dfnWorks is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers.

dfnWorks creates high-fidelity thee-dimensional networks 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 with 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.

dfnWorks has been designed to transform how flow and transport computations are performed in fractured rock—this software overcomes the limitations of continuum approaches used today to simulate flow and transport through fractured porous rock in the subsurface. Unlike continuum methods that use effective parameters to include the influence of the fractures on flow, dfnWorks uses geologic field investigations to create a network of fractures in which the geometry and properties of individual fractures are explicitly represented as intersecting planar polygons in three dimensions.

SimFrac is a Python-based library for constructing 3D sythenic rough fracture geometries. The software is designed to help researchers and practitioners investigate flow through fractures through direct numerical simulations of single/multi phase flow through fractures. One advantage of the Python implementation is that it allows for greater flexibility and customization compared to a GUI-based approach. With a Python-based interface, researchers can readily expand the development and test new fracture generation algorithms or modify existing methods to better match experimental data. pySimFrac offers a spectral-based and convolution-based generaion methods. pySimFrac also includes utilities for characterizing fracture properties such as the correlation length, moments, and probability density function of the fracture surfaces and aperture field. SimFrac is seamlessly integated with dfnWorks to create 3D DFNs with variable aperture fields.