Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning
Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multisca...
Gespeichert in:
Veröffentlicht in: | Water resources research 2021-04, Vol.57 (4), p.n/a |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | n/a |
---|---|
container_issue | 4 |
container_start_page | |
container_title | Water resources research |
container_volume | 57 |
creator | Wang, Ziyan Battiato, Ilenia |
description | Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small‐scale features (e.g., microcracks in shales) as an effective medium. Although this fracture‐matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, for example, microcrack clogging during mineral reactions. In this work, we employ deep learning in place of effective medium theory to upscale physical processes in small‐scale features. Specifically, we consider reactive transport in a fracture‐microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is first employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.
Key Points
We propose a hybrid framework for modeling reactive transport and clogging in multiscale fracture networks
Microcracks are upscaled by deep learning while main fractures are described by theory‐based modeling, ensuring a two‐way coupling
The framework provides a reliable and efficient upscaling method without relying on the existence of macroscopic equations |
doi_str_mv | 10.1029/2020WR029125 |
format | Article |
fullrecord | <record><control><sourceid>proquest_osti_</sourceid><recordid>TN_cdi_osti_scitechconnect_1774991</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2518476320</sourcerecordid><originalsourceid>FETCH-LOGICAL-a3952-32f32583ed4b171b4dd75bc5edd468814cf4c5cb12ad74e204ba4206c0985c93</originalsourceid><addsrcrecordid>eNp90D1PwzAQBmALgUQpbPwAC1YC_ozjEYVPqahSKOpoOY7TugQn2Cmo_55UYWBiupPu0enuBeAco2uMiLwhiKBlMXSY8AMwwZKxREhBD8EEIUYTTKU4BicxbhDCjKdiAuZvXTS6cX4FC6tN774sXATtY9eGHmpfwbxpV6v93Hn4utaNhS_OhNYEbd4jLHfwztoOzqwOflCn4KjWTbRnv3UKFg_3i_wpmc0fn_PbWaKp5CShpKaEZ9RWrMQCl6yqBC8Nt1XF0izDzNTMcFNioivBLEGs1Iyg1CCZcSPpFFyMa9vYOxWN661Zm9Z7a3qFhWBS4gFdjqgL7efWxl5t2m3ww1mKcJwxkVKCBnU1quGpGIOtVRfchw47hZHax6r-xjpwOvJv19jdv1Yti7wgnFBCfwDTPHd8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2518476320</pqid></control><display><type>article</type><title>Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning</title><source>Wiley-Blackwell AGU Digital Library</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Wang, Ziyan ; Battiato, Ilenia</creator><creatorcontrib>Wang, Ziyan ; Battiato, Ilenia</creatorcontrib><description>Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small‐scale features (e.g., microcracks in shales) as an effective medium. Although this fracture‐matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, for example, microcrack clogging during mineral reactions. In this work, we employ deep learning in place of effective medium theory to upscale physical processes in small‐scale features. Specifically, we consider reactive transport in a fracture‐microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is first employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.
Key Points
We propose a hybrid framework for modeling reactive transport and clogging in multiscale fracture networks
Microcracks are upscaled by deep learning while main fractures are described by theory‐based modeling, ensuring a two‐way coupling
The framework provides a reliable and efficient upscaling method without relying on the existence of macroscopic equations</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2020WR029125</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Boundary conditions ; Deep learning ; Effective medium theory ; Exact solutions ; fracture network ; Fractures ; Machine learning ; Microcracks ; Neural networks ; reactive transport ; Recurrent neural networks ; Sedimentary rocks ; Shale ; Shales ; Solute transport ; Solutes ; Transport ; upscaling</subject><ispartof>Water resources research, 2021-04, Vol.57 (4), p.n/a</ispartof><rights>2021. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3952-32f32583ed4b171b4dd75bc5edd468814cf4c5cb12ad74e204ba4206c0985c93</citedby><cites>FETCH-LOGICAL-a3952-32f32583ed4b171b4dd75bc5edd468814cf4c5cb12ad74e204ba4206c0985c93</cites><orcidid>0000-0002-8941-8926 ; 0000-0002-7453-6428 ; 0000000289418926 ; 0000000274536428</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2020WR029125$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2020WR029125$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,11493,27901,27902,45550,45551,46443,46867</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/1774991$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Ziyan</creatorcontrib><creatorcontrib>Battiato, Ilenia</creatorcontrib><title>Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning</title><title>Water resources research</title><description>Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small‐scale features (e.g., microcracks in shales) as an effective medium. Although this fracture‐matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, for example, microcrack clogging during mineral reactions. In this work, we employ deep learning in place of effective medium theory to upscale physical processes in small‐scale features. Specifically, we consider reactive transport in a fracture‐microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is first employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.
