Optimizing Well Trajectory Navigation and Advanced Geo-Steering Using Deep-Reinforcement Learning

摘要: The efficiency of modern drilling operations depends on the planning phase to determine possible well trajectories and the ability of the directional driller to traverse them accurately. Deviations from the planned trajectory while drilling often require updates to the original well plan, involving drilling engineers and rig personnel, which can be time-consuming due to several uncertainties, such as formation tendencies, survey measurement inaccuracy, or estimation errors. To address these challenges, this paper proposes an innovative solution that leverages artificial intelligence (AI) methods, specifically deep reinforcement learning (DRL) to dramatically reduce the need for continuous corrections to the well plan while drilling. In the DRL paradigm, the proposed approach eliminates the need for constant plan adjustments by training a drilling agent to imitate the driller's ability to dynamically adjust the well …