A Machine-Learning based generalization for an iterative Hybrid Embedded Fracture scheme

Abstract

Accurately simulating fractured systems requires treating the fracture's characteristics. Here we describe a novel framework that involves coupling the Hybrid Embedded Fracture (HEF) scheme with Machine Learning. In general, HEF is more accurate than continuum medium schemes and less reliable but more efficient than the Discrete Fracture Networks (DFN) schemes. In our work, the attributes used to estimate the HEF flux exchange parameters are extracted using image processing, Machine-Learning, and Artificial-Intelligence techniques. In addition, we formulate a pure Machine-Learning classifier and Deep-Learning topology design to deal with the extraction of hierarchical fracture features from low-level to high-level based on Neural-Network layers. Computations are visualized using velocity vectors that are controlled by fractures characteristics extracted automatically from the fractured systems images. Their results provide an understanding of the flow behavior and maps of pressure distributions.