Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases and inconsistent procedures. Academic attempts to solve geosteering decision optimization with greedy optimization and Approximate Dynamic Programming (ADP) showed promise but lacked adaptivity to realistic diverse scenarios. Reinforcement learning (RL) offers a solution to these challenges, facilitating optimal decision-making through reward-based iterative learning. State estimation methods, e.g., particle filter (PF), provide a complementary strategy for geosteering decision-making based on online information. We integrate an RL-based geosteering with PF to address realistic geosteering scenarios. Our framework deploys PF to process real-time well-log data to estimate the location of the well relative to the stratigraphic layers, which then informs the RL-based decision-making process. We compare our method's performance with that of using solely either RL or PF. Our findings indicate a synergy between RL and PF in yielding optimized geosteering decisions.

翻译：地质导向作为钻井作业的关键组成部分，传统上依赖于对测井数据等多种数据源的人工解释，这引入了主观偏差和不一致的操作流程。学术界尝试通过贪婪优化和近似动态规划（ADP）解决地质导向决策优化问题，虽展现出潜力，但缺乏对现实多样化场景的自适应能力。强化学习（RL）通过基于奖励的迭代学习促进最优决策，为解决这些挑战提供了方案。状态估计方法，如粒子滤波（PF），则为基于实时信息的地质导向决策提供了补充策略。我们将基于RL的地质导向与PF相结合，以应对现实地质导向场景。我们的框架利用PF处理实时测井数据，估计井眼相对于地层的位置，进而为基于RL的决策过程提供信息。我们将所提方法的性能与单独使用RL或PF的方法进行对比。研究结果表明，RL与PF在生成优化的地质导向决策方面具有协同效应。