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Besson, Olivier

Nom
Besson, Olivier
Affiliation principale
Email
Olivier.Besson@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/453

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  • Publication
    Accès libre
    An Improved Parallel Multiple-Point Algorithm Using a List Approach
    (2011-4)
    Straubhaar, Julien 
    ;
    Renard, Philippe 
    ;
    Mariethoz, Grégoire 
    ;
    Froidevaux, Roland
    ;
    Besson, Olivier 
    Among the techniques used to simulate categorical variables, multiple-point statistics is becoming very popular because it allows the user to provide an explicit conceptual model via a training image. In classic implementations, the multiple-point statistics are inferred from the training image by storing all the observed patterns of a certain size in a tree structure. This type of algorithm has the advantage of being fast to apply, but it presents some critical limitations. In particular, a tree is extremely RAM demanding. For three-dimensional problems with numerous facies, large templates cannot be used. Complex structures are then difficult to simulate. In this paper, we propose to replace the tree by a list. This structure requires much less RAM. It has three main advantages. First, it allows for the use of larger templates. Second, the list structure being parsimonious, it can be extended to include additional information. Here, we show how this can be used to develop a new approach for dealing with non-stationary training images. Finally, an interesting aspect of the list is that it allows one to parallelize the part of the algorithm in which the conditional probability density function is computed. This is especially important for large problems that can be solved on clusters of PCs with distributed memory or on multicore machines with shared memory.
  • Publication
    Accès libre
    Optimisation issues in 3D multiple-point statistics simulation
    (: Julián M. Ortiz and Xavier Emery, Mining Engineering Department, University of Chile., 2008-12)
    Straubhaar, Julien 
    ;
    Walgenwitz, Alexandre
    ;
    Froidevaux, Roland
    ;
    Renard, Philippe 
    ;
    Besson, Olivier 
    Multiple-point statistics simulation has gained wide acceptance in recent years and is routinely used for simulating geological heterogeneity in hydrocarbon reservoirs and aquifers. In classical implementations, the multiple-point statistics inferred from the reference training image are stored in a dynamic data structure called search tree. The size of this search tree depends on the search template used to scan the training image and the number of facies to be simulated. In 3D applications this size can become prohibitive. One promissing avenue for drastically reducing the RAM requirements consists of using dynamically allocated lists instead of search trees to store and retrieve the multiple–point statistics. Each element of this list contains the identification of the data event together with occurence counters for each facies. First results show that implementing this list based approach results in reductions of RAM requirement by a factor 10 and more. The paper discusses in detail this novel list based approach, presents RAM and CPU performance comparisons with the (classical) tree based approach.