A ML framework to predict permeability of highly porous media based on PSD
Authors
Yang, H
Ke, Y
Zhang, D
Publication Date
2021-03-01Journal Title
IOP Conference Series: Earth and Environmental Science
ISSN
1755-1307
Publisher
IOP Publishing
Volume
680
Issue
1
Language
en
Type
Article
This Version
VoR
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Yang, H., Ke, Y., & Zhang, D. (2021). A ML framework to predict permeability of highly porous media based on PSD. IOP Conference Series: Earth and Environmental Science, 680 (1) https://doi.org/10.1088/1755-1315/680/1/012080
Abstract
<jats:title>Abstract</jats:title>
<jats:p>Using machine learning (ML) method to predict permeability of porous media has shown great potential in recent years. A current problem is the lack of effective models to account for highly porous media with dilated pores. This study includes (1) generation of media (porosity = 0.8) via a Boolean process, (2) the pore size distribution (PSD) control by using different groups of homogeneous packed spherical particles (3) PSD data obtainment using the spherical contact distribution model (4) computation of the permeability via LBM simulations, (4) training of artificial neuron network (ANN) and (5) analysis of the model. It is found that the PSD could outperform the previous geometry descriptors as an input of ML framework to deal with highly porous structures with different fractions of dilated pores, however there is still room for precision enhancement.</jats:p>
Keywords
Paper
Identifiers
ees_680_1_012080, e6801080
External DOI: https://doi.org/10.1088/1755-1315/680/1/012080
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333093
Rights
Licence:
http://creativecommons.org/licenses/by/3.0/
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