Abstract
From exploration to reclamation, digital applications in mining are increasingly utilized in data collection and processing to solve challenges. In oil sands mining, access to ore quality data is key to optimizing mine plans and extraction processes. Currently, coring activities are conducted on an annual basis to characterize deposits and then used for mine planning. The characterization procedures are performed in a lab, requiring significant time and effort for sample preparation and analysis. New non-destructive sensing technologies offer the possibility to reduce the cost and time for ore sample and core analyses. These sensing technologies can enable the oil sands operators to access timely data to improve the process efficiency, productivity, and sustainability. As a result, bitumen can be extracted more efficiently and less tailings are generated at the same time. In this applied research project, we explored different technologies that can quickly measure key oil sands properties such as bitumen content, clay content, and particle size distribution. The technologies include hyperspectral imaging, infrared spectroscopy, nuclear magnetic resonance (NMR), micro-CT, X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), optical microscopy, and more. Each technology has their own advantages and limitations. In addition, machine learning and artificial intelligence techniques are utilized to predict some key properties. The findings and lessons learned from this applied research project will be discussed in this presentation.