The Alba Field Ocean Bottom Cable SeismicSurvey: Impact on Development,” paper SPE 56977, prepared for presentation at the 1999 Offshore EuropeanConference, Aberdeen, Scotland, September 7-9, 1999.Caldwell J, Christie P, Engelmark F, McHugo S, Özdemir H,Kristiansen P and MacLeod M: “Shear Waves ShineBrightly,” Oilfield Review 11, no. 1 (Spring 1999): 2-15.

Adjustment of the productivity index, anotherinput parameter, affects the quality of a historymatch. The productivity index, often expressed inunits of B/D/psi or Mcf/D/psi, is a measure ofhow much a well is likely to produce. If the skinvalue is known, the productivity index—usuallycomputed from model inputs that include theskin—can be computed more accurately. Whendiffering stimulation or completion techniquesamong field wells are used, productivity indexvalues often vary from well to well. For example,hydraulic fracturing of a single well in a field mightenhance permeability and, therefore, productivityof that well alone.

The options available to change a reservoirmodel to improve the match between one simu-lation run and a field’s production history mightappear endless. At some point, practical limits ondata collection, computational power and timefor modifying input parameters curtail simulationiterations. Independent analyses that support theinterpretations of other team members increaseconfidence in reservoir simulation. A proper sim-ulation workflow helps accomplish this goal.Working as a team ensures that all data are usedto validate the reservoir model.

Validating interpretations and models acrossdisciplines addresses complex problems that aredifficult to solve within the confines of a singlediscipline. A multidisciplinary team sharing adatabase and iteratively validating and updatingshared earth models, or geomodels, achieves thisgoal.15 Operating companies report increases onthe order of 10% in predicted ultimate recoverythrough proper data integration, simulation andreservoir development.16 Cycle time alsodecreases in many cases, probably because ofready access to data and interpretations foreveryone involved in the project.

. . . Validating interpretations and models across disciplines

addresses complex problems that are difficult to solve

within the confines of a single discipline . . . Multidisciplinary

validation of reservoir models increases the value of

data beyond the cost of data-gathering activities alone . . .

Modeling for Data AcquisitionIn addition to the general question of determin-ing how best to produce a reservoir, simulationcan demonstrate the best time to acquire addi-tional data. Time-lapse (4D) seismic surveys,which are repeated 3D surveys, can be acquiredat optimal times predicted by careful model con-struction and simulation.18 As oil and gas are pro-duced from a reservoir, the traveltime, amplitudeand other attributes of reflected seismic waveschange; simulation can demonstrate when thesechanges become visible.19 Because 4D seismicdata acquisition, processing and interpretationcan cost millions of dollars, it is critical to deter-mine from modeling studies whether reservoirvariations will be discernible in the new survey.This type of prediction differs from routine simu-lation techniques.

Traditionally, reservoir engineers have beeninterested in matching simulated well productionwith actual production data. These data comefrom one point in space—the wellhead—but arenearly continuous in time. There are other typesof history matching, though. Seismic data repre-sent a single point in time but offer almost com-plete spatial coverage of the reservoir. Also, themodeled and observed parameters are differ-ent—seismic amplitudes are of concern ratherthan fluid pressures, for example.

To perform seismic history matching, first, theseismic response to a saturated reservoir ismodeled. After some period of production, theseismic response to the depleted reservoir iscalculated. The seismic response might be com-plex and include a combination of changes inamplitude, phase, attenuation and traveltime.The initial and depleted reservoir responses dif-fer because the composition of the pore fluidsand the reservoir pressure change during produc-tion, both of which affect the seismic velocity ofthe reservoir. The synthetic responses are com-pared with recorded seismic data—the initial 3Dsurvey and the subsequent repeated survey. Thedifference in seismic character of the reservoirfrom the initial survey to the later survey is afunction of the compressional (P) and shear (S)seismic velocities and is interpreted as a changein fluid content and pressure.

Multicomponent seismic data and amplitudevariation with offset (AVO) analysis of compres-sional-wave data both reduce the ambiguity ofdistinguishing the effects of pressure changesfrom the effects of pore fluids. Without AVOprocessing, ordinary marine 3D seismic data arefundamentally ambiguous because they respondto compressional waves only, but compressionalwaves respond to both pressure and saturation.Multicomponent seismic data separate compres-sional and shear components, allowing the inter-preter to separate saturation effects and pressureeffects that influence porosity, because shearwaves do not respond to pore fluids.20

Interpretation of a seismic response changefrom an initial seismic survey to a repeated sur-vey enables detection and spatial calibration ofadditional faults, movement of oil-water contactsand gas coming out of solution. Small faults inthe reservoir section are often visible as linearfeatures of decreased amplitude in a seismicamplitude or coherency plot. Sealing faults alsoappear as patches of undrained hydrocarbonswhose well-defined edges represent the fault.Movements of oil-water contacts are visible aschanges in amplitude and possibly as ”flatspots.“ When the reservoir pressure drops below

32 Oilfield Review

18. Gawith DE and Gutteridge PA: “Seismic Validation ofReservoir Simulation Using a Shared Earth Model,”Petroleum Geoscience 2, no. 2 (1996): 97-103.

19. Pedersen L, Ryan S, Sayers C, Sonneland L and Veire HH:“Seismic Snapshots for Reservoir Monitoring,” Oilfield Review 8, no. 4 (Winter 1996): 32-43.

20. In the case of the Magnus field, located on the UK conti-nental shelf, the effect of pressure changes on time-lapse seismic data is greater than the effect of changesin pore fluids. For more information: Watts GFT, Jizba D,Gawith DE and Gutteridge P: “Reservoir Monitoring ofthe Magnus Field through 4D Time-Lapse SeismicAnalysis,” Petroleum Geoscience 2, no. 4 (November 1996): 361-372.

21. Pedersen et al, reference 19: 42.

Predicted Seismic Propertiest2

t3

Predicted Seismic Datat2

t3

Actual Seismic Datat2

t3

> Forward modeling to optimize data acquisition. Predicted properties of seismic data attime t2 (top left) are used to predict the appearance of seismic data (middle left). Thesepredictions are revisited after acquisition of actual seismic data at time t2 (bottom left).Seismic properties at time t3 (top right) are predicted next from actual t2 seismic data.By considering fluid changes in the reservoir and their effects on seismic waves, andthen modeling the seismic data that would result from surveying at time t3 (middle right),additional seismic surveys for reservoir monitoring will be acquired at the optimal timet3 (bottom right).

Summer 1999 33

bubblepoint and gas comes out of solution, P-wave seismic data typically show a significantbrightening of seismic amplitude. Multicomponentseismic data, which include shear-wave data,show no brightening because the shear wavesdo not respond to pore fluids. Such a responseconfirms the presence of gas.

Seismic history matching has benefited reser-voir management decisions in the Gullfaks field,where interpretation of 4D seismic data indi-cated the existence of previously unseen sealingfaults and the presence of bypassed oil. Thefaults themselves were not seen on the newdata, but were interpreted where fluid contentchanged abruptly in geophysical maps showingthe time-lapse results. After fluid transmissibilityacross faults was reevaluated, the potential forbypassed pay supported drilling an additionalwell.21 That well, drilled by Statoil, initially pro-duced 12,000 B/D [1900 m3/d] from a formerlyundrained compartment and confirmed the pres-

ence of a gas cap predicted by seismic data.Statoil also reentered and sidetracked an aban-doned well and produced 6300 B/D [1000 m3/d].

Currently, the scientists at SchlumbergerCambridge Research, Cambridge, England, arecombining information on the distribution of fluids,pressure and other properties from the reservoirsimulator with rock properties from the geo-model, or earth model, to generate a forwardmodel of seismic response (previous page). Inparticular, the porosity, bulk modulus and shearmodulus from the geomodel are combined withsaturation and pressure information from thesimulator. The forward model provides informa-tion about the elastic moduli and density of thereservoir, from which P-wave velocity, densityand acoustic impedance, or other properties, canbe derived.

Next, the synthetic seismic data are com-pared with actual seismic data. Numerous com-parisons can be made between vintages of

seismic data, such as maps of seismic attributes,prestack gathers for AVO studies and so on, buteach approach has the common goal of deter-mining the area of mismatch between predictedand recorded seismic data and analyzing the rea-sons for the differences.

One major challenge in interpretation of anobserved time-lapse seismic response is that thenon-uniqueness in a particular seismic responsemust be considered. For example, an observedchange in amplitude might represent a change insaturation of oil or free gas, a change in theamount of gas dissolved in the oil, a change inpressure, or, most likely, a combination of these.Clearly, it is important to know which of thesefactors are significant in the reservoir, and seismicmodeling can help determine this.

In the Foinaven study area, West ofShetlands, UK, normal-faulted, layered turbiditereservoirs form separate reservoir compartments(below). A preproduction baseline 3D survey was

DC2

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> Foinaven field. Located West of Shetlands (left), the Foinaven field produces from four main turbidite reservoirs. The reservoir map (top right) shows gascaps in red and the strike of the normal faults as black lines. The platforms and well locations are shown in black. The Foinaven study area is indicated bythe blue box. The cross section (bottom right), which extends from south of platform DC1 to north of platform DC2, shows the layered reservoirs that havebeen compartmentalized by normal faulting that must be drained by carefully constructed directional wells.

acquired in 1995 and a repeat survey was shot in1998 after 10 months of oil production. Theamplitude brightening in the synthetic seismicdata indicated that development of gas capswould be visible as the reservoir pressuredropped and gas came out of solution during pro-duction (above). The repeat survey verified theexistence of the expanded gas caps as high-amplitude events (left). Because seismic propertymodeling predicted a good match between thebaseline survey and the repeat survey—syntheticseismic sections from simulator model matchedreal seismic data at appropriate times—the reser-voir team had more confidence in the reservoirsimulator model, both in this area and, by extrap-olation, elsewhere in the field. As a result, addi-tional 4D seismic data will be acquired acrossFoinaven and neighboring Schiehallion field to val-idate the reservoir models and ultimately supportthe placement of a number of infill production andinjection wells.

Correct timing of data acquisition maximizesthe value of the data. Careful study before repeat3D survey acquisition can ensure optimal acqui-sition timing, which is field-dependent. In theFoinaven case, timely acquisition of 4D seismicdata helped determine where the reservoir wasconnected or segmented and where flow barriersexisted so the team could select optimal infill andwater-injection well placement.

34 Oilfield Review

Repeat3D survey1998

Baseline3D survey1995

1000 2000 3000 4000 5000 6000 7000

1000 2000 3000 4000 5000 6000 7000

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Water

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> Visible changes in repeated surveys. Cross-sectional synthetic seismic displays for the baseline survey and repeated survey (left)show the development of a gas cap. The actual seismic sections confirm the predictions from seismic modeling (right).

Repeat3D survey1998

Baseline3D survey1995

Surface Seismic 4D Response

Bright

T32 sand after 10months of production

T32 sand beforeproduction

Dim

T35 sands not yet on production

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-3.40-3.17-2.94-2.71-2.48-2.25-2.02-1.79-1.56-1.33-1.10-0.87-0.65-0.42-0.19 0.04 0.27 0.50

Free gas

0 500 1000 1500 2000 m

0 500 1000 1500 2000 m

N

> Amplitude changes. Map views of the 1995 baseline survey and the 1998 repeat survey clearlydisplay the changes in seismic amplitude that result from gas cap development. The small gascaps in the original survey and the synthetic data shown above it (top) enlarged significantlyafter 10 months of production (bottom). The oil-water contact (OWC) remains consistentbetween the two surveys.

Summer 1999 35

Future PossibilitiesReservoir simulation has already helped oil andgas producers increase predicted ultimate recov-ery, and further improvement is likely. In additionto ongoing software and shared earth modelenhancements, reservoir monitoring with down-hole sensors, 4D seismic surveys or other meth-ods is becoming increasingly cost-effective,particularly when new data are acquired at opti-mal times (above).22

A prudent, efficient team that works togetherto develop a field reduces cycle time andexpense. Sharing data and interpretations allowsthe team to maximize the value of its achieve-ments for more realistic reservoir simulation andimproved understanding of the reservoir. These inturn advance reservoir management, reserverecovery and project economics. Currently, thismethod relies heavily on the team’s motivation towork together. Software provides strong support,but is not yet fully integrated to handle the com-plete spectrum of oilfield data simultaneously.

In the future, new software that validates theshared earth model will incorporate measureduncertainties of data and interpretations. As themodel is refined with the capture of new data,any change in uncertainty will be addressedautomatically. Forward modeling will furtherreduce uncertainty and risk, and maximize thevalue of additional data. If the shared earthmodel is consistently updated and new data andinterpretations are incorporated, project teammembers will have another tool to better copewith increases in both the volume of data and theproductivity expectations of their companies.

—GMG

22. Watts et al: reference 20.

Interpretive data

Uncertainty analysisand risk management

Development and Production Planning

Reservoir modeling

Flow simulation

Production and reservesforecasting

Rese

rvoi

r Mon

itorin

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> Future reservoir management. Reservoir optimization is an iterative process that normally begins with reservoir characteriza-tion of a new discovery, but can be implemented at any stage in an existing field. Reservoir management will rely increasingly onmonitoring and modeling reservoir performance to optimize oil and gas production. The key additional element will be ongoingcollection of data at the reservoir scale, including seismic data and wellbore measurements, so that the development plan canbe assessed and, where necessary, modified. Monitoring the reservoir closely will overcome the current problems of historymatching using only the loose constraints of production data.

36 Oilfield Review

Real-Time Openhole Evaluation

Tom Barber Sugar Land, Texas, USA

Laurent Jammes Jan Wouter Smits Clamart, France

Werner Klopf Milan, Italy

Anchala Ramasamy BP Amoco ExplorationAberdeen, Scotland

Laurence Reynolds Aberdeen, Scotland

Alan Sibbit Houston, Texas

Robert Terry BP Amoco ExplorationHouston, Texas

For help in preparation of this article, thanks to David Allen,Schlumberger-Doll Research, Ridgefield, Connecticut, USA; Gordon Ballard, Kevin Eyl and Alison Goligher,Schlumberger Wireline & Testing, Montrouge, France;Vincent Belougne, Geco-Prakla, Gatwick, England; KeesCastelijns, Schlumberger Wireline & Testing, New Orleans,Louisiana, USA; Ollivier Faivre and Pascal Rothnemer,Schlumberger-Riboud Product Center, Clamart, France;Pierre Roulle, Schlumberger Wireline & Testing, Pau,France; Jay Russell, Schlumberger Wireline & Testing,Livingston, Scotland; Bob Mitchell, Schlumberger Wireline & Testing, Sugar Land, Texas, USA; Jim White,Schlumberger Wireline & Testing, Aberdeen, Scotland; and Technical editing Services (TeS), Chester, England.AIT (Array Induction Imager Tool), DLL (Dual LaterologResistivity), EPT (Electromagnetic Propagation Tool), FMI(Fullbore Formation MicroImager), GeoFrame, HRLA (High-Resolution Laterolog Array), InterACT, Platform Express,SlimAccess, UBI (Ultrasonic Borehole Imager) and Xtremeare marks of Schlumberger. HDLL (High-Definition LateralLog) is a mark of Baker Hughes, Inc.

Operators are gaining an accurate, at-the-wellsite first look at pay zones, thanks

to new technology that incorporates logging measurements environmentally

corrected in real time, fast forward modeling and inversion techniques. At

the heart of this technology are good science and innovative engineering.

