Hyperspectral fluorescence imaging: Robust detection of petroleum in porous sedimentary rock formations

Abstract

Examining hand samples can be a necessary step for geological studies, and effective mapping of such samples can be achieved through the high spectral and spatial resolutions of ground-based hyperspectral imaging (HSI) at the millimeter to centimeter scale. We present a simple approach to crude oil identification and characterization—feasible in 16 hours—based on hyperspectral data collected under ultraviolet lighting and normalized with respect to the fluorescence patterns of Spectralon diffuse reflectance material. The samples under consideration were extracted from a core acquired from an Early Cretaceous bituminous sandstone formation in the Athabasca basin located near Fort McMurray, Alberta, Canada. This basin contains the largest natural bitumen deposit in the world, where surface mining operations currently are viable only for approximately 20% of the estimated 164 billion barrels of total recoverable oil reserves. This deposit is unique in that its tar sands are water-wet, which facilitates the separation of bitumen from the sandstone via water-based gravity separation. However, large amounts of water are still required for oil recovery, so a fast and reliable way to mark portions of the deposit where ample petroleum has accumulated and assess its extractability based on its physical characteristics prior to mining can be helpful for optimizing resource usage. For this reason, we test and visually evaluate the ability of three classification methods—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Supervised Neural Network (SNN)—to distinguish between bitumen, Spectralon, and a non-fluorescent slate background based on the emission of visible light in response to absorbing ultraviolet light of different wavelengths. We also propose spectral indices useful for indicating concentrated bitumen in tar sands. Errors inherent to the methodology are discussed along with ways to mitigate them. After accounting for these, HSI can be a valuable asset alongside other techniques used for production economics evaluation.