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

Xiao, F., Shang, J. , Ayal, W. and Zhiye, 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. (doi: 10.1016/j.petrol.2021.109126)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Shang, Dr Junlong
Authors: Xiao, F., Shang, J., Ayal, W., and Zhiye, Z.
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
Journal Name:Journal of Petroleum Science and Engineering
ISSN (Online):1873-4715
Published Online:25 June 2021
Copyright Holders:Copyright © 2021 Elsevier B.V.
First Published:First published in Journal of Petroleum Science and Engineering 206: 109126
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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