Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin; Srinivasan, Gowri; Viswanathan, Hari S.

doi:10.1103/PhysRevE.96.013304

Title: Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

Journal Article · Mon Jul 10 00:00:00 EDT 2017 · Physical Review E

DOI:https://doi.org/10.1103/PhysRevE.96.013304· OSTI ID:1374351

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396; 20150763PRD4; 20170103DR

OSTI ID:: 1374351

Alternate ID(s):: OSTI ID: 1369102

Report Number(s):: LA-UR-17-22022; PLEEE8

Journal Information:: Physical Review E, Vol. 96, Issue 1; ISSN 2470-0045

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 39 works

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Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways Srinivasan, Shriram; Karra, Satish; Hyman, Jeffrey Computational Geosciences, Vol. 23, Issue 3 https://doi.org/10.1007/s10596-019-9811-7	journal	March 2019
Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning Srinivasan, Gowri; Hyman, Jeffrey D.; Osthus, David A. Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-30117-1	journal	August 2018

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