Geotechnical Estimates under Uncertainty

Published:January 01, 2001
Abstract
Risk and uncertainty are not synonymous (Megill, 1984; Rose, 1987). Risk connotes the threat of loss. Risk decisions weigh the level of investment against four considerations: net financial assets, chance of success/failure, potential gain, and potential loss. The last three considerations must rely on estimates, made under uncertainty, of the range of probabilities that some condition may exist or occur.
Every exploration decision involves considerations of both risk and uncertainty. Risk comes into play in deciding how much we are willing to pay for additional data or mineral interests, considering the high impact of frontend costs on project profitability. Uncertainty is intrinsically involved in all geotechnical predictions about the range of magnitude of the inferred mineral deposit, the chance of discovery, and the cost of finding and developing it. Therefore, once prospects have been identified, the problem in serial exploration decision making is twofold:
to be consistent in the way we deal with risk and uncertainty, and
to perceive uncertainty accurately and reduce it where possible.
Although extensive scientific and geotechnical work is indeed essential to successful modern petroleum exploration, we must also recognize that nearly all of the parameters required to assign expected monetary value to the exploratory prospect can only be estimates made under substantial uncertainty. Table 1 lists the most significant ones.
Given the importance of responsible estimating, it is quite remarkable that until recently so little effort has been made by most modern oil companies to monitor and improve their geotechnical staff's estimating performance.
Risk, Uncertainty, and Estimating
Risk and uncertainty are not synonymous (Megill, 1984; Rose, 1987). Risk connotes the threat of loss. Risk decisions weigh the level of investment against four considerations: net financial assets, chance of success/failure, potential gain, and potential loss. The last three considerations must rely on estimates, made under uncertainty, of the range of probabilities that some condition may exist or occur.
Every exploration decision involves considerations of both risk and uncertainty. Risk comes into play in deciding how much we are willing to pay for additional data or mineral interests, considering the high impact of frontend costs on project profitability. Uncertainty is intrinsically involved in all geotechnical predictions about the range of magnitude of the inferred mineral deposit, the chance of discovery, and the cost of finding and developing it. Therefore, once prospects have been identified, the problem in serial exploration decision making is twofold:
to be consistent in the way we deal with risk and uncertainty, and
to perceive uncertainty accurately and reduce it where possible.
Although extensive scientific and geotechnical work is indeed essential to successful modern petroleum exploration, we must also recognize that nearly all of the parameters required to assign expected monetary value to the exploratory prospect can only be estimates made under substantial uncertainty. Table 1 lists the most significant ones.
Given the importance of responsible estimating, it is quite remarkable that until recently so little effort has been made by most modern oil companies to monitor and improve their geotechnical staff's estimating performance. Many organizations, even today, persist in utilizing deterministic estimates—“singlevalue forecasts.” These are hopelessly inadequate given the wide uncertainties that usually exist and the multiplicity of interactive parameters involved in calculating the chanceweighted aftertax net present worth [Expected Net Present Value (After Tax) = ENPV(AT)] of a typical exploration prospect (Equation 2):
Dimensional  thickness, area, volume, depth 
Reservoir  net/gross, ϕ (porosity), K (permeability), HCrec %, GOR, S_{W}, etc. 
Well Performance  IP, % decline, etc. 
Geochemical Values  SRtype, TOC, maturity, composition 
Migration  impedance, dispersal, routes 
Trap Integrity  seal effectiveness, leakage, flushing, etc. 
Timing  migration vs. trap creation 
Frontend Costs  land, drilling, completion, geotechnical data acquisition, overhead 
Discovery Probability  geologic chance factors, commercial and economic reserve thresholds 
Wellhead Price  local vs. international influences, envelope of historical real oil prices 
Dimensional  thickness, area, volume, depth 
Reservoir  net/gross, ϕ (porosity), K (permeability), HCrec %, GOR, S_{W}, etc. 
Well Performance  IP, % decline, etc. 
Geochemical Values  SRtype, TOC, maturity, composition 
Migration  impedance, dispersal, routes 
Trap Integrity  seal effectiveness, leakage, flushing, etc. 
