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Title: 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:
ORCiD logo [1];  [2];  [2];  [3]
  1. Southern Methodist Univ., Dallas, TX (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. 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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