geologic description and modeling

1

INTEGRATION OF LOG AND

SEISMIC DATA

Laser Engineering And Resources Consultant Ltd

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Rumuodara Port Harcourt

2

Course Outline Course Objectives

Introduction

Analysis and Interpretation of Open-hole Logs

Review of Core and Cutting Data

Seismic Imaging

Reservoir Fluid Data

Data Integration

Mapping Techniques

Petrophysical Modeling

Uncertainty Management in Reservoir Management.

3

Introduction

The key to success in a reservoir description and modeling isto integrate geological, geophysical, petrophysical,engineering, and reservoir performance data to form themost accurate description of the reservoir.

Such a study is necessarily an ongoing process, continuallyrefining the working description of the reservoir throughoutthe producing life of the field.

4

Introduction

The first step in reservoir description is to identify the

geometry, continuity, and internal anatomy of the reservoir. Thisgeologic work provides the basic framework for acomprehensive reservoir description.

Other information — such as well test results, petrophysicaldata, reservoir pressure history, and production data — helps tofill in gaps and refine understanding.

5

Introduction

After the basic geologic framework is in place, the next step is to

quantify the reservoir quality of the various geologic facies units.

Reservoir quality is determined primarily by the distribution of

petrophysical properties such as porosity and permeability, pore

size distribution and pore geometry, and the presence of pore-

filling materials that may have an effect on productivity or

hydrocarbon recovery efficiency.

6

Introduction

Other important factors directly affecting recovery efficiencyand productivity include relative permeability and capillarypressure relationships. These latter properties involvewettability and other rock-fluid interaction effects.

Thus, it is necessary to have a knowledge of the compositionand characteristics of the reservoir fluids, as well as the rockpore system, in order to fully describe reservoir quality.

7

Introduction

The key to success in a reservoir characterization study is tointegrate geological, geophysical, petrophysical, engineering,and reservoir performance data to form the most accuratedescription of the reservoir. Such a study is necessarily anongoing process, continually refining the working descriptionof the reservoir throughout the producing life of the field.

8

Analysis and Interpretation of

Open-Hole Logs

Log evaluation/interpretation consists of a number of

consecutive steps or the process which result in the determination

of petrophysical parameters such as net pay, porosity, lithology,

hydrocarbon saturation, fluid content etc.

Measurable parameters as resistivity, bulk density, interval transit

time, natural radioactivity, and the hydrogen content of the rock

are translated to petrophysical parameters

Introduction/concept of log interpretation

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Direct and indirect information from logs are needed to estimate

the properties and to understand the nature of the reservoir, This

is the utmost concern of E & P industries.

So, having a firm grip on the basics of Well Log Interpretation is

very important.

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Direct and indirect information from logs are

needed to estimate the properties and to

understand the nature of the reservoir, This

is the utmost concern of E & P industries.

So, having a firm grip on the basics of Well

Log Interpretation is very important.

INTRODUCTION


DIRECT METHODS

• Give information directly about the rock and its fluid

contents. They are:

Mudlogging

Formation Sampling – Drilled cuttings, Coring,

Sidewall Sampling

Fluid Sampling – Wire Line Test, Drill Stem Test,

Production Test

INDIRECT METHODS

• Give information indirectly about the rock and its fluid

contents through the measurement of other physical

processes and properties of the formation. Indirect

12

INTRODUCTION


13

• Log curves plotted

against depth

• Multi-track displays

(3 tracks typical on a

field print)

• Multiple logs per

track

• Industry standard

scales

What do logs look like?


Manual and Wireline Log

Manual Logs are generated from directreading from drill cuttings, Drill-stempenetration rate e.g. LithoLog

Wireline Log: a cable connects the sondewith other equipment at the surface,sometimes power source.

Electrical

Nuclear

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OPEN HOLE LOGGING

Open hole data is

recorded using a

variety of

principles of physics

including

Resistivity, nuclear

and acoustic. These

measurements have

to be interpreted to

obtain rock and fluid

properties

OPEN HOLE LOGGING

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BOREHOLE CONDITIONS• Caliper, Borehole Geometry Tools• Deviation Surveys

LITHOLOGY/POROSITY• Gamma Ray, Spectral Gamma Ray• Density• Neutron• Sonic/Acoustic• Gamma Spectroscopy

PERMEABILITY FLUID TYPES, HYDROCARBON• SP• Resistivity• Induction


19

GEOLOGY•Sidewall Cores•Bottom Hole Cores•Dipmeter

FRACTURES•Borehole Televiewer•Sonic Wavetrain•Variable Density•Microscanner

FORMATION PRESSURE & SAMPLES• Repeat Formation Testers • Formation Interval Testers


Rock Properties From Well Logs

The evaluation of any petroleum reservoir, new or old, for

maximum rate of production and maximum recovery of the

hydrocarbons requires :-

a thorough knowledge of the fluid transport properties of rocks

and

the fluid-rock interactions that influence the flow of the fluids.