Timing  migration vs. trap creation 
Frontend Costs  land, drilling, completion, geotechnical data acquisition, overhead 
Discovery Probability  geologic chance factors, commercial and economic reserve thresholds 
Wellhead Price  local vs. international influences, envelope of historical real oil prices 
There are several important observations to be made about this equation. First, in an Americanstyle tax and royalty license, the owners of the producing property usually pay 100% of the costs but receive a reduced proportion—ordinarily from about 70% to 87.5%—of the revenues from production. This reduced proportion is the net revenue interest (NRI); the remainder goes to the royalty owner(s)—generally the landowner. In productionsharing contracts the formula is different, although the general principle still prevails that the operator provides most or all of the capital, especially the exploration investment, but receives only a part of the production revenues. Second, the equation expresses the profit or (loss) as if it were a “lumpsum” payment, whereas it is actually received over a long period of time: a complex net cashflow stream combining investments, production decline, price fluctuations, expenses (including taxes), and inflation. Third, in order to consider the time value of money, the net cash flows are expressed as a discounted cashflow stream so the entire venture can be compared with current alternative investments. Wherever a dollar value is expressed as Present Value (PV), it means that the value has been discounted to reflect the time value of money.
Uncertainty attends every item in this ENPV equation except the net revenue interest. These uncertainties are diverse, relating to geology, engineering, law, politics, economics, and acts of God. It is the special professional responsibility of geotechnical staff to estimate the magnitude of reserves, production rates, and costs; to reduce the level of uncertainty as much as possible through sound scientific and technological judgment (and additional investigation, where warranted); and to accurately and consistently convey estimates—as well as uncertainty levels—to management. Otherwise management's investment decisions may be misguided and imprudent. Thus the financial consequences of their geotechnical predictions and estimates constitute a weighty professional responsibility of the geotechnical staff.
Magnitude of Geotechnical Uncertainty
The earth is a coarse filter. Even though petroleum explorationists employ increasingly sophisticated and discriminating technology, our precision in measuring most of the important geotechnical factors and parameters bearing on prospect value is much more limited than many of us care to admit. Technology can reduce, but not eliminate, uncertainty. Moreover, exploration always operates at the cutting edge—the threshold of resolution—so that the potential effectiveness of new tools and concepts is constantly and aggressively being evaluated by applications in new and old exploration theaters. Accordingly, explorationists will always have to deal with uncomfortably large uncertainties. Figure 2 shows, by crossplot, four actual experiences of modern oil companies in attempting to estimate reserves contained in prospects that turned out to be discoveries (Capen, 1992). Figure 15 shows similarly wide uncertainties by a large number of companies exploring in the Norwegian North Sea over an extended period of time.
The crossplots in Figure 2 express two important attributes of prospect reserves forecasting:
Prevalent optimistic bias of estimates vs. actual outcomes—in crossplots B, C, and D, most of the outcomes are overestimates (crossplot A is an unbiased data set). We will address bias later.
Substantial uncertainty, expressed as a characteristic, wide “scatter” of estimates. Given a continuing and substantial number of exploration ventures (repeated trials), statistics offers a practical way to deal with the prevailing large uncertainties that characterize petroleum exploration.
This characteristic, wide “scatter” conveys a profound (if disagreeable) message that petroleum prospectors would do well to absorb: Explorationists cannot predict very well how much oil or gas their successful prospects (= discoveries) will contain. We can generally identify those closures that are too small to contain large volumes, but we cannot forecast how much oil or gas the large closures may contain, often because of unanticipated variations in trap fillup, reservoir quality, and dip rate.
Stated more pragmatically, we can usually distinguish between a 5millionbarrel (MM bbl) closure and a 50millionbarrel closure, but we cannot distinguish between 3million and 5millionbarrel closures, or 30million and 50millionbarrel closures. Moreover, we often cannot tell how much oil may be present in a closure having 50millionbarrel capacity—1 million, 5 million, 20 million, or even 50 million. This basic “fact of life” has profound (and often ignored) implications for explorationists.
Ranges and Probabilities
The problem is how to express our technical uncertainties realistically, and in a form by which they can be utilized in economic equations and formulae and subjected to subsequent evaluation. The most common convention in use today by modern petroleum corporations involves the formulation of a range of anticipated values for a given parameter, with probabilities— ordinarily 90%, 50%, and 10%—assigned to the values that constitute the range. For example, the geologist may believe there is a 90% chance that the anticipated payzone will be more than 10 feet thick, and she may be 50% confident that it will be more than 20 feet thick, but she is only 10% sure that it could be more than 40 feet thick. The same procedure may be applied to any parameter—productive area, reservoiryield, initial production rate, declinerate, wellhead price, drilling costs, and the like.
