ABSTRACT

Ore grade is one of the primary variables controlling the economic recovery of bitumen from oil sands reservoirs, hence there is a need for fast and reliable quantification of total bitumen content (TBC). This is typically achieved through laboratory-based Dean-Stark analyses of drill core samples. However, this method is time and labor intensive and destructive to the core sample. Hyperspectral imaging is a remote sensing technique that can be defined as reflectance spectroscopy with a spatial context, where high-resolution digital imagery (∼1 mm/pixel [0.04 in./pixel]) is acquired and reflectance measurements are collected in each pixel of the image. This study compares two hyperspectral models for the determination of TBC from imagery of both fresh and dry core samples. For three out of four suites of fresh core, TBC was predicted within ±1.5 wt. % of the Dean-Stark data with both spectral models achieving correlations of R2>0.97. For a fourth fresh core and the dry core, larger margins of error were found because of some instances of overestimation. Surface roughness because of uneven oil distribution and small-scale fracturing is a potential source of error in some of the spectral TBC results, particularly for the dry core. Producing results within minutes with the additional benefit of being nondestructive to the core sample, hyperspectral imaging shows great potential to become a viable alternative method for bitumen content determination in oil sands.

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