Multiple-point geostatistics: a powerful tool to improve groundwater flow and transport predictions in multi-modal formations

In this paper we introduce the use of multiple-point geostatistics in hydrogeology. Multiple-point geostatistical algorithms allow the generation of stochastic models reproducing more realistically geological shapes and connectivities as compared to more traditional variogram-based techniques. Multiple-point geostatistics relies on the concept of 3-dimensional training images from which higher order statistics can be borrowed. The training images are merely conceptual, i.e., they need not be conditioned to any local data, and depict the expected pattern of geological heterogeneity. We use the snesim (single normal equation simulation, see Strebelle 2002) code, a pixel-based algorithm that employs a fast and robust sequential simulation methodology. As an example problem, we use a reference field representing a typical fluvial deposition that consists of an interconnected network of permeable sand channels embedded in less permeable fine-grained floodplain material. The width and orientation of the sand channels in the area are nonstationary. We show how strongly heterogeneous models can be built from simple training images, whose patterns are modified based on local anisotropy. We investigate the impact of the facies geometry and intrafacies heterogeneity on groundwater flow and transport predictions. Results indicate that it is of the utmost importance to properly represent and locate the sand channels, because transport is mainly occurring through these high-permeable zones. We show how different sources of information, such as local conditioning data, angle and channel width information, improve the representation of the spatial heterogeneity, hence flow and transport predictions. We also apply a sequential indicator approach and compare the results with those of the multiple-point approach. Results show that this two-point correlation based technique is not able to represent the high-permeable interconnected channel network. As such, these 2 Luc Feyen and Jef Caers methods tend to underestimate contaminant movement within the channel network.