However, such estimates cannot be pulled out of the air! They must rely on objective considerations of all relevant data, especially maps, cross sections, geophysical data, borehole log interpretations, analogous production records, and the like. Moreover, geotechnical professionals must arrive at a final distribution for each parameter by repeated iterations—making trial estimates, examining the implications of various values in the distribution, determining through credibility checks and reality checks that the distribution makes sense, comparing it with analog data, considering the independently derived opinions of other professionals, and adjusting it repeatedly until finally becoming comfortable with all the estimates in the distribution, as constituting a “best fit” to the facts.
Biases in Estimating under Uncertainty
Bias is a more serious problem in geotechnical forecasting than is the characteristic wide uncertainty. If the exploration company's decisionmakers consistently receive biased estimates concerning prospect value, their investment decisions will be correspondingly flawed, leading to suboptimal economic performance of the company's exploration portfolio. The stockholder will suffer. Table 2 lists the most significant biases observed in modern exploration companies (Rose, 1987).
For the exploration company, three of these biases are especially dangerous.
Overconfidence
This bias typically leads to excessively narrow ranges: Technical specialists think they know more than they really do, so they tend naturally to set predictive ranges that correspond to a confidence significantly less than the ranges they think they are setting. A common result is that, for prospect reserves forecasts, the anticipated “lowside” reserves prediction is too large and the projected “highside” prediction is too small. The common operational symptom of the problem is that prospectors experience frequent surprises on reserve sizes of discoveries (Capen, 1976), as well as many other geotechnical parameters. Figure 2 indicates that the real ranges of newfield wildcat prospect reserves uncertainties are commonly about two orders of magnitude (powers of 10) at about the 90% confidence level.
Conservatism
This bias commonly leads to underestimates because professionals, fearing criticism if results are disappointing, may think it is worse to overestimate a project than to underestimate it. The psychology of an unexpected upward revision in project profitability is much more pleasant than a disappointing downward revision. In fact, however, either error may result in a loss to the investor (Rose, 1987). Overestimates result in overinvesting in projects, whereas underestimating may cause the firm to invest too little, or even decline to invest. Either result is a loss.
Overoptimism
This form of motivational bias leads to overestimates because of perceived career or economic selfinterest on the part of the professional. The most common example in exploration is prospectors inflating estimates of prospect reserves or probability of success in order to “sell the deal” and get the prospect drilled (Rose, 1987).
Lognormality
Basis
Statistics is routinely taught to students by employing the “normal” distribution—the wellknown symmetrical “bellshaped” curve. Even though students of mathematical statistics have long known the significance of the Central Limit Theorem, the lognormal distribution in petroleum science has only gained wide acceptance—and more importantly, routine analytical application—during the past decade.
The Central Limit Theorem states that distributions resulting from the natural addition of independent random variables will be “normal”—that is, a frequency distribution will tend to take the form of the familiar “bellshaped” curve, in which the vertical axis is ordinarily expressed as a percent of the total, and the horizontal axis is an arithmetic scale expressing some variable such as dimension or value (Figure 3a). Another convention for presenting the same data is the cumulative probability distribution, in which the vertical axis is 0100% and the horizontal axis displays a dimensional variable, using an arithmetic scale (Figure 3b). The power of the cumulative probability distribution is that, conceptually at least, it represents the full universe of all possible outcomes—100%—and probability is expressed as a cumulative percent of some outcome “equal to or less than” or “equal to or more than” a particular value^{1}. Thus the cumulative probability distribution is especially useful as a predictive tool.
A special type of cumulative probability graph paper has been developed on which the vertical probability axis is symmetrical around the 50% probability, with complementary probability intervals (4050% and 5060%; 30^0% and 6070%, etc.) that are equal but of increasing spans upward and downward (Figure 4a). Maximum and minimum probabilities are 1% and 99%, rather than 0% and 100%. The horizontal axis is arithmetic. The special property of the cumulative probability graph is that a cumulative probability distribution that is perfectly normal will plot as a straight, sloping line.
