Title : Autonomous geosteering: Integrating reinforcement learning and edge computing for real-time directional drilling optimization
Abstract:
The energy industry is undergoing a rapid digital transformation, where the integration of Artificial Intelligence (AI) and Machine Learning (ML) is no longer a luxury but a necessity for operational efficiency. This presentation explores the advancement of Automated Directional Drilling (ADD) through the implementation of Reinforcement Learning (RL) and Edge Computing. Traditional directional drilling faces significant challenges, including signal latency between the drill bit and the surface, which often results in excessive wellbore tortuosity and increased non-productive time (NPT).
By utilizing Edge-AI, the proposed system enables real-time lithological prediction and autonomous geosteering directly at the rig site. The model utilizes high-frequency Measurement While Drilling (MWD) data to make instantaneous steering adjustments, such as optimizing weight on bit (WOB) and revolutions per minute (RPM). Furthermore, the integration of Digital Twin technology allows for the continuous simulation of drilling scenarios, ensuring the well path remains within the most productive pay zone with high precision.
Preliminary data suggests that closed-loop automation can reduce NPT by up to 35% while significantly lowering the carbon footprint of extraction activities through reduced drilling days and optimized energy consumption. This study demonstrates how autonomous systems represent the future of sustainable and efficient engineering in complex reservoir environments
