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Title: Particle tracking approach for transport in three-dimensional discrete fracture networks: Particle tracking in 3-D DFNs

Journal Article · · Computational Geosciences

The discrete fracture network (DFN) model is a method to mimic discrete pathways for fluid flow through a fractured low-permeable rock mass, and may be combined with particle tracking simulations to address solute transport. However, experience has shown that it is challenging to obtain accurate transport results in three-dimensional DFNs because of the high computational burden and difficulty in constructing a high-quality unstructured computational mesh on simulated fractures. We present a new particle tracking capability, which is adapted to control volume (Voronoi polygons) flow solutions on unstructured grids (Delaunay triangulations) on three-dimensional DFNs. The locally mass-conserving finite-volume approach eliminates mass balance related problems during particle tracking. The scalar fluxes calculated for each control volume face by the flow solver are used to reconstruct a Darcy velocity at each control volume centroid. The groundwater velocities can then be continuously interpolated to any point in the domain of interest. The control volumes at fracture intersections are split into four pieces and the velocity is reconstructed independently on each piece, which results in multiple groundwater velocities at the intersection, one for each fracture on each side of the intersection line. This technique enables detailed particle transport representation through a complex DFN structure. Verified for small DFNs, the new simulation capability enables numerical experiments on advective transport in large DFNs to be performed. Furthermore, we demonstrate this particle transport approach on a DFN model using parameters similar to those of crystalline rock at a proposed geologic repository for spent nuclear fuel in Forsmark, Sweden.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396; AC05-00OR22725
OSTI ID:
1234426
Alternate ID(s):
OSTI ID: 1265666
Report Number(s):
LA-UR-14-28539; PII: 9525
Journal Information:
Computational Geosciences, Vol. 19, Issue 5; ISSN 1420-0597
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 65 works
Citation information provided by
Web of Science

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Cited By (12)

Review: Mathematical expressions for estimating equivalent permeability of rock fracture networks journal June 2016
Machine learning for graph-based representations of three-dimensional discrete fracture networks journal January 2018
Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics journal September 2018
Upscaled discrete fracture matrix model (UDFM): an octree-refined continuum representation of fractured porous media journal December 2019
Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways journal March 2019
An Efficient ELLAM Implementation for Modeling Solute Transport in Fractured Porous Media journal January 2018
Advanced computation of steady-state fluid flow in Discrete Fracture-Matrix models: FEM–BEM and VEM–VEM fracture-block coupling journal July 2018
Groundwater in fractured bedrock environments: managing catchment and subsurface resources – an introduction journal January 2019
Particle-Based Workflow for Modeling Uncertainty of Reactive Transport in 3D Discrete Fracture Networks journal November 2019
Stochastic modeling of flow and conservative transport in three-dimensional discrete fracture networks journal January 2019
Stochastic modeling of flow and conservative transport in three-dimensional discrete fracture networks posted_content August 2018
Machine learning for graph-based representations of three-dimensional discrete fracture networks text January 2017