Advances in Evaluation Methods for Artificial Fracture Networks in Shale Gas Horizontal Wells

In recent years, the accurate evaluation of artificial fracture networks has become a key challenge in enhancing the effectiveness of reservoir stimulation in shale gas development. This paper systematically reviews the research progress on evaluation methods for artificial fracture networks in shale gas horizontal wells, covering two major technical systems: direct monitoring and dynamic inversion. Direct monitoring methods focus on technologies such as microseismic monitoring, tracers, wide-field electromagnetic methods, and distributed fiber optics. Dynamic inversion methods utilize data from fracturing construction curves, shut-in water hammer effects, and flowback production, and combine numerical simulations with artificial intelligence algorithms to infer fracture network parameters, although the issue of non-uniqueness in solutions remains to be addressed. Research shows that no single technology can comprehensively characterize fracture network features. Future directions should involve the integration of multi-source data (geophysical, chemical, fiber-optic, and dynamic production data) to construct intelligent evaluation frameworks, validated by field experiments and dynamic data simulations. The introduction of artificial intelligence and big data technologies provides new ideas for fracture network parameter inversion, but their effectiveness still requires support from more case studies. This paper provides theoretical guidance and practical reference for the optimization and integration of fracture network evaluation technologies in efficient shale gas development.