In the drilling process of ultra-deep wells with large-diameter boreholes, the transport and deposition behavior of cuttings plays a critical role in maintaining wellbore cleanliness and ensuring operational safety. Due to the geometry of enlarged boreholes and their complex annular flow characteristics, conventional single-parameter control methods often fail to achieve effective cuttings transport. This study aims to identify the dominant influencing factors and optimize key parameters by focusing on the cuttings volume fraction as a primary evaluation metric. A numerical simulation approach is employed to systematically investigate the influence of stabilizer geometry and hydraulic parameters. Five variables—drilling fluid velocity, drill pipe rotational speed, number of stabilizers, flow area, and helical angle—are selected for analysis. An initial one-factor sensitivity analysis is conducted to evaluate local impacts and to establish relative sensitivity indices, thereby identifying key variables. A variance-based global sensitivity analysis is further applied to quantify first-order effects, full-order effects, and interaction contributions, revealing nonlinear coupling and synergistic mechanisms. The results indicate that drilling fluid velocity and rotation speed exhibit the most significant first-order influences, while stabilizer-related parameters show strong interaction effects that are often underestimated by traditional methods. Based on these findings, an optimized cuttings transport scheme for large-diameter boreholes is proposed. Additionally, a multi-parameter response model for the cuttings volume fraction is developed using sensitivity-weighted analysis, offering theoretical support and methodological reference for enhancing cuttings transport performance and structural design in large-diameter borehole drilling operations.

Aiming at the problem of the unclear sound wave attenuation in the signal transmission of the load-bearing drill string, an experimental device for the sound wave propagation characteristics in the curved drill string was set up. The influence of the drill string structure and acoustic excitation parameters on the sound propagation characteristics in the drill string under different loads is different. The results show that the curvature of the drill string has an influence on the propagation of the sound wave in the drill string, and its rule is related to the curvature of the drill string and the frequency of the sound wave. The pulse repetition rate, excitation voltage, and pulse width only affect the passband amplitude, and the effect is significantly greater than the curvature of the drill string. The main influencing factor of its sound transmission characteristics is the degree of drill string curvature. The low-frequency signal should be preferentially selected as the carrier of downhole information transmission after considering the influence of drill string bending on acoustic transmission.

In the process of oil and gas drilling, it is of great significance to accurately predict the drag coefficient and settling rate of cuttings in the drilling fluid for hydraulic parameters and borehole cleaning. In this paper, particle settling experiments were used to obtain the final settling velocity of 196 groups of spherical particles and 224 groups of terrestrial high-clay pure-shale cuttings in the power-law fluid. Based on data analysis, a settlement drag coefficient model suitable for irregular-shaped shale cuttings was established. The model can be used to predict the settlement drag coefficient and settling velocity of irregular-shaped shale cuttings in power-law fluid, which can provide theoretical guidance for wellbore cleaning and hydraulic parameter optimization in Daqing Gulong shale oil drilling operations.

Sticking is the most serious cause of failure in complex drilling operations. In the present work a novel “early warning” method based on an artificial intelligence algorithm is proposed to overcome some of the known problems associated with existing sticking-identification technologies. The method is tested against a practical case study (Southern Sichuan shale gas drilling operations). It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state; furthermore, the results from four groups of verification samples are also consistent with the actual downhole state. This shows that the proposed training-based model can effectively be applied to practical situations.