Abstract In order to systematically harness the substantial volume of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for real-time risk monitoring and warnings during drilling, an intelligent drilling auxiliary decision-making system has been developed. This system integrates various technologies, including physical models, intelligent algorithms, and trend analysis. By analyzing the characteristics of logging data under different operating conditions, an intelligent algorithm capable of automatically distinguishing six types of drilling operating conditions has been established. Taking into account the impact of rock cuttings on the stress on the pipe string and annular pressure loss during the drilling process, coupled frictional torque and cyclic pressure loss calculation models were developed under coupled conditions. Building upon this foundation and considering the trend of deviation between predicted and measured parameters, a monitoring algorithm for detecting stuck drilling, wellbore leakage, and overflow incidents was formulated. The development of the drilling intelligent decision-making system was completed based on a C/S three-layer architecture and a data center. This system has been successfully applied in more than 50 exploration wells of various types, including shale oil and gas horizontal wells and deep wells. The static analysis results demonstrated a compliance rate of 91.5% with on-site monitoring, playing a crucial role in drilling parameter optimization and risk monitoring. The practicality and feasibility of the system have been verified, providing essential support for efficient and safe drilling production.
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