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Title: Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities
Authors: Das, Diganta Bhusan
Thirakulchaya, Thanit
Deka, Lipika
Hanspal, Navraj S.
Keywords: Artificial neural network (ANN)
Two phase flow
Porous media
Dynamic coefficient
Dynamic capillary pressure
Porous medium heterogeneity
Issue Date: 2015
Publisher: Springer (© The Authors)
Citation: DAS, D.B., 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.
Abstract: An 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.
Description: This is an Open Access article. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Sponsor: This work was supported by the EPSRC [grant number GR/S94315/01].
Version: Published
DOI: 10.1007/s40710-014-0045-3
URI: https://dspace.lboro.ac.uk/2134/16222
Publisher Link: http://dx.doi.org/10.1007/s40710-014-0045-3
Appears in Collections:Published Articles (Chemical Engineering)

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