The Central Limit Theorem also provides that distributions resulting from the natural multiplication of independent random variables will be “lognormal”— that is, the frequency distribution will tend to form a symmetrical “bellshaped” curve where the horizontal axis is logarithmic (Figure 3c). When a lognormal distribution is plotted as a frequency curve on a regular coordinate graph (i.e., arithmetic scale), it takes the form of a severely rightskewed frequency curve (Figure 3d). Another special type of graph paper has also been developed for plotting “cumulative log probability,” in which the vertical axis is the cumulative probability scale, as described in the preceding paragraph, whereas the horizontal axis is a logarithmic scale. A cumulative probability distribution that is perfectly lognormal will appear as a straight, sloping line (Figure 3e). Figure 4b shows a cumulative log probability graph; note that maximum and minimum values on the vertical probability scale are 1% and 99%, and the horizontal axis is a log scale.
So natural multiplication of independent, random variables yields lognormal distributions. Most important geotechnical parameters involved with oil and gas occurrence are lognormal (Megill, 1984; Capen, 1984, 1992). Geoscientists who are aware of the prevalence of lognormality (and who constrain their estimates in the expectation of lognormality) will tend to make better predictions of most parameters having to do with oil and gas reserves (Rose, 1996c). A few petroleum parameters are exponential; fewer still are normal. Predictions of all such parameters should be constrained by the expected form of the distribution.
Fieldsize Distributions
Distributions of reserve sizes (projected ultimate recoveries) in fields in a given trend, play, or basin show a pronounced tendency to follow a conventional lognormal pattern:
just a few very small fields,
a great many small fields,
a handful of mediumsize fields, and
a very few very large fields
The reason, of course, why fieldsize distributions (FSDs) are lognormal is that the parameters controlling field size are multiplicative: Field Area χ Average Net Pay Thickness χ Hydrocarbon Recovery Factor = Field Reserves. Several attributes of FSDs are noteworthy. First, they typically shift toward smaller sizes as exploration progresses (Figure 5a). Second, where many small fields (1,000 to 100,000 BOE [barrels of oil equivalent]) are included, the FSD may depart from a straight line on a cumulative log probability graph, taking a concave form at the lower end (Figure 5a), because of incomplete sampling of smaller fields. Such smaller accumulations may be incompletely represented in the population of discovered fields because of economic and technological censoring:
anomalies recognized to be small may therefore not be drilled;
discoveries recognized to be very small by testing may not be completed for production; and
small anomalies may not be visible geotechnically, and therefore never drilled.
When FSDs are truncated at the low end to eliminate fields that are noncommercial, the resulting distribution typically fits a straight, sloping line, but in the lower part (the P99%P80% sector), the FSD has a characteristic convex shape at the lower end as a consequence of the arbitrary elimination of the small part of the sample (Figure 5b). FSDs of trends in economically demanding regions, such as the North Sea or deep Gulf of Mexico, where only larger discoveries qualify for platform installation, have already been severely truncated at the lower end by such minimum economic requirements. Parent distributions in such areas contain very many uncompleted accumulations, ordinarily reported as “shows,” many of which were not even tested. If such a trend were located onshore, however, many such “shows” would have been completed as small fields. This point is elaborated further on page 80 and in Appendix F.
Construction of FSDs sheds great light on exploration of most trends and basins. They are recognized as an indispensable tool by most modern companies, serving as “reality checks” and giving essential perspective on proposed exploration ventures.
Calculating the Mean of Lognormal Distributions
Statistically, the best single representation of a lognormal distribution is the mean, or average. Because events in the lowprobability end of the distribution have disproportionately much greater “weight” than in the highprobability part, the arithmetic mean of a lognormal distribution typically increases as sample size (n) increases. The statistical mean assumes a continuous distribution; that is, that n = °° and characteristically represents the largest possible mean value. On the other hand, if we simply calculate the arithmetic average of a lognormal distribution composed of a small number of values (say n = 6, or n = 10), that mean will be smaller than the statistical mean.
A practical problem with use of the statistical mean in exploration forecasting is that extremely lowprobability events (less than P1%), which have extremely large values, contribute to the mean. But such events are sufficiently unlikely that we are justified in treating them as “geologically impossible.” By truncating such distributions above P1%, the resulting mean values are more realistic.
