Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media
Abstract
Here, we statistically infer fluid flow and transport properties of porous materials based on their geometry and connectivity, without the need for detailed We summarize structure by persistent homology and then determines the similarity of structures using image analysis and statistics. Longer term, this may enable quick and automated categorization of rocks into known archetypes. We first compute persistent homology of binarized 3D images of material subvolume samples. The persistence parameter is the signed Euclidean distance from inferred material interfaces, which captures the distribution of sizes of pores and grains. Each persistence diagram is converted into an image vector. Here, we infer structural similarity by calculating image similarity. For each image vector, we compute principal components to extract features. We fit statistical models to features estimates material permeability, tortuosity, and anisotropy. We develop a Structural SIMilarity index to determine statistical representative elementary volumes.
- Authors:
-
[1];
- Southern Methodist Univ., Dallas, TX (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Carl Zeiss X‐ray Microscopy Inc., Dublin ,CA (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
- OSTI Identifier:
- 1667397
- Report Number(s):
- SAND-2020-8596J
Journal ID: ISSN 0043-1397; 690046
- Grant/Contract Number:
- AC04-94AL85000; SC0014664; SC0006883
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 55; Journal Issue: 11; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- fluid flow; persistent homology; REV; statistical inference; LASSO; Principal Component Analysis
Citation Formats
Moon, Chul, Mitchell, Scott A., Heath, Jason E., and Andrew, Matthew. Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media. United States: N. p., 2019.
Web. doi:10.1029/2019wr025171.
Moon, Chul, Mitchell, Scott A., Heath, Jason E., & Andrew, Matthew. Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media. United States. doi:10.1029/2019wr025171.
Moon, Chul, Mitchell, Scott A., Heath, Jason E., and Andrew, Matthew. Sun .
"Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media". United States. doi:10.1029/2019wr025171. https://www.osti.gov/servlets/purl/1667397.
@article{osti_1667397,
title = {Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media},
author = {Moon, Chul and Mitchell, Scott A. and Heath, Jason E. and Andrew, Matthew},
abstractNote = {Here, we statistically infer fluid flow and transport properties of porous materials based on their geometry and connectivity, without the need for detailed We summarize structure by persistent homology and then determines the similarity of structures using image analysis and statistics. Longer term, this may enable quick and automated categorization of rocks into known archetypes. We first compute persistent homology of binarized 3D images of material subvolume samples. The persistence parameter is the signed Euclidean distance from inferred material interfaces, which captures the distribution of sizes of pores and grains. Each persistence diagram is converted into an image vector. Here, we infer structural similarity by calculating image similarity. For each image vector, we compute principal components to extract features. We fit statistical models to features estimates material permeability, tortuosity, and anisotropy. We develop a Structural SIMilarity index to determine statistical representative elementary volumes.},
doi = {10.1029/2019wr025171},
journal = {Water Resources Research},
number = 11,
volume = 55,
place = {United States},
year = {2019},
month = {10}
}
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