Fast Inversion of Fracture Networks Created by Hydraulic Fracturing Treatment Using Transient Pressure History

As a critical component in the stimulation of unconventional oil and gas reservoirs, the accurate characterization of the spatial distribution of hydraulic fracture networks forms the basis for evaluating fracturing effectiveness and calculating hydrocarbon recovery. In this study, a coupled fluid-solid numerical model for hydraulic fracturing is employed to characterize the dynamic propagation behavior. of fracture networks, while simultaneously capturing the corresponding wellhead pressure responses. Based upon this, a Bayesian trans-dimensional inversion method is developed by integrating the Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) algorithm with a parallel computing framework, enabling real-time inversion of fracture network geometry based on field injection pressure curves. In this approach, discrete fracture elements within the network—characterized by their geometric properties and spatial distribution— are treated as stochastic variables. Given the independence of fracture growth, random sampling is carried out in parallel across multiple threads. By combining the physical model with observational data, the Metropolis-Hastings- Green (MHG) criterion is applied to screen the sampling results, efficiently generating a set of fracture network samples that align with the measured wellhead pressure data. Subsequently, a Greedy Algorithm is applied to select the fracture geometry with the best pressure-matching accuracy as the inversion result and to optimize the fracture network parameters for the subsequent time step, thereby improving inversion efficiency. Finally, to verify the feasibility of the proposed method, pressure data from Stage 4 of Well H in a shale gas field in southwestern China were used to invert the fracture network geometry, and the results were compared with microseismic monitoring data. The comparison demonstrates strong consistency in both time and space between the fracture geometries derived from inversion and those from microseismic monitoring. Although aspects of the inversion method, such as pressure data selection, still require improvement, the proposed approach undoubtedly provides a novel solution for the real-time evaluation of fracture geometries during hydraulic fracturing operations.