Prediction of Breakdown Pressure Using a Multi-Layer Neural Network Based on Supercritical CO2 Fracturing Data

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.