Ultra?deep wells of over 10 000 meters have been successfully drilled in China, and deeper breakthroughs has been made in drilling and completion equipment technology, which indicates that China has achieved a significant milestone in the field of oil exploration and development. It is crucial to improve drilling efficiency and ensure safety in this context. Intelligent drilling and completion technology is a core solution to the challenges faced in the industry. It offers significant advantages in improving drilling efficiency and safety. By integrating advanced automation control, real?time data monitoring, and machine learning technology, it optimizes drilling operations. This not only improves drilling efficiency but also dramatically enhances drilling and completion safety. This paper summarizes the current development status of intelligent drilling and completion equipment technology. It proposes a trinity research approach that includes automation control, real?time data monitoring, and machine learning technology. The paper focuses on analyzing the development journey and technological innovation of domestic and international innovative equipment, such as intelligent drill bits, intelligent guiding tools, intelligent drill pipes, intelligent slip sleeves, and intelligent drilling rigs. To achieve comprehensive development of intelligent drilling and completion equipment technology in the future, it is recommended to utilize artificial intelligence, intelligent optimization algorithms, and foster domestic and international cooperation.

Aiming at the problem of temperature tracking and balance control of each branch pipe of the heating furnace, an improved genetic algorithm was proposed to optimize the multi?deviation control of the temperature tracking and balance of the branch pipe temperature of the heating furnace. The scheme used the temperature deviation of the raw materials of each branch pipe after mixing and the temperature deviation of each branch pipe. By adjusting the feed flow rate and fuel flow rate, it not only ensured that the flow rate of the main pipe is constant during the regulation process, but also realized the dual goal of temperature tracking and balance of each pass. Multi?passes are analyzed as a whole, so the temperature comparison of adjacent branch pipes was avoided repeatedly. At the same time, the improved genetic algorithm was used to optimize the controller parameters of the differences control technique, which overcomes the difficulty of controller parameter tuning. The simulation results show the feasibility and effectiveness of the improved genetic algorithm to optimize the differences control scheme.