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

Abstract

Highlights: Contaminant transport was modeled using two heterogeneity models. Models were based on field data collected from a highly heterogeneous outcrop analog exposure. Transport results characterized the silty clay as the most influential material on tailing. Contaminant tailing was dominated by vertical diffusion in/out of low conductive units. Abstract:Coupled impacts of slow advection, diffusion and sorption were investigated using two heterogeneity models that differ in structure and in the mathematical framework that was used to simulate flow and transport and to quantify contaminant tailing. Both models were built using data from a highly heterogeneous exposure of the Borden Aquifer at a site located 2km northwest of the Stanford-Waterloo experimental site at Canadian Forces Base Borden, Ontario, Canada. The inclusions-based model used a simplified representation of the different materials found at the site, while the second model was based on transitional probability geostatistics of the formation. These two models were used to investigate sensitivity of contaminant tailing on model selection and on geometric and material properties. While simulations were based on data collected at Borden, models were exercised beyond the geometric and material properties that characterize the site. Various realizations have identified very low conductive silty clay, found at volume fraction of 23.4%, as the material with dominant influence on tailing, and vertical diffusion in and out of low conductive units, affected by sorption, as the dominant transport mechanism causing tailing. The two models yielded almost identical transport results when vertical correlation lengths of silty clay were matched. Several practical implications relevant for characterization of low conductive units were identified and briefly discussed.3

A travel time‐based approach to model kinetic sorption in highly heterogeneous porous media via reactive hydrofacies

“…This is so because late‐time behavior is controlled by kinetic sorption rather than by variations in hydraulic conductivity; it also confirms similar findings of Maghrebi et al . [].…”

“…Meanwhile, it is generally accepted that solute transport is not a stochastically random process but rather dominated by structured heterogeneity, i.e., geological facies with contrasting hydraulic properties, and the spatial organization and connectivity of high‐conductivity zones [e.g., Fogg et al ., ; Zheng and Gorelick , ; Zinn and Harvey , ; Knudby and Carrera , ]; see Molz [] for a chronology of discussions and gained scientific knowledge in the past 35 years. Therefore, bi‐ and multimodal models mimicking the distribution of facies‐like discrete zones have increasingly been investigated and applied [e.g., Rubin , ; Dagan and Lessoff , ; Lessoff and Dagan , ; Dagan and Fiori , ; Fiori and Dagan , ; Ritzi et al ., ; de Marsily et al ., ; Feyen and Caers , ; Werth et al ., ; Lee et al ., ; Massabo et al ., ; Coppola et al ., ; Ramanathan et al ., ; Fiori , ; Allen‐King et al ., ; Maghrebi et al ., ; Pryshlak et al ., ; Soltanian et al ., , ; Zarlenga and Fiori , ; Zhang and Zhang , ]. In this paper, we make use of the work of Bayer et al .…”

Modelling 3D permeability distribution in alluvial fans using facies architecture and geophysical acquisitions

“…where ρ is the saturated formation resistivity ( m), α the pore-geometry coefficient associated with the medium (0.5 ≤ α ≤ 2.5) and m the cementation factor (1.3 ≤ m ≤ 2.5) (Massoud et al, 2010;Khalil and Santos, 2013). α is set as 1.…”

Impact of geostatistical reconstruction approaches on model calibration for flow in highly heterogeneous aquifers

“…Increasing efforts have been devoted to investigate the effects of various geostatistical approaches on connectivity metrics (Bakshevskaia and Pozdniakov 2016;Dell'Arciprete et al 2012Lee et al 2007;Mohammadi et al 2020;Sharifzadehlari et al 2018;Vassena et al 2010). Other studies addressed the impact different geostatistical methods on the accuracy of estimated facies distribution (He et al 2009, Kessler et al 2013Marini et al 2019;Park et al 2007), hydraulic head and flux fields (Lee et al 2007;Bianchi et al 2015), or spreading of dissolved chemicals migrating in aquifer systems (Maghrebi et al 2015;Siirila-Woodburn and Maxwell 2015).…”