Download Statistical Methods for Estimating Petroleum Resources by P.J. Lee PDF

By P.J. Lee

This e-book describes tactics for picking out the entire hydrocarbon (petroleum) source or source capability in a sector. Statistical techniques and techniques hired in petroleum source review are the topic of the manuscript, broadly illustrated through various actual case stories. Prof. Lee's computer-aided Petroleum info administration and source evaluate method (PETRIMES) technique has been followed by means of governments around the globe and by means of significant multinational oil businesses to accomplish source overview and to foretell destiny oil and fuel construction. although this technique is so everyday, there isn't any "user's consultant" to it, and this ebook could be the definitive source for PETRIMES clients.

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Additional statistical assumptions (such as a constant ratio between two adjacent size classes) are not required in this approach. On the other hand, the estimation of reserves from drill stem test results is time-consuming and requires reservoir engineering expertise. 10. Box plots for in-place pool-size volume of several plays in the Western Canada Sedimentary Basin. A, Leduc reefs play; B, Beaverhill Lake play; C, Devonian sandstone play; D, Keg River reefs play; E, Cardium sandstone play; F, Viking sandstone play; G, Upper Mannville sandstone play.

A) Discrete distribution. (B) Continuous distribution. 100 Evaluation Models 13 sample set can be used to construct a histogram (Fig. 5A), a cumulative greater-than distribution (Fig. 5B), or a cumulative less-than distribution (Fig. 5C). These types of continuous distribution are considered to be superpopulations. The greater-than form is used to express probability distributions in petroleum resource evaluation. In reality, the sample sets of certain variables resulting from exploration are neither random nor large enough to represent the population.

If this problem can be overcome, then the estimation of population mean, variance, and correlation among variables can be achieved. The objective of this chapter is to explain the characterization of the discovery process by statistical formulation. To account for sampling bias, Kaufman et al. (1975) and Barouch and Kaufman (1977) used the successive sampling process of the superpopulation probabilistic model (discovery process model) to estimate the mean and variance of a given play. Here we shall discuss how to use superpopulation probabilistic models to estimate pool-size distribution.

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