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Application of Hyperspectral Imaging for the Detection of Coal in Oil Sand Previous HitDrillNext Hit Core

Abstract

Detecting the presence of coal in oil sands Previous HitdrillNext Hit core is of value for the management of the bitumen ore extraction process at oil sands mines. The presence of coal negatively impacts recovery using the bitumen flotation process and the quality of the froth in the floatation cell. Identifying coal in dark, fine grained bitumen-saturated sediment can be challenging during the description of Previous HitdrillNext Hit core. This study examines the use of longwave hyperspectral imaging, a remote sensing technique, to enhance the detection of coal in oil sands as well as to comment on the detectability of variable coal rank. We examined 20 meters of oil sands Previous HitdrillNext Hit core from the McMurray Formation bearing coal fragments and discrete coal beds. Coal-rich intervals were initially determined by visual inspection of the core. Also included in the study were museum specimens of relatively pure coals of varied rank (anthracite, bituminous coal, sub-bituminous coal, and lignite). Longwave spectral imagery of core and samples was acquired at a spatial resolution of 0.25 mm/pixel. The spectral imagery was collected for 32 spectral bands spanning 8-11 μm. Two broad observations can be drawn from the examination of the spectra of museum samples. First, there are minor differences in the shape of spectra across ranks and it may be possible to exploit these differences to estimate rank (not pursued in this study). Second, silicate minerals that contaminate portions of the samples are clearly detected and importantly as a group, the spectra of coal are similar and distinct from that of other geological materials. Average spectra for pixels encompassing coal-rich intervals in Previous HitdrillNext Hit core were compared to that of museum samples. Core spectra show many similarities to museum samples but display more variance in amplitude then what is observed for the latter. These results indicate that coal as a spectral class has a distinct signature that can be exploited for its detection in Previous HitdrillTop core. Coal intervals can thus be highlighted using principal component analysis of the spectral imagery. These findings point to the value of such imagery to differentiate coal from surrounding sediments in core. There is huge value in exploring applications of this technology at operations within an oil sands mine.