To maximize asset value, oil and gas operatorscontinually strive to characterize the location andextent of recoverable reserves. Traditionally, open-hole logs generated with ”triple-combo“ toolstrings have provided key information: porosityfrom neutron-density measurements and satura-tion from resistivity measurements. The introduc-tion of an advanced logging system in 1995,based on a platform of integrated sensors, hasresulted in a quantum change in data acquisitioncapability, reliability and efficiency. The shorter,lighter, operator-friendly tool string is capable offaster rig-up at the wellsite and better access todeviated holes. Flexible mechanical design andshort tool length enable the operator to drill shal-lower wellbores while retaining the ability to logimportant pay zones at the bottom of the well.Array-resistivity measurements, microresistivityand three-detector density tools are improvingaccuracy in difficult environments without sacri-ficing logging speed.

But there is more to the story than more flex-ible operations, reduced rig time and improvedaccuracy of standard measurements. Using thelatest technology, new-generation tools providemore complete reservoir characterization right at

the wellsite—opening up a wealth of opportuni-ties for locating and tapping additional reserves.Improved tool response in thin beds, better padapplication in poor holes, greater accuracy inhigh-weight muds and real-time corrected forma-tion evaluation—all presented in clear, easy-to-understand formats—aid decision-making. Newtools, calibration methods and processing tech-niques, combined with a comprehensive log qual-ity-control (LQC) system, allow engineers tomonitor tool measurements and environmentalconditions—validating data acquisition andensuring that high-quality analysis can be per-formed over the entire logged zone. In manywells, this saves valuable time by eliminating theneed for repeat passes for log verification. Fasterwellsite calibrations, real-time environmentalcorrections, quality control, depth matching and acomplete wellsite quick-look contribute to well-site efficiency and put formation evaluation datainto the operator’s hands more quickly.

In this article, we look at three aspects ofnew platform logging technology and illustratethe simultaneous improvement in operationalefficiency that can be achieved while accuratelydetermining formation characteristics underincreasingly difficult environmental conditions.

1. Goligher A, Scanlan B, Standen E and Wylie AS: “A First Look at Platform Express Measurements,”Oilfield Review 8, no. 2 (Summer 1996): 4-15.

Summer 1999 37

First, we discuss the foundations supportingreal-time environmental corrections, includingspeed and depth, as a vital first step toward theuse of forward models and inversion techniquesfor making real-time environmental correctionsto basic logging measurements. Model-basedinversions—along with new measurements,such as those from mud resistivity sensors,microresistivity measurements, array tools withmultiple-depth measurements and densitybackscatter detectors—contribute to a clearerpicture of the borehole and formation.

Second, the latest developments in openholelogging are highlighted, including the new HRLAHigh-Resolution Laterolog Array tool with multipledepths of investigation, a tool capable of resolvingthe effects of shoulder beds, invasion and dippingformations, thereby providing better resistivityevaluation in complex environments with salinemud. Post-log processing techniques, to interpretlogs in extreme environments, are discussed alongwith a new maximum-entropy-based inversiontechnique developed to quantitatively interpretinduction logs from highly deviated wells or withlarge shoulder-bed contrasts.

Finally, we look at two important benefits ofreal-time environmentally corrected loggingdata—complete wellsite log interpretation andlog quality control. We illustrate how LQC isenhanced by real-time environmental information.

Real-Time CorrectionsEvery logging tool suffersfrom environmental effects ofone sort or another. Real-time cor-rections are essential to get accurate logginginformation into the hands of the operator effi-ciently. As a first step, every tool raw sensormeasurement requires a speed-derived depthcorrection. Next, forward models are used to pre-dict each measurement response for a given setof borehole and formation properties. Finally, bycomparing predicted sensor responses with log-ging measurements—a process called inver-sion—the environmentally corrected formationproperties are determined. This sequence ofsteps is performed during data acquisition andapplied to openhole logging measurements madewith the Platform Express tool system firstreviewed in the Oilfield Review three years ago.1

It may seem surprising that real-time depthcorrection is such an important issue. After all,most log analysts can shift logs at the computingcenter simply by ”eye“ or with automatic mathe-matical correlation algorithms. The depth of thelogging tool is traditionally determined from thelength of unwound cable with an approximateadjustment for cable stretch. However, one of thegreatest uncertainties in wireline logging hasbeen the assignment of petrophysical data to thecorrect depth of the subsurface.

As the tool is pulled up the wellbore, chang-ing wellbore conditions such as caves or fric-tional drag will cause the tool speed to changeerratically, and even to stick and slip, while thecable speed measured at surface remains con-stant. Thus each sensor’s motion across the for-mation may not correlate with the motion of thecable at the surface. The high tool speed—up tofive times the normal logging speed—occurringafter a stuck zone often results in lost data.Speed-derived depth corrections are essential attwo basic stages.

First, at the measurement stage, some loggingmeasurements depend on integrating raw datafrom multiple sensors located at different loca-tions along the tool string. Irregular or nonuniformtool motion during the measurement cycle willinvalidate the assumption that all the data comefrom the same volume of the formation. Log pro-cessing will produce an incorrect or unstableresult, especially visible at layer boundaries.

Some tool designs, such as neutron and den-sity tools, have multiple asymmetrically posi-tioned sources and detectors and unequalsource-detector spacings, and their measure-ments—based on comparing the count rates ineach detector—can be affected by nonuniformtool motion. An example from the North Seaillustrates the effects of speed-corrected densitymeasurements (above).

Likewise, the AIT-H Array Induction Imagertool filter-based processing algorithm assumesthat the data from the eight asymmetrical arraysare regularly sampled every 3 in. Irregular toolmotion can give rise to artifacts on the logs.Caliper measurements and auxiliary mud resistiv-ity measurements must be correctly depthaligned with the AIT array measurements toderive AIT borehole corrections.

At the second stage, depth corrections areequally important when integrating logging datafrom different tools to perform petrophysical inter-pretations. For example, log analysts frequentlylook for gas by comparing density and neutronporosity logs. For the characteristic crossover tobe meaningful, the spectral count rates of eachgamma ray detector in the density tool mustchange in phase with the count rates in each neu-tron detector in the neutron porosity tool as eachpasses the gas-saturated bed in the formation.

In addition to speed-based depth corrections,resolution matching is equally important. Sincethe neutron measurement samples a slightlythicker region in the formation than does the den-sity measurement, these measurements must bematched volumetrically. This resolution-matchingprocess is important in all high-resolution inter-pretations, because it ensures that the sensors ofeach tool used in a combined measurement seeexactly the same formation thickness.

Finally, measurements with high vertical res-olution can have errors amplified by irregulartool motion because the acquisition systemobtains data at sampling rates that vary as afunction of the required bed resolution. Whentool speed differs substantially from the recom-mended, then over- or under-sampling results.Rapid acceleration following a stuck-toolepisode can result in lost data. Tool-speed-based depth corrections are a prerequisite togood high-resolution measurements.

Obtaining properly depth-matched high-reso-lution measurements is critical for OceanEnergy’s efforts to evaluate thinly bedded reser-voirs in the Gulf of Mexico (next page). In onewell, high-resolution invaded-zone resistivity,Rxo, measurements from the MicroCylindricallyFocused Log (MCFL) tool in combination withdensity logs clearly show the many thin beds—some less than 1-ft thick [0.3 m]—throughout thereservoir. Comparison with the FMI FullboreFormation MicroImager images confirms thepresence of thin beds, and the real-time porosityderived from the high-resolution density logenables accurate reserve calculations.

Crucial depth corrections are implemented inthe Platform Express system during acquisitionusing a built in tool-axis accelerometer. Thisdevice measures instantaneous tool accelerationto determine tool velocity and the true depth atwhich all the other tool measurements wererecorded. Stability of the depth-correction algo-rithm is maintained by the use of a Kalman filter-based optimization.2 This optimization minimizes

38 Oilfield Review

2. Belougne V, Faivre O, Jammes L and Whittaker S: “Real-Time Speed Correction of Logging Data,” Transactions ofthe SPWLA 37th Annual Logging Symposium, NewOrleans, Louisiana, USA, June 16-19, 1996, paper F.

Gamma ray, speed corrected MD1:200

ft

X150

X169

X183

X200

Standoff

Standoff(density)

in.

API0 75Density, speed corrected

g/cm31.95 2.95

Density, no speed correctionCaliperin.4 14

Gamma ray, no speed correction

API0 75 1 0

A

A

>Speed-corrected density logs. This example, from a 97º-deviated borehole in the North Sea, comparesthe speed-corrected Platform Express density log (red) with the uncorrected log (black) in track 2. The green band on the left of track 1 is the accelerometer LQC flag indicating—by turning black—excessive stick and slip experienced in Zone A. The gamma ray in track 1 also shows shifts whenspeed-corrected. The logs highlight the benefits of speed corrections in poor hole conditions. The twohigh-porosity thin beds seen at X169 ft and X183 ft are incorrectly identified in the uncorrected log.Potential hydrocarbons in these zones could be missed if uncorrected depths were used to guide theperforation interval.

Rxo (8 in.)

Rxo (1 in.)0 API

Gamma ray150

Rxo

0.2 ohm-mAIT 90-in. resistivityDepth

ft

X760

X770

X780

X790

X800

X810

X820

X830

20

0 FMI image

North

Rxo

ohm-m

360

0.5 8

g/cm3

Densityg/cm3

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1.65 g/cm3

Density (8 in.)2.65

60 p.u.Neutron porosity

0

>Detecting thin beds in the Gulf of Mexico. The FMI Fullbore Formation MicroImager tool image shown in track 1 confirms thepresence of many beds less than 1-ft thick detected by the high-resolution Micro-Cylindrically Focused Log (MCFL) Rxo log shownin track 2. The high-resolution (black) and very high-resolution (blue) density logs are shown in track 3. The high-resolution densitylogs are quantitative in beds over 8-in. thick. The high-resolution, deep induction log shown in track 2 detects many of the thinbeds and can be used quantitatively in beds 1 ft or thicker. Track 4 compares all the density logs with the neutron porosity log.

Summer 1999 39

the overall error in the depth correction by solv-ing a system of simultaneous equations linkingthe exact moment each sensor measurementwas made with the true tool position—derivedfrom the instantaneous accelerometer measure-ment. Cable depth is used as a constraint to helpstabilize the solution. All raw sensor measure-ments and optimization solutions are performedin the time domain. This allows them to be easilyconverted to true depth or cable depth,whichever is required. Time-domain processingalso helps to overcome limitations encounteredin high-frequency depth sampling during high-resolution logging operations.

Forward Modeling for Environmental CorrectionsIn the language of log analysts, the phrase for-ward modeling refers to computing a loggingsensor response in the presence of the environ-ment surrounding the logging tool. Almost anytool response can be linked to formation proper-ties through the physics of the sensor measure-ment and its interaction with the materials of theformation and borehole environment. Comptonscattering and photoelectric absorption governthe interaction of low-energy gamma rays usedto measure formation density. The physical prin-ciples embodied in Maxwell’s equations are well

understood, and with enough knowledge of aresistivity tool design and its environment, thevoltages and currents that make up the toolresponses are predictable.

Forward modeling is important because itallows prediction of tool response under anygiven conditions. These predictions can then becompared with observed measurements, in a pro-cess known as inversion—described later in thisarticle—to understand the real conditions underwhich the measurements were made. In this arti-cle, unless otherwise specified, tool or sensorresponse means the raw measurement, such ascount rate in a nuclear detector, or voltage andcurrent measured on an electrode or inductiontool antenna coil.

Mudcake FormationMud

b

c

a

b Short-spacingdetector

spectrumW2

W3

W4

W1

c Backscatterdetector

spectrumW2

W3

W1

a Long-spacingdetector

spectrum

W1

Window count rates (W)

W2

W3W4

Density

Formation

Mudcake

Mud

Distance

Coun

t rat

eCo

unt r

ate

Coun

t rat

e

Energy

A forward model is used in Platform Expressreal-time density analysis to calculate eachwindow count rate as a function of formation and mudcake properties. The formation modelgeometry consists of a homogeneous forma-tion and mudcake corresponding to a one-dimensional (1D) radial step profile varying indensity and photoelectric properties. Within thisframework, the different detector count-rateresponses depend on only five environmental

40 Oilfield Review

3. Allioli F, Faivre O, Jammes L and Evans M: “A NewApproach to Computing Formation Density and Pe(Photoelectric Factor) Free of Mudcake Effects,”Transactions of the SPWLA 38th Annual LoggingSymposium, Houston, Texas, USA, June 15-18, 1997,paper K.

4. Ellis D: Well Logging for Earth Scientists. New York, NewYork, USA: Elsevier Science Publishing Co., Inc, 1987.

5. Anderson B, Druskin V, Habashy T, Lee P, Luling M,Barber T, Grove G, Lovell J, Rosthal R, Tabanou J,Kennedy D and Shen L: “New Dimensions in ModelingResistivity,” Oilfield Review 9, no. 1 (Spring 1997): 40-56.

6. In this article, we shall confine our discussion to thedimensionality of the formation model—the number ofindependent coordinates needed to describe the way inwhich resistivity varies. See “The Vocabulary ofResistivity Modeling,” Anderson et al, reference 5.

>Three-detector density logging sonde. Multiple Compton scattering and photoelectric absorptionlead to a spectrum of gamma ray photons entering the detector windows from the borehole and forma-tion environment (right). The forward model represents a homogeneous formation behind a thick layerof mudcake (bottom). Each detector spectrum is partitioned into broad count-rate windows used toestimate properties of the gamma ray scattering environment (left).

>Density calibration database. The density for-ward-model database was recorded in the Envi-ronmental Effects Calibration Facility in Houston,Texas, USA, which was built for the characteriza-tion of nuclear logging tools. This database cov-ers the range of environments to which the toolwill be exposed. The facility manager, John Spal-lone, is shown lowering the Platform Expressdensity tool into one of the calibration blocks.Since the Platform Express tool first becameavailable, a continuous effort has been underway to enhance the density measurement capa-bility in heavy mud environments.

degraded photons from multiple Compton scatter-ing and photoelectric absorption contribute to anoverall continuous gamma ray spectrum seen inthe formation by each of the three detectors.

Typically, increasing density causes anincrease in the gamma ray flux near the sourcebecause there are more scattering targets in ahigher density material. This increases theobserved backscatter detector count rate. On theother hand, increasing density tends to cause adecrease in the observed count rates in the twodetectors spaced farther from the source becauseof the long attenuating path to these detectors.Also, changes in formation lithology can bedetected by variations in the low-energy windowcount rates due to photoelectric absorption.These count rates are also strongly affected bybarite in the mudcake, and its presence can makethe photoelectric effect measurement intractable.

Density forward models—The three-detectordensity tool in the Platform Express tool stringuses a gamma ray source that emits 662-keVphotons from a source capsule located in thelogging pad (above).3 Although density measure-ments are sensitive to a relatively small volume ofthe environment between the source and detector,the increasing source-to-detector spacing of eachdetector enables each to see progressively deeperinto the mudcake and formation.

Gamma rays from the source enter the mud-cake and formation and typically scatter severaltimes before being detected by each detector inthe logging pad. Each Compton scatteringencounter causes the incident gamma ray to loseenergy and change direction, eventually bendingmany gamma rays back towards the detectoraperture in the tool. The comined effect of energy-

Summer 1999 41

parameters—formation density and photoelec-tric factor; mudcake density and photoelectricfactor; and mudcake thickness.

