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Pirot, Guillaume

Nom
Pirot, Guillaume
Affiliation principale
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https://libra.unine.ch/handle/123456789/168

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  • Publication
    Accès libre
    Stochastic heterogeneity modeling of braided river aquifers: a methodology based on multiple point statistics and analog data
    (2015)
    Pirot, Guillaume 
    ;
    Renard, Philippe 
    ;
    Straubhaar, Julien 
    In this thesis a new pseudo-genetic method to model the heterogeneity of sandy gravel braided-river aquifers is proposed. It is tested and compared with other modeling approaches on a case study of contaminant transport. Indeed, in Switzerland or in mountainous regions, braided-river aquifers represent an important water resource that need to be preserved. In order to manage this resource, a good understanding of groundwater flow and transport in braided-river aquifers is necessary. As the complex heterogeneity of such sedimentary deposits strongly influences the groundwater flow and transport, groundwater behavior predictions need to rely on a wide spectrum of geological model realizations.
    To achieve realistic sedimentary deposits modeling of braided river aquifers, the proposed pseudo-genetic algorithm combines the use of analogue data with Multiple-Point Statistics and process-imitating methods. The integration of analogue data is a key feature to provide additional, complementary and necessary information in the modeling process. Assuredly, hydrogeologist are often subject to field data scarcity because of budget, time and field constraints. Multiple-Points Statistics recent algorithms, on one hand, allow to produce realistic stochastic realizations from training set with complex structures and at the same time allow to honor easily conditioning data. On the other hand, process-imitating methods allow to generate realistic patterns by mimicking physical processes.
    The proposed pseudo-genetic algorithm consists of two main steps. The first step is to build main geological units by stacking successive topography realizations one above the other. So, it mimics the successive large flood events contributing to the formation of the sedimentary deposits. The successive topographies are Multiple-Point Statistics realizations from a training set composed of Digital Elevation Models of an analogue braided-river at different time steps. Each topography is generated conditionally to the previous one. The second step is to generate fine scale heterogeneity within the main geological units. This is performed for each geological unit by iterative deformations of the unit bottom surface, imitating so the process of scour filling. With three main parameters, the aggradation rate, the number of successive iterations and the intensity of the deformations, the algorithm allows to produce a wide range of realistic cross-stratified sedimentary deposits.
    The method is tested in a contaminant transport example, using as reference Tritium tracer experiment concentration data from MADE site, Columbus, Mississippi, USA. In this test case, an assumption of data scarcity is made. Analogue data are integrated in the geological modeling process to determine the input parameters required -- characteristic dimensions and conductivity statistical properties -- for two variants of the proposed pseudo-genetic algorithm as well as for multi-gaussian simulation and object based methods. For each conceptual model, flow and transport simulations are run over 200 geological model realizations to cover a part of the uncertainty due to the input parameters. A comparison of the plume behavior prediction is performed between the different conceptual models.
    The results show that geological structures strongly influence the plume behavior, therefore the choice or the restriction to specific conceptual models will impact the prediction uncertainty. Though little information are available for the modeler, it is possible to achieve reasonable predictions by using analogue data. Of course, with limited information, it is impossible to make an accurate prediction to match the reference, and none of each conceptual model produces better predictions but all are useful to cover the uncertainty range. The results also underline the need to consider a wide exploration of the input parameters for the various conceptual models in order to recover the uncertainty.