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dc.contributor.authorDugstad, Martin Sandanger
dc.contributor.authorKumar, Kundan
dc.contributor.authorPettersen, Øystein
dc.date.accessioned2022-03-18T09:03:04Z
dc.date.available2022-03-18T09:03:04Z
dc.date.created2022-01-28T14:44:49Z
dc.date.issued2021
dc.identifier.issn1420-0597
dc.identifier.urihttps://hdl.handle.net/11250/2986075
dc.description.abstractDimensional reduction strategy is an effective approach to derive reliable conceptual models to describe flow in fractured porous media. The fracture aperture is several orders of magnitude smaller than the characteristic size (e.g., the length of the fracture) of the physical problem. We identify the aperture to length ratio as the small parameter 𝜖� with the fracture permeability scaled as an exponent of 𝜖�. We consider a non-Newtonian fluid described by the Carreau model type where the viscosity is dependent on the fluid velocity. Using formal asymptotic approach, we derive a catalogue of reduced models at the vanishing limit of 𝜖�. Our derivation provides new models in a hybrid-dimensional setting as well as models which exhibit two-scale behaviour. Several numerical examples confirm the theoretical derivations of the upscaled models. Moreover, we have also studied the sensitivity of the upscaled models when a particular upscaled model is used beyond its range of validity to provide additional insight.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleDimensional reduction of a fractured medium for a polymer EOR modelen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright The Author(s) 2021en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1007/s10596-021-10075-w
dc.identifier.cristin1992650
dc.source.journalComputational Geosciencesen_US
dc.source.pagenumber1753-1773en_US
dc.relation.projectNorges forskningsråd: 811716en_US
dc.identifier.citationComputational Geosciences. 2021, 25, 1753-1773.en_US
dc.source.volume25en_US


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