An ideal implementation of the gamma rayphysics in this formation forward model would begiven by an exact solution to the Boltzmannequation for gamma ray transport. Unfortunately,the Boltzmann equation has no simple analyticform for this environment.4 Instead, a proxy forthe exact sensor response physics parameterizesthe detector window count rates as exponentialfunctions in terms of formation and mudcakeproperties. Each window count-rate responsefunction contains empirical coefficients, whichaccount for source strength, detector collimation,average gamma ray track length for each partic-ular source-detector spacing, and energy-depen-dent Compton scattering and photoelectricabsorption cross sections. These coefficientsform the calibration for this nonlinear parametricforward model, and are determined by aweighted least-squares fit to a database of labo-ratory measurements.

The database measurements were obtained in a laboratory with a calibrated density tool in known formations and mudcake conditions(previous page, right). Today, over 1130 calibra-tions in boreholes with barite mudcake and 420calibrations with nonbarite mudcake have beenmade, and recent calibrations for the density for-ward model have extended the operating rangeof the density tool to mud weights of up to 17 lbm/gal [2.04 g/cm3].

Resistivity forward models—In contrast tonuclear measurements with their small volume ofinvestigation, all deep-resistivity responses,whether from induction or laterolog tools, areinfluenced by the resistivity distribution in a largevolume surrounding the logging instrument.Correct interpretation requires corrections for bore-hole, invasion and other large-scale environmentalor geometrical effects such as shoulder effects.

The response of a laterolog, such as the High-Resolution Azimuthal Laterolog Sonde (HALS)measurement, is computed by solving Laplace’sequation for the electrostatic potential in theborehole and formation environment. Laplace’sequation follows from Maxwell’s equations inthe low-frequency limit and dictates conserva-tion of current everywhere in the environmentaldomain. Knowing the voltage and currents every-where along the tool allows prediction of theapparent resistivity reading. Unfortunately, ana-lytical solutions exist for only a limited number ofproblems with simple geometries. In practice, the

solution requires the use of numerical methods.The two methods most frequently employed arethe Finite Element Method (FEM) and the FiniteDifference Method (FDM). Development of for-ward modeling techniques using FEM and FDMfor resistivity measurements was discussed twoyears ago in the Oilfield Review.5

Both FDM and FEM divide the environmentaldomain into grid cells and solve for the potentialin each cell. The interactions between neighbor-ing cells are controlled by Laplace’s equation.Combining all the cell interactions together withthe boundary conditions yields a large system oflinear equations, which is solved by the computerto find the electric potential at the vertices of each cell. Depending on the complexity and dimension of the formation model, eithertwo-dimensional (2D) or three-dimensional (3D) solutions of the electromagnetic field areneeded (right).6

Multidimensional forward modeling tech-niques have been successful in rapidly and accu-rately predicting the tool response of resistivitytools. The models are used to optimize tooldesigns, characterize their response and providethe basis for inversion procedures designed tofind formation characteristics. Although modelingspeed has increased considerably in recent years,it is frequently too slow to allow real-time inver-sion of measurements while logging, especiallywhen a 3D model is needed. When the number offormation parameters is limited, it is often morepractical to create a database of the toolresponse to variations in parameters and inter-polate from this database to build the numericalforward model used during the inversion.

1D-radial

1D-layer

2D

3D (2D plus dip)

3D

Formation model dimensions. In HALS laterologresistivity modeling, a 1D radial formation modelis concerned only with the influences of theradial invasion profile (A). It assumes that thereading is being taken in an infinitely thickformation bed. At other times, a 1D layered for-mation is used to model the effects of thick andthin shoulder beds with resistivity contrasts (B). A 2D model assumes that the formation is com-posed of homogeneous layers perpendicular tothe borehole (C). In this model, both the invasionof each layer and the shoulder-bed effect ofadjacent layers are taken into account, resultingin a more accurate Rt calculation in beds wheresignificant shoulder-bed effects exist. A 2D modelis defined by the values of Rxo, Rt, the invasionradii and thicknesses of the formation layers.The 2D model can be taken one step further byincorporating the formation dip relative to theaxis of the borehole. This results in a 3D-formationmodel (D). This scenario could be due to struc-tural formation dip, deviated borehole or both.More complicated 3D formation models can beconstructed in various ways. One way is to parti-tion the layers into azimuthal sectors (E). Thismodel takes into account azimuthal anisotropy,shoulder-bed effects and invasion as well asvariations in layer thickness with distance fromthe wellbore.

A

B

C

D

E

>

Boreholeradius AIT tool

Boreholemud resistivity

Standoff

Formationresistivity

rRm

Rf

The HALS tool uses two such FEM-deriveddatabases for processing its measurements.7 Thefirst database is used to correct apparent resis-tivities for borehole effects. It models the toolresponse in an uninvaded formation as a functionof formation-to-mud-resistivity contrast, bore-hole size and tool eccentricity. Because of tooleccentering, a 3D model was needed to constructthis database. The second database is used toinvert apparent resistivities for Rt. It describesthe tool response as a function of invasion radiusand contrast between formation resistivity, Rt,and invaded zone resistivity Rxo. Even though theresponse was originally determined using a 3D-based computation, the forward model iscalled 1D because it models resistivity variationsonly in the radial direction.

The AIT Array Induction Imager tool bases itsborehole corrections on a forward model thatincludes a 2D plus eccentricity effects (above).8

The borehole forward model is based on solutionsto Maxwell’s equations in a cylindrical boreholewith resistivity Rm surrounded by a homogeneousformation of resistivity Rt. The tool can be locatedwith a standoff anywhere in the borehole, but is assumed to be parallel to the borehole axis. Inthis model, the signal in any given AIT array ispredicted as a function of four environmentalparameters—Rm, Rt, borehole size and tool stand-off. This model is used to develop a database oftool responses that provides calibration for thefitted forward model used in real time to predicttool responses in any borehole environment.

From Forward Models to InversionsGiven a forward model with a system of equa-tions governing tool responses, one simply entersthe formation and borehole parameters into themodel and, after computation, the desired toolmeasurements are predicted. The prediction is aset of tool responses that would be observed if aphysical experiment were performed in the givenformation and borehole environment. When thegoverning equations are linear, the process isreversible—or invertible in one step. Iteration isnot needed. Given the tool response, the modelparameters can be estimated by multiplying thevector of observed tool responses by the gener-alized inverse of the same matrix used in thecorresponding forward problem.

Unfortunately, nature does not always poselinear problems. Density and resistivity toolresponses are not linear with respect to forma-tion properties. For these responses, a moreversatile technique is to find a solution by itera-tively solving the forward problem. Inversion isthe process of creating a model, mathematicallymodeling the physical response to that model,and then varying the parameters in the modeluntil the modeled response matches the oneseen by the logging tool. There are two ways toperform this task, manually and automatically. Alog analyst often uses manual inversion, adjust-ing model parameters based on previous experi-ence or knowledge, with advanced post-loggingprocessing programs at a computing center.

Automatic inversion is performed by an algo-rithm that computes the response to a model, thenfollows certain rules to modify the model to con-verge to a solution. Common algorithms minimizethe difference between the observed responseand the modeled synthetic response by adjustingthe model parameters to reduce the differences ateach step. Real-time environmental borehole cor-rections in the Platform Express system all dependon automatic inversion techniques.

Density inversion—Density inversion isbased on a maximum likelihood method that usesthe nonlinear parametric forward model dis-cussed above to link the depth-correctedobserved count rates in each of the detectorcount-rate windows to the formation parameters.At every depth, the inversion predicts the countrates in each detector window and comparesthem with those measured by the tool.Minimizing a cost function containing threeterms optimizes the solution.9

The first term in the cost function is based onthe best fit of all the observed window countrates. This is done by minimizing the ”recon-struction error,“ which is a term proportional tothe average squared difference between all themeasured and modeled count rates in eachdetector energy window—each weighted by theexpected error based on counting statistics andmodel uncertainty.

The second term in the cost function mea-sures the difference between the current model-predicted environmental parameters and those atthe previous logging depth. This term, called thesmoothness condition, helps ensure compatibil-ity between sampling rate and measurement ofvertical resolution.

The final cost term helps control the stabilityof the solution when formation and mudcakeparameter estimates are far from the databaserange, which can happen when large standoffsoccur. This term vanishes when the solution iswithin the limits of the database.

A powerful example of density inversionrobustness can be seen when comparing densitymeasurements derived during extreme condi-tions (down logging) with normal measurementstaken while logging uphole (next page).Occasionally, for precautionary reasons, logging

42 Oilfield Review

7. Smits J, Benimeli D, Dubourg I, Faivre O, Hoyle D,Tourillon V and Trouiller J-C: “High Resolution From a New Laterolog with Azimuthal Imaging,” paper SPE 30584, presented at the SPE Annual TechnicalConference and Exhibition, Dallas, Texas, USA, October 22-25, 1995.

8. Anderson BI and Barber TD: Induction Logging. Sugar Land, Texas, USA: Schlumberger Wireline &Testing, 1997.

9. The concept of a cost function comes from the mathe-matics of operations research and making decisionswith multiple objectives. A cost function is an equationthat contains a measure of the error in the decision-making or optimization problem. The function cancontain many terms, one of which usually describes the overall reconstruction (or fit) of the forward modelpredictions to the observed sensor responses. Themagnitude of the cost function decreases as the opti-mization improves.

10. Statistical (count rate) uncertainties and forward modelerrors are propagated through the density inversion pro-cessing by the measurement covariance calculation.

< AIT borehole correction model.A tool can be located anywherein the borehole with any valuestandoff. The borehole corrections are based on an iterative inversionprocessing.

Summer 1999 43

measurements are taken while going into theborehole. The caliper arm and pad are closed toprevent getting stuck against the borehole wallwhile going down the borehole. This results in alarge standoff with excessive mud and mudcakebetween the density pad and formation. Undersuch large standoff conditions, two-detector den-sity measurements based on a graphical spine

and ribs algorithm cannot account for theseextreme standoff conditions. The different radialsensitivity of each of the three density detectorshelps the inversion algorithm provide a final envi-ronmentally corrected density log that compen-sates for the extra standoff. This corrected logagrees well with the density log obtained undernormal logging conditions.

The use of a parametric forward-model-basedinversion for density measurements has severaladvantages. It makes the most efficient use of allthe sensor measurements while simultaneouslyobtaining formation and mudcake properties. Forexample, all density, photoelectric and standoffinformation contained in the entire spectrumfrom each gamma ray detector is automaticallytaken into account in the inversion algorithm,providing a strong degree of redundancy in theinformation available from the density tool. Bothstatistical uncertainties and forward modelerrors are considered in the inversion calculation,and provide realistic output uncertainties.10

These take into account count-rate statistics, cal-ibration errors and model errors, and establishreliable confidence limits for LQC and subsequentpetrophysical analysis.

Resistivity inversion—Inversion is central tothe borehole correction algorithm in the process-ing chain for AIT logs. It uses the AIT polynomialforward model to correct the eight depth-corrected raw array measurements for tooleffects in a nonstandard borehole environment.The parameters for the inversion are the fourcomponents of the database—borehole radius,tool standoff, mud resistivity and formation resis-tivity. The inversion is an optimization that findsthe set of borehole parameters that best repro-duces the four shortest arrays (6-, 9- 12- and 15-in. measurements). However, since thesemeasurements overlap considerably in theirinvestigation range, their information content isnot sufficient to solve for all borehole parameterssimultaneously.

In practice, the inversion process reliablydetermines only two of the four parameters. Theother two parameters are always measured orfixed. Since formation resistivity is always anunknown, there is only one additional freeparameter for the inversion to determine.Accordingly, there are three modes of boreholecorrection—depending on which parameter issought. If an accurate hole diameter from thedensity caliper and mud resistivity from the aux-iliary mud resistivity, Rm, sensor are used, thenthe borehole correction inversion determines toolstandoff. Mud resistivity needs to be measuredwithin 5% of its true value, which can be metwith the AIT mud sensor. By solving for formationresistivity and standoff, the borehole correctionproblem can be solved with no intervention fromthe engineer. Likewise, hole diameter can becomputed with an accurate standoff and mud-resistivity measurement, or else the mud resistiv-ity can be computed from accurate hole size andstandoff information.

Gamma ray

Depthft

Standoffupin.

API0 150Backscatter density (down)

g/cm31.65 2.65

Short-spacing density (down)

Inversion density logging downg/cm31.65 2.65

Inversion density logging up

Long-spacing density (down)

Backscatter-spacing density (up)

Short-spacing density (up)

Long-spacing density (up)

Caliperin.4 14

1 0

Standoffdown

in.1 0

X575

X600

>Using inversion to obtain the correct answer under extreme conditions. When a density tool is low-ered into the borehole, the caliper is kept closed to prevent getting stuck in the hole. Under theseconditions, the density pad is not pushed firmly against the borehole wall, resulting in a large standoff(black dashed) derived from the inversion algorithm shown in the depth track. All three detectors (bluecurves) see low density in track 2 because of extra mud and mudcake encountered in this configura-tion. However, the inversion algorithm is still capable of accounting for the extra mud and standoff inthis environment. It produces a density curve (blue) in track 3 that agrees with the density reading(red) obtained under normal conditions—logging up the borehole with the caliper open and padpushed against the formation.

invasion profiles. Typically, radial inversion isanother 1D four-parameter optimization that usesa monotonic-conductivity invasion profile modelto produce logs of Rxo, Rt, and the limits of a tran-sition zone (left).

Real-time wellsite processing for laterologsalso involves borehole corrections followed by a1D inversion for Rt. For example, HALS boreholecorrections adjust for the presence of the bore-hole, taking into account the borehole size andthe ratio of apparent resistivity to mud resistivityRa/Rm. It also includes an eccentricity correctionthat allows for the eccentered position of the toolin the borehole. The mud resistivity needed in the

44 Oilfield Review

Annulus profile

Rt

RannRxo

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atio

n re

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ivity

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file

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r1

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Distance from wellbore

Invasionmidpoint

Rxo

Ramp profile

Rt

ri

Step profile

Rxo

Rtri

>Resistivity invasion models. The simple piston-invasion, or step profile, and the ramp profilerequire three parameters; the slope profile is a four-parameter model; the annulus profile is a five-parameter model. A step profile is usedfor real-time HALS and HRLA radial inverse inva-sion modeling, and the slope profile is used forreal-time AIT radial invasion inversion.

Depthm

X150

X250

Gamma ray0 150API

Caliper6 11in.

HRLA array resistivity 1

ohm-m




HRLA array resistivity 50.1 10

DLL shallow resistivity

DLL deep resistivity0.1 10ohm-m

>Eliminating the Groningen effect. The DLL Dual Laterolog Resistivity deep-resistivity (red)and shallow-resistivity (blue dashed) curves in track 3 show a large separation (yellowshaded) caused by the Groningen effect. The HRLA curves in track 2 are not affected bythe Groningen effect because all the currents return to the tool string itself, rather than thesurface. The fact that all five HRLA curves are reading the same resistivity over this intervalalso establishes that the formation is uninvaded.