A widely used alternative is Swanson's Mean (Megill, 1984), which works well for (1) n values consistent with exploration experience (i.e., most trends do not contain an infinite number of fields), (2) distributions truncated at the upper end, beyond P1%, and (3) distributions of low to moderate variance, including distributions truncated toward the low end by economic threshold requirements. Appendix A illustrates and reviews various techniques for calculating the mean of a lognormal distribution.
Techniques for Improving Geotechnical Estimates
Exploration staffs can learn to improve their geotechnical estimating performance by using at least seven techniques (Table 3).
Geotechnical Analog Models
Since about 1950, geoscientists have increasingly developed and used “analog models”—exceptionally welldocumented and wellunderstood examples of various “type” geologic situations—to anticipate dimensions, patterns, and associations of newly encountered (and therefore poorly documented) counterpart geologic phenomena. The first such models were stratigraphic. One example is the very wellknown carbonate faciescomplex of the middle Permian Guadalupean shelfmargin of west Texas and New Mexico (Newell et al., 1953; Pray, 1988). Another is the modern delta of the Mississippi River feeding into the Gulf of Mexico (Fisk, 1954; Coleman and Prior, 1982). Stratigraphers familiar with such models can often make farreaching and insightful forecasts about newly encountered geologic situations, even though very little prospectspecific data exist. Now we also have structural models, such as the balanced structural models used to resolve and interpret seismic lines in complex thrusted terranes. Engineers routinely set up models of reservoir behavior. Economic models predict economic trends, given certain technologic and market developments. Such models effectively widen our conceptual and predictive ranges by providing flexible templates and characteristic associations that would never have been available to someone using only traditional geologic or economic principles. However, experienced geoscientists and engineers have learned that utilizing models too literally can lead to predictive errors; the lesson is to maintain flexibility in interpreting new geology based on analog models.
Multiple Working Hypotheses and Maps
T.C. Chamberlin's (1931) classic paper emphasizes the importance in scientific investigations of the conscious identification and evaluation of independent, multiple working hypotheses. To the exploration mind, it offers a disciplined method to widen predictive ranges because it forces the investigator to systematically construct and evaluate alternative interpretations of incomplete data sets. In its simplest practical form, it requires the prospector to make several possible maps of various prospect parameters, showing optimistic, intermediate, and pessimistic possible cases, or various possible structural or depositional interpretations of the geotechnical data.
1.  USE OF GEOTECHNICAL MODELS AS ANALOGS 
2.  USE OF MULTIPLE WORKING HYPOTHESES AND MAPS 
3.  INDEPENDENT MULTIPLE ESTIMATES 
“Delphi Rounds”  
Team Exploration  
Peer and Committee Reviews  
Technical Subcommittees in Joint Ventures  
4.  “NATURE'S ENVELOPES” 
Lognormality  
Known Ranges of Parameters Plausibility Checks  
5.  “REALITY CHECKS” 
Fieldsize Distributions  
Historical Record  
Comparisons with Worldwide Databases  
Iteration and Tests for Reasonableness  
6.  PROPER STATISTICAL PROCEDURES 
7.  PRACTICE AND COMPARISON OF PRIOR PREDICTIONS WITH OUTCOMES 
1.  USE OF GEOTECHNICAL MODELS AS ANALOGS 
2.  USE OF MULTIPLE WORKING HYPOTHESES AND MAPS 
3.  INDEPENDENT MULTIPLE ESTIMATES 
“Delphi Rounds”  
Team Exploration  
Peer and Committee Reviews  
Technical Subcommittees in Joint Ventures  
4.  “NATURE'S ENVELOPES” 
Lognormality  
Known Ranges of Parameters Plausibility Checks  
5.  “REALITY CHECKS” 
Fieldsize Distributions  
Historical Record  
Comparisons with Worldwide Databases  
Iteration and Tests for Reasonableness  
6.  PROPER STATISTICAL PROCEDURES 
7.  PRACTICE AND COMPARISON OF PRIOR PREDICTIONS WITH OUTCOMES 
Independent Multiple Estimates
When we are estimating under uncertainty, the consideration and reconciliation of independent multiple estimates of the parameter yields forecasts that are generally less biased and closer to reality than the more orthodox procedure of devoting more time, money, and technology to additional study by a single investigator. Modern exploration firms accomplish this by organizational means, such as multidisciplinary exploration teams, peer reviews of emerging projects, formal prospect review by a centralized exploration risk committee, or structured iterative estimating procedures called “Delphi Rounds.” Exploration joint ventures provide a practical way to achieve similar balance among participating partners who interact through technical subcommittees.
