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Title: Statistical Inference Over Persistent Homology Predicts Fluid Flow in Porous Media

Journal Article · · Water Resources Research
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)
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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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
Grant/Contract Number:
AC04-94AL85000; SC0014664; SC0006883
OSTI ID:
1667397
Report Number(s):
SAND-2020-8596J; 690046
Journal Information:
Water Resources Research, Vol. 55, Issue 11; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

References (24)

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Figures / Tables (12)

Table 1(p. 3)table Table 1
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Table 2(p. 5)table Table 2
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Table 3(p. 14)table Table 3

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Related Subjects

36 MATERIALS SCIENCE
fluid flow
persistent homology
REV
statistical inference
LASSO
Principal Component Analysis