Distributed by permission ofHart's E & P and James Murtha

2 Risk Analysis

Jim Murtha, a registered petroleumengineer, presents seminars and trainingcourses and advises clients in buildingprobabilistic models in risk analysis anddecision making. He was elected toDistinguished Membership in SPE in1999, received the 1998 SPE Award inEconomics and Evaluation, and was1996-97 SPE Distinguished Lecturer inRisk and Decision Analysis. Since 1992,more than 2,500 professionals have takenhis classes. He has published Decisions

Involving Uncertainty - An @RISKTutorial for the Petroleum Industry. In25 years of academic experience, hechaired a math department, taught petro-leum engineering, served as academicdean, and co-authored two texts inmathematics and statistics. Jim has aPh.D. in mathematics from the Uni-versity of Wisconsin, an MS in petroleumand natural gas engineering from PennState and a BS in mathematics fromMarietta College. ◆

Risk Analysis:Table of Contents

When I was a struggling assistant professor ofmathematics, I yearned for more ideas, for wewere expected to write technical papers andsuggest wonderful projects to graduate students.Now I have no students and no one is countingmy publications. But, the ideas have beencoming. Indeed, I find myself, like anyone whoteaches classes to professionals, constantlystumbling on notions worth exploring.

The articles herein were generated during afew years and written mostly in about 6months. A couple of related papers found theirway into SPE meetings this year.

I thank the hundreds of people who listenedand challenged and suggested during classes.

I owe a lot to Susan Peterson, John Trahanand Red White, friends with whom I argue andbounce ideas around from time to time.

Most of all, these articles benefited by the carefulreading of one person,Wilton Adams,who has oftenassisted Susan and me in risk analysis classes.During the past year, he has been especially helpfulin reviewing every word of the papers I wrote forSPE and for this publication.Among his talents area well tuned ear and high standards for clarity.I wish to thank him for his generosity.

He also plays a mean keyboard, sings agood song and is a collaborator in a certainperiodic culinary activity.

You should be so lucky. ◆

Acknowledgements

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A Guide To Risk Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 3Central Limit Theorem – Polls and Holes . . . . . . . . . . . 5Estimating Pay Thickness

From Seismic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Bayes’ Theorem – Pitfalls . . . . . . . . . . . . . . . . . . . . . . . . 12Decision Trees vs. Monte Carlo Simulation . . . . . . . . 14When Does Correlation Matter? . . . . . . . . . . . . . . . . . 20Beware of Risked Reserves . . . . . . . . . . . . . . . . . . . . 24Decisioneering Company Profile . . . . . . . . . . . . . . . . 26Landmark Company Profile . . . . . . . . . . . . . . . . . . . 28Palisade Company Profile . . . . . . . . . . . . . . . . . . . . . . 30

Table of Contents

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Biography

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RR

Risk Analysis 3

Risk Analysis:An Overview

Risk and decision analysis was born in the middleof the 20th century, about 50 years after some ofthe necessary statistics became formalized.Pearson defined standard deviation and skewnessin the late 1890s, and Galton introducedpercentiles in 1885.

The term Monte Carlo, as applied touncertainty analysis, was mentioned by Metropolisand Ulam: the Journal of the American StatisticalAssociation in 1940. D.B. Hertz published hisclassic Harvard Business Review article in 1964. Acouple of years later, Paul Newendorp beganteaching classes on petroleum explorationeconomics and risk analysis, out of which evolvedthe first edition of his text in 1975, the same yearas A.W. McCray and 2 years before R.E. Megillwrote their books on the subject.Ten years later,there was commercial software available to doMonte Carlo simulation.

During this 50-year period, decision analysis,featuring decision trees, also came of age. Raiffa’sclassic book appeared in 1968. By 1985, therewere several commercial applications of softwareon the market.

These developments, in many ways, paralleledthe development of petroleum engineering, withthe basics appearing in the 1930s, mature texts like

Craft and Hawkins appearing in the late 1950s, andreservoir simulation and other computer basedmethods emerging in the 1970s and 1980s.Similarly, reservoir characterization and geostatisticscame along in the second half of the 20th century.

Oddly, the oil and gas industry remainedskeptical about risk analysis throughout most of thisdevelopment period, usually limiting their accept-

ance to isolated departments within organizations.Managers were notoriously unwilling to embraceresults that presented probability distributions forreserves and net present value (NPV). Consultantsoffering services and software vendors know toowell these levels of resistance.

