Real-time autonomous decision-making in well construction

The drilling industry faces significant challenges in optimizing the wellbore construction process, which is often complicated by high-dimensional, continuous state spaces and unpredictable environmental conditions. These complexities can hinder operational efficiency and compromise safety, highlighting the need for innovative solutions to manage these dynamic factors effectively. The lack of standardized and precise approaches to wellbore cleaning and conditioning in drilling operations, particularly in highly inclined and extended reach wells, poses significant challenges to operational efficiency and well integrity. Decisions on hole conditioining, relying on conventional practices rather than data-driven insights. This reliance leads to inconsistent execution, excessive conditioning, and wasted time, underscoring the need for real-time monitoring systems and autonomous decision-making tools to enhance the consistency, efficiency, and reliability of well construction activities. This research aims to develop a robust reinforcement learning (RL) framework designed to automate the sequential processes involved in drilling operations. By leveraging the ability of RL to make real-time decisions and optimize long-term rewards, the dissertation seeks to enhance the wellbore conditioning practices that are vital for maintaining well integrity while reducing operational costs and environmental impact. A comprehensive RL framework was constructed to generate action sequences based on real-time operational feedback autonomously. The research began with a thorough literature review to establish foundational concepts and identify existing gaps in the field. The problem was then formulated as a Markov Decision Process (MDP), allowing for restructuring existing data to define the RL state and action spaces. Through this methodology, the system could adapt to varying environmental conditions, improving decision-making for wellbore conditioning operations. The developed framework successfully demonstrated its ability to automate wellbore conditioning processes, significantly accelerating drilling operations while also reducing costs and carbon emissions. Key insights from the research included the RL model's capability to suggest fewer reaming actions, thereby avoiding potential sticking and optimizing hole cleaning. Furthermore, the agent maintained the wellbore's desired condition, leading to reduced time and costs associated with hole conditioning. The RL framework also effectively kept operational parameters within safe limits, ensuring wellbore stability and enhancing overall safety. Overall, this research represents a significant advancement in the automation of drilling operations, providing a valuable tool for future enhancements in the field and laying the groundwork for further exploration into combining data-driven and physics-based methodologies.