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An efficient model for computing plume concentration in aquifers
Author(s) -
Ghori S. G.,
Heller J. P.,
Singh A. K.
Publication year - 1992
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.3170030202
Subject(s) - aquifer , streamlines, streaklines, and pathlines , plume , permeability (electromagnetism) , groundwater flow , hydrogeology , groundwater , dispersion (optics) , mechanics , geology , soil science , geotechnical engineering , meteorology , physics , chemistry , biochemistry , optics , membrane
Abstract Accurate monitoring of contaminated groundwater is one of the most important issues in environmental technology. This paper, composed of two main sections, presents an efficient method of computing plume concentration. In the first section, reservoir rock properties such as permeability are characterized by geostatistical methods for generating spatial property distributions, such as the source point method (SPM), augmented with conditional simulation from the known sample data. In case the data are not available the permeability field is generated from the SPM alone. In the second section of the paper, the generated random field along with source/sink flow data is used as input in the pressure equation to solve for the Darcy velocity. The Darcy velocity is then used in the convectivedispersion (C‐D) equation to solve for the flow of contaminant in the aquifer. A newly developed numerical method of solving the C‐D equation is used to follow the movements of the front. The numerical scheme recognizes both convection and dispersion. The physical dispersion includes both longitudinal and transverse dispersion along with the molecular diffusion. The new method is numerically stable and avoids any numerical dispersion. The fronts are displaced according to the direction and magnitude of the concentration gradient, rather than merely from the streamlines. There are two main advantages of this model. Firstly, the tracking of fronts gives a better representation of the contaminant movement in the heterogeneous reservoir. Secondly, the time record of the output concentration at the production wells will enhance the prediction of contaminant migration.