Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics

Srinivasan, Shriram; Hyman, Jeffrey De'Haven; Karra, Satish; O'Malley, Daniel; Viswanathan, Hari S.; Srinivasan, Gowri

doi:10.1007/s10596-018-9770-4

Title: Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics

Journal Article · Mon Sep 17 00:00:00 EDT 2018 · Computational Geosciences

DOI:https://doi.org/10.1007/s10596-018-9770-4· OSTI ID:1477661

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

In this paper, we propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. Lastly, the method allows for the level of accuracy to be a user-controlled parameter.

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

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1477661

Report Number(s):: LA-UR-18-23489

Journal Information:: Computational Geosciences, Vol. 22, Issue 6; ISSN 1420-0597

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 16 works

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58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING
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