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SPE 49030

Risk Analysis: Lessons LearnedJ.A. Alexander, SPE, and J.R. Lohr, Spirit Energy 76, Unocal

Copyright 1998, Society of Petroleum Engineers, Inc.

This paper was prepared for presentation at the 1998 SPE Annual Technical Conference andExhibition help in New Orleans, Louisiana, 27-30 September 1998.

This paper was selected for presentation by an SPE Program Committee following a review ofinformation contained in an abstract by the author(s). Contents of the paper, as presented,have not been reviewed by the Society of Petroleum Engineers and are subject to correctionby the author(s). The material, as presented, does not necessarily reflect any position of theSociety of Petroleum Engineers, its officers, or members. Papers presented at SPE meetingsare subject to publication review by Editorial Committees of the Society of PetroleumEngineers. Electronic reproduction, distribution, or storage of any part of this paper forcommercial purposes without the written consent of the Society of Petroleum Engineers isprohibited. Permission to reproduce in print is restricted to an abstract of not more than 300words; illustrations may not be copied. The abstract must contain conspicuousacknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

AbstractDuring the past 30 years the petroleum industry has generatedsignificant information on risk analysis methods for

exploration and development projects. However, some peoplecontinue to ask the question Is it worth the effort? orcomment It has not worked for me.

The authors have learned many lessons during the past fewyears that resulted in an improved risk analysis process. Thefollowing recommendations will ensure a successful risk

analysis process.

1. Written guidelines are necessary to avoid misapplicationof established methods. These guidelines should be welldocumented, communicated to everyone involved in theprocess and regularly updated.

2. Oversight of the risk evaluation process is necessary tomaintain consistency, eliminate bias, and check formisinterpretations that occur through lack of

understanding.3. Risk analysis training for both technical professionals and

management is essential.

4. Evaluation software should be accessible, standardized,easy to use and adaptable to changing technology andneeds.

5. An understanding of dependency between variables isvital.

6. Risk methods should be adaptive to allow integration ofnew techniques, such as seismic direct hydrocarbon

indicators (DHCIs), into the process.7. Results from risked projects should be understood and

tracked closely over time to facilitate adjustments and

enhancements to the methods.

IntroductionC.J. Grayson (1960) is credited with introducing risk analysisto the industry. In 1968, Paul Newendorps Risk Analysis inDrilling Investment Decisions introduced a methodology for

assessing and describing the degree of uncertainly involved inevaluating a prospects profitably. Since then, several otherpapers have been published, books have been written and

many training seminars have been offered to assist theindustry to understand risk analysis for exploration anddevelopment projects.

Despite these efforts, why do some continue to doubt the valueof risk analysis? We believe it is because they do not have all

the essential elements of the processes in place to ensuresuccess. Our experience has helped us identify seven keyelements of a successful risk analysis process. We will discuss

these elements in this paper while sharing lessons learned andcommon pitfalls we have noticed in risk analysis.

It is not our intent to debate or explain any of themathematical methods previously introduced in the industry

by authors such as Newendorp, Capen, Murtha, Rose, Garb,Smith and Megill.

Written Guidelines and CommunicationWritten guidelines are necessary to help avoidmisinterpretation of established methods. They should bewell-documented, fully communicated and regularly updated.

There are many things that can go wrong in risk analysis.Overestimation, underestimation, misidentifying critical risks,

overselling projects and underselling projects are some of theproblems. To ensure that risk analysis results in betterdecisions, it must be applied consistently. How can a company

choose between two projects if there is no consistency in the

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2 J.A. ALEXANDER, J.R. LOHR SPE 49030

risking? How can a company successfully manage a portfoliowithout consistent risking methods? Written guidelines arerequired to promote consistent risk analysis.

Our first experience was with a risk analysis manual

introduced in 1992. Unfortunately, users interpreted themanual differently, sometimes taking the guidelines tooliterally at the expense of sound professional judgment.

In the past 1-1/2 years our risk analysis team has reviewedmore than 100 projects drilled in 1997 and 1998 andcompleted a detailed lookback on 75 additional projects

drilled between 1994 and 1996. The inconsistencies weobserved during these reviews supported the need to updatethe risk analysis manual and to provide oversight of the risk

analysis process. Some of the inconsistencies we found duringthese reviews include:1) A variety of nonstandard and occasionally conflicting

risking tools being used,2) Misinterpretation of P10, P50 and P90 definitions when

working with distributions,

3) Risking of drilling costs was not commonly done or if itwas, the methods were not compatible,

4) Inconsistent methods for risking multiple object zonewere practiced,

5) Dependencies between zones and between projects weretreated in various ways,

6) Project teams range widely from optimistic to pessimistic7) Method for truncating or limiting reserve distributions

varied.

