Knowledge of the present-day in situ stress field (PIS) has important theoretical and practical significance for shale gas exploration and development. The Wufeng-Longmaxi Formation in the Luzhou Block serves as the main reservoir of marine shale production in southern China. However, there is no clear understanding of PIS in the Luzhou area, which leads to a series of production problems. To investigate the PIS of deep shale reservoirs in Luzhou, experimental and finite element methods (FEMs) were used. Data obtained from imaging logging data, well deviation data, multipole array acoustic logging (XMAC), and world stress map were used to determine the orientation of the horizontal maximum principal stress (S _H max). Additionally, acoustic emission experiments were employed to determine the magnitude of three principal stresses. Based on FEM, with the stress of a single well as a constraint, a three-dimensional (3D) stress field model was developed. The results show that the orientation of S _H max varies from 90 to 120 °E. Further, there are significant differences in stress regimes across the region, with strike-slip/reverse faulting stress regime (S _H max > S _v ≈ S _h min) in the northern region and strike-slip stress regime in the southern region (S _H max > S _v > S _h min). In addition, geomechanical evaluations of wellbore stability and natural activation of fractures were performed. The Luzhou Block mainly develops two groups of natural fractures (NE-SW trending and NW-SE trending), with NW-SE trending being the dominant fracture development orientation. During the fracturing operation, a pore pressure increment larger than 50 MPa is conducive to maintaining a high opening ratio of the reservoir and improving the gas production efficiency. Horizontal wells drilled in the direction of maximum horizontal principal stress in the Wufeng-Longmaxi Formation reduce the probability of wellbore instability. The research results provide a reference for the effective development of deep shale gas reservoirs in southern Sichuan.

During rock drilling, a drill bit will wear as it breaks the rock. However, there is no uniform grading standard for rock abrasiveness. To solve this problem, the wear mechanisms of a polycrystalline diamond compact (PDC) bit and the formation it is drilling into are analyzed in depth, and an abrasiveness evaluation method based on the fractal dimension of the rock surface topography is established. Initially, a three-dimensional digital model is generated from a scanning electron microscope image of the rock after drilling; next, an evaluation of the irregularities on the rock surface is performed using an adapted Weierstrass–Mandelbrot (W-M) function to ascertain the fractal dimensionality. Then, the microcontact characteristics of the contact surface between the formation and the PDC bit are analyzed, and the distribution of the microconvex contact points of the two-body friction pair in a region is obtained. Because the sliding friction between the drill bit and the rock produces a large amount of heat, according to the contact area formula of the friction surface and heat conduction theory, the temperature rise and overall temperature distribution of the formation and PDC bit under the condition of sliding friction are revealed, and the real contact area between the formation and the drill bit within a certain temperature range is obtained. Finally, the evaluation index of rock abrasiveness under sliding conditions is established by adopting the wear weight loss of the rock cutting tool per unit volume as the index of rock abrasiveness, and the model is verified by a microdrilling experiment. The research in this paper is highly important for improving the rock-breaking efficiency and bit service life during drilling.

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.