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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
    Direct simulation of non-additive properties on unstructured grids
    (2020-6)
    Mourlanette, Pauline 
    ;
    Biver, Pierre
    ;
    Renard, Philippe 
    ;
    Noetinger, Benoît
    ;
    Caumon, Guillaume
    ;
    Perrier, Yassine Alexandre
    Uncertainties related to permeability heterogeneity can be estimated using geostatistical simulation methods. Usually, these methods are applied on regular grids with cells of constant size, whereas unstructured grids are more flexible to honor geological structures and offer local refinements for fluid-flow simulations. However, cells of different sizes require to account for the support dependency of permeability statistics (support effect). This paper presents a novel workflow based on the power averaging technique. The averaging exponent đťś” is estimated using a response surface calibrated from numerical upscaling experiments. Using spectral turning bands, permeability is simulated on points in each unstructured cell, and later averaged with a local value of đťś” that depends on the cell size and shape. The method is illustrated on a synthetic case. The simulation of a tracer experiment is used to compare this novel geostatistical simulation method with a conventional approach based on a fine scale Cartesian grid. The results show the consistency of both the simulated permeability fields and the tracer breakthrough curves. The computational cost is much lower than the conventional approach based on a pressure-solver upscaling.
  • Publication
    Accès libre
    Constraining distance-based multipoint simulations to proportions and trends
    (2015-10)
    Mariethoz, GrĂ©goire 
    ;
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    ;
    Chugunova, Tatiana
    ;
    Biver, Pierre
    In the last years, the use of training images to represent spatial variability has emerged as a viable concept. Among the possible algorithms dealing with training images, those using distances between patterns have been successful for applications to subsurface modeling and earth surface observation. However, one limitation of these algorithms is that they do not provide a precise control on the local proportion of each category in the output simulations. We present a distance perturbation strategy that addresses this issue. During the simulation, the distance to a candidate value is penalized if it does not result in proportions that tend to a target given by the user. The method is illustrated on applications to remote sensing and pore-scale modeling. These examples show that the approach offers increased user control on the simulation by allowing to easily impose trends or proportions that differ from the proportions in the training image.