Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2310.04772v2
• Date: Wed, 22 Jan 2025 09:47:42 GMT
• Title: Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach
• Authors: Ressi Bonti Muhammad, Sergey Alyaev, Reidar Brumer Bratvold,
• Abstract summary: Trajectory adjustment decisions throughout the drilling process, called geosteering, affect subsequent choices and information gathering. We use the Deep Q-Network (DQN) method, a model-free reinforcement learning (RL) method that learns directly from the decision environment. For two previously published synthetic geosteering scenarios, our results show that RL achieves high-quality outcomes comparable to the quasi-optimal ADP.
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• Abstract: Trajectory adjustment decisions throughout the drilling process, called geosteering, affect subsequent choices and information gathering, thus resulting in a coupled sequential decision problem. Previous works on applying decision optimization methods in geosteering rely on greedy optimization or approximate dynamic programming (ADP). Either decision optimization method requires explicit uncertainty and objective function models, making developing decision optimization methods for complex and realistic geosteering environments challenging to impossible. We use the Deep Q-Network (DQN) method, a model-free reinforcement learning (RL) method that learns directly from the decision environment, to optimize geosteering decisions. The expensive computations for RL are handled during the offline training stage. Evaluating DQN needed for real-time decision support takes milliseconds and is faster than the traditional alternatives. Moreover, for two previously published synthetic geosteering scenarios, our results show that RL achieves high-quality outcomes comparable to the quasi-optimal ADP. Yet, the model-free nature of RL means that by replacing the training environment, we can extend it to problems where the solution to ADP is prohibitively expensive to compute. This flexibility will allow applying it to more complex environments and make hybrid versions trained with real data in the future.

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