Study on Hydraulic Fracture Propagation Path and Intelligent Prediction of Fracturing Effects in Coal Measure Strata

To address the challenges in controlling hydraulic fracture propagation trajectories and stimulated volumes within multi-lithologic coal measure strata - particularly those caused by formation interfaces and interlayer strength variations - this study employs a combined finite-discrete element method (FDEM) to investigate hydraulic fracture behavior. The research reveals twelve distinct propagation modes under multifactor coupling conditions, classifiable into four categories: interface penetration, penetration with interface extension, interface tracking, and interface arrest. We developed a novel hybrid artificial intelligence model integrating BP neural networks with differential evolution (DE) and grey wolf optimization (GWO) algorithms (BP-DEGWO) to predict fracture trajectories and stimulation effectiveness. Systematic analysis identified key controlling factors - including rock strength contrast coefficient, interface dip angle, interfacial strength, injection rate, and perforation angle - and quantified their relative importance under varying in-situ stress conditions. The study further proposes an intelligent optimization framework for hydraulic fracturing design in stratified formations based on the BP-DEGWO model. This approach provides a predictive tool for fracture geometry in heterogeneous coal-bearing strata, and a methodological reference for AI-assisted fracturing optimization. The findings offer significant implications for enhancing stimulation efficiency in multi-lithologic unconventional reservoirs.