Integrated fuzzy-stochastic modeling of petroleum contamination in subsurface

An integrated approach associated with fuzzy set theory, Monte Carlo simulation, and interval analysis are proposed in this study to address the uncertainties in simulating petroleum contamination in the subsurface. A numerical multiphase compositional modeling technique is implemented to examine the fate of petroleum contaminants in groundwater. The intrinsic permeability, longitudinal dispersivity, and soil porosity are considered as uncertain input parameters. A three-dimensional (3D) case of a petroleum contamination problem is presented to illustrate the suitability and capability of the proposed methods for managing uncertainties. The results show that the uncertainties in intrinsic permeability and porosity will have significant impacts on the modeling outputs. Neglecting these uncertainties may result in an unreasonable estimation of the contaminant fate in the subsurface.