After all the raw array measurements havebeen corrected for nonstandard borehole effects,they are processed by the usual AIT log-formingtechniques. This involves generating the stan-dard depth (10-, 20-, 30-, 60- and 90-in.) andsimultaneously resolution-matched (1-, 2- and 4-ft) logs by convolving the corrected raw arraymeasurements with the Born-approximation-based weighting functions.11 Subsequently, real-time interpretations are based on inverting theseprocessed measurements radially to obtain anestimate of Rt.12 This radial processing also givesa quantitative estimate of invasion geometry aswell as an accurate estimate of Rt in complex

correction can be derived from the tool itself orfrom an external mud-resistivity measurement.Following borehole correction, an inversionbased on a 1D three-parameter step-profile inva-sion model is used to determine the formationparameters—Rt and the invaded-zone radius—that best describe the borehole-corrected deepand shallow measurements. Since only twoformation parameters can be determined fromthe two measurements (shallow and deep resis-tivity), the value of Rxo must be supplied to theinversion. It is obtained from the MCFL tool,which gives a resolution-matched microresis-tivity measurement.

Summer 1999 45

A similar Rt inversion technique is used for thenew HRLA High-Resolution Laterolog Array tooldiscussed below.13 For this tool, there is enoughinformation in the five array measurements toallow an accurate estimate of Rt, and determinethe invasion profile—independent of an externalRxo under most conditions. However, if an addi-tional Rxo measurement is used, it helps constrainthe inversion processing and improve the derivedRt. In the HRLA inversion, weights are assigned toeach measurement based on the magnitude of itsborehole correction. The measurement with thesmallest borehole correction is given the highestweight in the inversion algorithm.

New High-Resolution Resistivity TechnologyAlthough the concept of an array laterolog toolhas existed since the 1950s, Shell was first torecognize that an array-resistivity tool couldimprove thin-bed saturation evaluation, and pro-posed a point-electrode array tool called theMulti-Electrode Resistivity Tool (MERT).14 Theirtool design is similar to a multiple-spaced”normal“ measurement to which lateral andsecond-difference voltage measurements areadded.15 Recently, Baker Atlas commercializedtheir HDLL High-Definition Lateral Log service,which is an implementation of the MERTarchitecture.16

In response to this need, Schlumbergerdeveloped a new HRLA High-Resolution LaterologArray tool that can be used with the PlatformExpress system. This tool achieves multipledepths of investigation through a segmented array of six simultaneous, symmetrical andactively focused laterolog measurements. Thisdesign gives a coherent set of high-resolutionresistivity measurements that can be inverted to correct the deepest measurements for theenvironmental influences of invasion and shoulderbeds. By having all the laterolog currents return to the tool body, the HRLA tool minimizes the two most unwanted laterolog parasitic distor-tions—reference effects and shoulder-bedeffects. In addition, the surface current return and insulated bridle are no longer needed—reducing cost and risk.

Reference effects—Traditionally, laterologtools operate in a deep mode with currentsreturning to a reference electrode at the surface.This requires isolating the logging cable from thetool by use of a long insulating bridle. A system-atic shift in the resistivity measurement, calledthe Groningen effect, arises when high-resistivityformation layers above the tool force returningcurrents—following the path of least resis-tance—into the borehole. This leads to a poten-tial drop along the cable, and subsequently thevoltage reference of the tool can no longer beconsidered to be infinitely far away. As a result,formation resistivity computed using this refer-ence reads artificially high (previous page, right).Long tool strings and drillpipe have a similareffect—artificially increasing the measured for-mation resistivity.

Shoulder-bed effects—Shoulder beds withlarge resistivity contrasts have a strong influenceon most laterolog measurements. The measure-ment and focusing currents from the laterologtool tend to flow along zones of least resistance(left). A distortion in the focusing current distribu-tion allows the measurement current to flow dif-fusely across intervals with significant resistivityvariations. This defocusing introduces a couplingbetween the vertical and radial response charac-teristics of the resistivity measurement.

11. Generalized (finite conductivity) 1D tool geometricalresponse functions are derived using a forward modelsolution to similar to the single scattering Bornapproximation formalism traditionally used in quantummechanics. See Gianzero S and Anderson B: “A NewLook at Skin Effect,” The Log Analyst 23, no. 1 (January-February, 1982): 20-34.Barber TA and Rosthal R: “Using Multiarray InductionTool to Achieve High-Resolution Logs with MinimumEnvironmental Effects,” paper SPE 22725, presented atthe 66th SPE Annual Technical Conference andExhibition, Dallas, Texas, USA, October 6-9, 1991.

12. Howard AQ: “A New Invasion Model for Resistivity LogInterpretation,” The Log Analyst 33, no. 2 (March-April,1992): 96-110.

13. Griffiths R, Smits J, Faivre O, Dubourg I, Legendre E andDoduy J: “Better Saturation from a New ArrayLaterolog,” Transactions of the SPWLA 40th AnnualLogging Symposium, Oslo, Norway, May 31-June 3, 1999,paper DDD.

14. Vallinga PM, Harris JR and Yuratich MA:”A Multi-Electrode Tool, Allowing More Flexibility in ResistivityLogging,” Transactions of the SPWLA 14th EuropeanFormation Evaluation Symposium, London, England,December 9-11, 1991, paper E. Vallinga PM and Yuratich MA: “Accurate Assessment of Hydrocarbon Saturation in Complex Reservoirs From Multi-Electrode Resistivity Measurements,”Transactions of the SPWLA/CWLS 14th FormationEvaluation Symposium, Calgary, Alberta, Canada, June13-16, 1993, paper E.

15. The use of computed focusing makes it possible, in prin-ciple, to obtain a Laterolog-7-style measurement fromthe MERT tool.

16. Itskovich GB, Mezzatesta AG, Strack KM and TabarovskyLA: “High-Definition Lateral Log-Resistivity Device: BasicPhysics and Resolution,” Transactions of the SPWLA39th Annual Logging Symposium, Keystone, Colorado,USA, May 26-29, 1999, paper V.

SqueezeAntisqueeze

>Squeeze and antisqueeze. Squeeze (top) occurswhen adjacent outer beds have significantlyhigher resistivity than the middle bed. Laterologfocusing currents tend to migrate toward the bedof interest and squeeze into this bed of compara-tively low resistivity. This results in a deeper mea-surement than in a homogeneous formation. Thesqueeze can result in resistivity curve separationthat imitates an invasion profile even in theabsence of invasion. When both invasion andsqueeze are present, the resistivity curve separa-tion will indicate deeper invasion than is actuallypresent. The reverse situation, antisqueeze (bot-tom), results when adjacent outer beds have lowerresistivity than the middle bed. In this case, mea-surement currents tend to flow into beds of lowerresistivity rather than flow through higher resistiv-ity beds. The resulting defocusing causes the deepmeasurements to have a reduced depth of investi-gation and thus read a lower formation resistivity ifthe invaded-zone resistivity is lower than Rt. Thiseffect can lead to underestimation of reserves.

The HRLA tool addresses these problemswith multiple modes of tightly focused arraymeasurements.17 Multifrequency operation of thesegmented electrodes enables the simultaneousresistivity measurement modes to be distin-guished (left). Software focusing by linear super-position of each mode is used to provide activefocusing. The shallowest mode is the most sen-sitive to the borehole and is used to estimatemud resistivity, Rm. The deepest mode has aresponse comparable to the deep-resistivitymeasurement of the HALS tool. The spacing ofthe other arrays is such that they have responsecharacteristics that optimize the information con-tent of their measurements with respect to theinvasion profile.

The addition of shallower curves improvesthe radial sensitivity to resistivity change, whichresults in greater log curve separation in thepresence of invasion (below). This is especiallyhelpful in thin beds, where deeper measure-ments tend to lose both depth of investigationand vertical resolution because of antisqueezeeffects. In addition, with reference effects goneand shoulder-bed effects reduced, the separationbetween the deep and shallow measurementscaused by these effects is also reduced. Theimproved invasion discrimination in thinly bed-ded formations leads to better vertical resolutionand more accurate inversion processing forformation resistivity.

46 Oilfield Review

17. Smits et al, reference 7.

150

100

50

0

-50

-100

-150

Mode-0 Mode-1 Mode-2

150

100

50

0

-50

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1.0

10

Appa

rent

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stiv

ity, o

hm-m

HRLA mode 1

HALS deep

HRLA mode 5

HRLA mode 4

HRLA mode 3

HRLA mode 2

HALS shallow

Radial response of resistivity tools. The bore-hole-corrected HALS deep-resistivity radialresponse compares well with the Mode-5response from the HRLA array measurement,while the HALS shallow-resistivity response isintermediate between the Mode-2 and Mode-3HRLA responses. The additional HRLA resistivitymeasurements provide improved definition ofradial resistivity changes, which helps evaluateinvasion profiles.

>Current distributions for HRLA focusing modes. By increasing the number of centralelectrodes that are kept at the same potential, the tool current return in the formation ismoved farther away, and the depth of investigation is increased. Six modes with increas-ing depth of investigation are used. In Mode-0, current flows directly from the centralelectrode to the nearest array electrodes. This mode is sensitive to the mud column envi-ronment and is used to estimate mud resistivity and borehole diameters. The deepestmode, Mode-5, sends current out from all but the outermost electrodes. The spacing ofthe array has been designed to optimize the information content of the measurement datawith respect to the invasion profile.

>

Summer 1999 47

An inversion for the HRLA tool based on a 2D-formation model with a piston-invasionprofile is currently being developed for theGeoFrame log interpretation system. The inver-sion processing proceeds by detecting bedboundaries and segmenting the log into discretebeds with average thickness of 1 to 2 ft [0.3 to0.6 m]. The inversion will iteratively refine for-mation parameters until the forward modelaccurately reproduces the input logs. Becausethe formation model includes the boreholeshape and mud resistivity, the input logs do notneed to be borehole-corrected.

More accurate representations of theformation environment lead to more accurateestimates of Rt, especially in thinly beddedformations. When the reservoir and water layerthickness are on the order of 2 to 5 ft [0.6 to 1.5 m], it is not unusual to see differences of 50 to 100% between the 1D- and 2D-inversionresistivity estimates (right). Usually the 1D-inversion estimates are too pessimistic. An obvi-ous extension of these models includes dippinglayers with 3D models.

Getting More Pay fromResistivity Logs


Rt from 2D inversion

Rxo from 2D inversion

Rt from 1D inversion

MCFL microresistivity

ohm-mohm-m




Rxo from 2D inversion


Rt from 2D inversionAttenuation

Propagation time Differential

EPT

1 30 1 30100 0 20700dB/m

XX00

XX10

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27 7ns/m 0 3in.

Crossover Washout

2D invasion

Invasion radius

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in.

Caliper

Rxo > Rt

Rxo < Rt

Rxo > Rt

Rxo < Rt

Improved Rt estimate with 2D inversion. The 2Dinverted formation resistivity Rt (wide red) andinvasion resistivity Rxo (green) are shown in track 3along with the raw HRLA curves. The shadingbetween Rxo and Rt indicates where the invasion isnormal (Rxo < Rt) or reversed (Rxo > Rt). In track 4,the 2D inverted resistivities Rt (red) and Rxo

(green) are compared with the 1D inverted forma-tion resistivity Rt (magenta) and the Rxo (black)from the MCFL tool. The 2D inversion shows a sig-nificant increase in Rt obtained in thin beds—suchas those between XX30 and XX70 ft—over the 1D-inversion results. A good match between the 2Dinversion-derived Rxo and the one independentlyobtained from the MCFL measurement—addsconfidence to the inversion results. The EPT Elec-tromagnetic Propagation Tool dielectric attenuationand propagation time curves, confirming the pres-ence of thin beds in track 1, were used to constrainthe inversion for the uninvaded formation model inthe shales.

>

48 Oilfield Review

Optimal array focusing is enhanced by thesymmetric tool design, ensuring that all the sig-nals are measured at exactly the same time andat the same logging tool position. This helpsavoid horns and oscillations produced by irregu-lar tool motion, and ensures that the measure-ments are exactly depth aligned. The coherentnature of the focused, depth-aligned, resolution-matched measurements from the HRLA toolproduces a more intuitive LQC, as the curves sep-arate following the invasion resistivity profile.(below). At the wellsite, operational safety andefficiency are improved by the elimination of thebridle and surface current system.

Tackling Difficult EnvironmentsModel-based inversion processing is a delicatebalancing act. Two conflicting factors need to beconsidered. On one hand, the accuracy of theresult depends on how much additional informa-tion can be built into the model. On the other, thespeed with which the result is delivereddecreases with the complexity of the model. Fast1D-inversion models are needed for real-timeenvironmental corrections to help the operatorprocess, interpret and evaluate logs quickly atthe wellsite. However, sometimes the wellsiteanswer isn’t enough. For example, various para-sitic effects on resistivity measurements—

shoulder beds, spiral boreholes and dipping for-mation beds with invasion—can continue tocause errors when computing Rt. For these, post-logging processing techniques available at thecomputing center can help.

Shoulder beds—The presence of shoulderbeds can lead to overestimating Rt in the”squeeze“ case and underestimating it in the”antisqueeze“ case. Consequently, in both cases,water saturation, Sw, estimation will be affected.For example, a log analyst will estimate thewater-filled resistivity, Ro, from a water-saturatedbed (squeeze case), and estimate Rt in the payzone (antisqueeze) using Archie’s equation. Errorsin both resistivities contribute to overestimatingwater saturation in the pay zone, which can leadto overlooked hydrocarbons.

How to tackle this problem? The 1D radialinversions used in real time are for simpler casesand do not address the fact that nearby adjacenthigh-contrast beds may influence the resistivityreading. Improved methods involve the combina-tion of 1D shoulder-bed corrections followed by a1D radial inversion, but they do not address thefact that the radial and vertical responses arecoupled, leading to significant errors in resistivitydetermination. It has been long recognized thatresistivity estimation can be improved by the useof inversion techniques that take into account true2D or 3D formation structure (see ”Getting MorePay from Resistivity Logs,“ previous page).Application of 2D formation models to the inver-sion technique can double the calculated reserves,particularly in thinly bedded formations.18

Spiral boreholes—Some drilling practicesproduce a borehole with a 3D shape that has aspiral groove on top of the bit-sized hole. Suchboreholes produce a quasi-periodic character inthe logs, and are variously referred to as”corkscrew“ or ”threaded hole.“ These havebeen associated with downhole drill motors andhigh-angle wells. Displaced stabilizers can pro-duce the same effect in vertical wells. The effectis seen as a periodic oscillation on the caliperlog. The effect on other logging tools depends on the physics of the measurement. The impactof spiral boreholes on induction measurementshas been extensively studied by BP AmocoExploration.19 Recently, filter techniques havebeen developed to reduce the effects of spiralborehole rugosity on logging tool measurements(see ”Dealing with Spiral Boreholes,“ next page).For density tools, the effect of the spiral-groovedhole is to produce a cyclic mudcake effect.

