Conditioning DFN models by GPR data
As part of the EU Horizon 2020 ENIGMA ITN project, ICSAS, the CNRS, and SKB proposed a PhD project entitled “Flow and transport in fracture networks: reducing uncertainty of DFN models by conditioning to geology and geophysical data”, to develop and test a methodology for rock characterization that would help in the decision-making process for an adequate location of the nuclear waste canister burying. The main objectives are to assess the contribution of the Ground Penetrating Radar (GPR) method for:
- 3-D fracture mapping and flow path imaging in a very low-permeability deep environment
- Reducing Discrete Fracture Network (DFN) models uncertainty for flow and transport predictions (scale of ≈ 1 – 10 m)
GPR data were acquired in a tunnel at the SKB Äspö Hard Rock Laboratory in Sweden, at 410 m depth:
- Static surface-based GPR to map individual fractures in subsurface up to 10 m depth
- Time-lapse surface-based GPR and tracer tests to map the connected network between two boreholes
A DFN model of the tunnel area was also developed, conditioned by tunnel and borehole trace data, and the acquired GPR data.
In very low permeable crystalline rock, GPR is able to:
- Detect ≈80% of open and sub-horizontal fractures with sub-mm apertures (correlation between GPR and borehole data)
- Image the fracture connectivity by detecting fracture aperture variation induced by high-pressure injection (combination with a hydromechanical study)
Finally, conditioning of stochastic DFN with deterministic GPR information leads to an improvement of the predictive capability of model (up to 40% more realizations) at least in terms of connectivity.
- Molron, J., Linde, N., Baron, L., Selroos, J. O., Darcel, C., & Davy, P. (2020). Which fractures are imaged with Ground Penetrating Radar? Results from an experiment in the Äspö Hardrock Laboratory, Sweden. Engineering Geology, 273, doi: https://doi.org/10.1016/j.enggeo.2020.105674
- Time-lapse GPR experiment and tracer test on video at: