This is a major rework of Paul Newendorp's 1975 best-seller, which became the standard reference in the field. This book is now structured as a handbook of over 330 important concepts in risk and economic decision analysis. As the title suggests, well over half the examples apply to petroleum exploration investment decisions. Perhaps 80% of the topics are generally applicable to capital investment, project management, and operations decisions. Topics in the book represent a composite of evaluation practices and problem-solving approaches now commonly used in oil &amp; gas and other capital-intensive industries. Several important and practical techniques were first published in the first edition. Decision analysis methods apply to any type of decision. The emphasis here is on quantitative methods useful in capital investment decisions and decisions to acquire additional information. This will be of special interest to anyone involved in the evaluation of property acquisitions, geophysical surveys, prospect drilling, and field development decisions. This book is intended for petroleum geologists, engineers, geophysicists, evaluation and planning analysts, and managers. This is not a first book in decision analysis. We presume the reader has a general familiarity with management, economics, decision analysis, and knowledge of the oil &amp; gas industry. As a handbook we are focusing on what is most important and practical. Major topic area include the decision analysis process, key concepts in probability and statistics (including Bayes' rule and easy equivalents), decision policy (including risk policy expressed as a utility function), popular economic metrics and concepts, project and enterprise modeling, decision tree analysis, Monte Carlo simulation, and various special topics. Value of information problems receive special attention. Over 270 figures help illustrate the concepts. The expected value (EV) concept is central throughout. Most often we assume a decision policy that maximizes EV. Most of the discussion presumes a business context and measuring outcome as net present value (NPV). We also describe approaches for multi-criteria decision making including HSE. Expected monetary value (EMV = EV NPV) 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. The material is organized into seven sections: Decision Analysis Process, Probability and Statistics, Decision Policy, Economic Matters, Modeling, Decision Tree Analysis, and Monte Carlo Simulation. Throughout, real-world exploration examples are presented to illustrate the risk and decision analysis methods. This revised 3.0 edition features a larger page format, an updated and expanded bibliography, and an extensive glossary. We also offer additional material online, including extended discussions, software resources, and example Excel spreadsheets.

Decision Analysis for Petroleum Exploration: 3.0 Edition

Business & Money

Newendorp, Paul D.

Schuyler, John R.

Management & Leadership

Planning Press

Decision analysis (DA) provides assistance in making logical, consistent decisions under uncertainty. This book instructs readers in applying DA to a wide range of project decisions. This is a wholly rewritten and expanded successor to the best-selling prior editions. The entire asset life cycle is covered, from an initial feasibility analysis, to the project plan, to the post-project review, and on to a look-back analysis of the capital investment decision. The book primarily uses a business investment perspective, and assumes maximizing value for the project owner is the objective. However, DA applies to all manner of decisions for individuals, government, and non-profit organizations. DA is a problem-solving process. There are three key features: 1) probabilities and probability distributions express best judgments about risks and uncertainties. 2) The organization has a decision policy expressed as a single metric (the objective function). 3) Probabilities and outcome values combine in the probability-weighting expected value calculation. This book aims to make decision making clear, simple, and logical. A clear decision policy can be elusive, and we offer suggestions for making trade-offs among conflicting objectives. Converting the three pillars of project management (cost, schedule and performance) into project value equivalents makes the trade-offs clear. This book is intended for serious PM students and practitioners. This is an essential concepts and how-to book. The scope is quantitative analysis, from project inception to post-project review. Project cost and schedule modeling, in modest detail, is essential to feasibility analysis. A general background in PM and corporate planning will be helpful. The methods are quantitative and straightforward. We presume the reader is comfortable with basic algebra. Experience with spreadsheets will be helpful, as Microsoft® Excel® is the basis for several downloadable calculation examples. The book has six pages of Suggested Reading annotated references (plus footnote additions), over 500 Glossary definitions, and over 2200 Index entries. Online supplements include several whitepapers and other documents, example calculation spreadsheets, color images of several important figures, four videos (including a critical chain simulation), and the Utility Elicitation Program (as a web app). Key topics include: Decision trees and Monte Carlo simulation for calculating outcome distributions and expected values • Probability concepts, including Bayes’ rule for value of information analysis • Popular probability distribution types and when they apply • Eliciting expert judgments, with attention to potential cognitive and motivational biases • Recognizing the three pillars project in terms of project value • A 10-step decision analysis process applicable to typical decisions • Project modeling concepts and techniques, with special attention to risk drivers and other correlations • Deterministic and stochastic sensitivity analysis • Decision policy that separates objectives, time value, and risk attitude • @RISK® with Microsoft® Project for project schedule simulations under uncertainty • Logical, consistent risk policy expressed as a utility function • Merge bias when task chains converge at a merge point • Tail estimate bias when estimating highly uncertain quantities • Optimizer’s curse, a portfolio forecasting bias • Winner’s curse, a bias characteristic of auctions • Overconfidence and other common cognitive biases • Using the best of critical chain and Monte Carlo simulation • Stochastic variance between a deterministic and a stochastic model • Modeling risk and uncertainty using probabilities, probability distributions, explicit formula relationships, correlation coefficients, risk drivers, conditional branching, and rework cycles.