Because of these inconsistencies our guidelines and processeshave been updated to improve the consistency of our risk

analysis. The results of these changes are positive and arediscussed latter. The following are details on a few of thelessons learned. These lessons are being shared to increase the

awareness of some of the common problems that surfaced.

Lesson 1. Distortions in characterization of reservedistributions have occurred because of the interchangeable useof P90 and P99. In other words, evaluators may have meantthe maximum reserve for the project was 10 bcf (absolute

maximum), but called it P90 in the calculations of the meanexpected reserves. This has a significant impact on the meanexpected reserves. Figure 1 is a plot of Percent of

Overestimation versus Ratio of P90/P10 Value. Forexample, if the P90 value is actually P99, P10 is truly P1, andthe ratio of P90 to P10 is 10, then the mean expected reserve is

overstated by 31%. This might represent a case with a reservedistribution between 1 and 10 bcf as shown in Figure 2.

This distortion is the result of only one variable beingmisused. Since most reserve distributions are a result of morethan one variable (i.e. area, pay, or recovery factor), this

problem becomes magnified if all individual factors aresimilarly misrepresented.

Lesson 2. Improper truncation of the lower end of a reservedistribution frequently causes problems. This occurs whenthere is confusion about whether the reserves should be

truncated at the reserve value that made the project profitableor at the reserve value that made the completion profitable.

Depending on the ratio of completion cost to dry hole cost,this can significantly impact the economics of the projects.The original reserve distribution should be truncated at thereserve value that represents the possible reserve outcomes if

the completion is profitable, not the project. When reserves aretruncated, the probability of success (POS) is also adjusted.Figure 3 illustrates this with a decision tree.

The four-point method and Monte Carlo simulation are bothvalid methods for risked economic evaluations. In either

method, a reserve distribution associated with the probabilityof finding hydrocarbons (POSg) is developed. The reservevalue at which to truncate the reserve distribution is then

determined. The truncated reserve distributions and therevised POS are then used in the economic evaluations. Weidentify the revised POS as POS(ic) for the probability of

incremental commercial success. Because we also need toknow the chance the project will be commercial with fullcycle economics, we use POSc to define the probability of the

project being profitable (commercial).

Lesson 3. Use of an Intranet site to maintain and update risk

analysis guidelines avoids problems associated with thedifficulty of maintaining and distributing hard copy manuals.An Intranet site also can be designed to accommodate

changing staff who have a variety of experiences with riskanalysis. At the Intranet site, staff also can gain access toexamples, lookback results, risk software, references and

lessons learned.

Risk Evaluation OversightProcess oversight is necessary to maintain consistency,eliminate bias, and check for misinterpretations that occurthrough lack of full understanding and use of flawedassumptions. One way to achieve this is to assign a cross-functional team the task of overseeing the risk process for allexploration and development projects. The teams

responsibility is to review the risk and reserve estimations formost of the exploration and development projects, update riskguidelines, conduct lookback analysis, and provide training

and coaching on a project basis.

Lesson 4. A major element of risk analysis is and always will

be subjective judgment. It is essential that people withknowledge and critical analysis skills are involved in theanalysis process. The peer review process is an effectiveway to incorporate this critical component.

The peer review is a useful tool for enhancing the risk analysis

evaluation if these recommendations are followed: First, thereview should be done early and not delayed until the final

approval stage. Second, peers should be selected that will give

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SPE 49030 RISK ANAYSIS LESSON LEARNED 3

constructive feedback and will not introduce their own biases.Third, the peers should have experience with projects similar

to the one being reviewed. Fourth, the results of the projectsshould be communicated back to the peers so the peers can

also learn from the results.

Training / UnderstandingRisk analysis training should be required of all employees whouse the process and particularly managers who make decisionsbased on the results. Optimally, this training should be offered

in a concentrated fashion, ensuring that the greatest number ofemployees can be trained within the shortest possible period.

Lesson5. Train everyone including managers in a short time

period. This improves communication and will increase thechance of having a successful risk analysis process.

