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Suslick,S.B., Schiozer,D., Rodriguez, M.R.THEMATICCONTRIBUTIONTERRÆ 6(1):30-41, 2009Uncertainty and Risk Analysis in PetroleumExploration and ProductionSaul B. SuslickUNICAMP, Institute of Geosciences and Center ofPetroleum StudiesDenis SchiozerUNICAMP, Department of Petroleum Engineering(FEM) and Center for Petroleum Studies – [email protected] Rebelo RodriguezPETROBRAS, Science and Petroleum EngineeringGraduate Program – FEM/IGAbstractDuring the past decades, there havebeen some significant improvements in uncertainty andrisk analysis applied to petroleum exploration and production. This paper presents a brief overview of the maincontributions made in the exploration and productionstages, followed by a summary of the main trends in thecontext of an exhaustible resource. Decisions related to petroleum exploration and production are still very complexbecause of the high number of issues involved in the process. However, uncertainty and risk analysis are becomingmore popular as new hardware and software advancesappear, contributing in an important manner to clarifythe range and the impacts of new discoveries as well asdevelopment and production assets.Keywordscally viable reservoirs, technology and oil price.Even at the development and production stagethe engineering parameters embody a high level ofuncertainties in relation to their critical variables(infrastructure, production schedule, quality ofoil, operational costs, reservoir characteristics etc.).These uncertainties originated from geologicalmodels and coupled with economic and engineering models involve high-risk decision scenarios,with no guarantee of successfully discovering anddeveloping hydrocarbons resources.Corporate managers continuously face important decisions regarding the allocation of scarceresources among investments that are characterized by substantial geological and financial risk.For instance, in the petroleum industry, managersare increasingly using decision analysis techniquesto aid in making these decisions. In this sense, thepetroleum industry is a classic case of uncertaintyin decision-making; it provides an ideal settingfor the investigation of corporate risk behaviorand its effects on the firm’s performance. Thewildcat drilling decision has long been a typicalexample of the application of decision analysis inclassical textbooks.Future trends in oil resource availability willdepend largely on the balance between the outcome of the cost-increasing effects of depletionand the cost-reducing effects of new technology.Based upon that scenario, new forms of reservoiruncertainty, risk analysis, decisionanalysis, portfolio.IntroductionExploration and production of hydrocarbons1is a high-risk venture. Geological concepts are uncertain with respect to structure, reservoir seal, andhydrocarbon charge. On the other hand, economicevaluations have uncertainties related to costs,probability of finding and producing economi1Exploration and production of hydrocarbons in this paper encompass allthe activities, such as: basin and play analysis, leads, prospect evaluation,development stages, facilities, logistics, management, etc.30

TERRÆ 6(1):30-41, 2009Suslick, S.B., Schiozer, D., Rodriguez, M.R.pricing and resource allocation in large monopolistic enterprises. Allais’ work was a useful means orpreview to demonstrate Monte Carlo methods ofcomputer simulation and how they might be usedto perform complex probability analysis, instead ofsimplifications of risk estimation of large areas.During this period, there were several attemptsto define resource level probabilities at various stages of exploration in a basin using resource distribution and risk analysis (Kaufman, 1963; Krumbeinand Graybill, 1965; Drew, 1967; Harbaugh et al.,1977; Harris, 1984; Harbaugh, 1984, Harris 1990).At that time governmental agencies (U.S. Geological Survey, Institut Français du Pétrole, etc.) werealso beginning to employ risk analysis in periodicappraisals of oil and gas resources (Figure 1).During the 1980’s and 1990’s, new statisticalmethods were applied using several risk estimationtechniques such as: (1) lognormal risk resourcedistribution (Attanasi and Drew, 1985), (2) Paretodistribution applied to petroleum field-size datain a play (Crovelli, 1995) and (3) fractal normalpercentage (Crovelli et al., 1997). Recently, USGShas developed several mathematical models forundiscovered petroleum resource assessment (Ahlbrandt and Klett, 2005) and forecast reserve growthof fields both in the United States (U.S.) and theworld (Klett, 