Author(s)

Runze Jiao, Guangye Chen

Corresponding Author

Runze Jiao

Abstract

This study introduces a novel approach utilizing deep reinforcement learning (DRL) to optimize process management in downhole pressure monitoring—an essential, yet complex task for ensuring safe and efficient drilling operations. Grounded in the context of drilling's evolving landscape, where traditional methods often fall short amidst the industry's dynamic and uncertain environment, our method leverages a deep neural network, a reward function, and a policy gradient algorithm. By employing a comprehensive simulation model that encompasses wellbore hydraulics, drill string dynamics, choke valve characteristics, and downhole sensor measurements, this study undertake a comparative analysis against conventional model predictive control and adaptive control methods. Our findings demonstrate the DRL method's superior ability to accurately maintain the pressure setpoint, adeptly navigate the system's nonlinearities and uncertainties, and significantly minimize pressure fluctuations. Moreover, it achieves these outcomes with reduced control effort, indicating potential for lower energy consumption and decreased equipment wear. The implications of our research extend across the industry, offering a path toward more sustainable and cost-effective drilling operations, while highlighting areas for future exploration and refinement.

Keywords

Downhole pressure monitoring; Process management optimiza-tion; Deep reinforcement learning