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dc.contributor.authorDas, Digantaen
dc.contributor.authorThirakulchaya, Thaniten
dc.contributor.authorDeka, Lipikaen
dc.contributor.authorHanspal, Navraen
dc.date.accessioned2017-10-18T11:51:47Z
dc.date.available2017-10-18T11:51:47Z
dc.date.issued2014-11-08
dc.identifier.citationDas, DB. Thirakulchaya, T. Deka, L. and Hanspal, NS. (2015) Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities. Environmental Processes, 2(1), pp.1-18.en
dc.identifier.issn21987491
dc.identifier.urihttp://hdl.handle.net/2086/14647
dc.descriptionOpen access article
dc.description.abstractAn artificial neural network (ANN) is presented for computing a parameter of dynamic two-phase flow in porous media with water as wetting phase, namely, dynamic coefficient (τ), by considering micro-heterogeneity in porous media as a key parameter. τ quantifies the dependence of time derivative of water saturation on the capillary pressures and indicates the rates at which a two-phase flow system may reach flow equilibrium. Therefore, τ is of importance in the study of dynamic two-phase flow in porous media. An attempt has been made in this work to reduce computational and experimental effort by developing and applying an ANN which can predict the dynamic coefficient through the “learning” from available data. The data employed for testing and training the ANN have been obtained from computational flow physics-based studies. Six input parameters have been used for the training, performance testing and validation of the ANN which include water saturation, intensity of heterogeneity, average permeability depending on this intensity, fluid density ratio, fluid viscosity ratio and temperature. It is found that a 15 neuron, single hidden layer ANN can characterize the relationship between media heterogeneity and dynamic coefficient and it ensures a reliable prediction of the dynamic coefficient as a function of water saturation.en
dc.language.isoen_USen
dc.publisherSpringer International Publishingen
dc.subjectArtificial Neural Networksen
dc.titleArtificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneitiesen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1007/s40710-014-0045-3
dc.researchgroupDIGITSen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC BYen
dc.date.acceptance2014-10-27en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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