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

Abstract

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.

Influence of fluvial crevasse-splay deposits on sandbody connectivity: Lessons from geological analogues and stochastic modelling

“…Crevasse-splay deposits are common components of subsurface fluvial successions, for which staticconnectivity predictions are needed in a variety of applications, including hydrocarbon production, exploitation of geothermal heat, underground carbon capture and storage, prediction of contaminant dispersion, and aquifer use and remediation (e.g., Hardage et al 1996;Ambrose et al 2008;Burns et al 2010a,b;Mastrocicco et al 2011;Hansen et al 2013;Lew et al 2016;El-Mowafy & Marfurt 2016;Willems et al 2017;Dall'Alba et al 2020).…”

“…Crevasse splays are conspicuous features of fluvial landscapes and of the sedimentary record of floodbasins. In subsurface fluvial successions, crevasse-splay deposits typically form relatively porous and permeable sandbodies, which can act as productive units in hydrocarbon reservoirs (e.g., Jirik 1990;Chapin & Mayer 1991, Ramón & Cross 1997Stuart et al, 2014;Heldreich et al 2017;Ba et al 2019), and as significant components of alluvial aquifers (e.g., Bersezio et al 2004;Grasby et al 2008;Maliva 2016;Dall'Alba et al 2020).…”

A Framework for the Cross‐Validation of Categorical Geostatistical Simulations

“…It is a simplified 2‐D version of the model build by Dall'Alba et al. (2020). The area is modeled considering four geological facies: river bed, crevasse splays, flood plains, and alluvial fans.…”

Extended GOSIM: MPS‐Driven Simulation of 3D Geological Structure Using 2D Cross‐Sections

“…Compared to the variogram‐based method, also known as two‐point geostatistics (TPS), multiple‐point statistics (MPS) (Guardiano & Srivastava, 1993; Journel & Zhang, 2006; Strebelle, 2002) is better at capturing realistic complex spatial characteristics and incorporating geological knowledge, and thus, MPS has attracted considerable attention in the field of geostatistics over the past two decades (Comunian et al., 2012; Bruna et al., 2019; Dall'Alba et al., 2020; Straubhaar & Renard, 2021; Hou et al., 2021). Recently, some studies have applied MPS algorithms to simulate 3D geological structures (Chen et al., 2018; Jones et al., 2013; Li et al., 2020; Mariethoz & Caers, 2015; Silva & Deutsch, 2014; Zhang, 2015).…”