The behavior of a specific reservoir, however, can only be

predicted from analyses of the petrophysical properties of the

reservoir and fluid-rock interactions obtained from core

samples of the reservoir.


Rock Properties From Well Logs

Analyses of the cores only yields data at point locations within

the reservoir; therefore, the petrophysical analyses must be

examined with respect to the

geological,

mineralogical, and

well-log correlations of the reservoir to develop a meaningful

overall performance estimate.


Lithology

Natural Gamma ray

Spontaneous Potential

Density –Photo – electric factor (PEF)

22


Gamma Ray• Detects the clay or shale content in reservoirs due to their radioactivity

• The GR log is plotted on a linear scale of 0-100% shale (API unit)

• The shales give a high GR log reading and low reading in clean

sandstones or carbonates except in cases of radioactive sands due to

zircon, glauconite etc.

Interpretation Steps

• Identify the average GR reading in a thick shale section of the reservoir

This value read-off is assumed to represent 100% shale and is called

shale – line.

• Identify the average GR reading in a thick sand section of the reservoir

This value read-off is assumed to represent 100% sand and is called

sand – line.

• A near vertical line in the middle between the shale line and sand line

(cut – off line) is also constructed

• All intervals where the GR log is on the left of this cut – off line are

sands.


Identify Reservoir


1233.0 2 IGRV sh

17.3083.0 2 IGRV sh

25

Identify Reservoir Cont.


Spontaneous Potential (SP)

• The curves usually defines a more or less

straight line on the log

•The SP log is plotted on a linear scale of 0-

200 (millivolt)

• In sands and more permeable formations,

the curves show excursion from straight line.

• Currents are developed from the interactions

which are electrochemical or electrokinetic

in nature.

• The direction of this deflection depends

primarily on the relative salinities of the

formation water and of the mud filtrate.

• There will be a deflection to the left in the

sand compared to the shale when the

resistivity of the mud filtrate (Rmf) is greater

than the resistivity of the formation water (Rw)

and will deflect to the right when Rmf < Rw.



PSP = pseudo-static spontaneous potential

(SP from water-bearing shaly sand

zone)

SSP= static spontaneous potential (maximum

SP value in clean sand zone)

NOTE: SP-derived volume of shale is

probably over-estimated

1001(%)

SSP

PSPV SH



Where S(SP) = SSP= static SP

= equivalent mud filtrate resistivity

= equivalent formation water resistivity

K= temperature dependent coefficient (average, 71

at 250 C

e

eKSPS

RR

w

mflog

eRmf

eRw


Density – Photo Electric Factor (PEF)

• It is a Litho-density tool. The parameter links the number

of gamma rays (r-ray) that are absorbed by photo-electric

absorption to Lithology.

• Photo- electric absorption is the disappearance of a low

– energy r-ray as it collides with an atom, causing the

ejection of an orbital electron.

• The PEF is a good matrix indicator.

• Low PEF factor corresponds to sandstone lithology.

1 2 3 4 5 6 7 8 9 10

SST SDSH SH

Identification of Lithology from PEF2929Laser –Enhancing Optimal Recovery Through Specialized Services

Porosity Tools

These include

• Sonic log

• Density log

• Neutron log

Note: Porosity calculated from these tools might

not be equal to one another


Sonic Log• It is usually inferior to neutron or density log calculated values.

•It measures formation interval transit time plotted on a linear reversed

scale of 40-140 (micrsecond /ft).

• The formula commonly used for this is Wyllie et al., 1958

Where

Sonic = Sonic derived porosity

tma = interval transit time of matrix (given)

t log = Interval transit time of formation

tF = Interval transit time of fluid in the well bore (Fresh mud =

189, salty mud = 185)

Ct pFsonic tma

tmat 1log


tma

tmat

tFsonic

log

32

Sonic Porosity


• Where a sonic log is used to determine porosity

in unconsolidated sands, an empirical compaction

factor or Cp. should be added to Wyllie et al.,

(1958) equation

Where:

Cp = compaction factor

tsh = Interval transit time of adjacent shale

C = a constant, normally 1.0 (Hilchie, 1978)

•

100

ctC

sh

p

33

Sonic Porosity


• Interval transit time (t) of a formation is

increased due to the presence of

hydrocarbons (i.e. hydrocarbon effect).

