Research on AI-based Analysis and Recognition Technology for Drilling Tool Coupling

During the drilling tripping operations in oilfields, although iron roughnecks are utilized for makeup and breakout operations, significant inherent errors in mechanical operations prevent the automatic identification of parameters such as thread connection status, drilling tool coupling connection positions, and drill pipe specifications. Consequently, manual assistance remains indispensable throughout the operational workflow, which fails to meet future intelligent requirements. Therefore, the development of methods capable of assisting in drilling pipe positioning, determining pre-makeup or breakout status, and enabling real-time calculation of drilling pipe diameters has emerged as a critical challenge requiring immediate resolution. To address this technical bottleneck, an artificial intelligence (AI)-based approach was proposed to optimize deficiencies in conventional algorithms, employing a deep learning-centric algorithm for identifying the alignment status of drilling bits. First of all, the local image of the buckle is extracted, and then the spatial position of the buckle and the width of the buckle seam are calculated. Subsequently, the results are transmitted to the lower-level controller for adaptive adjustments based on real-time conditions. Laboratory validations demonstrated that the AI-driven drilling tool coupling analysis and recognition technology achieved 100% identification accuracy across three tested tool coupling specifications. In summary, this technology can be widely used in oilfield drilling and workover sites. The significant reduction in errors and labor intensity in manual assisted operations lays the technical foundation for achieving automation and intelligence in drill pipe processing.