Characterization and quantification of aquifer heterogeneity using outcrop analogs at the Canadian Forces Base Borden, Ontario, Canada

Abstract

Accurate characterization of internal structures and geometries of aquifers is critical for evaluation of plume migration and dispersion of contaminants. For this reason, high-resolution transport study fi eld sites, such as the Waterloo Groundwater ResearchSite at the Canadian Forces Base Borden, Ontario, Canada, have been established. However, geological characterization at this site is based primarily on cores and shallow geophysical data. Outcrop analog studies offer detailed horizontal data that can help to build quantifi ed models of aquifer heterogeneity. We used a combination of groundbased light detection and ranging (LiDAR) and high-resolution photographs at a sand quarry cut into Borden aquifer sediment to evaluate and quantify the distribution of lithofacies and hydrofacies that control fl ow properties in the Borden aquifer. We exposed sixteen ~20 m × 1.5 m outcrops, LiDAR surveyed, photographed, and fi eld mapped lithofacies at each exposure, and segmented the lithofacies from photographs to produce maps of facies distributions. We grouped different lithofacies into hydrofacies based on overall facies geometry and estimated hydraulic properties. We used LiDAR scans and the segmented facies maps to produce a digital outcrop model (DOM) of the site. The resultant DOM combines lithofacies and hydrofacies derived from fi eld observations with high-resolution (5 mm) LiDAR data to reconstruct hydrofacies distributions in a three-dimensional (3-D) geomodel. The DOM offers a relatively complete horizontal correlation structure that was used in transition probability geostatistical modeling to create realizations of hydrofacies distributions in the aquifer at the study site.

Contaminant tailing in highly heterogeneous porous formations: Sensitivity on model selection and material properties

“…The size, shape, and the number of inclusions were selected based on the directional correlation lengths and the volume fraction of each material. The values for K and K d are based on published values for the material for the Borden Aquifer (Allen-King et al, 2015).…”

“…These impacts were investigated using numerical simulations that were based on two models of subsurface heterogeneity which differ in structure and in mathematical framework that was used to simulate flow and transport and to quantify contaminant tailing. The models are based on alternative geostatistical descriptions of the hydraulic conductivity K and perchloroethene (PCE) sorption distribution coefficient K d of highly heterogeneous sediment in an exposure of the Borden aquifer located approximately 2km north-west of the StanfordWaterloo experimental site at Canadian Forces Base Borden, Ontario, Canada (Weissmann et al 2015). The main goal of the present study is to quantify the impact of model selection on tailing and to identify the impacts of different geologic materials and their geometric and material properties on tailing.…”

“…Recent advances in site characterization techniques have enabled very high resolution descriptions of aquifer heterogeneity that capture mm-scale variability in aquifer materials (e.g., Weissmann et al 2015). However, incorporating such details into numerical models of flow and contaminant transport can be computationally prohibitive if the goal is to capture tailing and back diffusion processes.…”

“…This 3D dataset was created using high-resolution photography and ground-based LIDAR techniques that were applied to several vertical outcrop panels exposed at the site (Weissmann et al, 2015). The K(x) and K d (x) models used here were built using the geostatistical description of individual materials found in the formation (with unique K and K d values (Allen-King et al, 2015)), instead of the overall K(x) measures (mean, variance, overall correlation length) and assumptions of K(x) -K d (x) correlations. The description includes materials' volume fractions and correlation lengths.…”

“…This model is used to investigate transport and tailing at the spatial scale characterized by Weissmann et al (2015) (40m horizontal and 10m vertical) and larger scales, and to investigate tailing sensitivity on material and geometric properties of different geologic materials. In addition to this inclusions model, another model was created based on the transitional probability geostatistics (Carle andFogg, 1996, 1997) using TPROGS code (Carle, 1999) for comparison with the inclusions model.…”