Decision Analysis for Petroleum Exploration: 2.1 Edition

Superseded by the 3.0 edition, April 2014. This is another update of Paul Newendorp's 1975 best-seller, which became the standard reference in the field. This book introduces the reader to ways of applying risk and decision analysis to petroleum exploration investment decisions under uncertainty. Topics covered in the book represent a composite of evaluation practices and problem-solving approaches now commonly-used in the industry. Several important and practical concepts were first published in the first edition. Decision analysis methods apply to any type of business investment decision. The emphasis here is on quantitative methods useful in petroleum exploration decisions. This will be of special interest to anyone involved in the evaluation of property acquisitions, geophysical surveys, prospect drilling, and field development decisions. Ths books is for petroleum geologists, engineers, geophysicists, evaluation and planning analysts, and managers. A mathematics or statistics background is not required to follow the practical, applications-oriented discussions. First is a review of the advantages and disadvantages of various profitability criteria used for selecting and comparing drilling prospects. The important expected value (EV) concept is next introduced. Expected monetary value (EMV = EV net present value) is the principal decision criterion used in most examples. The EV calculation incorporates judgments about risks and uncertainties expressed as probabilities and probability distributions. EV is the cornerstone of formal, quantitative analysis for decisions under uncertainty. The key calculation methods are decision trees and Monte Carlo simulation. Small decision trees can be solved with a hand calculator, while larger trees and Monte Carlo simulation usually require a computer. Software supporting these methods is now widely available as Microsoft® Excel spreadsheet add-ins and for other platforms. Much of the book is devoted to problem framing, decision modeling, and decision policy (including risk policy as a utility function). Throughout, real-world exploration examples are presented to illustrate the risk and decision analysis methods. This revised edition, 2.1, features an updated and expanded bibliography and an extensive glossary.