Modeling Groundwater Contamination under Uncertainty: A Factorial-Design-Based Stochastic Approach

Abstract

A factorial-design-based stochastic modeling system (FSMS) was developed in this study to systematically investigate impacts of uncertainties associated with hydrocarbon-contaminant transport in subsurface. FSMS integrated a solute transport model, factorial analysis, and Monte Carlo technique into a general framework, and effectively analyze the individual and joint effects of input parameters’ uncertainties that are associated with hydrogeological conditions. Four input parameters (i.e. the mean and the variance of permeability as well as the mean and the variance of porosity) were assumed to be of uncertain nature, and the factorial design and Monte Carlo simulation algorithm were incorporated into a groundwater flow and solute transport model developed in this study. Under each factorial experiment, a number of Monte Carlo simulations were implemented. A pilot-scale physical modeling system was used to illustrate the applicability of the proposed methodology. The simulation results reveal that the uncertainties in input parameters pose considerable influences on the predicted output; especially, variations in the mean of porosity will have significant impacts on the modeling output. The results obtained from the systematic uncertainty analysis methods proposed in this study, such as mean, standard deviation, and percentile can provide useful information for further decision-making regarding the petroleum contamination problem.

Keywords: contaminant transport, factorial design, fuzzy modeling, Monte Carlo simulation