PhD 4 : Flow and transport in fracture networks: reducing uncertainty of DFN models by conditioning to geology and geophysical data

Objectives: Develop and test a general framework to condition discrete fracture network (DFN) models to geological mapping and geophysical data in order to reduce the uncertainty of fractured rock properties and flow patterns.

Expected Results: Predicting geological and hydrological structures and their impact on flow and mechanical processes is a major challenge for a large number of hydrological and geotechnical applications in fractured hard rock systems. This is mostly achieved by deriving DFN models from field and core mapping, and pumping tests. Conditioning the DFN models with geophysical imaging techniques is still largely unexplored although very promising to overcome the lack of actual 3D fracture data. The ESR aims at developing a methodology for geophysical conditioning of DFN models, by profiting from the complementary expertise of the Rennes group in DFN modelling, and of the Lausanne group in GPR imaging in hard rock environments. The DFN conditioning method will be applied to field-scale tracer experiments conducted at the Äspö Hard Rock Laboratory. The ESR will benefit from the important fracture database that has been acquired for twenty years at Äspö by SKB. Flow measurements with fibre-optic DTS in collaboration with ESR 6 will be used to validate hydrological prediction of the newly developed DFN. Data produced in this project on fracture, flow, transport and geophysics will constitute a unique dataset, made available to the community to understand flow and transport patterns in fractured media. The methodology will offer a new approach for SKB to assess confinement properties of the bedrock barrier around canisters for nuclear waste disposal.

Supervisors: ITASCA / Co-Supervisors : CNRS Rennes, UNIL Lausanne, SKB Sweden
Contact: Caroline Darcel c.darcel(at), Philippe Davy, philippe.davy(at)

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