Prediction of Fluid Pressure Dynamics in Deflagration Fracturing for Unconventional Reservoir Stimulation Based on Physics-Guided Graph Neural Network

Deflagration fracturing is a gas-dominated, water-free reservoir stimulation technology that has shown strong potential in unconventional, low-permeability, or water-sensitive reservoirs such as coalbed methane and shale gas formations. Accurate prediction of fluid pressure variations, critical for optimizing fracture propagation and stimulation performance, is challenging. While field experiments and numerical simulations offer reliable predictions, they are hindered by high risks, costs, and computational complexity due to multi-physics coupling, Moreover, purely data-driven machine learning methods often exhibit poor generalization and may produce predictions that deviate from fundamental physical principles. To address these challenges, a physics-guided graph neural network (PG-GNN) is proposed in this study to predict the evolution of fluid pressure, the key driving factor governing fracture propagation, from a mechanistic perspective. The proposed method integrates governing equations and physical constraints to construct geometric, physical, and hybrid features and employs a graph neural network encoder to capture the spatial correlations among these features, thereby forming a deep learning framework with strong physical consistency. A multi-task loss function is further employed to balance predictive accuracy and physical rationality. Finally, the proposed model is validated using a high-resolution dataset generated by a CDEM-based numerical simulator, achieving a minimum MAPE of 0.313% and a minimum MSE of 2.309 × 10−4 on the test dataset, outperforming baseline models in both accuracy and stability and demonstrating strong extrapolation capability.