Now, finally, there seems to be broaderacceptance of probabilistic methods, although asI write, my SPE Technical Interest Group digestcontains strong negativism from traditionalistsabout probabilistic prices. Nonetheless, considerthese items:

• the three most recent and five out of the lastseven recipients of the SPE Economics andEvaluation award have been strong proponentsof risk analysis;

• whereas the index to the last edition of thePetroleum Engineers’ Handbook only had tworeferences to “risk,” the forthcoming editionwill feature an entire chapter on the topic;

• my first paper on Monte Carlo simulation waspresented at the Eastern Regional Meeting in1987 and summarily rejected by the editorialcommittees for not being of adequate generalinterest. (It was a case study dealing with theClinton formation, but the methods wereclearly generic and used popular material

balance notions). Ten years later, I publishedMonte Carlo Simulation, Its Status and Future inthe Distinguished Author series;

• the most popular SPE Applied TechnologyWorkshop focuses on probabilistic methods;

• SPE, SPEE and WPC are working ondefinitions that include probabilisticlanguage; and

A Guide To –

Risk Analysis

The oil and gas industry remained skeptical aboutrisk analysis throughout most of this development period.

4 Risk Analysis

• the largest service companies acquiredsmaller companies that market probabilisticsoftware.

So, we advocates of Monte Carlo andDecision Trees should be jumping with glee thatthe rest of the world has caught up to us, right?Well, sort of…

The problem with this newly-found techniqueis that we are still learning things by our mistakes.Like anything else that evolves into widespread use,risk analysis requires more maturation. Only nowdo we have the critical mass on proponents torefine our applications, to discover what works, toeliminate faulty logic, and to test our ideas bypapers, courses, books and forums.

I have:• discovered things during the past 5 years that

make some of my papers out of date;• proposed methods that are too difficult to

explain and therefore impractical in anorganization;

• stumbled on limitations of the software I usethat force us to change procedures;

• witnessed numerous misuses, misquotations,and misinterpretations, many of them intechnical papers; and

• had ongoing unresolved arguments withreliable colleagues.

All this spells opportunity for the larger com-munity interested in risk analysis. There shouldbe better papers and more of them, and gooddiscussions and improved software. More peopledoing day-to-day modeling should come for-ward. I know of several companies where thehigh profile spokesmen are far less knowledge-able and less experienced than some of their lowprofile colleagues.

I am reminded of an experience while on myfirst sabbatical at Stanford in 1972, studyingoperations research and management science.The university granted me full access to all thelibraries, and I read more papers in 12 monthsthan any 5-year period before or since. I dis-covered the early years of what were twojournals then, Management Science and Operations

Research. Many of the authors had later becamef amous . Many of the paper s were luc id ,expositions, rather than arcane dissertations.Often they raised unanswered questions andstimulated others. It was clear that these peoplehad been mulling over these ideas anddiscovering them in fields that were stillimmature. Once the publications came intobeing, they exploded with their knowledge.

We have made some progress toward thisexposition as one can see by the increasednumber of papers at several of the SPE and AAPGmeetings. But we need to encourage papers andpresentations by people who have applied themethods for several years, testing and improving,discarding ideas that do not travel well from thecloister to the hearth, comparing alternative solu-tions, providing case studies, documenting look-backs, citing success stories within organizationsand touting benefits of company-wide prob-abilistic methods.

SPE and AAPG have provided ample oppor-tunities with forums, workshops and conferencesfor the general ideas to be promulgated.A naturalnext step would be to hold workshops foradvanced users.

About the articles in this publicationThis publication is in part a challenge to reviewsome of our assumptions and check our language. Itis intentionally provocative and open ended.

Themes articulated:• everyone should use probabilistic not deter-

ministic methods;• fallacies abound in both deterministic and

probabilistic methods;• teach people how to ask questions whose

answers require probabilistic methods;• we often abuse the language of probability

and statistics;• increased use of risk analysis presents more

opportunity to do things wrong; and• in order to talk about real options and

optimizations (buzzwords de jour), we need todo probabilistic cash flow properly. ◆

We need to encourage papers and presentations by people who have applied the methods for several years.

Risk Analysis:An Overview