XX20

XX00

1D Rt Increase

HALS invasion

Depthft

0

Invasiondiameter

HALS

in. 60

0

Bit size

in. 60

1

1D Rt from HRLA

ohm-m 100 1

1D Rt from HALS

ohm-m 1000

Invasiondiameter

HRLA

in. 60

MCFL microresistivity MCFL microresistivity

1

1D Rt from HRLA

A

ohm-m 100

HRLA array resistivity 5 HALS shallow resistivity 1D Rt from HALS

HRLA array resistivity 4 HALS deep resistivity




>Reducing shoulder-bed effects on laterolog measurements. In this example, HALS resistivities,shown in track 3, are out of sequence because of shoulder-bed effects. Therefore, the HALS real-timeprocessing forces invasion diameter to bit size—thus computing zero invasion. As a result, the HALS-derived formation resistivity, Rt, defaults to the deep-reading resistivity value because the data areinconsistent with the 1D formation forward model. Over the same interval, the HRLA tool is much lesssensitive to shoulder-bed effects, and the additional data from the array measurements, shown intrack 2, provide a realistic estimation of the invaded zone. The invasion corrections are applied to allHRLA array resistivity measurements for an accurate Rt evaluation. Results show that the maximumresistivity from HRLA real-time inversion is 45% higher than that estimated by the HALS measurement,leading to a 16% increase in reserves estimated in Zone A.

18. Smits et al, reference 7.19. Webster M: “AIT Performance in Prudhoe Bay,” BP

Exploration Memorandum, October 1997.

Summer 1999 49

For the AIT tool, saline mud combined withspiral or corkscrew boreholes can produce astrong signal and completely smear an arrayinduction tool log (below). The origin of theinduction tool response distortion is likely dueto changes in standoff.1 Practical experienceshows that the effect is worst in 6-in. holeswhere the standoff distance is limited. As thetool moves along the borehole, the standoff ribson the tool tend to fall into the grooves, allowingthe induction sonde to approach the boreholewall—periodically reducing tool standoff. Using the fact that the spiral borehole introduces a periodic effect or distortion to the log, several

signal-processing techniques effectively cancelthe unwanted periodic signal.

There are three main steps: automaticallydetecting the primary signal and its harmonics,estimation of their frequency, and subsequentremoval of all unwanted components. For AITmeasurements, the method involves frequencyspectrum estimation and peak identificationover short logging segments, in which each arraydata segment is replaced by a parametricautoregressive model. The advantage of thisapproach is automatic detection and frequencyestimation of the dominant periodic componentsin each segment. Typically, up to three sinu-

soidal harmonics are detected in AIT logs. Oncethe harmonic components are determined, thetask is to filter them out. For each array datasegment, a notch filter is designed with theappropriate transfer and phase characteristicsto remove—without phase distortion—all har-monics from the spiral borehole (bottom). Thefilter is applied to the raw signal just after bore-hole correction. Similar methods have beenproposed for handling the effects of corkscrewrugosity on nuclear logs.2

Dealing with Spiral Boreholes

1. Barber et al, reference 21, main text.2. Betts P, Blount C, Broman B, Clark B, Hibbard L,

Louis A and Oostoek P: “Acquiring and Interpreting Logs in Horizontal Wells,” Oilfield Review 2, no. 3 (July1990): 34-51.

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tral p

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, dB

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LQCValid flag1-ft2-ft4-ftORMag mud flagNonMag

LQCValid flag1-ft2-ft4-ftORMag mud flagNonMag

Hole sizein.

Array resistivity10 in.20 in.30 in.

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Induction logs in a spiral borehole. Onthe left is a Platform Express inductionlog from a Canadian well with a severespiral borehole (left). Filtering the datafrom the spiral borehole well hasremoved unwanted harmonic boreholenoise, and now the environmental flagsindicate that the 2-ft resolution logs arevalid (right). Chart-based C, bad hole Band magnetic mud M log quality-control(LQC) flags are shown to the left ofeach log. The color of the chart-basedand bad hole flag shows the recom-mended resistivity bed resolution. Thecolor of the magnetic mud flag indicateseither magnetic mud (red) or nonmag-netic mud (yellow).

Frequency spectrum and notch filter.A power spectrum of the logging datashows several peaks (arrows) causedby cyclic standoff variations in toolstandoff. A frequency response showsthe attenuation (top right) and phase-shift (bottom right) characteristics ofa three-frequency notch filter designedto remove periodic noise found in log-ging data.

>>

Handling dip—The large volume of investi-gation of induction tools complicates the inter-pretation of their logs. Modern inductiondevices such as the AIT tool are designed foruse in vertical wells, and are carefully focusedto limit their response to a relatively thin forma-tion layer perpendicular to the borehole.However, in wells at high relative dip, theresponse cuts across several beds, and themeasurement is no longer focused in an iso-lated single layer. The effect of high relative dip

50 Oilfield Review

Window 1Backscatter

Processing flag statistical analysisPEF flags upDensity flags up

BS average reconstruction errorSS average reconstruction errorLS average reconstruction error

W20.50%0.03%2.29%

W31.29%0.41%

W4

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%-10 10 % %

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Detector reconstructionerrors Black areas show that the corresponding error flag is set

Offset errorTau loop errorStabilization loop or crystal resolution error

0.7%0.0%Window 1

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0.70%

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A

>Density LQC logs. In a well drilled with nonbarite mud in the North Sea, the density inversion processing reconstruction errors (left) for all detectors are seento be nearly zero—closely tracking the center of each track, as expected. The density curve (yellow) from this interval is superimposed to highlight the largechange in densities computed across this interval. The global cost function log is low throughout the entire interval indicating good reconstruction and highconfidence in the inversion results. Statistical analysis of the reconstruction errors shows that every energy window is below its maximum bias level. Thehardware LQC logs (right) from this well show stable tool operation. As expected, the backscatter total count rates (black) in track 1 anticorrelate in Zones Aand B with those of the from the short-spacing detector (black) in track 2 and long-spacing detector (black) in track 3. No detector hardware error flags weredisplayed in the green columns shown at the left side of each track.

is to blur the log response and to introducehorns at the bed boundaries.

Traditional dip-correction algorithms forinduction logs are limited in practice to anglesless than 50º because of an increasing nonlinearresponse to dip and adjacent bed-boundary con-trasts. As a result, interpreting resistivity frominduction logs at apparent dip angles over 50ºhas been limited to iterative inversion using 1Dforward models. The presence of invasion hasadded additional complexity to the geometry,requiring processing based on 3D inversioncodes. Even with fast computers, the processing

has been excessively time-consuming for longlog sections or when many thin beds areencountered. Recently, a new algorithm basedon a maximum-entropy inversion of raw, bore-hole-corrected array data through a fast 1Dforward model has dramatically improved thespeed and ability to interpret multiarrayinduction logs in invaded formations at high rel-ative dip angles (see ”Interpreting InductionLogs in High Dip Angle Formations withInvasion,“ page 54).

Summer 1999 51

Window 1Backscatter

Processing flag statistical analysisPEF flags upDensity flags up

BS average reconstruction errorSS average reconstruction errorLS average reconstruction error

Window 2

Window 3% % %

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BackscatterTotal count rate

1650 V 1750

Black areas show that the corresponding error flag is setOffset errorTau loop errorStabilization loop or crystal resolution error

High voltage

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5 % 25

Short-spacingTotal count rate

Short-spacing Long-spacingBackscatter

0 cps 500K

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5 % 25

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0 cps 50K

W21.44%1.44%

W32.91%0.87%

W4

-2.48%

4.9%1.4%Window 1

-0.53%-0.72% -12.59% -2.13%

0.48%-1.04%

B

A

>Density LQC logs. In another 52º deviated well drilled with heavy barite-weighted mud for a different operator in the North Sea, a section of the density pro-cessing LQC logs tells a different story. The long-spacing and backscatter detector reconstruction errors (left) shown in tracks 1 and 3 are large because ofthe presence of heavy barite mud (14 lbm/gal) [1.67 g/cm3] and a highly fractured borehole environment through a known coal bed. In other parts of thewell—with better borehole conditions—reconstruction errors were lower. The density algorithm automatically detects the presence of barite and changesthe weighting of the window count rates from each of the detectors to obtain the most accurate answer in this difficult environment. The hardware LQC logs(right) from a higher section in this well show an unusual phenomenon. The total count rates (black) in the lower coal bed in Zone A, from the backscatterdetector (track 1) anticorrelate with those from the short-spacing detector (track 2) and long-spacing detector (track 3) as before, and as expected. However,in Zone B, they all decrease uncharacteristically. This is apparently due to increased attenuation in the backscatter gamma ray flux due to thick barite mud-cake in this zone. The density standoff curve (red) in the depth track confirms the increased thickness of mudcake over this zone.

indicates a problem in calibration, excessive padwear or tool standoff. High intermittent values inthe reconstruction errors indicate an abnormalnoise level on the measurement (hardware prob-lems), or instabilities in the inversion processthat may be associated with bad borehole condi-tions. A log of the cost function used in the inver-sion modeling helps to evaluate confidence in theestimations. Hardware LQC logs along withdetector count-rate logs are used to help confirmtool response, calibrations and stabilization inunusual environments such as in thick mudcakewith heavy muds (above).

Active Log Quality ControlMany questions arise when logs appear strange.Raw data may be fine, but the computed forma-tion parameter, such as density, may look abnor-mal. This leads to questions: Is the tool in anunusual formation? Is the software correct? Is thecalibration correct? Is the tool working properly?Good log quality control (LQC) resolves theseissues, and with the addition of real-time envi-ronmental corrections provides insight duringacquisition into both the effects of the loggingenvironment on every tool measurement and theway these measurements are being processed.

Numerous LQC analyses, logs and flags areavailable in the Platform Express system toensure quality measurements and processing.

These literally form a log quality-control hierar-chy from the top level of environmental process-ing down to the bottom level of tool-specificsensor performance and calibration. Followingare some examples showing how LQC activelyworks to provide better logging answers both atthe wellsite and afterwards.20

Density LQC—Window count-rate reconstruc-tion errors from the density inversion algorithmare a measure of the ”health“ of the inversionprocess (previous page). They pinpoint significantdifferences between modeled window countrates and those measured for each detector. Alarge systematic bias in the observed reconstruc-tion error log for more than one energy window

20. For more on LQC and tool specifics see: PlatformExpress User’s Guide. Houston, Texas, USA:Schlumberger Wireline & Testing, 1999.

52 Oilfield Review

XX850

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A

>Density profile caused by pipe groove. In this 97º-deviated wellbore, pipe groove filled withmudcake produces a mudcake density profile, shown in the depth track. The backscatter (dotted),short-spacing (short dashed) and long-spacing (long dash) detector density curves show anincreasing density profile as they each see deeper into the formation. LQC flags, shown on theleft of track 1, verify that the tool readings in this environment are normal. The UBI UltrasonicBorehole Imager tool images—amplitude (track 1) and transit time (track 2)—confirm that thepipe groove contains mudcake. In other sections of the wellbore that have been scraped cleanof mudcake, each detector reads the same density. The characteristic parallel lines caused bythe drillpipe scraping the borehole wall shown in Zone A confirm this. The standard resolutionformation density (solid blue) derived from inverse modeling is shown in track 2.

A typical example of significantly high recon-struction errors occurs when the logging engi-neer selects the wrong mud-type-processingoption—Barite or No-Barite. These two modescorrespond to different tool-response models aswell as specific inversion schemes, and thechoice has to be made according to the impor-tance of photoelectric absorption in the mud,which is linked to the barite content. If the engi-neer does not have exact knowledge of the mudcomposition, the wrong option might be selected,resulting in poor estimation of density and Pe.However, the raw count rates are alwaysrecorded, and since processing is applied on theraw counts, it is always possible to recomputecorrect results regardless of the acquisition mode.

Recently, a new ”switchless“ mud-densityalgorithm was designed to eliminate problemswith incorrect mud-algorithm selection. Currentlybeing field-tested, the algorithm uses a uniquetool-response model, valid for all mud properties,and a generalized inversion scheme based on abarite indicator. The barite indicator provides anestimation of the amount of barite in the mud ateach depth, and is based on low- and high-energy window count-rate ratios in the back-scatter detector.

Another example of how LQC provides activecontrol of the log acquisition process is theautomatic offset recalibration for high-resolutiondensity logging measurements. The robust low-resolution window count-rate reconstructionerror helps identify slowly varying count-rate off-set errors on the backscatter and short-spacingdetector measurements—used primarily for thehigh-resolution density logs. Any offset count-rate error detected in these measurements is cor-rected before being used in the inversionalgorithm to estimate the high-resolution forma-tion parameters. Whenever LQC detects extremerugosity or bad borehole, the more robust low-resolution density logs are recommended.

Summer 1999 53

An example from the North Sea shows howLQC helps build confidence in the tool measure-ments when the unexpected happens in unusualborehole conditions (previous page). Each of thethree detectors in the Platform Express densitytool sees progressively farther into the formation.Like the invasion profile produced by an array-resistivity tool, array density measurements pro-duce density profiles. These profiles depend onthe mud weight used. In wells drilled with lightnonbarite mud, the density profile tends toincrease from low to higher density, as the detec-tors look deeper into the formation. Typically, inwells drilled with high-density barite mud, thenormal density profile will be from high to low aseach detector looks farther into the formation.

When first seen in boreholes, unexpectedapparent density profiles were thought to be dueto hardware problems. However, LQC quickly ver-ified that the tool was functioning correctly.Further examination revealed the answer: Indeviated wells, where pipe grooves frequentlyoccur, the borehole is scraped clean of mudcake,giving an unpredicted density response. Nowthat the phenomenon is well understood, theinversion algorithms are designed to accommo-date this effect.

Environmental LQC for resistivity—The oper-ational limits of the AIT induction measurementhave been incorporated into a ”fuzzy-logic“algorithm that uses real-time inputs of caliperand mud-resistivity measurements to determinethe best resolution logging output consistent

with the environment (above).21 The output of the logic has four possible states—1-ft valid,2-ft valid, 4-ft valid or “Out of Range.” The laststate is flagged when environmental parametersare completely outside the range of the leastrestrictive induction tool measurement. Thismeans laterolog tools would provide better resis-tivity measurements.

However, the chart-based induction tool LQCalgorithm is based on smooth boreholes, anddoes not always detect when the environment isunfavorable for induction measurements. Inwells where the borehole is very rough or whenstandoff is inadequate, spurious spikes and otheranomalies might render high-resolution logsunusable. Research has shown that high-fre-quency induction array signals come from nearthe borehole, confirmed by the extremely sharpspikes near the tool axis seen on the shortestarray Born-response tool sensitivity function. Inthese cases, a rugosity-detection algorithm com-bines high-pass-filtered, short-array data withmud-resistivity information to make certain thatrugosity detection is dominant. The default well-site presentation is the most appropriate com-posite-resolution log, which varies smoothlybetween the 1-ft to 4-ft resolution log—basedon the combination of the chart and hole-rugositylogic. In all cases, the three basic-resolution andcomposite-resolution logs are always recorded.

Remote witnessing—Recently, BP AmocoExploration initiated a program of remote wit-nessing on their wells in the Andrew field in theNorth Sea by combining the capabilities ofPlatform Express real-time LQC and interpreta-

tion with the InterACT communications system.The InterACT system provides the capability forreal-time transmission of log data and wellsitegraphics to distant locations. This allows directand immediate communication and interactionbetween the offshore wellsite and consultants inAberdeen and London during log acquisition forbetter and more timely decision-making.

For example, in one well, a wireline loggingtool was unable to reach target depth due towellbore deviation. The situation was confirmedwhile logging. An immediate decision was madeto pull out of hole and go straight to a drillpipe-conveyed logging option. In other cases, irregu-larities in borehole dimensions shown on thecaliper were witnessed during logging and aquick decision was taken to pull out of the holeand rig down—eliminating the possibility of astuck tool. In all cases, real-time LQC providedconfidence in the tool measurements during log-ging, enabling operators to make appropriatedecisions based on environmental constraints,and not on limitations in the tool performance.

