dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

Abstract

11dfnWorks is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using dfnGen, which combines fram (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in an intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code pflotran. A Lagrangian approach to simulating transport through the DFN is adopted within dfnTrans to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.

Implementation of a Time-Domain Random-Walk Method into a Discrete Element Method to Simulate Nuclide Transport in Fractured Rock Masses

“…Therefore, water flow process in fractured rock masses should be studied firstly and foremost. Till now, five models have been found in literatures to simulate the water flow in fractured rock masses, that is, continuous model [3][4][5], dual-continuum model [6][7][8], equivalent continuum model [9][10][11], discrete fracture network (DFN) [12][13][14], and hybrid model [15][16][17]. In continuous model, rock masses are treated as continuum porous media with porosity and permeability and continuum-mechanics formulations can be used to describe flow and transport in media.…”

Stochastic modeling of flow and conservative transport in threedimensional discrete fracture networks

“…CC BY 4.0 License. numerous models that are based on the DFN approach to model the flow or transport in fractured formations (Cacas et al, 1990a;Cacas et al, 1990b;de Dreuzy et al, 2013;Hyman et al, 2015a;Liu and Neretnieks, 2006;Long et al, 1985;Pichot et al, 2012;Xu and Dowd, 2010). Advanced 3D DFN approaches typically include procedures of fracture generation, DFN meshing, and flow and transport or particle tracking (de Dreuzy et al, 2013;Erhel et al, 2009;Hyman et al, 2014;Pichot et al, 2012;Xu and Dowd, 2010;Zhang, 2015).…”

“…Advanced 3D DFN approaches typically include procedures of fracture generation, DFN meshing, and flow and transport or particle tracking (de Dreuzy et al, 2013;Erhel et al, 2009;Hyman et al, 2014;Pichot et al, 2012;Xu and Dowd, 2010;Zhang, 2015). Particle tracking algorithms are usually preferred to simulate DFN transport 5 and have recently been widely implemented to evaluate the time resistance of contaminants for fractured formations (Hyman et al, 2015a;Hyman et al, 2015b;Makedonska et al, 2015;Painter et al, 2008;Stalgorova and Babadagli, 2015;Wang and Cardenas, 2015). The objective of the Lagrangian approach is to avoid numerical difficulties in solving the advection dispersion equation (ADE) in complex DFN domains.…”

“…Such DFN transport models use the released particles to represent the contaminant with a specified mass or concentration. Many previous studies have discussed the issues in treating particle 10 movement in fracture networks (Hyman et al, 2015a;Hyman et al, 2015b;Johnson et al, 2006;Makedonska et al, 2015;Painter et al, 2008;Park et al, 2003;Wang and Cardenas, 2015;Zafarani and Detwiler, 2013).…”

“…Over the years, many studies have focused on developing flow and transport models and integrating DFN simulation workflows for 3D fracture networks (Hyman et al, 2014;Hyman et al, 2015a;Lee and Ni, 2015). Specifically, the DFN transport was mainly modeled based on Lagrangian approaches such as particle tracking and random walk algorithms (e.g., 15 Makedonska et al, 2015;Painter et al, 2008;Stalgorova and Babadagli, 2015;Wang and Cardenas, 2015) .…”