2005).Throughout 1960’s, the concepts of risk analysis methods were more restricted to academia andwere quite new to the petroleum industry whencontributions appeared from Grayson (1960), Arpsand Arps (1974), Newendorp (1975, edited as Newendorp and Schuyler, 2000) and Megill (1977).Newendorp (op.cit.) emphasized that decisionanalysis does not eliminate or reduce risk and willnot replace professional judgment of geoscientists,engineers, and managers. Thus, one objective ofdecision analysis methods, as will be discussed laterin this paper, is to provide a strategy to minimizethe exposure of petroleum projects to risk and uncertainty in petroleum exploration ventures.The assessment to risk model preferences ofdecision makers can be achieved using a utilityfunction provided by Utility Theory. If companiesmake their decisions rationally and consistently,then their implied risk behaviors can be describedby the parameters of a utility function. DespiteBernoulli’s attempt in the 18th century to quantifyan individual’s financial preferences, the parameters of the utility function were formalized onlyexploitation and management will appear wherethe contributions of risk and decision models areone of the important ingredients. This trend canbe seen in the last two decades. The new internationally focused exploration and productionstrategies were driven in part by rapidly evolvingnew technologies. Technological advances allowedexploration in well-established basins as well asin new frontier zones such as ultra-deep water.Those technology-driven international exploration and production strategies combined with newand unique strategic elements where risk analysisand decision models represent important components of a series of investment decisions.This paper covers a brief review of previousapplications involving the following topics: (1)Risk and Decision Analysis in Petroleum Exploration; (2) Field Appraisal and Development, andUncertainty in Production Forecasts, (3) the Decision Making Process and Value of Informationand (4) Portfolio Management and Valuation Option Approach. This paper describes some of themain trends and challenges and presents a discussion of methodologies that affect the present levelof risk applied to the petroleum industry aimed atimproving the decision-making process.Risk Analysis: ExplorationThe historical origins of decision analysis canbe partially traced to mathematical studies of probabilities in the 17th and 18th centuries by Pascal,Laplace, and Bernoulli. However, the applicationsof these concepts in business and general management appeared only after the Second WorldWar (Covello and Mumpower, 1985; Bernstein,1996). The problem involving decision-makingwhen there are conditions of risk and uncertaintyhas been notorious since the beginnings of theoil industry. Early attempts to define risk wereinformal.The study by Allais (1956) on the economicfeasibility of exploring the Algerian Sahara is a classic example because it is the first study in which theeconomics and risk of exploration were formallyanalyzed through the use of the probability theoryand an explicit modeling of the sequential stagesof exploration. Allais was a French economist whowas awarded the Nobel Prize in Economics in 1988for his development of principles to guide efficient31

Proved ProbableContigentResourcesLowEstimateProved Probable PossibleHighEstimateBestEstimateProject MaturityCommercialSub-commercialDiscovered veredPetroleumIniatially-inPlaceTotal Petroleum - Iniatially in PlaceProductionstatuct topmlevDectspeProLowRiskTERRÆ 6(1):30-41, 2009HighRiskSuslick, S.B., Schiozer, D., Rodriguez, BestEstimateyPlaUnrecoverableRange of UncertaintyFigure 1 – Petroleum Resource Classification Scheme (modified from Ross, 2004 and SPE/WPC/AAPG, 2000)300 hundred years later by von Neumann andMorgenstern (1953) in modern Utility Theory.This seminal work resulted in a theory specifyinghow rational individuals should make decisionsin uncertain conditions. The theory includes a setof axioms of rationality that form the theoreticalbasis of decision analysis. Descriptions of this fullset of axioms and detailed explanations of decisiontheory are found in Savage (1954), Pratt (1964), andSchailfer (1969). Cozzolino (1977) used an exponential utility function in petroleum exploration toexpress the certainty equivalent that is equal to theexpected value minus a risk discount, known as therisk premium. Acceptance of the exponential formof risk aversion leads to the characterization of riskpreference (risk aversion coefficient), which measures the curvature of the