Although supercritical CO₂ (SC-CO₂) fracturing has shown promise in oil and gas development with demonstrated potential, its application in shale gas extraction remains in its infancy globally. In this study, fracturing experiments were conducted with water, liquid CO₂ (L-CO₂), and SC-CO₂, as well as SC-CO₂ at varying pump rates. The results reveal that SC-CO₂ fracturing produces a highly complex fracture network characterized by fractures of varying numbers, deflection angles, and tortuosity. Analysis of CO₂ temperature and pressure data showed a sharp drop in injection pressure and temperature at breakdown, followed by fluctuations until injection stopped. Acoustic emission (AE) monitoring demonstrated that energy released during main fracture initiation significantly exceeded that from CO₂ phase transition-driven fracture extension, underscoring the dominant role of main fractures in energy dissipation. Compared to hydraulic fracturing, SC-CO₂ fracturing created a seepage area 2.2 times larger while reducing the breakdown pressure by 37.2%, indicating superior stimulation performance. These findings emphasize the potential of SC-CO₂ to form intricate fracture networks, offering a promising approach for efficient shale gas extraction.

Hydraulic fracturing is a widely employed technique for stimulating unconventional shale gas reservoirs. Supercritical CO₂ (SC-CO₂) has emerged as a promising fracturing fluid due to its unique physicochemical properties. Existing theoretical models for calculating breakdown pressure often fail to accurately predict the outcomes of SC-CO₂ fracturing due to the complex, nonlinear interactions among multiple influencing factors. In this study, we conducted fracturing experiments considering parameters such as fluid type, flow rate, temperature, and confining pressure. A fully connected neural network was then employed to predict breakdown pressure, integrating both our experimental data and published datasets. This approach facilitated the identification of key influencing factors and allowed us to quantify their relative importance. The results demonstrate that SC-CO₂ significantly reduces breakdown pressure compared to traditional water-based fluids. Additionally, breakdown pressure increases with higher confining pressures and elevated flow rates, while it decreases with increasing temperatures. The multi-layer neural network achieved high predictive accuracy, with R, RMSE, and MAE values of 0.9482 (0.9123), 3.424 (4.421), and 2.283 (3.188) for training (testing) sets, respectively. Sensitivity analysis identified fracturing fluid type and tensile strength as the most influential factors, contributing 28.31% and 21.39%, respectively, followed by flow rate at 12.34%. Our findings provide valuable insights into the optimization of fracturing parameters, offering a promising approach to better predict breakdown pressure in SC-CO₂ fracturing operations.

The mechanical behavior of sand–fines mixtures is governed by their limiting void ratios, which are sensitive to fines content and particle morphology. Conventional empirical correlations often fail to generalize to a wide range of soils, limiting their applicability in engineering design. This study develops an integrated approach combining laboratory calibration, discrete element method (DEM) simulations incorporating realistic particle morphologies and machine learning to predict maximum and minimum void ratios. Glass beads were first tested to validate DEM contact parameters, after which sand particles obtained through 3D scanning were employed to capture morphological effects. Correlation and partial least squares analyses confirmed fines content as the dominant factor, while particle shape also contributed to packing behavior. A fully connected neural network (FCNN) was trained to establish predictive relationships, demonstrating closer agreement with DEM simulations than traditional empirical formulations. The proposed approach provides a reliable and generalizable tool for evaluating packing characteristics and offers new insights into the role of particle morphology in the mechanical response of sand–fines mixtures.