Remote witnessing has also decreased costsand improved safety by reducing personnel andtransportation requirements at the wellsite.Logs and evaluations are immediately availableto the experts who need them, and real-timeLQC ensures that logging measurements arevalid and can be trusted. If problems occur,expert opinions are available to help with con-tingency plans and decisions.

Limit of 4-ft logs

Possible large errors on shallow logs – 2-ft limit

Limit of 1-ft logs

AIT-family toolsrecommended operating range

using computed standoff method

Possible largeerrors

on all logs

(Rt/Rm)(hole diameter/8)2 (1.5/standoff)

R t, o

hm-m

1000

100

10

0.01 0.1 1 10 100 1,000 10,000

1

Possible large errors on all logs

Possible large errors on 2-ft logs

Possible large errors on 1-ft logs

Recommended range

R t, o

hm-m

1000

100

10

0.01 0.1 1 10 100 1,000 10,000

1

(Rt/Rm)(Bit size/8)2

>Operating range for AIT induction measurements. Borehole resistivity, Rm, hole diameter and standoff limit the range of acceptableAIT induction measurements (left). The chart shows that the effect of the borehole environment is most significant on the high-resolu-tion logs. A web-based job planner can be used to determine what resolution is usable for a range of expected formation resistivity andborehole parameters: Rt, Rm and bit size (right). The scatter of data represents the range of uncertainty on the input parameters.

21. Barber T, Sijercic Z, Darling H and Wu X: “InterpretingMultiarray Induction Logs in Difficult Environments,”Transactions of the SPWLA 40th Annual LoggingSymposium, Oslo, Norway, May 31-June 3, 1999, paper YY.

A new algorithm based on maximum-entropyinversion of borehole-corrected multiarrayinduction data through a fast 1D forward modelhas been developed and tailored for highly devi-ated wells.1 This algorithm provides the sameinterpretation for invasion that has previouslybeen available only for vertical wells. The key to maximum-entropy inversion is a fast forwardmodel. For this model, an analytical solution is used to compute the response of the AIT toolin a layered formation with dip. The response of each array is computed by finding exactsolutions to Maxwell’s equations for the beds at a given dip angle. In implementation, it is desirable to have the layer thickness lessthan the resolution of the sensors—typicallylayers 6- to 12-in. [15- to 30-cm] thick.

The inversion is formulated on finding theunknown formation conductivity that minimizesa cost function. Like the other inversions usedin the Platform Express system, the first term in this cost function is a measure of how wellthe forward model predicts each array raw

measurement—weighted by the expected erroror noise in each measurement. For example, if the model predicts array voltages that agreewell with those observed on each AIT receivercoil, then its contribution to the cost function is low. The second term is one proportional to the total entropy in the resistivity log. Thisterm adds stability to the solution. Finally, there is an empirical smoothing term includedin the cost function. The smoothing term also helps add stability to the solution, but is used sparingly because it tends to decreasevertical resolution.

The concept of entropy as applied to log datais not intuitive. In physics, entropy is a measureof the degree of disorder in a system, and thesecond law of thermodynamics states that thetotal entropy of a system can never decreaseduring a change. Applied to log data, entropy is a measure of the departure of log values fromlocally averaged values. For example, by settingup a simple least-squares inversion of the thinlylayered model—done by including only the first

term in the cost function—the results can havemany high-frequency “wiggles” (below).Including the entropy term in the cost functionhelps smooth out all the extraneous high-frequency information content—meaninglessinformation below the resolution capability of the tool.

The implementation of maximum-entropyinversion processing is more robust when pairsof arrays are inverted at the same time to obtainformation resistivity. As a result, rational sets of array pairs are inverted together, with thesurprising property that the depth of investiga-tion of each pair is the same as that of the deeper-reading array. This means that radialresponse functions for the arrays can be used to define the radial response of the invertedlogs. Furthermore, by weighting the results of inverting the array pairs, the inverted logscan be focused radially to give logs with stan-dard AIT depths of investigation.

The combination of maximum-entropyprocessing, resolution-matched inverted forma-tion resistivities and radial focusing is calledMaximum Entropy Resistivity Log INversion(MERLIN). This processing works on AIT data at all dip angles from 0º to approximately80º (next page, top). In principle, it will work up to 90º, but in practice, the parameterizationrequires that the wellbore cut all beds of interest. MERLIN processing replaces theBorn-response function filter-based standardAIT processing. The resulting logs can be inverted for invasion parameters as if theywere in a vertical well, but at any dip angle. In addition, although maximum-entropy inver-sion was developed to remove the effects of high dip, the exact solution forward model at the heart of the method works at any dipangle, including zero.

Interpreting Induction Logs in High Dip Angle Formations with Invasion

Maximum-entropy solution. A simple least-squaresinversion of the thinly layered model results in a logwith a high noise component (left). In the high-entropy solution (right), the dotted curve indicatesthe increased entropy found in the maximum-entropy solution.

54 Oilfield Review

Resistivity, ohm-m Depthft

Highentropy

Lowentropy

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

1.0 10.0 100.0 1.0 10.0 100.0

Resistivity, ohm-m

>

For example, formations with high shoulderconductivity and with shoulder-bed contrastscan be modeled correctly with the exact-solution-based MERLIN processing. In a verticalwell drilled with oil-base mud, anomalies areseen on the bottom and top of the reservoirsection in the real-time AIT logs (right). Thehigh contrast between the reservoir resistivity(100 ohm-m) and that of the conductive clayshoulder beds (less than 1 ohm-m) causes thestandard logs to overshoot in the resistive reser-voir. This happens because standard AIT pro-cessing—based on Born-based responsefunctions—includes contributions from manydepth intervals around the tool. Although thesefunctions are accurate models of the inductionresponse in low-to-moderate-contrast envi-ronments, when the shoulder-bed contrastapproaches 500:1, the Born-based approxima-tion for the tool response is poor. The MERLINlogs are well-behaved in these difficult induction-logging environments.

1. Barber TD, Broussard T, Minerbo G, Sijercic Z andMurgatroyd D: “Interpretation of Multiarray InductionLogs in Invaded Formations at High Relative Dip Angles,”The Log Analyst 40, no. 3 (May-June, 1999): 202-217.

AIT 10 in.

Wellsite (1-ft)resistivity

AIT 20 in.

AIT 30 in.

AIT 60 in.

ohm-m

AIT 90 in.

AIT 10 in.

MERLIN-processedresistivity

AIT 20 in.

AIT 30 in.

AIT 60 in.

ohm-m

AIT 90 in.

AIT 10 in.


AIT 20 in.

AIT 30 in.

AIT 60 in.

ohm-m0.2 2K 0.2 2K 0.2 2K 0.2 2K

AIT 90 in.

AIT 10 in.


AIT 20 in.

AIT 30 in.

AIT 60 in.

ohm-m

AIT 90 in.

Bit size

12 in.

Caliper

17

0 API

Gamma rayDepthm

X240

X245

X250

X255

X260

X265

150

Correcting for dip. In a section of a deviated welldrilled with oil-base mud, real-time processed AITinduction wellsite logs show non-normal invasion profiles, resistivity horns and overshoots in four zones,shown in track 2. The first post-log MERLIN processingresults in track 3 assumed that the wellbore was at 75ºrelative to the beds, but better results in track 4 wereobtained assuming a 65º deviation. The final resultshows coherent resistivity separation in the top zoneand a dramatic increase in Rt, indicating more pay in each zone.

AIT 10 in.

Wellsite-processedresistivity

AIT 20 in.

AIT 30 in.

AIT 60 in.

ohm-m

AIT 90 in.

AIT 20 in.

MERLIN-processedresistivity (75 deg)

AIT 30 in.

AIT 60 in.

ohm-m

AIT 90 in.

AIT 20 in.

AIT 10 in. AIT 10 in.

MERLIN-processedresistivity (65 deg)

AIT 30 in.

AIT 60 in.

ohm-m

AIT 90 in.-40 degrees

Azimuth

360

70 degrees

Hole deviation

120

4

Bit size

14Caliper

C2

in.4

C1

14 0 API

Corrected gamma ray

Depthm

150 0.2 2K 0.2 2K 0.2 2K

X830

X840

X850

<Resolving shoulder-bed anomalies. The gamma raycurve (green) in track 1 clearly shows shale bedsabove and below the reservoir section. The wellsiteAIT 1-ft induction logs in track 2 show anomalousbehavior with large resistivity oscillations and a non-coherent separation over a large zone near the bottomand top of the reservoir. The 2-ft and 4-ft logs (tracks4 and 5) display similar behavior. The anomalies arecaused by high shoulder-bed contrast coupled with low-resistivity shoulders which violates the Born-approxi-mation used in the wellsite processing. After MERLINprocessing, the anomalies are eliminated from the logs in track 3, leaving the middle thick section of the reservoir unaffected.

Summer 1999 55

>

Real-Time InterpretationsWellsite interpretation during log acquisition isanother benefit of real-time environmental cor-rections. Platform Express interpretations featureintegrated petrophysical computations andgraphic presentations that help operators maketimely decisions about the reserves in their fieldduring the logging run. These include a completeformation volume and lithology evaluation, fluidsaturation analysis, invaded-zone gas saturationand a special horizontal well presentation.

For example, formation porosity is derivedfrom the traditional crossplot of tool-measuredbulk density, ρB, and the thermal neutron poros-ity. Comparing neutron response with that of thedensity measurement, a lithology-corrected for-mation porosity is determined. With theseresults, porosity-corrected formation grain den-sity, ρmaa, and volumetric matrix photoelectricfactor, Umaa, are computed, to provide inputsneeded for a standard ρmaa-Umaa crossplot-basedmineral interpretation. By its nature, this cross-plot method is independent of porosity. Finally,clay volume, Vcl, derived from gamma ray orspontaneous potential (SP) measurements,provides a third dimension to the standardmineralogy crossplot (below).

56 Oilfield Review

increasesVCI

Quartz

Umaa

ρmaa

Dolomite

Anhydrite

Calcite

> Formation lithology analysis. The ρmaa-Umaacrossplot, derived from neutron and density toolmeasurements, forms the basis for a standardmineralogy interpretation (right). Porosity iseffectively removed from these inputs, resultingin a crossplot that is a function only of the miner-alogy in the formation. End-points for pure anhy-drite, sandstone (quartz), dolomite and limestone(calcite) are shown. The lithology column colorchange depends on clay volume, Vcl, and isderived from the SP or gamma ray measurement.Colors assigned to the plot are derived from acolor cube with the corners of yellow, green,cyan and white on one face, which representszero Vcl, and corners of red, black, blue andmagenta on the opposite face, which represents100% Vcl. The Platform Express lithology columnshows significant lithology changes in the lowersection of the BP Amoco Catoosa test well (above).

70Depth

ft

Standoff(resistivity)

Standoff(density)

ohm-m

Computed Rt120

0.2 ohm-mAIT 90-in. resistivity

2K

0.2 ohm-m

Rxo

2K

1.95 g/cm3Density

2.95

45 p.u.Neutron porosity

-15

0

Zone of interest

Pe

10

90 0

10.0020.0030.0040.0050.0060.0070.0080.0090.00

RadialSw image

90

0 lbf

Tension

5K

0 MV

SP

200

6 in.

Caliper

16

0 API

Gamma ray

150

1600

1500

Summer 1999 57

The clay volume and the mineralogy crossplotdrive the real-time lithology column profile. Thecolor change depends on Vcl and the position ofeach point on the ρmaa-Umaa crossplot. This lithol-ogy image provides a valuable method to observelithology changes in the formation. Although notintended to provide a volumetric interpretation ofthe mineralogy of the formation, it can be usedeffectively for well-to-well correlation.

Next, porosity and resistivity measurementsare coupled with Archie’s equation to determinewater saturation. An optional invaded-zone gas orsteam saturation—computed from the density-neutron crossover separation—is used to com-pute a gas-corrected neutron-density porosity.

Finally, a special wellsite Platform Express pre-sentation has been developed for highly deviatedand horizontal wellbores (above). A real-time truevertical depth (TVD) computation—based on thedeviation derived from the built-in tool-axisaccelerometer—is used to plot a wellbore shapeversus cable depth using the lithology image as anarea fill in the wellbore trajectory curve. The avail-ability of a real-time well deviation and lithologyprofile at the well helps to explain unexpected log-ging results often encountered when crossingbeds and fault zones in highly deviated wells.

The Road AheadMore than two-thirds of the ”triple-combo“logging operations performed by Schlumberger in 1998 were done with the new-generationplatform logging technology. Real-time depth and environmental corrections provide the key togetting accurate formation information into thehands of the operator when and where it doesthe most good—at the wellsite. Real-time,speed-based corrections are being implementedon every tool platform that contains a built-inaccelerometer, such as the Xtreme quad-combotool string designed for high-pressure and high-temperature environments and the SlimAccessquad-combo tool string designed for the slim andcomplex-geometry borehole environment. Newtechnology, such as the HRLA tool, addresses thetraditional problems encountered in conductiveboreholes and promises to bring more accurateresults in a wider range of environments.

Improved 2D and 3D forward models arehelping to clarify increasingly more difficultlogging environments. Breakthroughs in com-puting speed will eventually lead to theirincreased application. Advanced processing, suchas MERLIN and HRLA 2D processing are availablein computing centers and may be availablesomeday for real-time operation. These develop-ments promise an exciting time for geologists,reservoir engineers and petrophysicists who useopenhole-logging measurements. —RH

Well trajectory

Anhydrite

Anhydrite

Sand reservoir

Resistivity PorosityDepth, m

X100

X150

X200

X250

X300

Horizontal well presentation. In this well,the wellbore penetrated salt and anhydritelayers, and entered the sand reservoir. Belowthe 75-m sand reservoir, the lithology log intrack 3 indicates that the well appears tohave entered another anhydrite layer andthen turned back into a second sand interval.However, the well trajectory plot (track 1)clearly shows that the wellbore turnedupwards prior to entering the anhydritelayer—suggesting that the well simply reen-tered the caprock anhydrite and then turneddown—back into the first sand reservoir.Without the well trajectory plot, analysiswould have been delayed until other logs anddeviation information could be correlated tothe log data to explain the lithology changes.

>

Walt Aldred, Drilling Performance Product Cham-pion, is based in Houston, Texas, USA. Previously, he was seconded from Anadrill to Schlumberger Cambridge Research in England, where he worked onthe Real-Time Wellbore Stability project and projectsrelated to drilling learning. This involved leading theSchlumberger PERFORM* team, a group dedicated to reducing drilling costs and improving drilling efficiency. In 1980, he joined the company, working inWest Africa and the North Sea. He later spent fiveyears in Sugar Land, Texas, developing drilling interpretation products and then was in Nigeria for four years working on drilling optimization andengineering. Holder of a patent in drilling motor optimization, Walt earned a BS degree (Hons) ingeology and chemistry from the University ofDurham, England.

