South-Central Section - 51st Annual Meeting - 2017

Paper No. 27-3
Presentation Time: 2:15 PM


SUN, Lei, Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204 and KHAN, Shuhab, Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004,

This study used ground-based hyperspectral imaging to map outcrops of the Eagle Ford Formation in west Texas. The Eagle Ford Formation consists of alternating layers of limestones, marlstones and volcanic ashes with high total organic content deposited during the Cenomanian-Turonian oceanic anoxic event. It is one of the few unconventional source rock and reservoirs that have surface representations.

Hyperspectral remote sensing acquires electromagnetic radiation in numerous bands in a continuous spectrum and holds great potential to resolve mineralogical compositions without physical damage. Ground-based hyperspectral imaging scans the geologic outcrops at close ranges with very fine spatial resolution (millimeters to centimeters). Pixel-based spectra matching of study material with reference standards are performed by mixture tuned matched filtering (MTMF). Automatic classification and spectral absorption modeling allowed quantification of the variations of calcite and kaolinite concentrations among the alternating layers. Laboratory spectroscopy is used to assist with mineral identification and image classification. Thin section petrography and X-ray diffraction data verified the classifications of hyperspectral data.

Detailed imaging of three outcrops of the Eagle Ford Formation are used to study the contents and characteristics of organic matters. This work also shed light on the distribution of bentonites. Hyperspectral remote sensing data helped in creating a virtual outcrop model with detailed mineralogical compositions, and provided geologic analogs to extract compositional and geo-mechanical characteristics. The utilization of these new techniques in geo-statistical analysis provides a workflow for employing remote sensing in resource exploration and exploitation.