Lesson 6. Several papers and books have been written on riskanalysis. All of those involved in the risk analysis process

should be encouraged to read and review this material toincrease their understanding of risk analysis.

Lesson 7. External training should be supplemented withfocused internal training, mentoring and coaching. This is theresponsibility of the risk analysis team. It is achieved during

the peer reviews, the project reviews, lunch and learnmeetings, lookback meetings, and individual sessions. Thishas proven an effective technique because it addresses theteams immediate concerns and specific needs. This method of

training also keeps the risk analysis team close to the teams

and in touch with the current problems.

Risk Evaluation SoftwareEvaluation software should be accessible, standardized, easy

to use, and adaptable. Over the past years, many versions andvarieties of software have been developed to assist those doingrisk analysis. A tremendous effort has been invested by

individuals developing their own software to meet theirspecific needs. This software proliferation leads tononstandard and sometimes conflicting risk software. Teamsmust reevaluate all risk analysis software to ensure the needsof individual teams are met while providing a consistentevaluation framework. Some of the questions to ask are:

1. Should the software be internally or externally designed?2. Should it be customized or off-the-shelf?3. Should the software do risking, reserves, economics,

portfolio management and deal screening?4. Should the programs be PC- or Unix-based?5. Should it be on the network or stand alone?6. Does the program correctly calculate multiple zones

reserves and POS?7. Does the software correctly handle dependencies?8. Should the program do Monte Carlo simulation?

Dependencies / CorrelationsAn understanding of dependency or correlations between

variables is also essential for proper risk analysis to preventoverstating or understating the risk and uncertainty. Even

though authors such as Newendorp (1975), Garb (1988),Smith (1992), Murtha (1994,1996) and others have written onthis subject, people are still struggling with it.

Dependency deals with existence or Is it there? whilecorrelation deals with the size or How big is it?

Consideration of both provides a seamless fit with the correctand balanced view of risk and uncertainty.

Dependency is the alteration in probability of the existence of

a factor at location or zone X given the presence or absence ofthe same factor at location or zone Y. These factors caninclude reservoir presence, trap (vertical and seal),

timing/migration and source. For example, if reservoir isfound at location one, the probability of it being present in

location two increases by 50%.

Correlation on the other hand, is the quantitative or qualitative

similarity between existing characteristics in two differentlocations or zones. For example if the permeability in locationone is at P70, then the permeability in location two should be

between P60 and P80.

Much of this problem can be solved first by recognizing thedependencies or correlations and second by understanding

how to handle the effect in the risk analysis.

The best tool for recognizing correlations is the cross plot. Ifactual data for two variables are plotted against each other anda trend is noticed, then a correlation exists between the

variables. Most spreadsheet programs can determine thedegree of correlation.

Some examples of variables with correlations include:Area and Pay ThicknessPorosity and Water SaturationRate and Pay ThicknessNumber of Wells and Ultimate RecoveryRecovery and Well Location in the trap

Lesson 8. One of the major correlations not always handledcorrectly is that of net pay thickness and area. This occurs

when a single point value at the wellbore or a single point inthe reservoir is used to reflect the distribution of net pay in thereservoir. In reality the net pay thickness may vary within the

reservoir, but its thickness is correlative to the area. If this isnot recognized, the reserve distribution model will includeextreme cases that may not represent any reasonable

geological model.

This problem causes overestimation of the P90 net pay

thickness and therefore the P90 volume and reserves. When

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using distributions for area, pay and recovery factor, theexpected reserves can be overestimated by more that 30% ifthe pay is overstated as shown in Figure 4. A simple check of

this problem is to multiply the P90 area and P90 pay togetherand ask the evaluators if about 5% of the time the reservoir

volume would be larger than the resultant product. The answerwill be, no, if there is a correlation that has been handledincorrectly. The authors advocate building P90 and P50reservoir volume maps as a way to have more confidence that

the uncertainty of the reservoir volumes is understood.

Lesson 9. The existence of dependencies between zones in a

multiple-zone prospect is another item that can causeproblems. Understanding how the result of one zone impactsthe result of other zones is essential. Let us consider a prospect

with four zones that have each been assigned a POS of 30%. Ifthe zones are independent of each other, the POS of one of thefour zones finding hydrocarbons is 87%. However, if the four

zones are fully dependent, the POS of finding hydrocarbons isonly 30%. This is a material difference that must beunderstood. Depending on the degree of dependency between

the zones, the correct POS is between 30% and 87%.