• Hilchie, (1978) suggests that

• = sonic x 0.7 gas

• = sonic x 0.9 oil

34

Sonic Porosity


Density logThe bulk density log measures formation bulk density.

It is plotted on a linear scale(2g/cc to 3g/cc)

Porosity from the density log is calculated using the equation

where

Den = Apparent density porosity

ma= Matrix density

b = Bulk density log reading

f = Fluid density

fma

bma

Den

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36

Density log


Neutron logs

The neutron logs measure the hydrogen ion

concentration in a formation.

It measures liquid-filled porosity in clean formation.

Neutron porosity decreases in the presence of gas (gas

effect).

Unlike other logs it must be interpreted from specific

charts.

A typical example of the neutron log is the compensated

neutron log (it is less affected by borehole irregularities.


Permeability

• This is the ability of a

rock to transmit fluids.

It is related to porosity

but it is not always

dependent upon it.

•To be permeable a

rock must have

interconnected

porosity


Permeability Estimation from Wireline Logs

Permeability can be expressed with the following equations below

Where

K=permeability (millidarcies)

= Porosity

= Irreducible water saturation

F= Formation resistivity factor

S wirr

K2

4.4

136.0

swirrK )(345402655230722

swirr

2000

2

1

F

15.2

62.0


Resistivity logs• Does not provide the absolute permeability value, only

used as an indicator of permeable formations.

• Basically, two curves, deep and shallow, separation between the two curves, with the deep reading higher indicates mud cake and therefore permeability.

• When there is no separation between the two curves, it indicates an impermeable stratum

• When there is a negative separation, it might indicative of a change in lithology type.


Resistivity logs

• Resistivity logs are plotted on a logarithmic scale (0.2-2000 ohm. meter).

• Their principal quantitative use is to find hydrocarbon

• Resistivity logs includeMicro-log normal/inverse

Micro-laterolog

Proximity log

Micro-spherically focused log

Laterolog

Induction log


Hydrocarbon Saturations

Resistivity logs are used to calculate water

saturation from which the hydrocarbon saturation

is calculated. When water saturation (Sw) is not

100% , the reservoir rock contains hydrocarbon.

(1-Sw) = Shc; (Shc = hydrocarbon saturation)


Hydrocarbon Saturations

Irreducible water saturation is the fraction of the pore

volume occupy by water in a reservoir at maximum

hydrocarbon saturation.

For most reservoir rocks it ranges from less than 10% to

more than 50%.

Irreducible water is a nonmobile water held to grains by

capillary pressure.

Hydrocarbon production at the zone of irreducible water

is water free.

Irreducible water saturation can be determined from

calculation and also from plots( Hingle & Picket‘ plots)


Water Saturations

This is the water saturation in the virgin zone. It

is the percentage of pore volume occupied by

formation water.

It is very important in reservoir evaluation

Underestimation of water saturation can lead to

gross overestimation of the life expectancy of a

well and its capacity to produce clean oil.


Saturation model are used for this calculation. A

typical model is the Archie model.

Where,

a = tortuosity factor

m = cementation factor

Rw = Water Resistivity

Rt = True Resistivity

R

RS

t

m

w

w

a


Bulk Volume of Water is the product of the

formation‘s water saturation and porosity.

BVW =

If values of the calculated bulk volume water

at several depths are constant or very close to

constant, it then mean the zones are

homogenous and at irreducible water

saturation.

sw


Formation Water Resistivity (Rw)

The resistivity of the formation water is

determined from the following sources

Mathematically

• SP logs. This is done using a series of

charts or.

• Apparent Water Resistivity

Charts

• Salinity Charts form measured data


Hydrocarbon Type• Neutron – Density logs are used to

discriminate between gas and oil in

a formation.

• A separation of the Neutron and

Density log with the Neutron deflecting

to the right and Density log to the

left indicates gas.

• A balloon shape typifies gas while

in an oil reservoirs the two curves

normally track together.


Fluid Contact

• The deep resistivity log (LLd, ILd) is used to

determine the extent of hydrocarbon thickness

in a formation.

• A combination of the Neutron – Density log

further confirms the contact point.

