Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm

In modern oil and gas exploration and development, wellbore trajectory optimization and control is the key technology to improve drilling efficiency, reduce costs, and ensure safety. In the drilling operation of non-vertical wells in complex formations, the traditional static trajectory function, combined with the classical optimization algorithm, has difficulty adapting to the parameter fluctuation caused by formation changes and lacks real-time performance. Therefore, this paper proposes a wellbore trajectory optimization model based on deep reinforcement learning to realize non-vertical well trajectory design and control while drilling. Aiming at the real-time optimization requirements of complex drilling scenarios, the TD3 algorithm is adopted to solve the problem of high-dimensional continuous decision-making through delay strategy update, double Q network, and target strategy smoothing. After reinforcement learning training, the trajectory offset is significantly reduced, and the accuracy is greatly improved. This research shows that the TD3 algorithm is superior to the multi-objective optimization algorithm in optimizing key parameters, such as well deviation, kickoff point (KOP), and trajectory length, especially in well deviation and KOP optimization. This study provides a new idea for wellbore trajectory optimization and design while drilling, promotes the progress and development of intelligent drilling technology, and provides a theoretical basis and technical support for more accurate, efficient, concise, and effective wellbore trajectory optimization and design while drilling in the future.