This concern is also valid for multiple-well prospects or

multiple-prospect trends. The dependency between these wellsor prospects needs to be understood to conduct proper riskanalysis.

Lesson 10. When using Monte Carlo simulation as a riskanalysis tool, each iteration must be a possible real life

scenario. Sometimes this tool is treated as a black box andthe evaluator does not understand the effect of the variation ininput parameters. If the input has been described correctly and

reflects the dependencies correctly, the Monte Carlo outputwill represent the possible outcomes for the project.

AdaptiveRisk analysis methods should be adaptive to allow integrationof new techniques such as seismic direct hydrocarbonindicators (DHCIs) into the process.

Lesson 11. We have learned from reviewing the results of

close-in exploration projects between 1994-1996 that theproblem of getting on the reserve distribution for projectsassociated with seismic amplitudes was underestimated by

20% (Figure 6). However, POSg estimates were very close toactual for non-amplitude associated projects during this time.It appears the positive impact of direct hydrocarbon indicators

on prospect risk was understated. Many of the wells in thesampling were drilled to Gulf Coast Tertiary sand objectivesin and around existing fields. This is a geologic environmentwhere amplitude anomalies are generally reliable indicators ofhydrocarbons.

Since 1992 the authors have used a geotechnical risk matrix toestablish POSg for projects. A portion of the matrix, shown in

Figure 10, illustrates the probability of the presence of

hydrocarbon system variables of trap, top seal, reservoir rock,source and timing and migration. Descriptors for each elementrelate certainty to data quality, data type, analogies and trend

information. While the matrix was not designed specificallyfor evaluating the impact of DHCIs, careful considerations of

the key risk factors that relate to the DHCIs will help applythem appropriately to risking. Each of the principalhydrocarbon system elements must be evaluated for the realimpact of seismic amplitude anomalies. Although the matrix

has proved to be an effective tool, it must continuallyaccommodate improvements in technology such as seismicattributes.

Trap (lateral seal) is the risk element most affected by DHCIs.Hydrocarbon saturation implies the presence of trapped

hydrocarbons and a working hydrocarbon system greatlyreducing the chance of failure. Seismic anomalies in generalcan be either qualitative or quantitative when using the risk

matrix. Seismic anomalies, particularly when used as DHCIs,should be tested and calibrated by modeling and verified byanalogies. Matching expected versus actual seismic responses

is the cornerstone of a thorough quantitative analysis.Qualitative indicators of the presence of hydrocarbons canalso be effective risk reducers. They include hydrocarbon

related fluid contact flat spots, velocity sags, amplitude dim-outs and phase changes.

Reservoir risk is not as easily defined by seismic attributes,but some estimates of quality and thickness are possible.Seismic inversion/impedance characteristics that relate

velocity to porosity and lithology may define minimumthreshold for reservoir quality. Under some circumstances,thickness may be estimated reliably and related to minimum

requirements for an active hydrocarbon system.

Timing and migration, source and top seal are less directlyrelated to seismic attributes. However, it is possible to distortthe risk of an amplitude supported project if non-definitive

estimates of these three elements are considered to have thesame weight as quantifiable DHCIs related to definition oftrap. Caution must be exercised in over- or underestimating

the impact of seismic amplitude anomalies on prospect risks.

Tracking / Understanding ResultsResults from risked projects should be tracked closely overtime to facilitate adjustments and enhancements to themethods and process. The results tracking (lookbacks) should

incorporate both technical review and statistical evaluation ofresults.

A formal process of technical post-project reviews can bedesigned to promote consistency in risk and reserveestimations, technical best practices, technology transfer and

tracking of results. Coordinators from various technicaldisciplines who serve as facilitators, process champions andmentors can sponsor these reviews. Post-project peer reviews

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need not be a formal part of the review system for all projects,but the information from pre-drill predictions of risk, reserves,

value and cost should be compiled on the riskier wells andwells with more uncertainty.

These data, accumulated since 1993, provide the basis for thestatistical plots presented in this paper. The information shouldbe disseminate to everyone from the project team tomanagement in a discussion format that maximizesunderstanding and tailors applications of the risking process.

The types of information and depth of detail of the datacollection have changed over the past few years to enablemore accurate measurement of results, identification of the

critical success/failure causes and portfolio management. Therisk and reserve team currently manages the project reviewand results analysis process to enhance the understanding and

application of the best and most appropriate riskingtechniques. Predicted and actual performance data indicate

that improvements in the process are having a positive effect.

