Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization

In three linesSequential decision optimization framework for geosteering under geological uncertainty. Integrates particle filtering for probabilistic subsurface interpretation with value-based reinforcement learning. Compares three decision policies: Approximate Dynamic Programming, Deep Q-learning, and Dual DRL with dueling decomposition, validated on industrial simulator with realistic noise and drilling constraints.

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