Tom Barber has worked on induction modeling, arraydesign and environmental corrections since he joinedSchlumberger Well Services in Houston, Texas, in 1978.There he developed log processing algorithms for theAIT* Array Induction Imager family of tools, and the first commercial signal-processing algorithm forresistivity tools, Phasor* processing. His most recentwork has involved interpreting AIT logs at high welldeviation and other difficult environments. Author of numerous papers and holder of eight patents, hewas awarded the SPWLA Distinguished Technical Achievement Award in 1993 for significant contribu-tions in electromagnetic logging. He previously workedon magnetic susceptibility logging measurements atSchlumberger-Doll Research, Ridgefield, Connecticut,USA. Before joining Schlumberger he worked at theNational Aeronautics and Space Administration(NASA), Marshall Flight Center, Huntsville, Alabama,USA, and at Brookhaven National Laboratory, Upton,New York, USA. He has a BA degree in physics fromVanderbilt University, Nashville, Tennessee, USA, anddid graduate work on low-temperature magnetism atthe University of Georgia, Athens, USA.

Jack Bouska, Geophysical Associate at BP Amoco inSunbury on Thames, England, has been involved in 3Dacquisition design, and 4D and 4C seismic imaging atBP Amoco since January 1999. In 1981 after receiving aBS degree in geophysics from the University of Albertain Edmonton, Canada, and studying electronics engi-neering at Southern Alberta Institute of Technology,Canada, he joined Seiscom Delta United. From 1983 to1985, he was with Western Geophysical in Calgary,Alberta, Canada, and spent the next three years withDome Petroleum Ltd. in Calgary. He began with Amocoin 1988, working first in Calgary and then in London,England. In 1995 Jack won the best theme paper awardat the Canadian Society of Exploration Geophysicsnational convention.

Ian Bradford is a senior research scientist in the WellConstruction department at Schlumberger CambridgeResearch in England. He joined Schlumberger in 1991and, most recently, worked on the Real-Time WellboreStability project. Prior to that, his activities werefocused on well planning, sanding and bit mechanics.Ian holds BS and PhD degrees in applied mathemat-ics from the University of Nottingham, England.

John Cook leads the Geomechanics group in theWell Construction department at Schlumberger Cambridge Research in England, where he works on wellbore stability control, sand management, perforating strategies and improvements to thedrilling process. John is a graduate of the Universityof Cambridge with a BA degree in materials scienceand a PhD degree in physics.

Mike Cooper, a consulting geophysicist with a strongbackground in seismic processing, does 4D seismictechnical consulting, seismic inversion studies, amplitude variation with offset (AVO) analysis andseismic processing quality control. Before this (1996to 1999), he was a development geophysicist on theFoinhaven field for BP Amoco. He also served as ageophysical analyst for the West of Shetlands area andspent four years as seismic processing geophysicist(central North Sea), responsible for seismic dataquality in the central North Sea asset group (ETAP).He began his career in London, England, with ShellUK Exploration & Production Ltd. as an assistant geo-physicist (1981 to 1982). Following this he moved toSeismograph Services Ltd. in Bromley, Kent, as seniorseismologist. His next assignment was as seismic processing geophysicist in Glasgow, Scotland (1988 to1990). Mike earned a BS degree in physics and geophysics (Hons) from University of Bath, England.

Chip Corbett, Principal Engineer for SchlumbergerHolditch-Reservoir Technologies (H-RT) in Houston,Texas, joined Schlumberger in 1981 as a wireline field engineer in Sacramento, California, USA. Duringthe next eight years he had various engineeringassignments, primarily in California. Since 1990 hehas been committed to software marketing for GeoQuest and integrated field studies for H-RT, andhas authored or co-authored several papers on reservoir characterization. Chip has a BS degree in mechanical engineering from the University of California at Berkeley and an MS degree in petroleumengineering from the University of Houston.

William (Liam) Cousins, Drilling Superintendent forthe Mungo Wells team, works for BP Amoco in Dyce,Aberdeen, Scotland. After joining BP in 1980, he heldvarious drilling engineering and drilling supervisorypositions in the North Sea and in Russia. He has beendrilling superintendent since late 1997. Liam holds a BS degree in geology from National University of Ireland in Cork.

John Fuller is the Schlumberger Holditch-ReservoirTechnologies coordinator for geomechanics in Europe,Africa and the CIS. He joined Schlumberger as a wireline field engineer in 1980 in Abu Dhabi, UnitedArab Emirates, and for the next 10 years had variousfield assignments in the Middle East. In 1990 hemoved to Paris, France, to work on applications andproducts during the launch of the DSI* Dipole ShearSonic Imager tool. After transferring to the LondonComputing Centre later that year, John worked in theUK and Norway on several geomechanics projects supporting drilling and production operations. He hasserved as Technical Vice-President of the Londonchapter of the SPWLA and is a member of the 1999SPE Forum steering committee for sanding. Johnreceived a BS degree in physics from the University ofPortsmouth, England.

Vidhya Gholkar, a Senior Research Scientist withSchlumberger Oilfield Research, is based at Schlumberger Cambridge Research in England. Hejoined the laboratory in 1992 and currently works in the Real-Time Drilling Decisions group. He has contributed to various projects, including MSM*(muds solids monitor), sonic labeling, quantitativerisk analysis, drillstring failure, multiphase flowwater-cut determination, real-time wellbore stabilityand drilling problem avoidance. Two years ago, Vidhyaorganized and cochaired the Schlumberger SignalProcessing Conference in Austin, Texas. He has BS and PhD degrees in electrical and electronic engineering from the University of Birmingham, England, and a Certificate in Management from theOpen University in Milton Keynes, England.

Shuja Goraya is working as a Schlumberger PERFORM engineer in Cabinda, Angola, on severalprojects involving drilling efficiency improvement,and on the design and execution of a shallow gasextended-reach drilling project. Shuja joined Anadrillin 1994 as a drilling services engineer and worked asmeasurements-while-drilling and logging-while-drilling, directional drilling and drilling engineer invarious parts of West Africa. He obtained a BS degree(Hons) in electronics engineering from University ofEngineering and Technology, Lahore, Pakistan.

Laurent Jammes, who is in the Interpretation Engineering department in Clamart, France, is leaderof the Invasion-2.0 Interpretation project. He beganwith Schlumberger in 1988 as a development engineerand physicist, working on density measurements. From 1992 to 1998, he was project leader for PlatformExpress* software and data integration, responsible for sensor physics studies, real-time measurement processing and interpretation products for the Platform Express tool. Laurent is a graduate of EcoleCentrale de Paris and earned a doctorate degree innuclear physics at Commissariat à l’Energie Atomiquein Saclay, France.

Werner Klopf, senior petrophysicist in Milan, Italy for Schlumberger, works on interpretation develop-ment, particularly for nuclear magnetic resonancemeasurements. After joining the company in 1978, hehad various field assignments in South America. From1984 to 1994, he was a log analyst with several postingsin Milan and Paris. Werner has been in his currentposition since 1994. His degree in petroleum engineer-ing was earned at University of Leoben, Austria.

Alberto Malinverno is a research scientist in theReservoir Optimization department at Schlumberger-Doll Research, Ridgefield, Connecticut. He is workingon inverse techniques that combine different datasources to construct a model of the reservoir that isconsistent with the available information and to quantify the uncertainty associated with the modeldescription. Before joining the company in 1992,Alberto spent three years doing postdoctoral studiesand as an associate research scientist at the Lamont-Doherty Earth Observatory of Columbia University,Palisades, New York. His undergraduate degree in geological sciences is from Università degli Studi diMilano, Italy; his MS and PhD degrees, also in geologi-cal sciences, are from Columbia University, New York.

Contributors

58 Oilfield Review

Reginald Minton, Well Integrity Technology ChallengeLeader for BP Amoco and Project Manager for thecompany’s “No Drilling Surprises” R&D objective, ispresently based in Aberdeen, Scotland. He first joinedBP in 1976 and worked in drilling fluids before movingto Anchor Drilling Fluids in 1983 as technical directorand then UK operations manager. He returned to BP in 1986 and has held a series of drilling engineering,R&D project management and exploration drillingoperations management posts prior to assuming hiscurrent position in February 1999. He has a BS degreefrom Hatfield Polytechnic, Hertfordshire, England, and a PhD degree from the University of Aberdeen in Scotland. Reginald has been an SPE DistinguishedLecturer and received the Stavanger SPE Engineer ofthe Year award in 1997.

Andrew (Andy) O’Donovan is a reservoir engineerand geophysicist with the BP Amoco West of Shetlands Subsurface Team in Aberdeen, Scotland.Prior to this (1996 to 1999), he worked on field development and production with the BP ExplorationFoinaven Subsurface Team. His 11 years with BPExploration have been spent in frontier exploration,prospect evaluation, appraisal, development and production in the UK, Vietnam and China. Andy holdsa BS degree (Hons) in physics with geophysics fromthe University of Bath in England and Masters degreesin petroleum engineering from Heriot-Watt University,Edinburgh, Scotland, and in geophysics from ImperialCollege in London, England.

Dick Plumb is Principal Consultant, Geomechanics at Schlumberger Holditch-Reservoir Technologies in Houston, Texas. Previously, he was responsible forcase studies in the interpretation and geomechanicsdepartment at Schlumberger Cambridge Research,England. He also worked at Schlumberger-DollResearch, Ridgefield, Connecticut, where he developed log interpretation techniques for fracturecharacterization, in-situ stress measurements andhydraulic fracture containment. Dick received a BA degree in physics and geology from Wesleyan University, Middletown, Connecticut; an MA degree in geology from Dartmouth College, Hanover, NewHampshire, USA; and a PhD degree in geophysics from Columbia University in New York.

Michael Prange is a senior research scientist atSchlumberger Doll Research (SDR), Ridgefield, Connecticut, where he is working on the ValidationGauntlet, a suite of tools to validate shared earthmodels against all available data. A validated modelwill include a description of uncertainty on all model components. Michael earned a PhD degree in geophysics from Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts, USA in 1989, and held postdoctoral positions as a Fulbright Scholar at Elf Aquitaine in Pau, France, in1990 and at the Earth Resources Laboratory at MIT.He joined SDR in 1991.

Anchala Ramasamy is a petrophysicist who works on the completions team for high-value wells at BPAmoco in Aberdeen, Scotland. There she providespetrophysical support for the company’s IntelligentWells project, with particular emphasis on electricalarrays, optical logging methods, and installation ofpermanent sensors in wells and their integration withother disciplines to maximize data value. She works ona team specializing in analysis of cased-hole nuclearand production logs and their integration with otherdata sources to optimize production and reduce risks and uncertainties. She began her career withSchlumberger in 1990 as a field engineer working with Shell in Aberdeen. From 1993 to 1994, she was asenior field engineer for open- and cased-hole servicesin Kuwait. In 1994 she joined GeoQuest in Aberdeen as geoscientist and petrophysicist for multiclient dataprocessing and analysis for GeoFrame* environments.Two years later, she became operational petrophysicistinvolved in data acquisition for the BP Andrew field.She moved to BP Amoco in 1998. Anchala holds a BS degree (Hons) in aeronautical engineering fromCity University, London, England.

Laurence Reynolds, Petrophysicist for the Schlum-berger Formation Evaluation group in Aberdeen,Scotland, provides support for openhole wirelineoperations, sales and marketing, as well as interpre-tation training, in an integrated group involving LWD(Anadrill), Wireline and GeoQuest. He joined thecompany in 1986 and spent seven years as a fieldengineer in various European locations for land andoffshore cased-hole and openhole wireline opera-tions. From 1993 to 1997, he was engineer in chargefor AGIP in Aberdeen, Scotland. Laurence has a BS degree in engineering physics from Queen’s University, Kingston, Ontario, Canada.

Sarah Ryan has been the program manager of theseismic reservoir characterization and monitoringgroup at Schlumberger Cambridge Research (SCR) in England since 1998. From 1996 to 1998, she was aresearch scientist at SCR involved in seismic reservoircharacterization and time-lapse seismic studies. She began with Schlumberger in 1990 as a wireline-logging engineer, and spent three years working inIndonesia and Australia. Sarah has also worked forBHP petroleum and Woodside Offshore petroleum.Her degrees include a BS degree in geology from theUniversity of Melbourne, Victoria, Australia; and a BS(Hons) degree and a PhD degree in petroleum geologyand geophysics, both from the University of Adelaide,South Australia, Australia.

Jan Wouter Smits, who is based in Clamart, France,has been project manager charged with developing the new HRLA* High-Resolution Laterolog Array tooland inversion answer products. He started withSchlumberger in 1991 in Clamart, working as an electronics design engineer on the ARI* AzimuthalResistivity Imager tool and subsequently on resistivitymeasurements for the Platform Express tool. Jan hasan MS degree in electrical engineering from Delft University of Technology in The Netherlands.

Alan Sibbit is an Interpretation Advisor (Petrophysics)and manager of the Schlumberger Center for AdvancedFormation Evaluation in Houston, Texas. Previously heworked in various interpretation assignments in thefield and in research. He joined Schlumberger in 1975.Alan holds BA and MA degrees in mathematics from St. John’s College, University of Cambridge, England.

Robert Terry is a petrophysical associate working ininternational operations and doing nuclear magneticresonance processing and job planning for BP Amocoin Houston, Texas. After receiving a BS degree inphysics with a minor in geophysics at the Georgia Institute of Technology in 1975, he joined Schlum-berger as a field engineer in West Texas. He spent threeyears working in various open- and cased-hole field-engineering assignments before taking a series of management and log interpretation positions. In 1988he joined Amoco to perform international log analysis.He is a graduate of the Amoco Petrophysics trainingprogram, where in subsequent years he taught loganalysis. An active member of the SPWLA and author of several papers, Bob is chairman of the Log Characterization Consortium.

Dean Tucker, a senior well engineer at the Schlumberger Integrated Project Management (IPM)group in Aberdeen, Scotland, has spent the past fouryears in research and operational assignments. He iscurrently Project Coordinator for the Upstream Technology Group, BP Amoco. This is a joint venture ofSchlumberger and BP Amoco to develop a process forimproving drilling performance using 3D visualizationtechnology. He is also lead engineer in the review of Mungo field drilling performance. In 1985, after earning a BS degree in engineering from MemorialUniversity of Newfoundland in St. John’s, Canada, hejoined Chevron Canada Resources, Calgary, Alberta, as a drilling engineer and spent the next five years in field and office operations. From 1991 to 1995, he served as a petroleum engineer in technical, administrative and managerial assignments. Deanjoined Sedco Forex in Paris, France, as a seniordrilling engineer in the engineering division to providedrilling engineering support for drilling operationsworldwide. He transferred to Aberdeen two years later. He assumed his current position in 1997.

Summer 1999 59

An asterisk (*) is used to denote a mark of Schlumberger.

• Sequence Stratigraphy of the Westphalian in the Northern Part of the Southern North Sea

• Modelling of Sandbody Connectivityin the Schooner Field

• Application of High-Frequency Cycle Analysis in High-ResolutionSequence Stratigraphy

• Index

The book is well illustrated andnicely produced.

...it will be of great value to geologists working in the southernNorth Sea. It also provides some useful lessons from this basin that have broad applicability to other areas.

Sweet M: AAPG Bulletin 82, no. 7 (July 1998):

1440-1441.

