Fracture Analysis and Flow Simulations in the Roselend Fractured Granite
Part of the
Quantitative Geology and Geostatistics
book series (QGAG, volume 15)
Understanding of flow and transport in fractured media requires a good knowledge of fractures and fracture networks, which are privileged pathways for water and solutes. The Roselend underground laboratory (French Alps) gives the opportunity to fully investigate flow and solute transport through such a medium. Fracture traces and water fluxes have been determined along the Roselend tunnel.
The major objectives of this work are to derive a three dimensional fracture network consistent with the observations to calculate its percolation properties, and the macroscopic permeability of the medium.
In the tunnel, fractures can be classified into two families of large and small fractures. While large fractures intersect entirely the tunnel, small fractures partially intersect it. Variograms of both trace length and fracture orientation do not show any significant correlation with distance along the tunnel axis or with distance between fractures.
A stereological analysis of the trace length probability densities of the small fractures provides the fracture diameter probability density distribution which is best described by a power law. The large fractures are assumed monodisperse, with a radius equal to 5 m. Numerical simulations show that the networks obtained by combining large and small fractures do percolate while networks constituted of small fractures only do not percolate.
For three different sections along the gallery reflecting the major contrasts in dripping water fluxes, fracture networks are repeatedly generated according to the observed fracture densities. The permeability of these networks is systematically calculated. Results compare well to conductivity properties of similar media and show good consistency with observed water fluxes.
KeywordsHydraulic Conductivity Flow Simulation Fracture Network Fracture Medium Fracture Permeability
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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