Nature's Envelopes
Most geologic and engineering variables involved in petroleum occurrence and production are distributed lognormally; similarly, our estimates of such parameters are also distributed lognormally. A few are distributed exponentially; fewer still are distributed normally. By understanding the probable distribution of a given parameter, we can make estimates that honor and are constrained by the expected distribution. Such “natural envelopes” lead to reduced bias and more realistic predictive ranges. Another natural envelope is provided by the known natural ranges of parameters. For example, we know that the largest known hydrocarbon recovery factor is about 1200 barrels per acrefoot (bbl/af); also, any oil reservoir yielding less than about 50 bbl/af is likely to be physically unresponsive. All geotechnical predictions should be made in observance of such natural envelopes. By projecting distributions out to the extremes, provisional P1% and P99% values may be checked to see if such large or small values are plausible; that is, do they constitute values that, when honoring available data, represent credible extreme highside and lowside values?^{2} If such extreme values are not plausible, the distribution must be shifted until they are.
Reality Checks
Once a preliminary estimate has been made, it should be tested repeatedly against known examples to ensure reasonability and obtain a bestfit. FSDs provide such a “reality check” against which prospect reserves estimates can be compared. The historical drilling record can provide a basis for evaluating estimates of discovery probability. Comparison of predicted prospect parameters against parameters measured in fields of similar type in the trend or basin, or against worldwide databases, can help evaluate those predictions. Comparison of the prospect's reserves variance against observed variance of analogous prospect types also provides useful reality checks (see p. 26).
Proper Statistical Procedures
Predictions of prospect parameters should be made using 80% confidence ranges, which calls for estimating highside (P10%) and lowside (P90%) cases. Special attention should be given to the mean and median values in all parameter distributions. Because the mean reserves case is the expected outcome of every prospect, the economic viability of the mean must be assessed, so the key cashflow model required is ordinarily based on the mean reserves case. Nevertheless, it may also be important to carry out discounted cashflow (DCF) analyses on the P90%, P50%, and P10% reserves cases, especially for largepotential, costly exploration prospects. This is especially true where the relationship between project reserves and project net present value (NPV) is not constant, such as with productionsharing contracts where the host country takes an increasing percent as field reservesize increases, or in offshore projects where “stepfunctions” may be introduced because of varying costs for offshore production facilities. For such situations, what is needed is the mean of NPVs of all reserves outcomes, rather than the NPV of only the mean reserves case. Mode, or “most likely,” is a widely misunderstood statistical term that commonly leads to overoptimistic reserves forecasts, and its use by geotechnical and economic staff should be discouraged. Many explorationists say “most likely” when they are really thinking about an average, median, or “best guess” value. Accordingly, I recommend that “most likely” be expunged from use in forecasting or estimating.
Practice and Comparison of Prior Predictions with Outcomes
Discussion, justification, and refinement of geotechnical estimates among professional staff provides an excellent way to clarify, standardize, and improve their ability to make sound and consistent estimates of prospect parameters. In addition, disciplined comparison of predictions with actual outcomes provides objective feedback as to individual, team, and organizational performance in predicting prospect parameters. This requires systematic recording of predictions and periodic review of actual outcomes over a year or more in order to acquire an adequate sample and to observe the result of learning as expressed by continual improvement in predictive performance. Commonly, this requires persistence, strong management encouragement, and monitoring, if it is to produce a permanent change in organizational values and professional behavior (Rose, 1987). Companies such as Chevron (Otis and Schneidermann, 1997), Amoco (McMaster and Carragher, 1996), Unocal (Alexander and Lohr, 1998), and Santos (Johns et al., 1998), have published compelling accounts about the improvement of exploration performance through such methods.
The exploration expression of reserves is thereby made compatible with the traditional expression of “proved reserves,” as an expression of high confidence in the presence of some specified conservative reserves value, or more;
Explorationists, being keenly aware that oil companies are particularly interested in large discoveries, naturally prefer to focus on the potential for large reserves—thus the “or more” expression is more natural and appropriate;
It eliminates the disturbing possibility that statistically naîve decisionmakers may be seduced into believing there is a 90% probability of finding the highside (P90%) outcome or more, rather than only a 10% chance; and
Commercial truncation is directly expressed as the proportion of the reserves distribution that is of commercial size or larger, rather than as the (1Pc [probability of commercial success]) expression required by the < convention.