Post-drill information from project reviews conducted shortly

after completing wells is collected to compare predicted toactual reserves and to review technical lessons learnedregardless of the result. The feedback step is critical to the

success of any process because without it, no process could bemodified and improved. This is similar to Chevrons methodof project risk evaluation and results tracking presented in Otisand Schneidermanns paper (1997).

Lesson 12. It is necessary to constantly dig deeper todetermine what is driving the results. Figure 5 shows theresults of predicted versus actual POS, reserves and NPV (netpresent value) for 75 close-in exploration projects drilled

between 1994-1996. This summary shows the results asacceptable. However, as the details are examined, severalobservations become evident.

It became apparent that one of the 75 projects was driving theresults of the summary. This project contributed 37% of thereserves and 52% of the NPV for the 75 well total. When thisproject is removed from the summary, the predicted versusactual results for the three-year period dropped to 66% for

reserves and 54% for NPV. This level of predictability is notsufficient for the evaluation of individual projects or balancinga portfolio. However, the impact of the one project

demonstrate the need for a balanced portfolio.

When examining the results of lookbacks, one should focus on

risk (i.e. POS) and uncertainty (i.e. reserves) separately. Thetwo must be separated to understand the results and impact ofeach parameter.

Some of the various ways the results for POS prediction wereanalyzed for 1994-1996 are presented in Figure 6. It shows

POS prediction results by year, API well category, POS level,

and seismic amplitude response. Such data indicates thatpredictions for POS were conservative during the three years.

The results also show an overall conservative POS driven byconservative estimates for API category 3 & 4 wells, high

POS wells and wells with seismic direct hydrocarbonindicators. These results have been shared with the technicalprofessionals and managers to provide the basis for changes tothe interpretation of the geotechnical risk matrix andimprovements in the results.

The POS for the 25 close-in exploration projects drilled in1997 was also conservative (131% over prediction) but, for adifferent reason than noticed in the 1994-1996 results. Theunderestimation in 1997 was driven by six successful wells

which were treated as independent wells when in fact suchwells were dependent. The strong dependency between thewells was not reflected in the original recommendation. If

dependency had been represented correctly in the originalrecommendation, the actual results would have been closer to

100%. This information should be communicated throughoutthe organization to improve future results.

Figure 7 shows some of the same methods for examiningaccuracy of predicting the uncertainty of reserves. These dataindicate the predictions were understated for API category 3

and 4 wells and overestimated for most of the others. Figure 7shows that 48% of the completed wells will recover less thanP20 reserves. Figure 8 shows that 80% of the completed wellstesting seismic amplitude anomalies, found less than P50

reserves. It also shows the wells testing nonamplitude

objectives had a more balanced reserve distribution (61%below P50 and 41% above P50).

The reason POS is understated and reserves overstated is due

to problems described earlier in this paper. Spirit Energy 76has improved its drilling results in 1997 and 1998 by utilizingthe seven-element process. The actual reserves found for the

25 close-in exploration wells drilled in 1997 are 103% ofprediction. No single project overly influenced these results aswe saw in the 1994-1996 results.

Other Reasons and Lame Excuses for Failure in aRisk Analysis Process

Seven requirements for a successful risk analysis process havebeen discussed along with several lessons the authors havelearned in their pursuit to improve risking results. Below is a

list of other legitimate reasons that will cause the risk analysisprocess to fail, along with a list of lame excuses that are oftengiven.

Reasons

Difficulty in quantifying risk and uncertainty or definingprobabilities.

Inexperienced people doing the analysis.

Risk specialists not involved with day-to-day problems ornot working as a member of the technical teams.

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Education process is short-circuited.

Incorrect analogy data used.

Lame Excuses

Poor communication between individuals providing inputon the range of uncertainty.

Lack of support from management.

Lack of professional judgment.

Belief that risk analysis is only for the statisticians.

Not using the experts.

Not willing to change from traditional ways.

Misinterpreting data (logs, samples, maps).

Personnel turnover.

Conclusions

As an expansion of the fundamental evaluation techniquesused by the industry, risk analysis is well worth the effort, if it

is done properly. Seven essential elements of a successful riskanalysis program have been presented along with some of thelessons learned by the authors. Successful application of these

lessons requires commitment and endorsement from bothmanagement and technical professionals. To be successful, theprocess should include close oversight of risk evaluations

including written guidelines, appropriate software, anunderstanding of dependency, adaptive processes, and resultstracking. In the end, all risk evaluations need to be tempered

by sound technical and professional judgment. Risk analysisdoes not replace professional judgment. It can supplement

judgment, and if properly used, can improve projectevaluations and selection.

