Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization
A new framework integrates particle filtering with reinforcement learning to optimize geosteering decisions under geological uncertainty. This approach uses particle filtering for probabilistic subsurface interpretation and value-based reinforcement learning for sequential decision-making. The framework was evaluated against Approximate Dynamic Programming and Deep Q-learning, demonstrating improved steering smoothness and operational insight. AI
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