The deep shale gas resources of the Sichuan Basin are abundant and constitute an important component of China’s natural gas production. Complicated by fault zones and other geostructures, the in situ stress state of the deep shale gas reservoirs in the Luzhou block remains poorly understood. This study integrated multiple datasets, including acoustic logging, diagnostic fracture injection testing (DFIT), imaging logging, and laboratory stress measurements, for calibration and constraint. A high-precision geomechanical model of the Luzhou block was constructed using the finite element method. This model characterizes the geomechanical properties of the reservoir and explores its applications in optimizing shale gas horizontal well placement, drilling processes, and fracture design. The study findings indicate that the Longmaxi Formation reservoir demonstrates abnormally high pore pressure, with gradients ranging from 16.7 to 21.7 kPa/m. The predominant stress regime is strike-slip, with an overburden stress gradient of 25.5 kPa/m and a minimum horizontal principal stress gradient ranging from 18.8 to 24.5 kPa/m. Based on a three-dimensional geomechanical model, a quantitative delineation of areas conducive to density reduction and pressure control drilling was conducted, and field experiments were implemented in well Y65-X. Utilizing an optimized drilling fluid density of 1.85 g/cm³, the deviated horizontal section was completed in a single trip, resulting in a 67% reduction in the drilling cycle compared to adjacent wells. Similarly, the Y2-X well demonstrated a test daily output of 506,900 cubic meters following an optimization of segmentation clustering and fracturing parameters. Studies indicate that 3D geomechanical modeling, informed by multi-source data constraints, can markedly enhance model precision, and such geomechanical models and their results can effectively augment drilling operational efficiency, elevate single-well production, and are advantageous for development.

In recent years, significant breakthroughs have been made in the exploration of deep to ultra-deep oil and gas reserves onshore in China. These conventional deep to ultra-deep reservoirs are typically buried at depths exceeding 4500 m, with bottom-hole temperatures surpassing 150 °C. The high temperatures at the bottom of the well are more likely to cause deterioration in drilling fluid properties, altering its rheological properties and reducing cutting transport efficiency, which can lead to wellbore cleaning issues. In this paper, the numerical simulation method is used to analyze the influence of cutting particle size, drilling fluid flow rate, drill pipe rotation speed, and drill pipe eccentricity on the annular cutting concentration under different wellbore drilling fluid temperature conditions. The results show that at the same cutting particle size, as the drilling fluid temperature increases, the cutting concentration in the annulus increases sharply. The increase is the largest when the particle size is 3 mm, and when the drilling fluid temperature is 220 °C, the cutting concentration increases by 79.7% compared to at 200 °C and by 279% compared to at 180 °C. When the flow rate increases from 0.5 m/s to 1.0 m/s, the annular cutting concentration at drilling fluid temperatures of 220 °C and 200 °C decreases by 70.5% and 50.4%, respectively. The higher the drilling fluid temperature, the better the cutting removal effect when increasing the drill pipe rotation speed. However, when the rotation speed exceeds 120 rpm, the change in cutting concentration with increasing rotation speed becomes insignificant. When the drill pipe eccentricity is small, an increase in drilling fluid temperature leads to a significant rise in annular cutting concentration. However, when the drill pipe eccentricity is large, changes in drilling fluid temperature have a smaller impact on cutting concentration. The research findings can provide engineering guidance and theoretical support for the design of drilling fluid hydraulic parameters for cutting transport and rheological parameters in high-temperature wellbores.

Drilling fluids are critical in oil and gas well drilling, particularly deep shale gas drilling. In recent years, applying nanoparticles as additives in drilling fluids has received widespread attention to address the various challenges associated with deep shale gas drilling. This study focused on the performance of three nanoparticle-enhanced oil-based drilling fluids (OBDFs), carbon nanotubes (CNTs), silicon dioxide (SiO₂), and aluminums oxide (Al₂O₃) in terms of improving thermal capacity and cooling efficiency. The potential of the nanoparticles to improve the thermal management capability of the drilling fluids was evaluated by measuring specific heat capacity and thermal conductivity. The results showed that CNTs exhibited the most significant improvement, with thermal conductivity increasing by 7.97% and specific heat capacity by 19.38%. The rheological properties and high temperature and high pressure (HTHP) filtration performance of the nanoparticle-enhanced OBDFs were evaluated, demonstrating that CNTs and SiO₂ significantly improved the rheological stability of the drilling fluids and reduced the filtration loss under high temperature conditions. When 3% CNTs were added, the HTHP filtration loss was reduced by 42.86%, exhibiting excellent sealing properties. The cooling effect of different nanoparticles was evaluated by calculating their effects on the bottomhole temperature. The results showed that CNTs performed the best in lowering the bottomhole temperature by 4.53 °C, followed by SiO₂ by 1.47 °C and Al₂O₃ by only 0.88 °C. The results showed that CNTs were the most effective in lowering the bottomhole temperature. These results indicated that nanoparticles as additives to drilling fluids could significantly increase the thermal capacity and cooling efficiency of OBDFs, making them effective additives for high-temperature deep shale gas drilling applications.

