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dc.contributor.authorZhang, Tao
dc.contributor.authorLi, Yiteng
dc.contributor.authorCai, Jianchao
dc.contributor.authorMeng, Qingbang
dc.contributor.authorSun, Shuyu
dc.contributor.authorLi, Chenguang
dc.date.accessioned2020-11-03T12:42:12Z
dc.date.available2020-11-03T12:42:12Z
dc.date.issued2020-11-02
dc.date.submitted2020-07-09
dc.identifier.citationZhang, T., Li, Y., Cai, J., Meng, Q., Sun, S., & Li, C. (2020). A Digital Twin for Unconventional Reservoirs: A Multiscale Modeling and Algorithm to Investigate Complex Mechanisms. Geofluids, 2020, 1–12. doi:10.1155/2020/8876153
dc.identifier.issn1468-8123
dc.identifier.issn1468-8115
dc.identifier.doi10.1155/2020/8876153
dc.identifier.urihttp://hdl.handle.net/10754/665790
dc.description.abstractThe special mechanisms underneath the flow and transport behaviors in unconventional reservoirs are still challenging an accurate and reliable production estimation. As an emerging approach in intelligent manufacturing, the concept of digital twin has attracted increasing attentions due to its capability of monitoring engineering processes based on modeling and simulation in digital space. The application potential is highly expected especially for problems with complex mechanisms and high data dimensions, because the utilized platform in the digital twin can be easily extended to cover more mechanisms and solve highly complicated problems with strong nonlinearity compared with experimental studies in physical space. In this paper, a digital twin is designed to numerically model the representative mechanisms that affect the production unconventional reservoirs, such as capillarity, dynamic sorption, and injection salinity, and it incorporates multiscale algorithms to simulate and illustrate the effect of these mechanisms on flow and transport phenomena. The preservation of physical laws among different scales is always the first priority, and simulation results are analyzed to verify the robustness of proposed multiscale algorithms.
dc.description.sponsorshipThe work of Tao Zhang, Yiteng Li, and Shuyu Sun was supported by funding from the National Natural Scientific Foundation of China (Grants Nos. 51874262 and 51936001) and King Abdullah University of Science and Technology (KAUST) through the Grant no. BAS/1/135101-01.
dc.publisherHindawi Limited
dc.relation.urlhttps://www.hindawi.com/journals/geofluids/2020/8876153/
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA Digital Twin for Unconventional Reservoirs: A Multiscale Modeling and Algorithm to Investigate Complex Mechanisms
dc.typeArticle
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentComputational Transport Phenomena Laboratory, Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalGeofluids
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
dc.contributor.institutionState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China
dc.contributor.institutionKey Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan 430074, China
dc.contributor.institutionChina National Oil and Gas Exploration and Development Company Limited, China
dc.identifier.volume2020
dc.identifier.pages1-12
kaust.personZhang, Tao
kaust.personLi, Yiteng
kaust.personSun, Shuyu
kaust.grant.numberBAS/1/135101-01
dc.date.accepted2020-09-15
refterms.dateFOA2020-11-03T12:43:13Z
dc.date.published-online2020-11-02
dc.date.published-print2020-10-31


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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.