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Drilling trajectory closed-loop control in directional drilling is a key technology for achieving high-precision drilling. However, due to the complex geological conditions, and engineering limitations of drilling tools, traditional control methods of drilling often face challenges, such as error accumulation, response delays, and control instability. To address these issues, this paper proposes an intelligent closed-loop steering method based on online adaptive optimization. The core of this method lies in the construction of an integrated “perception–optimization–execution” intelligent steering framework. First, real-time attitude feedback is used to accurately perceive trajectory deviations. Then, an optimization model is triggered, aiming to minimize deviations under the dogleg severity constraint, and genetic algorithms are employed to dynamically calibrate the PID controller online, effectively eliminating error accumulation. Finally, based on the optimization results, real-time calculations of tool face angle and steering tool force are performed to ensure precise execution of steering commands. Simulation results show that, compared to the traditional PID and PID-APF methods, the proposed method demonstrates significant advantages in trajectory control accuracy and wellbore quality. Under noise-free conditions, the normal distance accuracy improves by 88.89% and 34.02%, respectively, and dogleg severity is reduced by 6.30% and 5.81%. Under noise interference, the normal distance accuracy improves by 56.73% and 54.97%, respectively, and dogleg severity is reduced by 23.38% and 4.85%. In conclusion, the proposed intelligent closed-loop control method not only significantly enhances the system’s real-time response capability and control precision but also exhibits stronger robustness, with broad potential for engineering applications.
Sun et al. (Tue,) studied this question.