• In resistivity logs fluids contacts is inferred

where there is a sharp contrast in resistivity

values at the hydrocarbon zone. (see illustration)


Fluid Contact


GROSS LITHO

IDENTIFICATION



IDENTIFY RESERVOIR ROCKS

56

CORRELATION

Similarities in

shape &

magnitude of

Curves

Mapping Similar Sands Across Wells



DISCRIMINATING HC ZONE

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DISCRIMINATING HC ZONE

Mapping HC zone by Log combinationsLaser –Enhancing Optimal Recovery Through Specialized Services

59

GAS-OIL DISCRIMINATION



GAS-OIL DISCRIMINATION


ESTABLISH POROSITY


CALCULATE SATURATION

64

SUMMARY OF QUALITATIVE INTERPRETATION


Review of Core and Drill Cutting Data


67

Review of Drill Cutting Data

Drill cuttings are very useful in early field development to

determine

Gross lithology (e.g. sandstone, shale, or carbonate)

Drill cuttings yield information for reservoir description

and modelng such as:-

Grain size

Sorting

Mineralogy

Other laboratory measurements can also be made on

cuttings e.g. fossil identification, oil show, etc

68

Review of Core Data

Cores provide valuable data not just for geological analysis, but

for petrophysical and engineering purposes as well.

Cores are the backbone of the investigation of depositional

environments.

Cores are used for the measurement of rock and fluid

properties( porosity, permeability, wettability, capillary

pressure, etc) as well as flow test.

Cores allow for geologic facies interpretation (lithology, grain

size, sorting, etc,) as well as environment of deposition

(current-generated features and their orientation).

Cores are used to calibrate well logs for better characterization

of reservoir and non reservoir facies.

69

Review of Core Data

Cores provide valuable data not just for geological

analysis, but for petrophysical and engineering purposes

as well.

70

Seismic Imaging

Seismic imaging and processing can help define continuity on a

large scale in some reservoirs.

To generate a seismic pulse from which the subsurface can be

imaged, acoustic energy is put into the earth at the surface.

The acoustic energy is reflected back from many geological

formations in the subsurface, the record of the reflections from

various depths is produced in the form of a seismic section.

Seismic reflection data can be used to define the extent of

prospective reservoirs and to locate faults.

Improved 3D seismic processing techniques and cross-hole

tomography has resulted to better imaging, description and

characterization of the subsurface formations.

71

Seismic Imaging

By correlating detailed velocity, density, and lithology

data from cores and well logs, the composite seismic

reflection response in a particular rock sequence can be

predicted (synthetic seismogram).

72

Seismic Imaging Changes in certain reservoir properties, such thickness,

can be modeled on the expected seismic reflected

response (seismic modeling).

By comparing the actual seismic response with various

models for different parts of the reservoir, it is possible

to predict the probable lateral variations in reservoir

characteristics.

Seismic modeling can be used to anticipate the response

to reservoir heterogeneity e.g. discontinuous sand

lenses.

Comparison between actual and modeled seismic

responses can sometimes help to predict reservoir

continuity.

73

Seismic Imaging Seismic interpretation techniques can also allow for

detailed correlation of identified lithologic units as well

as estimates of the porosity (seismic inversion).

74


Reservoir fluid data are very important for better

understanding of the behavior and characteristics of a

petroleum reservoir.

Reservoir fluid data will be useful in future development plan

and production that will maximize profit.

Thus, accurate description of the physical properties of

reservoir fluids is of considerable importance in reservoir

management.

Data on most of the reservoir fluid properties are usually

determined by laboratory experiments (PVT analysis)

performed on samples of actual reservoir fluids and also from

empirically derived correlations.

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Physical properties of primary interest in petroleum reservoir

characterization include:-

Fluid gravity

Specific gravity of the solution gas

Gas solubility

Bubble-point pressure

Oil formation volume factor

Isothermal compressibility coefficient of under-saturated

crude oils

Oil density

Total formation volume factor

Crude oil viscosity

Surface tension



Crude oil viscosity can be used to characterize fluids into

Dead oil

Saturated oil and

Undersaturated oil

Some physical properties such as gas-oil ratio, appearance,

composition and pressure temperature phase diagram are

used to characterize fluid into

Ordinary black oil

Low-shrinkage crude oil

High-shrinkage (Volatile) crude oil

Near-critical oil



Fluid Property Monitoring• Obtain fluid samples (recombined surface sample or

MDT) and perform PVT

• Results include Pb, Bo, m, gas composition, Rs, wax

content, etc

• Routine samples taken to get GOR, WOR, oil gravity, etc

• Water samples analyzed to understand water chemistry

problems and source of reservoir water


m

Rs

Bo

PB


A = Wet Gas

B = Dry Gas

IC-2 has no initial gas

IC-3 has initial gas

(Associated Gas)

P

GasCondV-oilB-oilH-oilIC-1

IC-2

IC-3

T

Gas-oilA B

A = Wet Gas

B = Dry Gas

IC-2 has no initial gas

IC-3 has initial gas

(Associated Gas)

P

GasCondV-oilB-oilH-oilIC-1

IC-2

IC-3

T

Typical Petroleum Fluid Envelope

80

Data Integration

The use of integrated, multidisciplinary teams has proven to

be an effective way to increase recovery and profits.

