A Machine-Learning based generalization for an iterative Hybrid Embedded Fracture scheme
KAUST DepartmentComputational Transport Phenomena Lab
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2020-05-08
Print Publication Date2020-11
Embargo End Date2022-07-26
Permanent link to this recordhttp://hdl.handle.net/10754/664537
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AbstractAccurately 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.
CitationZ. Amir, S., Sun, S., & F. Wheeler, M. (2020). A Machine-Learning based generalization for an iterative Hybrid Embedded Fracture scheme. Journal of Petroleum Science and Engineering, 194, 107235. doi:10.1016/j.petrol.2020.107235
SponsorsThis project is funded by King Abdullah University of Science and Technology (KAUST), and KAUST (BAS/1/1351-01-01) research fund awarded through the KAUST-KFUPM Initiative (KKI) (REP/1/2879-01-01) Program.