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Renard, Philippe

Nom
Renard, Philippe
Affiliation principale
Site web
Fonction
Directeur de Recherche
Email
Philippe.Renard@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/150
_
0000-0003-4504-435X

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  • Publication
    Accès libre
    Impact of a stochastic sequential initiation of fractures on the spatial correlations and connectivity of discrete fracture networks
    (2016)
    Bonneau, F
    ;
    Caumon, G
    ;
    Renard, Philippe 
    Stochastic discrete fracture networks (DFNs) are classically simulated using stochastic point processes which neglect mechanical interactions between fractures and yield a low spatial correlation in a network. We propose a sequential parent-daughter Poisson point process that organizes fracture objects according to mechanical interactions while honoring statistical characterization data. The hierarchical organization of the resulting DFNs has been investigated in 3-D by computing their correlation dimension. Sensitivity analysis on the input simulation parameters shows that various degrees of spatial correlation emerge from this process. A large number of realizations have been performed in order to statistically validate the method. The connectivity of these correlated fracture networks has been investigated at several scales and compared to those described in the literature. Our study quantitatively confirms that spatial correlations can affect the percolation threshold and the connectivity at a particular scale.
  • Publication
    Accès libre
    Issues in characterizing heterogeneity and connectivity in non-multiGaussian media
    (2008)
    Kerrou, Jaouhar 
    ;
    Renard, Philippe 
    ;
    Hendricks Franssen, Harrie-Jan
    ;
    Lunatic, Ivan
    The performances of kriging, stochastic simulations and sequential self-calibration inversion are assessed when characterizing a non-multiGaussian synthetic 2D braided channel aquifer. The comparison is based on a series of criteria such as the reproduction of the original reference transmissivity or head fields, but also in terms of accuracy of flow and transport (capture zone) forecasts when the flow conditions are modified. We observe that the errors remain large even for a dense data network. In addition some unexpected behaviours are observed when large transmissivity datasets are used. In particular, we observe an increase of the bias with the number of transmissivity data and an increasing uncertainty with the number of head data. This is interpreted as a consequence of the use of an inadequate multiGaussian stochastic model that is not able to reproduce the connectivity of the original field.The performances of kriging, stochastic simulations and sequential self-calibration inversion are assessed when characterizing a non-multiGaussian synthetic 2D braided channel aquifer. The comparison is based on a series of criteria such as the reproduction of the original reference transmissivity or head fields, but also in terms of accuracy of flow and transport (capture zone) forecasts when the flow conditions are modified. We observe that the errors remain large even for a dense data network. In addition some unexpected behaviours are observed when large transmissivity datasets are used. In particular, we observe an increase of the bias with the number of transmissivity data and an increasing uncertainty with the number of head data. This is interpreted as a consequence of the use of an inadequate multiGaussian stochastic model that is not able to reproduce the connectivity of the original field.