Super-resolution imaging of multiphase fluid distributions in porous media using deep learning
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Super-resolution imaging techniques use deep learning to create large-scale, high-resolution images by combining a low-resolution image encompassing a large volume with high-resolution images on a smaller volume; however, applications to date have been limited to determining the pore structure only. We have successfully applied an enhanced deep super-resolution (EDSR) method to three-dimensional X-ray images of two fluid phases in the pore space of water-wet and mixed-wet Bentheimer sandstone, producing high-resolution results that capture both the pore space and two fluid phases within it, while expanding the field of view. We calculated and compared the geometrical and physical properties, including porosity, permeability, saturation, interfacial area, interfacial curvature, and contact angle derived from high-resolution, super-resolution, and low-resolution images. This comparison confirms that our super-resolution outcomes are consistent with the ground truth and far superior to low-resolution results.
Date Issued
2025-10-01
Date Acceptance
2025-07-28
Citation
Transport in Porous Media, 2025, 152 (10)
ISSN
0169-3913
Publisher
Springer
Journal / Book Title
Transport in Porous Media
Volume
152
Issue
10
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Subjects
BENCHMARKS
CURVATURE
Deep learning
Engineering
Engineering, Chemical
FLOW
Pore space and fluid phase analysis
PORE-SCALE
Science & Technology
Super-resolution imaging
Technology
Publication Status
Published
Article Number
85
Date Publish Online
2025-09-13