Experience confirms that putting the right people with the

right skills (and tools) in the right projects—with firm

management support—is the key to success


INTEGRATED RESERVOIR

CHARACTERIZATION AND MODELING

Modern reservoir characterization workflows attempt to integrateall available, reliable, and appropriate sources of data into 3Dgeocellular or numerical earth model(s). These models providevarious properties such as lithofacies, porosity, permeability,hydrocarbon/water saturation at each grid cell.

Reservoir simulation is then performed on the geocellular modelto predict reservoir performance and production history.Geocellular models allow geoscientists to integrate various datafrom many different sources to calculate oil and gas volumetric,perform efficient well planning, forecast reservoir performance,and optimize reservoir depletion schemes.




In addition, within the same model, the stochastic simulationprocess can generate multiple equiprobable realizations, all ofwhich honor the input data. The differences among theserealizations provide a quantitative envelope of the uncertaintydue to the limited reservoir information and data for thesubsurface.

In reservoir or geocellular modeling, different types of data areused, including geologic, geophysical, petrophysical, andengineering data. Some input data may be highly conceptualand not necessarily limited to a specific reservoir; for example,geologic knowledge can describe the reservoir at all possiblescales.




There are also various types of measured data specific to thereservoir, such as conventional core, well logs, 3D seismic,well testing, and hydrocarbon production. These datameasure the reservoir at different scales.

For example, well log data can provide centimeter-to-meterscale resolution, while seismic data are at comparativelylower resolution and provide larger-scale stratigraphic andstructural information.

These different data types need to be incorporated into thegeocellular model at their correct scales. We propose aworkflow integrating various types of data including geologic,geophysical, well/petrophysical, and associated knowledge attheir respective scales.




Data integration is a fundamental principle of geostatistics/

reservoir modeling. The goal is to explicitly account for all of the

available data. Some data that are considered:

Well log data (surface tops, rock type, porosity and

permeability) by zone.

Core data (porosity and permeability by rock type) by zone

Sequence stratigraphic interpretation/ Layering ( a definition of

the continuity and trends within each layer of the reservoir)

Trends and stacking patterns available from a regional

geological interpretation




Analog data from outcrops or densely drilled similar fields (size

distributions, measures of lateral continuity)

Seismic derived attributes (vertically averaged rock type

proportions and porosity)

Well test and production data ( interpreted permeability

thickness, interpreted channel widths, connected flow paths,

barriers.




Mapping Techniques

Sequence Stratigraphy

Definition of Key Sequence Stratigraphy Terms

Sequence stratigraphy:

• The study of rock relationships within a chronostratigraphic framework of cyclic, genetically

related strata, bounded by surfaces of stratal continuity & discontinuity.

• An integrated interpretation of stratal patterns from seismic, well logs, cores, high resolution

biostratigraphic & outcrop data within the framework of depositional environments and facies.

Depositional Sequence:

• The basic unit in (Exxon) sequence stratigraphy.

• A relatively conformable succession of genetically related strata bounded above and below

by unconformities and their correlative conformities.

Systems Tract:

• Contemporaneously linked depositional systems defined on the basis of stratal geometries

at bounding surfaces, position within the sequence & internal parasequence stacking patterns.

• Defined on the basis of objective stratal, lithofacies and biofacies crtieria.

Depositional System:

• A three-dimensional assemblage of lithofacies, genetically linked by active (modern) or

inferred (ancient) depositional processes and environments (e.g. delta, river, barrier island, etc.

(after Fisher & Brown, 1977).

Parasequence:

• A relatively conformable genetically related succession of beds and bedsets bounded above

and below by marine flooding surfaces and their correlative surfaces (Van Wagoner et al,

1990).

