Prediction and Risk Control of Interwell Fracturing Interference in Deep Coalbed Methane Reservoirs

Interwell fracturing interference increasingly affects production as deep coalbed methane development advances towards dense well patterns and large-scale stimulation. Predicting offset-well responses requires relation-specific geological information and surrounding well-network context. Using data from 131 horizontal wells in the Daning–Jixian Block, this study develops an integrated prediction and treatment-design framework. Three-dimensional sampling corridors describe geological and stress conditions between treatment stages and offset wells. Random forest recursive feature elimination selects key predictors. An edge-enhanced graph attention network combines these descriptors, treatment parameters and offset-well states in time-truncated bipartite graphs. Calibrated probabilities guide a genetic algorithm that retains treatment input while satisfying risk and engineering constraints. The model predicts frac-hit risk with high accuracy, and risk-constrained parameter optimisation reduces predicted interference risk. Subsequent stage-level field comparisons show fewer recorded frac hits and weaker offset-well pressure responses following parameter adjustment, with small differences in early whole-well production. The framework supports stage-specific risk management and coordinated stimulation design in densely developed coalbed methane reservoirs.