Advancing graph-based algorithms for predicting flow and transport in fractured rock

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

doi:10.1029/2017WR022368

Advancing graph-based algorithms for predicting flow and transport in fractured rock

Journal Article · Tue Jul 31 00:00:00 EDT 2018 · Water Resources Research

DOI:https://doi.org/10.1029/2017WR022368· OSTI ID:1463552

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

Discrete fracture network (DFN) models are a powerful alternative to continuum models for subsurface flow and transport simulations because they explicitly include fracture geometry and network topology, thereby allowing for better characterization of the latter's influence on flow and transport through fractured media. Recent advances in high performance computing have opened the door for flow and transport simulations in large explicit three-dimensional DFN, but this increase in model fidelity and system size comes at a huge computational cost because of the large number of mesh elements required to represent thousands of fractures (with sizes that can range several orders of magnitude, from mm to km). In most subsurface applications, fracture characteristics are only known statistically and numerous realizations are needed to bound uncertainty in flow and transport in the system, thereby exacerbating the computational burden. blackGraphs provide a simple and elegant way to characterize, query, and interrogate fracture network connectivity. We propose a DFN model reduction framework where various graph-representations are used in conjunction with high-fidelity DFN simulations to increase computational efficiency while retaining accuracy of key quantities of interest. The appropriate choice of a graph-representation, namely, which attributes of the DFN are to be represented as nodes and which ones as edges connecting those nodes, depends on the relevant scientific questions. We demonstrate that the proposed DFN model reduction framework provides an efficient means for DFN modeling through both system reduction of the DFN using graph-based properties and combining DFN and graph-based flow and transport simulations.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1463552

Report Number(s):: LA-UR--17-30945

Journal Information:: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 9 Vol. 54; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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