We hope this paper will add value to the industry by helping

others avoid many of the common problems noted.

NomenclatureDHCI Direct Hydrocarbon Indicator (seismic)P10: 10% probability the occurrence is less than this levelP50: 50% probability the occurrence is less than this level

P90: 90% probability the occurrence is less than this levelPOS: Probability of success.POSg: Probability of geological success.

POSic: Probability of incremental commercial success.POSc: Probability of commercial success.Reserves: Proved and unproved reserves

AcknowledgementsWe thank Bill Haskett, John Collins and our many colleagues

and supporters for the insight they provided for this paper.

References

1. Grayson, C.J., Decisions Under Uncertainty: Drilling Decisionsby Oil and Gas Operations, Harvard Univ., Div. Of Research,Graduate School of Business Administration (1960), 402.

2. Newendorp, P.D.: Risk Analysis in Drilling InvestmentDecisions, JPT(June 1968) 579-85.

3. Newendorp, P. D.: Decision Analysis for PetroleumExploration, PennWell Publishing Co., Tulsa, Okla. (1975).

4. Newendorp, P.D.: A Method for Treating Dependencies

Between Variables in Simulation Risk Analysis Models, JPT(Oct. 1976) 1145-50.

5. Garb, G.A.: Assessing Risk in Estimating HydrocarbonReserves and in Evaluating Hydrocarbon-Producing Properties,JPT(June 1988) 765-776.

6. Smith, M.D. and Jones, D.R.: Trend Analysis, The Business ofPetroleum Exploration, The American Association of Petroleum

Geologist, Tulsa, Okla. (1992) p.215-229.7. Murtha, J.A.: Incorporating Historical Data into Monte Carlo

Simulation, SPECA (April 1994).8. Murtha, J.A.: Estimating Reserves and Success for a Prospect

with Geological Dependent Layers, SPERE(Feb. 1996).

9. Megill, R.E.: An Introduction to Risk Analysis, PetroleumPublishing Co., Tulsa, Okla. (1977).

10. Rose, P.R.: Exploration Economics, Risk Analysis and ProspectEvaluation (Telegraph Exploration, Inc. 1996 edition).

11. Capen, E.C.: The Difficulty of Assessing Uncertainty, JPT(August 1976) 843-850

12. Otis, R.M. and Schneidermann, N.: A Process for EvaluatingExploration Prospects AAPG (July 1997) 1087-1109 AAPGpaper

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16

Ratio of Original P90 to P10 Value

OverestimateofMeanValue(%)

Two variables (I.e. pay and area)

One Variable (I.e. reserves)

Fig.1. Percent reserves are overstated if the estimator's P90 & P10estimates are actually P99 & P1 values. The effect for various P90/P10

ratios are shown for one and two variables. Note the compounding effect

of two variables being misrepresented.

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0.1

1

10

100

P1 P10 P50 P90 P99Probability

Reserves

(BCF)

Interpretation 2

Interpretation 1

Fig. 2. Impact of referring to P99 as P90 and P01 as P10 is illustrated.

The expected mean reserves for Interpretation 1 is 4.7 bcf and 3.6 bcf for

Interpretation 2. Expected mean for Interpretation 1 is 31% higher than

Interpretation 2.

30% 8%

70% Completed Well

Reserve Distribution.

40% 11%

30% 8%

Chance to

completewell

40%

30% 12%

Chance to

find HC

60% 60%

DrillDecision

Yes

No

Yes

No

P10

P50

P90

Dontdrill

Drill

Fig. 3. - Decision tree illustrating why reserve distributions should be

truncated at the reserve level that makes the completion profitable.

Fig. 4. - Example of area and pay correlation. As drainage area

increases, the average pay thickness decreases for location X, however

increases for location Y.

0

20

40

6080

100

120

140

POS Reserves NPV

Actual/Predicted(%)

Fig. 5 - Prediction results (actual / predicted) for 75 close-in exploration

projects drilled between 1994 and 1996. Summary results are acceptable.

However, one project contributed 37% of the reserves and 52% of the

NPV.

