Integration of near-surface geophysical, geological and hydrogeological data with multiple-point geostatistics in alluvial aquifers

This work takes place in the context of the following PhD research topic. Alluvial plains constitute essential geological bodies for environmental studies such contaminated sites remediation, low-enthalpy geothermal energy or groundwater resources. The general objective of this work is to contribute to a better quantification of the heterogeneity of the parameters governing flow and transport processes by combining near-surface geophysics and geostatistics. Two-points geostatistical approaches (variogram based) have been developed to quantify the heterogeneity of one geological formation but fail to reproduce the heterogeneity of fluvial deposits with multiple facies. Multiple-points geostatistics introduced the training image concept to replace the variogram within an extended sequential simulation framework. The role of the training image is to depict the conceptual geological patterns. Previous studies have demonstrated the need for more conditioning data to generate an efficient training image. The use of geophysics in this context has been studied in the petroleum research with wave-based methods (seismic reflection data). However, little research has been done to assess the use of near-surface geophysical measurements, relying on potential and wave methods, to condition multiple-point geostatistics for environmental studies. This research project will focus on three specific objectives. The first will consist in computing conceptual training images from the combination of near-surface geophysics images, geological information and borehole data. The second objective will use these images to build models of the subsurface properties with multiple-point geostatistics conditioned to site-specific near-surface geophysical data and borehole information. Finally, groundwater model parameter estimation will be performed using a geologically consistent perturbation approach based on tracer experiments, using well sampling and time-lapse geophysical data. The approach will be demonstrated on both synthetic benchmarks and real field sites. This presentation shows preliminary research results on the use of combined geophysical techniques to improve geophysical models, the building of geological scenarios (training images) in the Meuse alluvial aquifer and the use of time-lapse ERT data to improve our understanding of hydrogeological reservoirs.