Key Points
We propose a hybrid framework for modeling reactive transport and clogging in multiscale fracture networks
Microcracks are upscaled by deep learning while main fractures are described by theory‐based modeling, ensuring a two‐way coupling
The framework provides a reliable and efficient upscaling method without relying on the existence of macroscopic equations</description><subject>Algorithms</subject><subject>Boundary conditions</subject><subject>Deep learning</subject><subject>Effective medium theory</subject><subject>Exact solutions</subject><subject>fracture network</subject><subject>Fractures</subject><subject>Machine learning</subject><subject>Microcracks</subject><subject>Neural networks</subject><subject>reactive transport</subject><subject>Recurrent neural networks</subject><subject>Sedimentary rocks</subject><subject>Shale</subject><subject>Shales</subject><subject>Solute transport</subject><subject>Solutes</subject><subject>Transport</subject><subject>upscaling</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp90D1PwzAQBmALgUQpbPwAC1YC_ozjEYVPqahSKOpoOY7TugQn2Cmo_55UYWBiupPu0enuBeAco2uMiLwhiKBlMXSY8AMwwZKxREhBD8EEIUYTTKU4BicxbhDCjKdiAuZvXTS6cX4FC6tN774sXATtY9eGHmpfwbxpV6v93Hn4utaNhS_OhNYEbd4jLHfwztoOzqwOflCn4KjWTbRnv3UKFg_3i_wpmc0fn_PbWaKp5CShpKaEZ9RWrMQCl6yqBC8Nt1XF0izDzNTMcFNioivBLEGs1Iyg1CCZcSPpFFyMa9vYOxWN661Zm9Z7a3qFhWBS4gFdjqgL7efWxl5t2m3ww1mKcJwxkVKCBnU1quGpGIOtVRfchw47hZHax6r-xjpwOvJv19jdv1Yti7wgnFBCfwDTPHd8</recordid><startdate>202104</startdate><enddate>202104</enddate><creator>Wang, Ziyan</creator><creator>Battiato, Ilenia</creator><general>John Wiley & Sons, Inc</general><general>American Geophysical Union (AGU)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0002-8941-8926</orcidid><orcidid>https://orcid.org/0000-0002-7453-6428</orcidid><orcidid>https://orcid.org/0000000289418926</orcidid><orcidid>https://orcid.org/0000000274536428</orcidid></search><sort><creationdate>202104</creationdate><title>Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning</title><author>Wang, Ziyan ; Battiato, Ilenia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3952-32f32583ed4b171b4dd75bc5edd468814cf4c5cb12ad74e204ba4206c0985c93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Boundary conditions</topic><topic>Deep learning</topic><topic>Effective medium theory</topic><topic>Exact solutions</topic><topic>fracture network</topic><topic>Fractures</topic><topic>Machine learning</topic><topic>Microcracks</topic><topic>Neural networks</topic><topic>reactive transport</topic><topic>Recurrent neural networks</topic><topic>Sedimentary rocks</topic><topic>Shale</topic><topic>Shales</topic><topic>Solute transport</topic><topic>Solutes</topic><topic>Transport</topic><topic>upscaling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Ziyan</creatorcontrib><creatorcontrib>Battiato, Ilenia</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>OSTI.GOV</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Ziyan</au><au>Battiato, Ilenia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning</atitle><jtitle>Water resources research</jtitle><date>2021-04</date><risdate>2021</risdate><volume>57</volume><issue>4</issue><epage>n/a</epage><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small‐scale features (e.g., microcracks in shales) as an effective medium. Although this fracture‐matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, for example, microcrack clogging during mineral reactions. In this work, we employ deep learning in place of effective medium theory to upscale physical processes in small‐scale features. Specifically, we consider reactive transport in a fracture‐microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is first employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.
Key Points
We propose a hybrid framework for modeling reactive transport and clogging in multiscale fracture networks
Microcracks are upscaled by deep learning while main fractures are described by theory‐based modeling, ensuring a two‐way coupling
The framework provides a reliable and efficient upscaling method without relying on the existence of macroscopic equations</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2020WR029125</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-8941-8926</orcidid><orcidid>https://orcid.org/0000-0002-7453-6428</orcidid><orcidid>https://orcid.org/0000000289418926</orcidid><orcidid>https://orcid.org/0000000274536428</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0043-1397 |
ispartof | Water resources research, 2021-04, Vol.57 (4), p.n/a |
issn | 0043-1397 1944-7973 |
language | eng |
recordid | cdi_osti_scitechconnect_1774991 |
source | Wiley-Blackwell AGU Digital Library; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Algorithms Boundary conditions Deep learning Effective medium theory Exact solutions fracture network Fractures Machine learning Microcracks Neural networks reactive transport Recurrent neural networks Sedimentary rocks Shale Shales Solute transport Solutes Transport upscaling |
title | Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T08%3A12%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_osti_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Upscaling%20Reactive%20Transport%20and%20Clogging%20in%20Shale%20Microcracks%20by%20Deep%20Learning&rft.jtitle=Water%20resources%20research&rft.au=Wang,%20Ziyan&rft.date=2021-04&rft.volume=57&rft.issue=4&rft.epage=n/a&rft.issn=0043-1397&rft.eissn=1944-7973&rft_id=info:doi/10.1029/2020WR029125&rft_dat=%3Cproquest_osti_%3E2518476320%3C/proquest_osti_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2518476320&rft_id=info:pmid/&rfr_iscdi=true |