Investing in an oil and gas field is capital intensive and usually involves many risks. Nevertheless, the economic evaluations of these types of investment opportunities often ignore risk or are based on subjective risk assessments that are frequently biased. Furthermore, as the life of the field progresses, well and seismic data become available and the risk level of the project changes. In this paper we demonstrate a decision and risk analysis (DRA) method that minimizes the subjectivity in the evaluation and that can be consistently updated as the life of the field progresses.
We use a tank production model and Monte Carlo (MC) methods to simulate the possible economic outcomes, and ensuing risk, of a primary oil recovery project as a function of the uncertainties associated with the reservoir properties. During the early stages of the field (i.e. pre-discovery) the uncertainty information comes from the analysis of analog fields. In the post-discovery stage, the initial uncertainties are updated with well and seismic data using an application of Bayes theorem.
The method demostrated here gives decision makers a consistent and relatively transparent way to evaluate E&P investment opportunities. It also allows them to track how the risk level of a project changes through the life of the field.