Discriminating Hydrocarbon Alteration Zones Using Airborne Hyperspectral Remote Sensing

V. Sivakumar
Geomatics Solution Development Group (GSDG), C-DAC, Ministry of Communications & Information Technology, Govt. of India, Pune, India

The advanced hyperspectral remote sensing technology is playing an important role in Hydrocarbon exploration. The hyperspectral data have hundreds of discrete spectral bands in visible to long-wave infrared regions (0.4 to 14 µm) of electromagnetic spectrum, which allow direct identification of minerals using fully resolved spectral signatures. The seeping hydrocarbons geochemically alter the mineral assemblages present in surface rock and soils, which can be distinguished by spectroscopic study. Existing literature on hyperspectral remote sensing of oil and gas exploration study recommends that i) lower wavelengths region (visible) is useful for mapping of the oil and Gas fluorescence, pyrite (which is the dominant sulfide mineral in the hydrocarbon induced alteration zones), rare earth elements in phosphate minerals and bitumen, ii) VNIR region (400nm-1000nm) is useful for identifying iron oxide minerals, and iii) SWIR region (1600nm-2500nm) is useful for discriminating clay minerals, hydroxyls and carbonate bearing minerals (alunite, buddingtonite and kaolinite).

This rapidly developing technology is expected to dominate mapping of the earth's surface, and also provide a cost-effective technique to focus budget-heavy exploration on the most promising energy targets. This paper reviews the related literature available on the subject and presents capabilities and challenges of hyperspectral imagers pertaining to mapping and characterization of minerals in hydrocarbon exploration regions.

GGS - Spectrum have effectively utilised this technique on many proprietary and multi client surveys. Figure 1 shows an example from the Eastern Mediterranean, where the results give a dramatic improvement over the more conventional time processed sequence. Similar improvements have been produced on surveys from offshore Brazil, the west coast of India, US Gulf of Mexico and many other areas. In some of these locations, prospects have been identified in the 2D PSDM data which were not seen on 3D data processed through a more conventional time sequence.

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