Intelligent Inversion Method of Fracturing Fracture Morphology Based on G-Function Constraints and CNN-LSTM

Addressing the challenges in quantitative characterization of post-fracture fracture geometry in unconventional reservoirs such as tight gas and shale, for which existing prediction methods provide only limited accuracy, this study proposes an intelligent inversion method for fracture morphology by integrating G-function post-fracture diagnosis with a CNN-LSTM network. The number of branch fractures determined from G-function analysis of pressure decline curves is employed as a hard constraint. Latin hypercube sampling (LHS) is adopted to generate multi-dimensional fracture geometry samples, and a CNN-LSTM model is constructed to establish the nonlinear mapping from fracture morphology to production response, thereby forming a sample database that correlates production with fracture geometry. Leveraging this database, intelligent inversion of post-fracture fracture geometry can be efficiently realized. Field validation demonstrates that the inverted fracture half-length deviates by less than 3% from microseismic monitoring results, while the production prediction error remains below 6%, significantly outperforming the 10% tolerance threshold commonly accepted in field engineering. The proposed method requires only single-well pressure decline curves and production data to accomplish post-fracture fracture geometry evaluation, exhibiting broad application prospects.