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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-96
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-96
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 May 2020

14 May 2020

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A revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

3D Multiple-point Statistics Simulations of the Roussillon Continental Pliocene Aquifer using DeeSse

Valentin Dall'Alba1, Philippe Renard1, Julien Straubhaar1, Benoit Issautier2, Cédric Duvail3, and Yvan Caballero2 Valentin Dall'Alba et al.
  • 1Center of Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Rue Emilie Argand 11, CH-2000, Neuchâtel, Switzerland
  • 2French Geological Survey (BRGM)
  • 3GEOTER SAS, FUGRO Group

Abstract. This study presents a novel workflow to model the internal heterogeneity of complex aquifers using the multiple-point statistics algorithm DeeSse. We illustrate the applicability of this workflow on the Roussillon's aquifer in the region of Perpignan (southern France). This work is part of a project aiming at assessing the groundwater dynamics of this Mediterranean aquifer in the context of a growing population, climate change, and increasing pressure on the freshwater resources. We focus here on the geological heterogeneity of the Continental Pliocene layer because it is expected to influence possible saltwater intrusion process and its corresponding uncertainty quantification. The main aim of the paper is therefore to describe the procedure that is used to model the aquifer heterogeneity with a relatively small number of direct geological observations and a well defined geological concept. When few direct observations are available, the traditional geostatistical approaches cannot be applied easily because variogram inference is difficult. On the opposite, multiple-point statistics simulations can rely on a conceptual geological model. Here, the conceptual model consists not only of a training image displaying the spatial organization of the main sedimentological elements in space, but also in a set of additional information such as general trends and paleo orientations of the sedimentological features. The direct sampling algorithm DeeSse can be used in this context to model the expected heterogeneity. The workflow involves creating 2D non-stationary training images (TI) coupled during simulation with auxiliary information and controlled by hard conditioning data obtained from interpreted electrofacies. To control the non-stationarity, a 3D trend map is obtained by solving numerically the diffusivity equation as a proxy to describe the spatial evolution of the sedimentary patterns, from the source of the sediments to the outlet of the system. A 3D continuous rotation map is estimated from paleo orientations of the fluvial system. Both trend and orientation maps are derived from geological insights gathered from outcrops and general knowledge of processes occurring in these types of sedimentary environments. Finally, the 3D model is obtained by stacking 2D simulations following the paleo-topography of the aquifer. The vertical facies transition between two 2D simulations is controlled by both the hard conditioning data set and by simulating conditional data points from one simulation to another. This process allows to bypass the creation of a 3D training image while preserving the vertical continuity of the sedimentary objects.

Valentin Dall'Alba et al.

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Valentin Dall'Alba et al.

Valentin Dall'Alba et al.

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Latest update: 19 Sep 2020
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Short summary
Due to climate and population evolution, an increased pressure is put on the groundwater resource, which calls for better understandings and models. In this paper, we describe a novel workflow to model the geological heterogeneity of coastal aquifers and apply it to the Roussillon plain (Southern France). The main strength of the workflow is its capability to model aquifer heterogeneity when only sparse data are available while honoring the local geological trends and quantifying uncertainty.
Due to climate and population evolution, an increased pressure is put on the groundwater...
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