Inverse modeling for characterization of uncertainty in transport parameters under uncertainty of source geometry in heterogeneous aquifers

SummaryIn petroleum hydrocarbon contamination scenarios, assessment of the dimensions of contaminant plumes and prediction of their fate requires predictions of the rate of dissolution of contaminants from residual non-aqueous-phase-liquid (NAPLs) into the aquifer and the rate of contaminant removal through biodegradation. The central idea in this paper is to present an inverse modeling methodology for characterization of structural uncertainty in these parameters by tailoring their estimation to the distributions of source geometry and hydraulic conductivity field. For this purpose, a synthetic study site with two reference cases is established and a Monte Carlo type inverse modeling methodology is presented where dissolution and first-order biodegradation rates of the contaminants are estimated for joint realizations of source and hydraulic conductivity. The joint realizations are constructed by the distance-function (DF) and sequential self-calibration (SSC) approaches and a gradient-based optimization is adapted to solve the inversion problem. The results show larger uncertainty in the estimated dissolution rate and a moderate positive correlation between the two parameters. It is also observed that tailoring the estimation of the parameters to the constructed joint realizations can effectively reduce the uncertainty in the shape and size of the plume, and this uncertainty, as well as the uncertainty in the source size, can be further reduced by ranking and screening the conditional realizations based on the value of the objective function. The effects of measurement errors in head and concentration observations on uncertainty in the predicted parameters are shown to be as expected; measurement error translates to substantially greater uncertainty.

► Stochastic inversion for characterization of uncertainty in mass transport parameters.
► Uncertainty in source geometry through distance function algorithm.
► Reduction in uncertainty through conditioning to dynamic data.
► Larger uncertainty in dissolution rate relative to biodegradation rate.
► Moderate positive correlation between the two parameters.