Understanding Heterogeneous and Anisotropic Porous Media Based on Geometric Properties Derived From Three‐Dimensional Images

Abstract Natural porous media are generally heterogeneous and anisotropic. The structure of porous media plays a vital role and is often the source of their heterogeneity and anisotropy. In physical processes such as fluid flow in porous media, a small number of dominant features, here referred to as wide channels, are responsible for the majority of the flow. The thickness and orientation of these channels often determine the permeability characteristics of the media. Typically, identifying such dominant features requires extensive and costly simulations. Here, we propose a prompt approach based on geometric properties derived from three‐dimensional (3D) images. The size or radius of the dominant features is obtained via distance maps, and their orientations are determined using Principal Component Analysis. Subsequently, we visualize these dominant features with color and color brightness according to their orientation and size, together with their location and distribution in 3D space. The combined visualization of anisotropy (orientation) and heterogeneity (size) in a single plot provides a straightforward way to enhance our understanding of pore structure characteristics. Besides, we propose a refined stereographic projection method to statistically illustrate both heterogeneity and anisotropy. Based on these insights, we further present a potential approach to reduce model size in numerical simulations, significantly reducing computational costs while preserving essential characteristics.

Authors

Institutions

Publication Details

Journal
Water Resources Research
Published
2024-11-29
DOI
https://doi.org/10.1029/2024wr037205
Citations
1
Primary Topic
Enhanced Oil Recovery Techniques
Type
article
Field-Weighted Citation Impact
0.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding Heterogeneous and Anisotropic Porous Media Based on Geometric Properties Derived From Three‐Dimensional Images

Chen Yu, Xiangbo Gao, Liang Lei, S. A. Galindo‐Torres et al.
1 citations
Water Resources Research
Enhanced Oil Recovery Techniques
0.34
article

Understanding Heterogeneous and Anisotropic Porous Media Based on Geometric Properties Derived From Three‐Dimensional Images

Chen Yu, Xiangbo Gao, Liang Lei, S. A. Galindo‐Torres, Tingchang Yin, Rongrong Tian, Jiazuo Zhou, Xingyu Zhang, Yanmei Tian
article en
1 citations

Abstract

Abstract Natural porous media are generally heterogeneous and anisotropic. The structure of porous media plays a vital role and is often the source of their heterogeneity and anisotropy. In physical processes such as fluid flow in porous media, a small number of dominant features, here referred to as wide channels, are responsible for the majority of the flow. The thickness and orientation of these channels often determine the permeability characteristics of the media. Typically, identifying such dominant features requires extensive and costly simulations. Here, we propose a prompt approach based on geometric properties derived from three‐dimensional (3D) images. The size or radius of the dominant features is obtained via distance maps, and their orientations are determined using Principal Component Analysis. Subsequently, we visualize these dominant features with color and color brightness according to their orientation and size, together with their location and distribution in 3D space. The combined visualization of anisotropy (orientation) and heterogeneity (size) in a single plot provides a straightforward way to enhance our understanding of pore structure characteristics. Besides, we propose a refined stereographic projection method to statistically illustrate both heterogeneity and anisotropy. Based on these insights, we further present a potential approach to reduce model size in numerical simulations, significantly reducing computational costs while preserving essential characteristics.

Water Resources ResearchVol. 60(12)
Westlake University (CN), Institute of Rock and Soil Mechanics (CN), University of Chinese Academy of Sciences (CN), Zhejiang University (CN)
Openalex Percentile: Top 38%
Enhanced Oil Recovery Techniques
0.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.