The challenge of a deep-water oil leak is that a significant quantity of oil remains in the water column and possibly changes properties. There is a need to quantify the oil settled within the water column and determine its physical properties to assist in the oil recovery. There are currently no methods to map acoustically submerged oil in the sea. In this paper, high-frequency acoustic methods are proposed to localize the oil polluted area and characterize the parameters of its spatial covariance, i.e., variance and correlation. A model is implemented to study the underlying mechanisms of backscattering due to spatial heterogeneity of the medium and predict backscattering returns. An algorithm for synthetically generating stationary, Gaussian random fields is introduced which provides great flexibility in implementing the physical model of an inhomogeneous field with spatial covariance. A method for inference of spatial covariance parameters is proposed to describe the scattering field in terms of its second-order statistics from the backscattered returns. The results indicate that high-frequency acoustic methods not only are suitable for large-scale detection of oil contamination in the water column but also allow inference of the spatial covariance parameters resulting in a statistical description of the oil field.