Petroleum Industry Contingency Planning Using Auditing Theories and Inferential Statistics

Abstract

This chapter demonstrates how to apply a novel Bayesian auditing statistical technique with a predictive probability distribution model to predict petroleum accidents. The goal was to first develop the petroleum oil spill patterns for a given population, develop a predictive distribution model, and validate that using Bayesian audit statistical techniques. Sample data were taken from a U.S.-based state and fixed interval random sampling was used to select the records for the audit. A beta distribution was generated illustrating the materiality parameters along with the misstatement results.