Probabilistic Aggregation of Oil and Gas Field Resource Estimates and Project Portfolio Analysis

Abstract

Probabilistic aggregation and dependency estimation are essential in portfolio methods, production forecasting, and resource estimation. The use of arithmetic addition understates the true value of the resource estimates within a portfolio of fields. Potentially, this could result in deferral of a project, or loss of lucrative business and commercial opportunities, such as project investment, facility-sizing decisions, or incremental gas-supply commitments. A statistically robust method for aggregation of resource estimates that appropriately uses expert opinion is presented in this paper. Using two integrated-project examples, this paper introduces new methods for (1) probabilistic aggregation of the resource estimates for multiple fields and (2) estimating a measure of dependency between the resource estimates of individual fields. The new analytical method for probabilistic aggregation is based on multivariate skew-normal (MSN) distributions, which can model a wide range of skewness through a shape parameter and are used heavily in financial and actuarial applications.In studies of the fields in which the multiple-realizations approach is used as a basis for the uncertainty framework, tornado diagrams are generated routinely to describe the dependence of the field resources on reservoir parameters. The improved method for evaluating measures of dependency between the resource estimates within a portfolio of fields uses these tornado diagrams as a basis. Incorporating the expertise and knowledge of geologists and petroleum engineers is a critical element of the method. These methods for probabilistic aggregation and estimating dependencies were developed within the context of the oil industry, but their use is not limited to the oil industry. They are general and can be used in other probabilistic-aggregation problems. Application of these techniques requires limited time and effort, compared to individual-field studies, and can have a profound impact on the uncertainty range of the total resources for the portfolio of fields.