A numerical three‐dimensional conditioned/unconditioned stochastic facies type model applied to a remediation well system

In this study a three-dimensional stochastic facies-based aquifer model was developed. The model can be used to numerically simulate flow and solute transport in heterogeneous groundwater aquifers. The stochastic generation process can be conditioned by using available facies information in one vertical plane or two orthogonal vertical ones. In this study the information was obtained from a facies interpretation of a vertical georadar profile in a natural gravel formation in Switzerland. In the domain outside the known profile, unconditioned lenses and layers were generated at random according to statistical information on coherent sedimentary structures based on observations in adjacent gravel pits [Jussel et al., 1994a]. The method was applied to a single extraction well designed to capture an initially block-shaped contaminant plume. A total of 80 conditioned and unconditioned synthetic aquifers was generated. The flow and transport simulations were performed using a finite element flow model and a random walk transport model. The results are presented as the ensemble of integral solute mass recovery curves of single realizations. One would expect conditioning to reduce the bandwidth of the recovery curves representing the uncertainty, but the results show that the bandwidth even increased. This effect was attributed to a discrepancy in the mean volumetric fraction of the different facies types in the conditioned and the unconditioned cases. Moreover, a simulation using a homogeneous model with constant equivalent flow and transport parameters overestimated the remediation efficiency. The influence of a linear, reversible equilibrium sorption on the remediation well efficiency was taken into account by an uncorrelated random field of the retardation factor based on values from the literature. However, the impact of the variability in hydraulic conductivity clearly exceeded the effect of the variability in the retardation factor.

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