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Title: Assessing fluid flow in rough rock fractures based on machine learning and electrical circuit model
Authors: Xiao, Fei
Shang, Junlong
Wanniarachchi, Ayal
Zhao, Zhiye
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: 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-.
Journal: Journal of Petroleum Science and Engineering
Abstract: 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.
ISSN: 0920-4105
DOI: 10.1016/j.petrol.2021.109126
Schools: School of Civil and Environmental Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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