Browsing by Author "Hammond, Robert Kincaid"
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Item Decision impact of stochastic price models in the petroleum industry(2011-08) Hammond, Robert Kincaid; Bickel, J. Eric; Dyer, James S.; Smith, James E.Stochastic price models have proven material to decision making in the oil industry when accurate valuations are important, but little consideration is given to their impact on decisions based on relative project rankings. Traditional industry economic analysis methods do not usually consider uncertainty in oil price, although the real options literature has shown that this practice underestimates the value of projects that have flexibility. Monetary budget constraints are not always the limiting constraints in decision making; there may be other constraints that limit the number of projects a company can undertake. We consider building a portfolio of upstream petroleum development projects to determine the relevance of stochastic price models to a decision for which accurate valuations may not be important. The results provide guidelines to determine when a stochastic price model should be used in economic analysis of petroleum projects.Item Discrete approximations to continuous distributions in decision analysis(2014-05) Hammond, Robert Kincaid; Bickel, J. EricIn decision analysis, continuous uncertainties (i.e., the volume of oil in a reservoir) must be approximated by discrete distributions for use in decision trees, for example. Many methods of this process, called discretization, have been proposed and used for decades in practice. To the author’s knowledge, few studies of the methods’ accuracies exist, and were of only limited scope. This work presents a broad and systematic analysis of the accuracies of various discretization methods across large sets of distributions. The results indicate the best methods to use for approximating the moments of different types and shapes of distributions. New, more accurate, methods are also presented for a variety of distributional and practical assumptions. This first part of the work assumes perfect knowledge of the continuous distribution, which might not be the case in practice. The distributions are often elicited from subject matter experts, and because of issues such as cognitive biases, may have assessment errors. The second part of this work examines the implications of this error, and shows that differences between some discretization methods’ approximations are negligible under assessment error, whereas other methods’ errors are significantly larger than those because of imperfect assessments. The final part of this work extends the analysis of previous sections to applications to the Project Evaluation and Review Technique (PERT). The accuracies of several PERT formulae for approximating the mean and variance are analyzed, and several new formulae presented. The new formulae provide significant accuracy improvements over existing formulae.