Figures & Tables
Dimensional  thickness, area, volume, depth 
Reservoir  net/gross, ϕ (porosity), K (permeability), HCrec %, GOR, S_{W}, etc. 
Well Performance  IP, % decline, etc. 
Geochemical Values  SRtype, TOC, maturity, composition 
Migration  impedance, dispersal, routes 
Trap Integrity  seal effectiveness, leakage, flushing, etc. 
Timing  migration vs. trap creation 
Frontend Costs  land, drilling, completion, geotechnical data acquisition, overhead 
Discovery Probability  geologic chance factors, commercial and economic reserve thresholds 
Wellhead Price  local vs. international influences, envelope of historical real oil prices 
Dimensional  thickness, area, volume, depth 
Reservoir  net/gross, ϕ (porosity), K (permeability), HCrec %, GOR, S_{W}, etc. 
Well Performance  IP, % decline, etc. 
Geochemical Values  SRtype, TOC, maturity, composition 
Migration  impedance, dispersal, routes 
Trap Integrity  seal effectiveness, leakage, flushing, etc. 
Timing  migration vs. trap creation 
Frontend Costs  land, drilling, completion, geotechnical data acquisition, overhead 
Discovery Probability  geologic chance factors, commercial and economic reserve thresholds 
Wellhead Price  local vs. international influences, envelope of historical real oil prices 
1.  USE OF GEOTECHNICAL MODELS AS ANALOGS 
2.  USE OF MULTIPLE WORKING HYPOTHESES AND MAPS 
3.  INDEPENDENT MULTIPLE ESTIMATES 
“Delphi Rounds”  
Team Exploration  
Peer and Committee Reviews  
Technical Subcommittees in Joint Ventures  
4.  “NATURE'S ENVELOPES” 
Lognormality  
Known Ranges of Parameters Plausibility Checks  
5.  “REALITY CHECKS” 
Fieldsize Distributions  
Historical Record  
Comparisons with Worldwide Databases  
Iteration and Tests for Reasonableness  
6.  PROPER STATISTICAL PROCEDURES 
7.  PRACTICE AND COMPARISON OF PRIOR PREDICTIONS WITH OUTCOMES 
1.  USE OF GEOTECHNICAL MODELS AS ANALOGS 
2.  USE OF MULTIPLE WORKING HYPOTHESES AND MAPS 
3.  INDEPENDENT MULTIPLE ESTIMATES 
“Delphi Rounds”  
Team Exploration  
Peer and Committee Reviews  
Technical Subcommittees in Joint Ventures  
4.  “NATURE'S ENVELOPES” 
Lognormality  
Known Ranges of Parameters Plausibility Checks  
5.  “REALITY CHECKS” 
Fieldsize Distributions  
Historical Record  
Comparisons with Worldwide Databases  
Iteration and Tests for Reasonableness  
6.  PROPER STATISTICAL PROCEDURES 
7.  PRACTICE AND COMPARISON OF PRIOR PREDICTIONS WITH OUTCOMES 
Contents
Risk Analysis and Management of Petroleum Exploration Ventures
During the 1990s, many international petroleum companies improved their exploration performance significantly by using principles of risk analysis and portfolio management, in combination with new geotechnologies. While exploration risk cannot be eliminated, it can certainly be reduced substantially, on a portfolio scale. And the widespread adoption of standardized risk analysis methods during the 1990s brought badly needed discipline to petroleum exploration. By the mid1980s, most wellinformed major international petroleum firms that were engaged in exploration recognized that, globally, the average size of new discoveries was diminishing. Not coincidentally, the class of exploratory prospects categorized as “high risk/highpotential” was showing marked signs of underperformance. For major companies, when all such ventures, which averaged around a 10% perceived probability of success, were considered, less than 1% actually discovered profitable oil and gas reserves, and the sizes of these discoveries were generally far smaller than predicted. All in all, such exploration for new giant fields destroyed value, rather than creating it, in the 1980s and early 1990s. Consequently, exploration, as a corporate function, lost credibility. It badly needed to begin delivering on its corporate promises. It needed to become more efficient, and thereby more profitable. To optimize the allocation of exploration capital, concepts of portfolio management began to be considered.