Dynamic Decision Making for Graphical Models Applied to Oil Exploration

TL;DR

This paper introduces a framework for sequential decision making in graphical models, applied to oil exploration, using approximate dynamic programming to improve exploration strategies based on potential outcomes.

Contribution

It proposes and compares various approximation methods for dynamic decision making in graphical models, specifically tailored for oil exploration scenarios.

Findings

Proposed strategies outperform simple heuristics.

Application to oil exploration models improves decision quality.

Strategies are computationally efficient and adaptable.

Abstract

This paper has been withdrawn by the authors. We present a framework for sequential decision making in problems described by graphical models. The setting is given by dependent discrete random variables with associated costs or revenues. In our examples, the dependent variables are the potential outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to develop an optimal selection strategy that incorporates a chosen utility function within an approximated dynamic programming scheme. We propose and compare different approximations, from simple heuristics to more complex iterative schemes, and we discuss their computational properties. We apply our strategies to oil exploration over multiple prospects modeled by a directed acyclic graph, and to a reservoir drilling decision problem modeled by a Markov random field. The results show that the suggested strategies clearly improve the simpler intuitive constructions, and this is useful when selecting exploration policies.