utility function. Lercheand MacKay (1999) showed a more comprehensible form of risk tolerance that could intuitivelybe seen as the threshold value, whose anticipatedloss is unacceptable to the decision maker or tothe corporation.An important contribution that provides richinsight into the effects of integrating corporate ob-jectives and risk policy into the investment choiceswas made by Walls (1995) for oil and gas companies using the multi-attribute utility methodology(MAUT). Walls and Dyer (1996) employed theMAUT approach to investigate changes in corporate risk propensity with respect to changes in firmsize in the petroleum industry. Nepomuceno Fo etal. (1999) and Suslick and Furtado (2001) appliedthe MAUT models to measure technological progress, environmental constraints as well as financialperformance associated with exploration and production projects located in offshore deep waters.More recently, several contributions devisepetroleum exploration consisting of a series ofinvestment decisions on whether to acquire additional technical data or additional petroleum assets(Rose, 1987). Based upon these premises exploration could be seen as a series of investment decisions made under decreasing uncertainty whereevery exploration decision involves considerationsof both risk and uncertainty (Rose, 1992). Theseaspects lead to a substantial variation in what ismeant by risk and uncertainty. Some authors suchas Knight (1921) make a distinction between risk32

TERRÆ 6(1):30-41, 2009Suslick, S.B., Schiozer, D., Rodriguez, M.R.(where probabilities are known) and uncertainty(where one is unable to assign probabilities) focusing their analysis on uncertainty. Meanwhile,Megil (1977) considered risk an opportunity forloss. Risk considerations involve size of investmentwith regard to budget, potential gain or loss, andprobability of outcome. Uncertainty refers to therange of probabilities in which some conditionsmay exist or occur.Rose (2001) pointed out that each decisionshould allow a progressively clearer perception ofproject risk and exploration performance that canbe improved through a constructive analysis of geotechnical predictions, review of exploration tacticsversus declared strategy, and year-to-year comparison of exploration performance parameters. Thesefindings showed the importance of assessing therisk behavior of firms and managerial risk attitudes.Continued monitoring of the firm’s level of riskaversion is necessary due to the changing corporateand industry environment as well as the enormouscontribution generated by technological development in E&P. Over any given budgetary period,utilization of an established risk aversion level willresult in consistent and improved decision makingwith respect to risk.without significant precision loss. Simplificationsare possible, for instance, in the modeling tool,treatment of attributes and in the way several typesof uncertainties are integrated.One of the simplest approaches is to work withthe recovery factor (RF) that can be obtained fromanalytical procedures, empirical correlations or previous simulation runs, as presented by Salomão andGrell (2001). When higher precision is necessary, orwhen the rate of recovery significantly affects theeconomic evaluation of the field, using only theexpected recovery factor may not be sufficient.Techniques such as experimental design, response surface methods and proxy models havebeen used by several authors (Damsleth et al., 1991;Dejean, 1999; Ligero et al., 2007) in order to accelerate the process. Another possible approach is touse faster models such as a streamline simulation ora fast coarse grid simulation as proposed by Hastings et al. (2001), Ballin et al. (1993), Subbey andChristie (2003), and Ligero et al. (2003).The integration of risk analysis into the definition of production strategy can also be very timeconsuming because several alternatives are possibleand restrictions have to be considered. Alternativesmay vary significantly according to the possible scenarios. Schiozer et al. (2004) proposed an approachto integrate geological and economic uncertaintieswith production strategy using geological representative models to avoid large computational effort.Integration is necessary in order to (1) quantify the impact of decisions on the risk of theprojects, (2) calculate the value of information, asproposed by Demirmen (2001) and (3) quantifythe value of flexibility (Begg and Bratvold, 2002;Hayashi et al., 2007). The understanding of theseconcepts is important to correctly investigate thebest way to perform risk mitigation and to addv