Fuzzy-based Integration of Sentinel-2 MSI Data for Locating Potential Hydrocarbon Microseepag

Abstract

Abstract The vertical migration of hydrocarbons from oil and gas reservoirs to the surface and/or subsurface (i.e., microseepage) can induce mineral alteration that results in concentration of ferrous iron, clay, and carbonate minerals at the Earth’s surface. These altered zones may be detected in remote sensing imagery based on their specific reflectance spectral characteristics; zones where three types of mineral alteration occur simultaneously reflect possible hydrocarbon microseepage locations with the potential for underlying oil and gas reservoirs. In this study, a fuzzy set based approach was used to integrate the results of principal component analysis (PCA) and band ratios (BRs). First, each selected indicative principal component (PC) and BR was treated as a fuzzy set, and a corresponding fuzzy membership function defined. The membership degree of each pixel, which indicates the possibility of the presence of specific altered minerals, was then calculated. Subsequently, the Gamma operator was used to fuse all fuzzy sets to create a new fuzzy set, which was then defuzzied and used to indicate the possible locations of hydrocarbon microseepages. The Sentinel-2 MSI data were used to illustrate the approach and the zones of hydrocarbon microseepage mapped.