LNAPL Volume Calculation: Parameter Estimation by Nonlinear Regression of Saturation Profiles |
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| Authors: | Paul D. Lundegard Brett S. Mudford |
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| Affiliation: | Paul D. Lundegard;is a principal advising scientist with the Unocal Environmental Technology Group (376 S. Valencia Ave., Brea, CA, 92623;(714) 577–1625;fax (714) 577–2367). He received an M.S. in geology from the University of Cincinnati, and a Ph.D. in geology from the University of Texas at Austin. His current responsibilities include environmental litigation support and forensic geochemistry. Brett S. Mudford;is the director of geoscience applications with Economic Analysis Systems Inc. (1021 Main St., Ste. 1860, Houston, TX 77002;(713) 655–7400;fax (713) 655–7403). He received an M.Sc. in applied mathematics from Waikato University in New Zealand, and a Ph.D in plasma physics from Oxford University in England. His current responsibilities include economic risk analysis and multiphase flow modeling of sedimentary basins. |
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| Abstract: | An estimation of the volume of light nonaqueous phase liquids (LNAPL) is often required during site assessment, remedial design, or litigation. LNAPL volume can be estimated by a strictly empirical approach whereby core samples, distributed throughout the vertical and lateral extent of LNAPL, are analyzed for LNAPL content, and these data are then integrated to compute a volume. Alternatively, if the LNAPL has obtained vertical equilibrium, the thickness of LNAPL in monitoring wells can be used to calculate of LNAPL in monitoring wells can be used to calculate LNAPL volume at the well locations if appropriate soil and LNAPL properties can be estimated.
A method is described for estimating key soil and LNAPL properties by nonlinear regression of vertical profiles of LNAPL saturation. The methods is relatively fast, cost effective, and amenable to quantitative analysis of uncertainty. Optionally, the method allows statistical determination of best-fit values for the Van Genuchten capillary parameters (n, αoil-water and αoil-air), residual water saturation and ANAPL density. The sensitivity of the method was investigated by fitting field LNAPL saturation profiles and then determining the variation in misfit (mean square residual) as a function of parameter value for each parameter. Using field data from a sandy aquifer, the fitting statistics were found to be highly sensitive to LNAPL density, αoil-water and αoil-air moderately sensitive to the Van Genuchten n value, and weakly sensitive to residual water saturation. The regression analysis also provides information that can be used to estimate uncertainty in the estimated parameters, which can then be used to estimate uncertainty in calculated values of specific volume. |
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