The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, complex geological conditions frequently lead to gas kicks, posing significant challenges to well control and safety. Compared to traditional kick detection methods, artificial intelligence technology can improve the accuracy and timeliness of kick detection. However, there are limited real-world kick data available from drilling operations, and the datasets are extremely imbalanced, making it difficult to train intelligent models with sufficient accuracy and generalization capabilities. To address this issue, this paper proposes a kick data augmentation method based on a time-series generative adversarial network (TimeGAN). This method generates synthetic kick samples from real datasets and then employs a long short-term memory (LSTM) neural network to extract multivariate time-series features of surface drilling parameters. A multilayer perceptron (MLP) network is used for data classification tasks, constructing an intelligent kick detection model. Using real drilling data from ultra-deep wells in the SY block of the Sichuan Basin, the effects of k-fold cross-validation, data dimensionality, various imbalanced data handling techniques, and the sample imbalance ratio on the model’s kick detection performance are analyzed. Ablation experiments are also conducted to assess the contribution of each module in identifying kick. The results show that TimeGAN outperforms other imbalanced data handling techniques. The accuracy, recall, precision, and F1-score of the kick identification model are highest when the sample imbalance ratio is at 1 but decrease as the imbalance ratio increases. This indicates that maintaining a balance between positive and negative samples is essential for training a reliable intelligent kick detection model. The trained model is applied during the drilling of seven ultra-deep wells in Sichuan, demonstrating its effectiveness and accuracy in real-world kick detection.

Lost circulation is a major challenge in oil and gas drilling operations, severely restricting drilling efficiency and compromising operational safety. Conventional bridging and plugging materials rely on precise particle-to-fracture size matching, resulting in low success rates. Self-healing gels penetrate loss zones as discrete particles that progressively swell, accumulate, and self-repair in integrated gel masses to effectively seal fracture networks. Self-healing gels effectively overcome the shortcomings of traditional bridging agents including poor adaptability to fractures, uncontrollable gel formation of conventional downhole crosslinking gels, and the low strength of conventional pre-crosslinked gels. This work employs stearyl methacrylate (SMA) as a hydrophobic monomer, acrylamide (AM) and acrylic acid (AA) as hydrophilic monomers, and graphene oxide (GO) as an inorganic dopant to develop a GO-based self-healing organic–inorganic hybrid plugging material (SG gel). The results demonstrate that the incorporation of GO significantly enhances the material’s mechanical and rheological properties, with the SG-1.5 gel exhibiting a rheological strength of 3750 Pa and a tensile fracture stress of 27.1 kPa. GO enhances the crosslinking density of the gel network through physical crosslinking interactions, thereby improving thermal stability and reducing the swelling ratio of the gel. Under conditions of 120 °C and 6 MPa, SG-1.5 gel demonstrated a fluid loss volume of only 34.6 mL in 60–80-mesh sand bed tests. This gel achieves self-healing within fractures through dynamic hydrophobic associations and GO-enabled physical crosslinking interactions, forming a compact plugging layer. It provides an efficient solution for lost circulation control in drilling fluids.

Deep and ultra-deep shale gas resources have great potential, but well drilling faces many challenges. The Polycrystalline Diamond Compact (PDC) bit has become the primary rock-breaking instrument for oil and gas drilling. Reasonable bit structure designs can promote rock-breaking efficiency and extend service life. In this study, reverse modeling technology is used to analyze the structural characteristics of PDC bits collected in the field, and the influence of the structural characteristics of the bit at a specific interval on the rate of penetration (ROP) and drill footage is investigated using the Spearman rank correlation coefficient method. The number of blades, cutting angle of the cutters, crown rotation radius, internal cone angle, and diameter of the cutters are discovered to be the main structural characteristics that affect the ROP and footage of the bits, and the degree of influence varies depending on the formation conditions. The number of blades, crown rotation radius, inner cone angle, and cutting angle of the cutters have a significant impact on the ROP, whereas blade thickness, gauge length, gauge width, nozzle equivalent diameter have a significant impact on the bit footage. In addition, a back propagation (BP) neural network is utilized to build a prediction model of bit footage and ROP over a certain interval based on the structural characteristics of the bit. The goodness of fit of the model is greater than 85%, and its accuracy is high. Based on the usage of the bit, the evaluation and prediction of the bit can provide a reference for the structural design and optimization of the bit in a specific interval, guide the bit selection work, rationally plan the drilling operation, and reduce the drilling cost.