Learning the Matrix-Fractures Transfer Rate Using a Convolutional Neural Network

N. Andrianov, H.M. Nick

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

One of the key elements in constructing of representative dual porosity/dual permeability models is to provide the mass transfer rate between the matrix and the fractures. Whereas it is possible to compute numerically this transfer rate for specific geometries, it is challenging to estimate the transfer function without running the CPU intensive computations. In this work, we demonstrate that a convolutional neural network can approximate a transfer function using the encoded fracture geometry and the precomputed fine-scale simulation results.
Original languageEnglish
Title of host publicationConference Proceedings, First EAGE Digitalization Conference and Exhibition
Number of pages5
Volume2020
PublisherEuropean Association of Geoscientists and Engineers
Publication date2020
Edition1
DOIs
Publication statusPublished - 2020
EventFirst EAGE Digitalization Conference and Exhibition - Online
Duration: 30 Nov 20203 Dec 2020
Conference number: 1

Conference

ConferenceFirst EAGE Digitalization Conference and Exhibition
Number1
LocationOnline
Period30/11/202003/12/2020

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