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POS Prediction Results

0

20

40

60

80

100

120

140

160

180

3year

To

tal

1994

1995

1996 1 2 3 4 5

Yes

No

0-2

5

25-5

0

50-7

5

75-1

00

Analysis Detail

Actual/Predicted

(%)

Annual API Well Category Seismic

DHCI

(Amplitude)

POS Estimate

Fig. 6. - Various ways to look at POS prediction results for 75 close-in exploration wells drilled between 1994-1996. Results show the POS was understated

for API Well Category 3 and 4 wells, the wells with direct hydrocarbon indicators and the high POS wells. The results for the other categories were closer to100%. The actual/predicted results for POS for 25 close-in exploration wells drilled in 1997 was 131%. The 1997 results were driven by a strong dependency

between six wells that was not originally recognized.

Reserves Prediction Results

0

50

100

150

200250

300

350

3year

To

tal

1994

1995

1996 1 2 3 4 5

Yes

No

0-2

5

25-5

0

50-7

5

75-1

00

Analysis Detail

Actual/Predicted(%) Annual API Well Category POS EstimateSeismic

DHCI

(Amplitude)

Fig. 7. - Various ways to look at reserve prediction results for 75 close-in exploration wells drilled between 1994-1996. Although the results for the three year

total were acceptable, the detail analysis suggest significant room for improvement. The actual/predicted results for 1997 was 103% with no single project

overly influenced the results as in 1994-1996.

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SPE 49030 RISK ANALYSIS LESSONS LEARNED 9

Reserve Prediction Results

0

10

20

30

40

50

60

0-20 20-40 40-60 60-80 80-100

P Value (%)

Occurance(%)

Fig. 8. - Actual reserve P values (probability from original reserve

prediction) for completed close-in exploration wells drilled in 1994-1996.

Reserve P value results for the 1997 wells are more evenly distributed.

Reserve Prediction Results for Seismic

Hydrocabon Indicators

0

20

40

60

80

100

0-50 50-100P Value

Occurance

(%)

DHCI

No DHCI

Fig. 9 - Results from 75 close-in exploration wells drilled in 1994-1996indicate more low reserve P values occur when seismic hydrocarbon

indicators were present.

PROBABILITY

(Principle Descriptors

(=what am I risking)

TRAP

Closure of any trap(includes: structure, stratigraphic

and faulting) Presence of trap hydrocarbon retention

capability (lateral sealing)

0.95 1.00

Condition is virtual to absolutely

certain and data quality/control is

excellent.

Identical trap in immediate vicinity successfully tested

and defined by unambiguous data (such as seismic,

well control, outcrop, and engineering) clearly verified

closure and lateral seal.

0.65-0.95

Condition is most probable and data

quality/control is good.

Most likely interpretation.

Analogous trap within trend successfully tested or

defined by convincing data (such as well control,

seismic, outcrop, engineering), indicating a probable

closure and lateral seal.

0.35-0.65

Condition is probable or data

quality/control is fair. Less favorable

interpretations possible.

Similar trap within other trend/trends successfully

tested and/or limited data suggests a probable closure

and lateral sealing capacity.

0.05-0.35

Condition is possible or data

quality/control is poor. Less favorable

interpretations more likely.

Trap is poorly defined and/or structurally complex, or

on geological concepts only, unconvincing seismic

and/or well data hints at closure and lateral sealing

capacity.

0.00-0.05

Condition is virtually to absolutely

impossible and data quality/control is

excellent.

Identical trap proven unsuccessful in trend, and

unambiguous data (such as seismic, well control,

outcrop, and engineering) clearly establishes the

absence of both closure and lateral seal.

Fig. 10. - Part of Geotechnical Risk Matrix used to risk trap potential. The remaining elements, top seal, reservoir rock, source rock and timing/migration are

handled similarly. Wording and descriptions are used to facilitate communication and consistency. The basic assumption of the matrix is that risking is done

to the probability of a P1 resource outcome. For purpose of this exercise, assume that P1 resource is the resultant of P10 area, recovery and net pay.

Trap = probability of closure covering P10 area.

Source = probability of presence of adequate source rock within the fetch area to produce P1 resource.

Vertical Seal = probability of continuous interval with sealing capacity over an area of P10 acres.

Reservoir Rock = probability of P10 thickness in average net pay over a P10 area with a P10 recovery factor.

Timing/Migration = probability of timely migration route available to P1 resource into P10 area.

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Fig. 11. - API well categorie

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