Lowstand Systems Tract - Basin Floor Fan Complex

Basin Floor Fan

Lowstand Systems Tract - Basin Floor Fan Complex:

Facies Characteristics

Depositional Systems1. Soils

2. Basin floor fan deposits

3. Basin floor contourites

4. Basin plain muds

Major Lithofacies1. Soils

2. Basin floor fans/amalgam. sands:

• Sheets (proximal

turbidites)

• Channels

• Mounds

• Debris flows/mass

transport

3. Fan fringe & levee deposits:

• Distal turbidites

Depositional & Erosional

Processes1. Pedogenesis

2. Incised valleys (shelf)

3. Headward erosion (slope)

4. Slumping: canyons & scars

5. Gravity flow deposits

6. Basinal contour currents


Facies Characteristics


Characteristic Well Log Response

Upper boundary:

• Hemipelagic shale or channel-levee facies

above the boundary

• Sharp boundary with minimal transition

Basin floor fan:

• Amalgamated, massive turbidite sands

• Interbedded turbidite sands & shales

• Internal erosion surfaces

• Bidirectional downlapping & mounds (seismic)

Sequence boundary:

• Sharp boundary

• Massive sand above hemipelagic shale

• Often non-erosional base (except in proximal

areas)


Exploration Aspects

Reservoir:

• Excellent porosity & permeability in sediment derived from

quartz rich areas

• Poor porosity & permeability in sediment derived from feldspar &

lithic rich areas

• Typically good continuity, but variable in areas of strong

mounded topography

Traps:

• Typically stratigraphic, but can be structurally enhanced


Exploration Aspects

Source:

• Vertical leakage from deeper horizons

• Possible top and lateral pelagic shales

Seal:

• Major problem

• Typically comprises hemipelagic shales of Lowstand Prograding

Complex (silty)

• Poor seal when overlain by sand-bearing parts of the Slope Fan

Complex

Migration:

• Vertical from older source rocks

• Possible downward and lateral migration from contemporary

pelagic shales

Lowstand Systems Tract - Slope Fan Complex

Lowstand Systems Tract: Slope Fan Complex

Lowstand Systems Tract: Basin Floor Fan Complex

Lowstand Systems Tract - Slope Fan Complex:



Exploration Aspects

Reservoir:

• Thick (100’s m) channel sand bodies, commonly narrow,

sinuous & discontinuous

• Thinner (cm-dm) turbidites in levee/channel margins,

potentially complex

Traps:

• Typically stratigraphic (channel & levee pinch-out against slope

muds)

• Some structural enhancement (e.g. in salt basins)


Exploration Aspects

Source:

• Mainly requires vertical migration from deeper horizons

Seal:

• Good vertical & lateral seals provided by contemporaneous

pelagic shales

• Additional top seal from the condensed horizon shales

Migration:

• Vertical from older source rocks

• Possible leakage from breached basin floor fan traps

Lowstand Systems Tract - Prograding Complex

Lowstand Systems Tract: Prograding Complex



Lowstand Systems Tract - Prograding Complex:


Lowstand Systems Tract - Reservoir Distribution:

Deepwater Basin with Shelf-Slope Break

1. Incised valley fill sands

2. Coastal/deltaic sands

3. Channel-levee sands

4. Levee/lobe sands

5. Basin floor fan complex

6. Shingled turbidites of the

Prograding Complex (opposite)

Prograding Complex with Shingled Turbidites


Exploration Aspects

Reservoir:

• Updip stacked fluvial, deltaic & shoreface sands with variable

continuity

• Proximity to source areas can yield thick, high quality reservoir

successions

• Mud-dominated in distal depositional settings

Traps:

• Structural growth faults are common

• Possible compaction closure

• Stratigraphic traps where muddy coastal/delta plain facies

provide updip seal


Exploration Aspects

Source:

• Vertical migration from deeper horizons

• Possible local source from immediately overlying condensed

horizon (oil prone)

Seal:

• Good seal provide by the regionally extensive TST shales

Migration:

• Vertical migration along fault conduits from deeper source rocks

• Possible downward migration from contemporary condensed

horizon shale

Transgressive Systems Tract





Transgressive Systems Tract:


Transgressive Systems Tract: Exploration Aspects

Reservoir:

• Beach/shorface sands with excellent reservoir properties

• Estuarine/tidal sands of more variable reservoir quality

• Thick sands trapped within transgressive incised valleys

• Transgressive sands often thin/condensed beds

Traps:

• Stratigraphic traps in isolated/detached basal sands

• Generally requires structural closure

Transgressive Systems Tract: Exploration Aspects

Source:

• Good source rocks in contemporaneous MFS shales

• Local source rocks in transgressive estuary/lagoon settings

Seal:

• Transgressive/flooding surfaces provide good top seals

Migration:

• Typically downward and lateral from MFS/condensed horizon

shales

Condensed Section

Main characteristics:

• Thin chronostratigraphic interval of hemipelagic & pelagic

sediments

• Formed on the inner to outer shelf, slope and abyssal plain

• Deposited during extremely slow rates of sediment accumulation

during a period of maximum relative sea-level rise & maximum

transgression of the shoreline

• Characterised by authigenic minerals and maximum abundance &

diversity peaks of planktonic fossils

• The maximum flooding surface (MFS) within the condensed section

provides a chronostratigraphic surface correlating shelf and slope

sediments

Condensed Section

Highstand Systems Tract






Highstand Systems Tract:


Highstand Systems Tract: Exploration Aspects

Reservoir:

• Excellent reservoirs in late HST (e.g. fluvial-deltaic)

• Dominated by variable & discontinuous deltaic facies

• Reservoir characteristics will depend on delta type

Traps:

• Predominantly structural

• Early timing is critical

Highstand Systems Tract: Exploration Aspects

Source:

• Often a problem, usually relying on deeper horizons

• Contemporaneous shales typically lean and gas prone

Seal:

• Major problem, with updip and lateral leakage

• Local transgressive/flooding surfaces usual provide top seals

Migration:

• Gas and lean oil are typical from contemporaneous source rocks

• Good quality oil typically requires vertical migration from deeper

horizons

Idealised Sequence Stratigraphic cross-section showing

Systems Tracts & Well Log Patterns






Sequence Development in a Growth Fault Setting

Distribution of facies/environments, GR log profiles and

stratal patterns through an idealised depositional sequence

Schematic sand body stacking patterns through an

idealised depositional sequence (sensu Exxon)

Sequence stratigraphy and play analysis


Petrophysical modeling can be divided into

Deterministic

Stochastic or

Combination of both

The distribution of petrophysical properties within the

reservoir can be modeled using several algorithms.

The choice of the algorithm is depended on the general

understanding of the subsurface geology, stratigraphy and

environment of deposition.


Available algorithms for petrophysical modeling include:

Sequential Gaussian Simulation (SGS)

Kriging (Interpolation)

Kriging by GSLIB (Interpolation)

Moving average (Interpolation)

Functional (Interpolation)

Closest (Interpolation)

Assign values

User defined algorithm


Deterministic modeling uses interpolation algorithms to

distribute data between well control points.

The interpolation algorithms in the deterministic method are

used to weight the nearby data points, using a set of user-

controlled variables, in order to calculate values for empty

cells between control points.

The weighting equations employs two important variables:-

Search radius

Power factor

Petrophysical Modeling Inherent limitations of the deterministic data distribution are

It uses linear and non-linear equations which will caused the resultant trend of data to be centered around an average.

The interpolation equation only works well on normally distributed data.

The method gives only a single model result.

The quality of the result is limited by data spacing (well spacing must be equal or less than minimum horizontal facies tract dimension for a realistic model).

Where heterogeneity affect flow, the deterministic method may be ideal, unless the power factor of the linear interpolation algorithm and the search radius distance have to be varied for different depositional environment.

Generally, the lower the power factor and the greater the search radius, the greater the extrapolation distance, and vice versa.

The search radius can be weighted and bias to follow known depositional trend.


The stochastic methods attempt to honor the geologic

texture and full range of the data.

The method can be used if

1. If the well spacing is greater than the minimum facies tracts

dimension or in textural variations within facies tracts are

significant.

2. If textural variations within facies tracts can not be

correlated

3. If textural variations within facies tracts are on a scale less

than the well spacing.

The stochastic approach results in many ―equi-probable‖

models, called realizations, which are conditioned to the

data.


Many stochastic techniques use a statistical tool called a semi-

variogram.

The semi- variogram attempts to determine the spatial correlation of some reservoir properties in scale and direction (e.g. porosity).

In the absence of horizontal data, a semi-variogram model can be defined based on outcrop analogs, 3-D seismic data, tracer test data, or any other type of data that might provide relevant information as to the horizontal correlation range.

As well spacing increases, the confidence in the stochastic distribution of data decreases as the variogram model used is less well constrained.

The inherent limitation is in the scale of the data used to define the statistics.

Uncertainties and Reservoir Management


Uncertainty is inevitable in reservoir description and management.

Hence by quantifying uncertainty, areas of the reservoir that require more detailed analysis can be determined, and more accurate assessments and predictions of reservoir performance can be generated for the purpose of guiding development and operational decisions. The result will be reduced levels of financial risk.

