Advances in Hydraulic Fracture Propagation Research in Shale Reservoirs

Abstract

The characterization of artificial fracture propagation law in the fracturing process of shale reservoirs is the basis for evaluating the fracture conductivity and a key indicator of the reservoir stimulated effect. In order to improve the fracture stimulated volume of shale reservoirs, this paper systematically discusses the current status of research on artificial fracture propagation law from the research methods and main control factors and provides an outlook on its future development direction. The analysis finds that the study of fracture propagation law by using indoor physical simulation experiments has the advantages of simple operation and intuitive image, and the introduction of auxiliary technologies such as acoustic emission monitoring and CT scanning into indoor physical model experiments can correct the experimental results so as to better reveal the propagation mechanism of artificial fractures. At present, the numerical simulation methods commonly used to study the propagation law of artificial fractures include the finite element method, extended finite element method, discrete element method, boundary element method and phase field method, etc. The models established based on these numerical simulation methods have their own advantages and applicability, so the numerical algorithms can be integrated and the numerical methods selected to model and solve the different characteristics of the propagation law of artificial fractures in different regions at different times can greatly improve the accuracy of the model solution and better characterize the propagation law of artificial fractures. The propagation law of artificial fracture in the fracturing process is mainly influenced by geological factors and engineering factors, so when conducting research, geological factors should be taken as the basis, and through detailed study of geological factors, the selection of the fracturing process can be guided and engineering influencing factors can be optimized.