Hydraulic-Mechanical-Damage Multifield Coupling Control Mechanism and Gas Production Prediction of Fracturing in...

Efficient coalbed methane (CBM) development through hydraulic fracturing in tectonically deformed coal seams is governed by the multifield coupling of the hydraulic (H), mechanical (M), and damage (D), and it is urgent to build a data-driven gas production prediction model. Using multifield coupled numerical simulation, this study systematically analyzes the effects of elastic modulus (E), initial permeability (k ₀), and fracturing displacement rate (H _f) on the fracturing-induced damaged area (D _A), maximum postfracturing permeability (k _m‑max), and gas production. The applicability of three models of support vector machine (SVM), random forest (RF), and generalized regression neural network (GRNN) in gas production prediction is analyzed. The results show that the initial permeability and fracturing displacement rate have significant positive effects on gas production, increasing cumulative gas production by 341.44% and 48.47%, respectively. The elastic modulus exhibits a negative effect, reducing cumulative gas production by 22.21%. Postfracturing damaged area and permeability also show significant positive effects. The RF model demonstrates the best prediction performance on both the training and test sets, achieving R² values of 0.9871 and 0.9551, respectively, with error metrics significantly lower than those of the SVM and GRNN models. The RF model is therefore more suitable for gas production prediction in CBM fracturing wells under multifeature parameter conditions. The HMD multifield coupling control mechanism+machine learning drive fusion gas production prediction method realized the technical path from physical mechanism revelation to engineering prediction of hydraulic fracturing coalbed methane development in structurally deformed coal seams.