Deep reinforcement learning for automated decision-making in wellbore construction

The drilling industry continuously seeks cost reduction through improved efficiency, with automation seen as a key solution. The drilling industry continuously seeks cost reduction through improved efficiency, with automation viewed as a key enabler. However, due to the complexity of drilling operations, uncertainty in subsurface conditions, and limitations in real-time data, achieving reliable autonomy remains a major challenge. While physics-based models support automation, they often face limitations under real-time constraints and may struggle to adapt effectively in the presence of uncertain or incomplete data. This study contributes to automation efforts by employing Reinforcement Learning (RL) to model hole conditioning, an essential part of drilling operation. Using a Q-learning approach, the RL agent optimizes operational decisions in real time, adapting based on environmental feedback. This artificial intelligence (AI) -driven agent identifies the ideal sequence of actions for circulation, reaming, and washing, maximizing operational safety and efficiency by aligning with target parameters while navigating operational constraints. The RL model decisions were benchmarked against real-case actions, demonstrating that the agent strategy can outperform expert choices in several areas. Specifically, the RL model provided better solutions in three key examples: avoiding poor hole cleaning, lowering the operational time, and preventing wellbore stability issues. The proposed system contributes to the growing body of research applying deep reinforcement learning for automated hole conditioning, representing an innovative engineering application for AI. This approach not only enhances real-time decision-making capabilities but also establishes a foundation for further automation in well construction, integrating engineering requirements with advanced AI-driven strategies. Through the combination of AI and practical engineering design, this work advances both automation and safety in drilling operations, signaling a promising step forward for future developments in wellbore construction. Illustration of the RL algorithm used in this study • The work bridges petroleum engineering and AI, merging traditional methods with machine learning for enhanced automation. • Pioneers Reinforcement Learning(Q-learning) for modeling hole conditioning in drilling to optimize real-time actions. • RL agent adapts actions (circulation, reaming, washing) to real-time feedback, ensuring safe, efficient drilling. • RL model beats human experts in hole cleaning, time reduction, and wellbore stability. • Dynamic step optimization cuts redundant operations, saves time, and lowers production costs.