With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering.

The deterioration of rocks’ mechanical properties during the late stage of water injection development significantly reduces the rock-breaking efficiency of PDC bits. In this study, X-ray diffraction mineral composition analysis and triaxial compression mechanics tests were used to systematically characterize the weakening mechanism of water injection on reservoir rocks. Based on an analysis of mechanical experimental characteristics, this study proposes a multi-scale collaborative optimization method: establish a single tooth–rock interaction model at the micro-scale through finite element simulation to optimize geometric cutting parameters; at the macro scale, adopt a differential bit design scheme. By comparing and analyzing the rock-breaking energy consumption characteristics of four-blade and five-blade bits, the most efficient rock-breaking configuration can be optimized. Based on Fluent simulation on the flow field scale, the nozzle configuration can be optimized to improve the bottom hole flow field. The research results provide important theoretical guidance and technical support for the personalized design of drill bits in the later stage of water injection development.