Field/project development

Reinforcement Learning Enables Field-Development Policy Optimization

This paper describes an artificial intelligence deep Q network for field-development plan optimization.

Heat map of maximum return per candidate drilling location.
Heat map of maximum return per candidate drilling location. Overlay best well layouts evaluated by brute force: producers (black), injector (white). Each pixel representing cell index of geocellular model.

A field-development plan consists of a sequence of decisions. Each action taken affects the reservoir and conditions any future decision. The presence of uncertainty associated with this process, however, is undeniable. The novelty of the approach proposed by the authors in the complete paper is the consideration of the sequential nature of the decisions through the framework of dynamic programming (DP) and reinforcement learning (RL). This methodology allows moving the focus from a static field-development plan optimization to a more-dynamic framework that the authors call field-development policy optimization.

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