Risk And Decision Analysis In Projects: 3.0 Edition

Decision analysis (DA) guides executives toward logical, consistent decisions under uncertainty. This book instructs readers in applying DA to feasibility analysis, project estimation, and project risk management. This is a wholly rewritten and expanded successor to the best-selling first and second editions. The entire investment lifecycle is covered, from conception, to the project plan, to the post-project review, and to a look-back analysis of the capital investment decision. DA applies to all manner of project management (PM) decisions for individuals, government, and non-profit organizations. The book uses a business investment perspective and assumes that maximizing value for the project owner is the objective. DA is a problem-solving process. There are four key features: 1) probabilities and probability distributions express best judgments about risks and uncertainties. 2) The organization has a decision policy expressed as a single metric (the objective function). 3) Probabilities and outcome values combine in the probability-weighting expected value calculation. 4) The organization as a policy to choose the best expected value alternative. This book aims to make decision making clear, simple, and logical. A clear decision policy can be elusive, and the author offers suggestions for making trade-offs among conflicting objectives. Converting the three pillars of project management (cost, schedule, and performance) into project value equivalents makes the trade-offs clear. This book is intended for serious PM students and practitioners. This is an essential concepts and how-to book. The scope is quantitative analysis, from project inception to post-project review. Project cost and schedule modeling, in modest detail, is essential to feasibility analysis and risk management. A general background in PM and corporate planning will be helpful. The methods are quantitative and straightforward. The reader should be comfortable with basic algebra and Microsoft® Excel®. The book has eight pages of Suggested Reading annotated references (plus footnote additions), over 250 figures, approximately 600 Glossary definitions, and over 2400 Index entries. Online supplements include several whitepapers and other documents, example calculation spreadsheets, detailed color images of several important figures, four videos (including a critical chain simulation), and the Utility Elicitation Program (a web app, free for most users). Key topics include: Decision trees and Monte Carlo simulation for calculating outcome distributions and expected values • Probability concepts, including Bayes’ rule for value of information analysis • Popular probability distribution types and when they apply • Eliciting expert judgments, with attention to potential cognitive and motivational biases • Recognizing the three pillars project in terms of project value • A 10-step decision analysis process • Project modeling concepts and techniques, with special attention to risk drivers and other correlations • Deterministic and stochastic sensitivity analysis • Decision policy that distinguishes objectives, time value, and risk attitude • @RISK® with Microsoft® Project for project simulations under uncertainty • Logical, consistent risk policy expressed as a utility function • Merge bias when task chains converge at a merge point • Tail estimate bias when estimating highly uncertain quantities • Optimizer’s curse, a portfolio forecasting bias • Winner’s curse, a bias characteristic of auctions • Using the best of critical chain and Monte Carlo simulation • Stochastic variance between a deterministic and a stochastic model • Modeling risk and uncertainty using probabilities, probability distributions, explicit formula relationships, correlation coefficients, risk drivers, conditional branching, and rework cycles.

Risk and Decision Analysis in Projects 3.1 Edition

CreateSpace Independent Publishing Platform

Is there anything more important to the success of a project than making good decisions? This skill is certainly at or near the top of the list. Yet, few of us have had formal training in decision making. Decision analysis is the discipline that helps people choose wisely under conditions of uncertainty. This book introduces risk and decision analysis applied to project management. Probability is the language of uncertainty. Fortunately, a few basic concepts in probability and statistics go a long way toward making better decisions. The evaluation calculations are straightforward, and many everyday problems can be solved with a hand-held calculator. Schuyler also explains and demystifies key concepts and techniques, including expected value, optimal decision policy, decision trees, the value of information, Monte Carlo simulation, probabilistic techniques, modeling techniques, judgments and biases, utility and multi-criteria decisions, and stochastic variance.

Decision Analysis in Projects

Project Management Inst

See the best price to sell, buy, or rent books by Schuyler, John R.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Schuyler, John R.

See the best price to sell, buy, or rent books by Schuyler, John R.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Decision Analysis for Petroleum Exploration: 3.0 Edition

Risk And Decision Analysis In Projects: 3.0 Edition

Risk and Decision Analysis in Projects 3.1 Edition

Decision Analysis in Projects