DFN.Lab: a software platform for Discrete Fracture Network modelling

Etienne Lavoine and Philippe Davy and Caroline Darcel and Romain Le Goc and Benoît Pinier and Simon Cléris and Diane Doolaeghe and Silvia De Simone. ( 2020 )
in: 2020 RING Meeting, ASGA

Abstract

Numerical modelling of fracture networks is an important step for the simulation of physical processes in fractured rock mass", for many industrial applications including safety assessment for long-term nuclear waste storage, geothermal applications, mining, etc. We present here a modular computational suite to deal with three-dimensional Discrete Fracture Networks (DFN) models from generation to simulation and analysis of connectivity, flow, mechanical and transport properties. Core modules are developed in C++ for high performances and a Python API is provided for easy use. The main originality of DFN.Lab is in its capacity to deal with multiscale heterogeneities at both the fracture and network scales. For each fracture, the hydraulic properties (transmissivity and aperture) can vary locally either deterministically, or statistically according to correlated random fields. A “sealing” algorithm was developed to model fracture patches that are clogged by mineralization. A graph algorithm was developed to derive the connectivity of open patches at the network scale. At the network scale, thanks to its computing capacities, DFN.Lab can deal with fracture sizes ranging over more than 3 orders of magnitude. Another originality of DFN.lab is in its DFN generation modules, where genetic generation models [Davy et al., 2013; Davy et al., 2010] have been developed as an alternative to the classical Poisson (e.g., randomly distributed) models bootstrapped on statistical descriptions of the fracture properties (size, position, orientation). For the same distribution of fracture sizes, orientations and transmissivities, genetic models behave significantly differently from Poisson's models [Maillot et al., 2016]. Example applications of this suite for nuclear waste repository," solute transport and heat transport will be presented.

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BibTeX Reference

@INPROCEEDINGS{ETIENNE_RM2020,
    author = { Lavoine, Etienne and Davy, Philippe and Darcel, Caroline and Le Goc, Romain and Pinier, Benoît and Cléris, Simon and Doolaeghe, Diane and De Simone, Silvia },
     title = { DFN.Lab: a software platform for Discrete Fracture Network modelling },
 booktitle = { 2020 RING Meeting },
      year = { 2020 },
 publisher = { ASGA },
  abstract = { Numerical modelling of fracture networks is an important step for the simulation of physical processes in fractured rock mass", for many industrial applications including safety assessment for long-term nuclear waste storage, geothermal applications, mining, etc. We present here a modular computational suite to deal with three-dimensional Discrete Fracture Networks (DFN) models from generation to simulation and analysis of connectivity, flow, mechanical and transport properties. Core modules are developed in C++ for high performances and a Python API is provided for easy use. The main originality of DFN.Lab is in its capacity to deal with multiscale heterogeneities at both the fracture and network scales. For each fracture, the hydraulic properties (transmissivity and aperture) can vary locally either deterministically, or statistically according to correlated random fields. A “sealing” algorithm was developed to model fracture patches that are clogged by mineralization. A graph algorithm was developed to derive the connectivity of open patches at the network scale. At the network scale, thanks to its computing capacities, DFN.Lab can deal with fracture sizes ranging over more than 3 orders of magnitude. Another originality of DFN.lab is in its DFN generation modules, where genetic generation models [Davy et al., 2013; Davy et al., 2010] have been developed as an alternative to the classical Poisson (e.g., randomly distributed) models bootstrapped on statistical descriptions of the fracture properties (size, position, orientation). For the same distribution of fracture sizes, orientations and transmissivities, genetic models behave significantly differently from Poisson's models [Maillot et al., 2016]. Example applications of this suite for nuclear waste repository," solute transport and heat transport will be presented. }
}