This paper was prepared for the Rocky Mountain Regional Meeting of the Society of Petroleum Engineers of AIME, to be held in Casper, Wyoming, May 15–16, 1973. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgment of where and by whom the paper is presented. Publication elsewhere after publication in the JOURNAL OF PETROLEUM TECHNOLOGY or the SOCIETY OF publication in the JOURNAL OF PETROLEUM TECHNOLOGY or the SOCIETY OF PETROLEUM ENGINEERS JOURNAL is usually granted upon requested to the Editor PETROLEUM ENGINEERS JOURNAL is usually granted upon requested to the Editor of the appropriate journal, provided agreement to give proper credit is made.

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Abstract

Application of Monte Carlo techniques to oil field economic problems has not reached its full potential in spite of several recent papers on the subject. This paper describes the calculation techniques paper describes the calculation techniques used to generate triangular and bicubic probability distributions for use in Monte probability distributions for use in Monte Carlo simulations and presents a numerical example which involves development of a remote gas prospect. Justification is given for the use of simple triangular probability distributions and advantages probability distributions and advantages and pitfalls of the method are discussed.

Introduction

Over the past ten years or so there have been several papers published concerning the use of the Monte Carlo Method (MCM) in the simulation of investment problems. The first well known paper was that by Hertz, which outlines MCM in a general way. In 1967, Walstrom et al, applied the technique to several reservoir engineering problems. Since then several authors have used the method as an aid in evaluating engineering alternatives. Still, it appears that the average practicing engineer does not appreciate the potential and the ease of application of Monte Carlo Methods. The authors feel that this is due in large measure to the fact that previous authors have outlined methods without revealing detail. An engineer who has only a sketchy knowledge of statistics then feels that the key to Monte Carlo simulation—the generation of random variables with a given probability distribution—is a complex problem which is better left to experts. problem which is better left to experts. This paper is an attempt to present techniques and justification for the use of simple probability distributions, and to further explain the application of the results of MCM to an engineering problem.

AN ILLUSTRATIVE EXAMPLE

Suppose that ABC Oil Company has discovered a small gas field located 30 miles from the nearest gas transmission line. Uncertainty in reserves results from the fact that the pay zone is a shaley sand with production confined to small non-communicating lenses. production confined to small non-communicating lenses. Construction of a connecting transmission lateral will be delayed until the pipeline company feels there is sufficient reserve to justify it.

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