Modeling dense nonaqueous phase liquid mass removal in nonuniform formations: Linking source-zone architecture and system response

  • Lemke L
  • Abriola L
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Abstract

Dense nonaqueous phase liquid (DNAPL) source zones comprise persistent
sources of groundwater contamination that are recalcitrant to complete
remediation using conventional ( e. g., pump and treat) or emerging
( e. g., surfactant flushing) technologies. Increased attention to
the assessment of the benefits of partial mass removal from such
contaminant source zones has intensified efforts to model multiphase
flow and transport behavior. This paper describes the simulated recovery
of a tetrachloroethene (PCE) spill in a statistically homogeneous
but nonuniform aquifer, incorporating nonuniformity in both nonaqueous
phase liquid saturation and pore velocities. We developed a ganglia-to-pool
metric to quantify DNAPL source-zone architecture, and explored the
correlation of this metric with dissolved mass flux behavior in response
to partial DNAPL mass removal. Dissolution of 20% - 70% of PCE
mass from models exhibiting low ganglia-to-pool ratios resulted in
a larger predicted reduction of dissolved contaminant mass flux than
models with high ganglia-to-pool ratios. Results of this study suggest
that DNAPL source-zone characterization at field sites with homogeneous,
nonuniform aquifers would benefit from inclusion of an estimate of
the overall ganglia-to-pool ratio. Simulations demonstrate that flux
reduction behavior depends on the source-zone architecture, which
is not readily predictable using a priori assumptions about the spatial
correlation of physical aquifer parameters. Model results further
suggest that stochastic investigations of DNAPL source remediation
at field sites should avoid reliance upon Leverett scaling of capillary
entry pressures to permeability fields, which can artificially narrow
the range of simulated behaviors.

Author-supplied keywords

  • Contaminant mass flux
  • DNAPL remediation
  • DNAPL source zone
  • Heterogeneity
  • Stochastic modeling

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Authors

  • Lawrence D. Lemke

  • Linda M. Abriola

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