Abstract
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
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
| Original language | English |
|---|---|
| DOIs | |
| Publication status | Published - 31 Oct 2025 |