With new discoveries becoming smaller, evaluating uncertainty an essential means of maximizing the value of assets and optimizing production from one‘s reservoirs.


There are different approaches to uncertainty prediction

Traditional approaches to reservoir uncertainty are based on a single base case model that is then taken through to flow simulation.

It does not make provision for a broad enough range of scenarios to be tested, there is little spatial information and all decisions are based on a static criterion, such as volume.

Modern approaches to uncertainty have tended to focus almost exclusively on reservoir simulation based on the understanding that only the dynamic analysis of the reservoir can fully quantify what the impact of the uncertainties will be on reservoir performance.

It utilizes higher resolution, finer-scale simulation of oil and gas reservoirs enabling a supposedly more accurate prediction of field performances and better targeted capital expenditure.


Both single base case models and simulation within a dynamic environment can help contribute to managing reservoir uncertainty.

Often time both approaches fail to take into consideration one essential element of uncertainty - a realistic geological model.

Reservoir uncertainty requires a completely integrated approach where uncertainty is evaluated across the entire reservoir model -covering both static and dynamic modeling workflow and which is based on a shared earth model consistent with all known geological information.

Uncertainty management should not just include tools that work on the dynamic model but also tools that work back towards the original geological models - the original source of the data input.


One key means of basing uncertainty around realistic geological models is through 3D modeling, which despite its rapid uptake over the last few years, has surprisingly rarely been linked to uncertainty.

Properly utilizing multiple scenarios and realizations for risk analysis is a technique that requires a powerful and wide-reaching modeling package across the entire reservoir workflow. From well planning to fault seal analysis, 3D modeling can bring uncertainty management to the entire reservoir workflow

Geomodeling provides a fully accurate geometric representation of the structural and stratigraphic compartments within the exploration reservoir, thereby helping operators to increase their structural understanding of fields, rank prospects accordingly and significantly reduce their subsurface risk and uncertainty.


With geology being naturally three dimensional, a scalable 3D product of geologic modeling will provide a realistic, highly visual view of the data and helps to unify exploration and development activity

With special geologic software and well correlation tools, it is possible to group wells by classification, create well fence diagrams, view well trajectories and log data in 3D alongside other important reservoir data such as seismic, fault information and existing maps.

The result is greater well intelligence, reduced subsurface risk, optimized production and greater quantification of uncertainty.


Uncertainty management should not stop at just geological modeling.

Uncertainty management should extend to include a range of other uncertainties and scenarios, such as structure, porosity/permeability, water saturation, fluid contacts, and flow assurance.

This is where flow simulation has such an important role to play, provided that it is based on realistic geology.

Effective simulation can quantify uncertainties.


Uncertainty management should allow for uncertainties to be quantified across the complete reservoir characterization and development workflow.

Uncertainties in depth conversion, structural modeling, geological property modeling and dynamic reservoir simulation should all be able to be simultaneously evaluated ensuring that the full impact of these often independent uncertainties is captured through realistic 3D static and dynamic reservoir models.

The results are models that integrate all available data including seismic, well log, and other geological data and attempt to quantify all structural and reservoir property uncertainties.

Furthermore, through an extensive range of sensors, gauges and flow meters, which measure temperature, pressure, sand and the flow rates of oil, water, and gas in the well stream, models can be updated in real-time.

Current modeling technology only offers ‗what-if‘ type analysis - allowing for multiple realizations of what would happen if a well was choked back, essence being to close the loop and use right-time data to rapidly update the reservoir model.


Closing the loop is the ultimate goal of reservoir simulation. In this way, geometrically accurate models can be built up and then created into simulation models, consistent with all known geological information. This equates to uncertainty management across the complete static and dynamic workflow.

One essential tool for closing the loop, particularly in existing reservoir, is history matching - the act of adjusting a reservoir model until it closely reproduces its past behavior.

History matching‘s accuracy is almost completely dependent on the quality of the model as well as the accompanying production data. However, if this is achieved, history matching can help simulate future reservoir behavior with a high degree of uncertainty as well as provide a vital link between dynamic simulation and the underlying geological data.


The integration of geological descriptions of the reservoir, and the concept of history matching and uncertainty estimation can be used to predict how a field will perform. In this way, it brings geological modeling and simulation closer together and provides valuable information on the economics of the reservoir.

Uncertainty in reservoir management can be reduced with the use of geostatistics.

Generating a statistical framework will foster rapid understanding of production behavior and create robust estimates from a shared earth model.

The net outcome is a shared earth model with uncertainty and simulation models that are fully consistent with their underlying geological interpretation.

geologic description and modeling

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