Abstract: Well control shut-in involves coordinated regulation of multiple pieces of equipment, yet it still relies on manual operation by engineers at present, which results in delayed shut-in responses and frequent operational errors. This paper proposes an intelligent shut-in control scheme covering six drilling working conditions, ten shut-in modes and linkage of five types of equipment. A real-time identification model for the operating status of well control equipment integrating logical judgment and visual neural networks is established, and a physical simulation experimental system for intelligent shut-in control under multiple working conditions is developed. Indoor test results show that the action connection time among five types of equipment, including winch, top drive, mud pump, blowout preventer and choke manifold, is less than 1 second; the execution time of blowout preventers and choke manifolds is less than 90 seconds, reduced by more than half compared with traditional manual control. The whole-process soft and hard shut-in time for six working conditions (drilling, tripping drill pipes, tripping drill collars, running casing, wireline logging and empty hole) is less than 2 minutes, and the shut-in success rate reaches 100% in 60 repeated tests. This research provides important guidance for promoting the intelligent development of well control technology in China.