Perhaps no industry is more vitally concerned with risk than the oil and gas industry, and few professional men other than petroleum engineers are required to recommend higher investments on the basis of such uncertain and limited information. In recent years, the number of methods dealing with risk and uncertainty has grown extensively so that the classical approach, using analytical procedures and single-valued parameters, has undergone a significant transformation. The use of stochastic variables, such as those frequently encountered in the oil industry, is now economically feasible in the evaluation of an increasing number of problems by the application of Monte Carlo techniques.

This paper defines the Monte Carlo method as a subset of simulation techniques and a combination of sampling theory and numerical analysis. Briefly, the basic technique of Monte Carlo simulation involves the representation of a situation in logical terms so that, when the pertinent dataare inserted, a mathematical solution becomes possible. Using random numbers generated by an "automatic penny-tossing machine" and a cumulative frequency distribution, the behaviour pattern of the particular case can be determined bya process of statistical experimentation. In practical applications, the probabilistic data expressed in one or several distributions may pertain to geological exploration, discovery processes, oil-in-place evaluations or the productivity of heterogeneous reservoirs. The great variety of probability models used to date (e.g., normal, log-normal, skewed log-normal, linear, multi-modal, discontinuous, theoretical, experimental) confirms a broad rangeof experimental computations and a genuine interest in realistic representations of random impacts encountered in practice.

Emphasis in this paper is directed to the salient characteristics of the Monte Carlo method, with particular reference to applications in areas relatedto the oil and gas industry. Attention is focused on reservoir engineering models. Nevertheless. management facets of the oil and gas business are considered along with other applications in statistics, mathematics, physics and engineering. Sample size reducing techniques and the use of digitalcomputers are also discussed.

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