Evaluation of Exploratory Drilling Ventures By Statistical Decision Methods

McCray, Arthur W.

doi:10.2118/2220-pa

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…5. Probabilistic economic models should be data oriented and should require only that data that can be estimated at the time of the analysis.…”

Section: Discussionmentioning

confidence: 99%

Probability Models for Petroleum Investment Decisions

Smith

1970

Journal of Petroleum Technology

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The time, effort, and money required to develop a computer program for a general probabilistic economic model are great, but a single investment decision made more sound by the use of the model can more than justify the expense. Introduction When petroleum investments are viewed in perspective, it is possible to define four distinct environments for exploration and production decisions. From the inception through the final period these four environments can be identified as the trend, prospect, development, and production phases. Early exploration decisions concerning geological and geophysical reconnaissance projects are investigated with the entire trend as the basic unit. After prospects have been defined, the trend phase ends and the prospect phase begins. This phase, which continues until the exploratory test is drilled, is the environment in which various land trading and drilling decisions are made. The development phase, which is contingent upon a discovery, is the time when investments for field wells are considered. Decisions relating to the sale or acquisition of producing properties characterize the production phase, which extends to abandonment. production phase, which extends to abandonment. The risk and uncertainty associated with petroleum decision-making decrease as the environment changes from the trend to the prospect to the development and on to the production period, until at abandonment absolute certainty exists. Unfortunately, many of the most important decisions in petroleum exploration and production must be made during the early phases when dry-hole risk and uncertainty about economic factors are greatest. In order to quantify risk and uncertainty, four stochastic models, one for each of the four periods, can be defined. Although the first paper describing probability methods for petroleum investment decisions was published more than 30 years ago, the evolution of published more than 30 years ago, the evolution of techniques that can be applied to practical problems has been painfully slow. C. Jackson Grayson, Jr., stimulated renewed interest in probability theory with his classic, Decisions Under Uncertainty: Drilling Decisions by Oil and Gas Operators. Several recent investigators have offered procedures for implementing probability methods in investment decisions. Of the probability methods in investment decisions. Of the techniques proposed, the Monte Carlo method seems to represent the most promising approach for problems involving risk and uncertainty. problems involving risk and uncertainty. Probability theory, like any other science, has its own set of descriptive terms. For the convenience of the reader, a brief glossary of probability terms has been included in the Appendix. Probability Distributions for Probability Distributions for Subjective Random Variables Although probability theory is endowed with an abundance of laws and theorems relating the parameters of various probability distributions to statistics such as the mean or standard deviation, petroleum economics problems are usually unable to take advantage of them because of insufficient statistical data. The acceptable minimum statistical sample usually is not available to the petroleum investigator. JPT P. 543

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“…5. Probabilistic economic models should be data oriented and should require only that data that can be estimated at the time of the analysis.…”

Section: Discussionmentioning

confidence: 99%

Probability Models for Petroleum Investment Decisions

Smith

1970

Journal of Petroleum Technology

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“…(19) and then search only the local neighborhood of these µ 2 and a values. Again, such a strategy calls for some external assessment of local neighborhood and can also suffer from the same disadvantage as the more general Training Procedure, producing multiple domains where p 0 is equally maximal (Lerche, 1997; McCray, 1969;Megill, 1971;Rose, 1987). To obviate all of these difficulties and drawbacks, it is appropriate to invoke Bayesian updating (see Appendix A).…”

Section: The Limit Of Large Number Of Possible Events N Mmentioning

confidence: 99%

Predicting the future from observable sequences of events in astrophysics I. Maximum Likelihood Estimation for a fixed number of parameters

Röken

Lerche

2007

Astrophys. Space Sci. Trans.

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Abstract. Using analytical and numerical methods, estimates are given of future predictions in astrophysics that can be gathered from a sequence of observed events, for example for γ -ray bursts. Some general probability considerations are provided and then a maximum likelihood estimation, together with an approximation for the large number limit of possible events. Illustrations are given of the numerical maximum likelihood estimation programs in the situations of both a large number and a finite number of events. The effects of data uncertainty are also considered. Numerical calculations and comparisons with theoretical expectations are presented too.

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Hydrocarbon Assessment Using Subjective Probability and Monte Carlo Methods

Roy¹

1979

Methods and Models for Assessing Energy Resources

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Evaluation of Exploratory Drilling Ventures By Statistical Decision Methods

Cited by 6 publications

References 2 publications

Probability Models for Petroleum Investment Decisions

Probability Models for Petroleum Investment Decisions

Predicting the future from observable sequences of events in astrophysics I. Maximum Likelihood Estimation for a fixed number of parameters

Hydrocarbon Assessment Using Subjective Probability and Monte Carlo Methods

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