Petroleum Geology of Southeast AsiaA.J. Fraser, S.J. Mathews and R.W. Murphy (eds)Geological Society Publishing HouseUnit 7, Brassmill Enterprise Centre,Brassmill LaneBath, Somerset BA1 3JN UK1997. 436 pages. $125.00 ($65.00 forGeological Society members)ISBN 1-897799-91-8

This book comprises papers presentedat a two-day 1995 conference of thePetroleum Group of the Geological Society of London. The papers covermost of the active areas of petroleumexploration in Southeast Asia, with the majority pertaining to Indonesiaand Vietnam.

Contents:

• Energy Trends in SE Asia

• Cenozoic Plate Tectonic Reconstructions of SE Asia

• Characterizing Petroleum Charge Systems in the Tertiary of SE Asia

• Exploring the Lake Basins of East andSoutheast Asia

• Exploration in the Gulf of Thailand in Deltaic Reservoirs, Related to theBongkot Field

Petroleum Geology of the Southern North Sea: Future PotentialK. Ziegler, P. Turner and S. R. Daines (eds)Geological Society Publishing HouseUnit 7, Brassmill Enterprise Centre,Brassmill LaneBath, Somerset BA1 3JN UK1997. 210 pages. $99.00 ($60.00 for AAPG members)ISBN 1-897799-82-9

The book reviews the topics that areimportant in the exploitation of gas inthe southern North Sea hydrocarbonprovince. It describes the advancedtechniques and technologies needed for stratigraphic correlation and forimproving production efficiency.

Contents:

• Introduction

• History of Exploration in theSouthern North Sea

• Recent Advances in Understandingthe Southern North Sea Basin: A Summary

• Permian (Upper Rotliegend) Synsedimentary Tectonics, BasinDevelopment and Palaeogeography of the Southern North Sea

• Climatic Cyclicity and Accommodation Space in Arid toSemiarid Depositional Systems: AnExample from the Rotliegend Groupof the UK Southern North Sea

• Compartmentalization of Rotliegendes Gas Reservoirs by Sealing Faults, Jupiter Fields Area,Southern North Sea

• Diagenetic Controls on ReservoirQuality in Permian RotliegendesSandstones, Jupiter Fields Area,Southern North Sea

• Probing the Lower Limits of the Fairway: Further Pre-Permian Potential in the Southern North Sea

• The Structure of the Westphalian inthe Northern Part of the SouthernNorth Sea

• Structure, Stratigraphy andPetroleum Geology of the SE NamCon Son Basin, Offshore Vietnam

• Predicting Reservoir Quality DuringExploration: Lithic Grains, Porosityand Permeability in Tertiary Clasticsof the South China Sea Basin

• Miocene Carbonate Buildups OffshoreSocialist Republic of Vietnam

• Exploration History of the OffshoreSE Sumatra Production Sharing Contract, Java Sea, Indonesia

• N. Sumatra/Java Oil Families

• Fault Seal Analysis of SE Asian Basins with Examples from West Java

• Permeability Heterogeneity Withinthe Jerudong Formation: An OutcropAnalogue for Subsurface MioceneReservoirs in Brunei

• The Tectonic Evolution andAssociated Sedimentation History of Sarawak Basin, Eastern Malaysia: A Guide for Future HydrocarbonExploration

• Platform-Top and Ramp Deposits of the Tonasa Carbonate Platform,Sulawesi, Indonesia

• A Review of the Petroleum Potentialof Papua New Guinea with a Focus onthe Eastern Papuan Basin and thePale Sandstone as a Potential Reservoir Fairway

• An Overview of the HydrocarbonPotential of the Spratly IslandsArchipelago and Its Implications for Regional Development

• The Tectonostratigraphic Evolution of SE Asia

• Cenozoic Deformation in Sumatra:Oblique Subduction and the Development of Sumatran Fault System

• Tertiary Response to ObliqueSubduction and Indentation in Sumatra, Indonesia: New Ideas forHydrocarbon Exploration

• A Tectonic Model for the OnshoreKutai Basin, East Kalimantan

• New Observations on the Sedimentaryand Tectonic Evolution of the TertiaryKutai Basin, East Kalimantan

• Gravity Anomalies and DeepStructural Controls at the Sabah-Palawan Margin, South China Sea

• Index

...because of that diversity [of topics], there should be somethingof interest for most explorationistsattracted to Southeast Asia.

Hatley AG: APG Bulletin 82, no. 9

(September 1998): 1763-1764.

Whole Earth Geophysics:An Introductory Textbook for Geologists and GeophysicistsRobert J. LilliePrentice Hall, Inc.Upper Saddle River, New Jersey 07458 USA1999. 361 pages. $73.00 ISBN 0-13-490517-2

The book focuses on how each geo-physical technique provides informationon the Earth’s internal structure andtectonic development. Each chaptercontains definitions of terms used andhas many diagrams, a bibliography anda set of questions.

Contents:

• Introduction

• Plate Tectonics

• Seismic Waves

• Seismic Refraction Interpretation

• Seismic Reflection: Acquisition, Processing, and Waveform Analysis

• Structural and Tectonic Interpretation of Seismic Reflection Profiles

• Earthquake Seismology

• Gravity and Isostasy

• Magnetic Interpretation

• Heat Flow

• Appendices, Index

While not a replacement for more traditional introductory geophysics texts...a valuable additionto the library of any geo-professionalscharged with educating newcomers to the field or those moving fromsmall-scale prospecting to regionalgeophysical interpretation.

Avakian B: The Leading Edge 17 (November 1998):

1610-1611.

NEW BOOKS

60 Oilfield Review

• Fracture Distribution in FaultedBasement Blocks: Gulf of Suez, Egypt

• Polygonal Faulting in the Tertiary ofthe Central North Sea: Implication forReservoir Geology

• Fault and Fracture Characteristics of a Major Fault Zone in the Northern North Sea: Analysis of 3DSeismic and Oriented Cores in theBrage Field (Block 31/4)

• Structural Geology of the GullfaksField, Northern North Sea

• Index

It comes packaged in a slim 266pages with clear illustrations...Everypaper has an extensive bibliography.

I learned so much and heartilyrecommend it to all geoscientists andreservoir and petroleum engineers.

Hossack J: Marine and Petroleum Geology 15, no. 8

(December 1998): 842-843.

Geological Processes on Continental Margins: Sedimentation, Mass-Wasting and StabilityM.S. Stoker, D. Evans and A. Cramp (eds) Geological Society Publishing HouseUnit 7, Brassmill Enterprise Centre,Brassmill LaneBath, Somerset BA1 3JN UK1998. 355 pages. $120.00 ($72.00 for AAPG members) ISBN 1-987799-97-7

This collection of papers focuses oncontinental margin processes that are becoming crucial to the safe development of deep-water oil fields.

Contents:

• Geological Processes on Continental Margins:Sedimentation, Mass-Wasting and Stability: An Introduction

• Large Submarine Slides at the NE Faeroe Continental Margin

• Turbidite Flux, Architecture and Chemostratigraphy of theHerodotus Basin, Levantine Sea, SE Mediterranean

• Sediment Delivery to the Gulf ofAlaska: Source Mechanisms Along aGlaciated Transform Margin

• Large Scale Debrites and SubmarineLandslides on the Barra Fan, W ofBritain

• Morphology and Sedimentation on the Hebrides Slope and Barra Fan,NW UK Continental Margin

• Debris Flows on the Sula Sgeir Fan,NW of Scotland

• Shallow Geotechnical Profiles, Acoustic Character and DepositionalHistory in Glacially Influenced Sediments from the Hebrides andWest Shetland Slopes

• Mechanical Properties of Terrigenous Muds from Levee Systems on the Amazon Fan

• The Var Submarine Sedimentary System: Understanding Holocene Sed-iment Delivery Processes and TheirImportance to the Geological Record

• Cenozoic Changes in the Sedimentary Regime on the Northeastern Faeroes Margin

• The Southeast Greenland GlaciatedMargin: 3D Stratal Architecture ofShelf and Deep Sea

• Recent Geological Processes in the Central Bransfield Basin (Western Antarctic Peninsula)

• Seismic Stratigraphy of PalaeogeneDepositional Sequences: NortheastRockall Trough

• Sediment-Drift Development on theContinental Margin off NW Britain

• Cyclic Sedimentation on the Faeroe Drift 53-10 Ka BP Related toClimatic Variations

• Late Quaternary Stratigraphy andPalaeoceanographic Change in theNorthern Rockall Trough, NorthAtlantic Ocean

• Upper Slope Sand Deposits:The Example of Campos Basin, LatestPleistocene/Holocene Record of theInteraction Between Alongslope andDownslope Currents

• Hemipelagites: Processes, Facies and Model

• Late Glacial to Recent AccumulationFluxes of Sediments at the Shelf Edgeand Slope of NW Europe, 48-50Degrees N

The papers in this volume ...have considerable actual or potential commercial value.

AAPG Bulletin 82, no. 10 (October 1998): 1880.

Unlocking the StratigraphicRecord: Advances in Modern StratigraphyPeter Doyle and Mathew R. Bennett (eds)John Wiley & Sons1605 Third AvenueNew York, New York 10158 USA1998. 532 pages. $64.95ISBN 0-471-97463-3

This multiauthored book describes how new geophysical and laboratory techniques are being used to quantifyproperties and relationships amongrocks and to interpret stratigraphic successions.

Contents:

• Establishing the Sequence: Lithostratigraphy:Principles and Practice: RemoteSensing and Lithostratigraphy;Field Interpretation of Complex Tectonic Areas; Evolutionary Concepts in Biostratigraphy; EventStratigraphy: Recognition andInterpretation of Sedimentary Event Horizons; Cyclostratigraphy;Strontium Isotope Stratigraphy;Borehole Data and Geophysical Log Stratigraphy; Principles of Seismic Stratigraphy; SequenceStratigraphy; Stratigraphical Applications of Radiogenic IsotopeGeochemistry; Chronostratigraphy(Global Standard Stratigraphy):A Personal Perspective

• Interpreting the Record:Interpreting the Record: FaciesAnalysis; Interpreting Sea Level;Interpreting Palaeoenvironmentsfrom Fossils; Interpreting Paleoclimates; Interpreting Orogenic Belts: Principles and Examples

• Index

Highly recommended for all earth science libraries.

Beck JH: Choice 36, no. 2 (October 1998): 346.

Summer 1999 61

Structural Geology in Reservoir CharacterizationM.P. Coward, T.S. Daltaban and H. Johnson (eds)Geological Society Publishing HouseUnit 7, Brassmill Enterprise Centre,Brassmill LaneBath, Somerset BA1 3JN UK1998. 266 pages. $115.00 ($57.00 forGeological Society members)ISBN 1-897799-94-2

Intended for geologists, geophysicistsand engineers, the book aims to capture the wide range of research onreservoir characterization and also topromote synergy between geoscientistsand engineers.

Contents:

• The Role of Structural Geology inReservoir Characterization

• Reservoir Characterization and Modelling: A Framework for FieldDevelopment

• Fault Seal Prediction: The Gouge Ratio Method

• Experimental Fault Sealing:Shear Band Permeability Dependency on Cataclastic Fault Gouge Characteristics

• Identification and Spatial Distribution of Fractures in Porous,Siliciclastic Sediments

• Flow Through Fault Systems in High-Porosity Sandstones

• Physical Character and Fluid-FlowProperties of Sandstone-DerivedFault Zones

• Assessment of the Effects of Sub-Seismic Faults on Bulk Perme-abilities of Reservoir Sequences

• Reservoir Characterization: How Can Anisotropy Help?

• Numerical Simulation of Fluid Flowin Complex Faulted Regions

• Curvature Analysis of Gridded Geological Surfaces

• Strain Partitioning During Flexural-Slip Folding

• Index

Petroleum Geology of the North Sea: Basic Concepts and Recent AdvancesK.W. Glennie (ed)Blackwell ScienceCommerce Place350 Main StreetMalden, Massachusetts 02148 USA1998. 636 pages. $85.00ISBN 0-632-03845-4

Longer than previous versions, the 4thedition reflects expanded knowledgeabout North Sea hydrocarbons. Itincludes a historical review of NorthSea exploration and a summary of the geologic controls on hydrocarbondistribution in this area.

Contents:

• Historical Review of North Sea Exploration

• Origin, Development and Evolution of Structural Styles

• Devonian

• Carboniferous

• Lower Permian—Rotliegend

• Upper Permian—Zechstein

• Triassic

• Jurassic

• Cretaceous

• Cenozoic

• Source Rocks and Hydrocarbons ofthe North Sea

• North Sea Plays: Geologic Controls on Hydrocarbon Distribution

• References, Index

...the overall presentation iscrisper and neater than before.

The book will be essential to thosewith an interest in the North Sea, butit will also be valued by a far widerstratigraphic, petroleum-geologicaland structural readership.

Journal of Petroleum Geology 21, no. 4

(October 1998): 483.

Understanding Materials Science: History, Properties,ApplicationsRolf E. HummelSpringer-Verlag175 Fifth AvenueNew York, New York 10010 USA1998. 407 pages. $59.95ISBN 0-387-98303-1

Intended for engineering, physics and materials science students, this textbook links elementary, butquantitative explanations of phenom-ena to historical eras, the history andgrowth of understanding and control,and issues of environment, substitu-tion of materials, and recycling andreserves of raw materials.

Contents:

• The First Materials (Stone Age and Copper-Stone Age)

• Fundamental Mechanical Properties of Materials

Coming in Oilfield Review

Managing Production. Focusedefforts are required to maximizethe economic potential of anyasset. By outsourcing oilfield management, operators are nowable to realize the most benefitfrom their available infrastructure,resources and services. Utilizingnew technology, production analysts and operating personnelfrom service companies developtechniques to increase oil and gas field longevity, productivity,profitability and reserve recovery.

Seismic-Data Acquisition. Thenewest addition to the fleet of Schlumberger vessels—the Geco Eagle—was designed not only according to seismic-survey acquisition requirements,but also with the needs of workers in mind. In this article, we explain how application of theprinciples of ergonomics results in the design of equipment andwork-area environments that help increase operational safety, efficiency and productivity.

Remote Well-Process Control.Real-time reservoir management, maximizing reserve recovery andincreasing asset value requiremore than simply selecting anddeploying completion equipment in a wellbore. We discuss newtechnology—measurements from sophisticated monitoringequipment and downhole sensors, software for modelingand interpretation, and specialdevices that control flow fromreservoirs remotely—to help fine-tune well performance andoptimize field production.

Scale Removal and Control. Thebuildup of scale can eventuallyshut down a producing well.Traditional scale treatments have not always been successfuland were highly specialized for specific types of scale. This article discusses the causes ofscale formation and how newremoval and control techniqueswere developed. Armed with these innovations, scale can beremoved from wells quickly, andat low cost.

Oilfield Review62

• Mechanisms

• The Bronze Age

• Alloys and Compounds

• Atoms in Motion

• The Iron Age

• Iron and Steel

• Degradation of Materials

• The Age of Electronic Materials

• Electrical Properties of Materials

• Magnetic Properties of Materials

• Optical Properties of Materials

• Thermal Properties of Materials

• No Ceramics Age?

• From Natural Fibers to Man-Made Plastics

• Gold

• Economic and Environmental Considerations

• What Does the Future Hold?

• Appendices, Index

He has succeeded admirably...inrendering intrinsically complicatedtopics, such as polymerization, palatable and digestible.

This mix of proper science andrespectable history is something new among the plethora of materialsscience books....As a first-level introduction to materials science, I can recommend it unreservedly.

Cahn R: MRS Bulletin 23, no. 12

(December 1998): 48.

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