Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

Abstract

The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work.

Artificial Neural Network to Determine Dynamic Effect in Capillary Pressure Relationship for Two-Phase Flow in Porous Media with Micro-Heterogeneities

“…In the present work, we include an additional independent variable, i.e., intensity of heterogeneity (Das et al 2004;Mirzaei and Das 2007) to account for the amount of heterogeneity in the computational domain. Furthermore, an average permeability of the heterogeneous domain is used instead of the homogeneous domain permeability as previously used by Hanspal et al (2013). Similar to Hanspal et al (2013), we argue that the effects of any other system parameters (e.g., interfacial tension, entry pressure of porous domain) that are not explicitly accounted for in the ANN structure are lumped in the values of the saturation (input) and dynamic coefficient (output).…”

“…Furthermore, an average permeability of the heterogeneous domain is used instead of the homogeneous domain permeability as previously used by Hanspal et al (2013). Similar to Hanspal et al (2013), we argue that the effects of any other system parameters (e.g., interfacial tension, entry pressure of porous domain) that are not explicitly accounted for in the ANN structure are lumped in the values of the saturation (input) and dynamic coefficient (output). Of course, an ANN such as the present one may include as many parameters as one would like as input and output variables.…”

“…In the last decade, a number of studies have determined the significance of τ in different circumstances Dahle et al 2005;Manthey et al 2005;Das et al 2007Das et al , 2014Das 2007, 2013;Peszynska and Yi 2008;Juanes 2009;Sakaki et al 2010;Bottero et al 2011;Mumford and O'Carroll 2011;Hanspal and Das 2012;Mirzaei 2012, 2013;Hanspal et al 2013;Hou et al 2014;Zhang et al 2014). In particular, it has been shown that the heterogeneity in porous media affects the dynamic coefficient significantly by altering the rate to flow equilibrium (Manthey et al 2005;Mirzaei and Das 2007;Sakaki et al 2010;Das and Mirzaei 2013).…”

“…Incorporation of multiple neurons or processors in different layers (input, output and hidden) of the ANN architecture (feed-forward or feed-backward) can determine the performance of the ANN in terms of efficiency and computational run-times (Hanspal et al 2013;Ren 2009;Kurtulus and Razack 2006). In the literature, many such examples of ANN based tools in the context of flow and transport in porous media can be referred, e.g., determination of dynamic effect in homogeneous porous media (Hanspal et al 2013), oil well logging and testing (Baldwin 1991;Kumoluyi and Daltaban 1994;Elkamel et al 1996), prediction of petrophysical properties (Karimpouli et al 2010;Omole et al 2009;Boadu 2001), simulation of groundwater contamination, pollutant infiltration and surface run-off (Akratos et al 2008;Tabach et al 2007;Parida et al 2006), and immiscible flooding simulations (Elkamel et al 1996;Gharbi et al 1995;Li and Lake 1995;Shook et al 1992). However, an ANN-based tool that can specifically characterize the dynamic coefficient in heterogeneous porous domains, addressing the points discussed above, is still missing.…”

“…Conceptually, the presented approach is somewhat motivated by an ANN structure previously reported to compute dynamic coefficient in homogeneous domain (Hanspal et al 2013). However, the work in this paper does not have any other link to the previous work as the objective in this paper is completely different and we use different ANN structure and reference data sets.…”