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

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    Name:
    A machine learning.pdf
    Size:
    26.62Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2022-07-26
    Download
    Type
    Article
    Authors
    Z. Amir, Sahar
    Sun, Shuyu cc
    F. Wheeler, Mary
    KAUST Department
    Computational Transport Phenomena Lab
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    BAS/1/1351-01-01
    REP/1/2879-01
    Date
    2020-05-08
    Online Publication Date
    2020-05-08
    Print Publication Date
    2020-11
    Embargo End Date
    2022-07-26
    Submitted Date
    2019-10-16
    Permanent link to this record
    http://hdl.handle.net/10754/664537
    
    Metadata
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    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.
    Citation
    Z. 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
    Sponsors
    This 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.
    Publisher
    Elsevier BV
    Journal
    Journal of Petroleum Science and Engineering
    DOI
    10.1016/j.petrol.2020.107235
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S092041052030320X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.petrol.2020.107235
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computational Transport Phenomena Lab

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