Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model

What hinders current models for fluid transportation in three-dimensional (3D) fracture system from considering fracture roughness is model complexity, which makes it hard to get convergent results. Therefore, we propose an electrical circuit (EC) model to simulate fracture flow, with each rough roc...

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Main Authors: Xiao, Fei, Shang, Junlong, Wanniarachchi, Ayal, Zhao, Zhiye
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160830
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1608302022-08-03T05:51:43Z Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model Xiao, Fei Shang, Junlong Wanniarachchi, Ayal Zhao, Zhiye School of Civil and Environmental Engineering Engineering::Civil engineering Equivalent Hydraulic Aperture Electrical Circuit What hinders current models for fluid transportation in three-dimensional (3D) fracture system from considering fracture roughness is model complexity, which makes it hard to get convergent results. Therefore, we propose an electrical circuit (EC) model to simulate fracture flow, with each rough rock fracture taken as an EC with distributed electrical resistances, where the voltage and current are taken as the counterparts of pressure and flow rate, respectively. The robustness of EC model is validated against the computational fluid dynamics (CFD) simulations and laboratory experiments. Additionally, the EC model exhibits a very high computational efficiency (takes several seconds) compared with that of the CFD model (takes a couple of minutes). The proposed EC model is expected to have broader applications in fracture flow analysis as it applies not only to persistent fractures with tiny mechanical apertures but also to non-persistent fractures having substantial portions of contact areas. The authors thank the "Start-up Funding for New Faculty" provided by the Nanjing University of Aeronautics and Astronautics. 2022-08-03T05:51:43Z 2022-08-03T05:51:43Z 2021 Journal Article Xiao, F., Shang, J., Wanniarachchi, A. & Zhao, Z. (2021). Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model. Journal of Petroleum Science and Engineering, 206, 109126-. https://dx.doi.org/10.1016/j.petrol.2021.109126 0920-4105 https://hdl.handle.net/10356/160830 10.1016/j.petrol.2021.109126 2-s2.0-85109450045 206 109126 en Journal of Petroleum Science and Engineering © 2021 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
Equivalent Hydraulic Aperture
Electrical Circuit
spellingShingle Engineering::Civil engineering
Equivalent Hydraulic Aperture
Electrical Circuit
Xiao, Fei
Shang, Junlong
Wanniarachchi, Ayal
Zhao, Zhiye
Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
description What hinders current models for fluid transportation in three-dimensional (3D) fracture system from considering fracture roughness is model complexity, which makes it hard to get convergent results. Therefore, we propose an electrical circuit (EC) model to simulate fracture flow, with each rough rock fracture taken as an EC with distributed electrical resistances, where the voltage and current are taken as the counterparts of pressure and flow rate, respectively. The robustness of EC model is validated against the computational fluid dynamics (CFD) simulations and laboratory experiments. Additionally, the EC model exhibits a very high computational efficiency (takes several seconds) compared with that of the CFD model (takes a couple of minutes). The proposed EC model is expected to have broader applications in fracture flow analysis as it applies not only to persistent fractures with tiny mechanical apertures but also to non-persistent fractures having substantial portions of contact areas.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Xiao, Fei
Shang, Junlong
Wanniarachchi, Ayal
Zhao, Zhiye
format Article
author Xiao, Fei
Shang, Junlong
Wanniarachchi, Ayal
Zhao, Zhiye
author_sort Xiao, Fei
title Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
title_short Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
title_full Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
title_fullStr Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
title_full_unstemmed Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
title_sort assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
publishDate 2022
url https://hdl.handle.net/10356/160830
_version_ 1743119570716065792