Applications of Remote Hyperspectral Sensing in the Characterization of Alberta's Oil Sands Tailings

  • Author / Creator
    Entezari Najafabadi, Iman
  • The bitumen production from oil sands surface-mining operations produces large volumes of mineral wastes called tailings. Characterization of the oil sands tailings is of importance to monitor their state for trafficability and reclamation issues, to assess the tailings operation performance, and to develop more effective measures for tailings operations and management. This thesis investigates the use of hyperspectral remote sensing data to develop spectral models capable of predicting key characteristic of oil sands tailings surfaces including: 1) water content and normalized evaporation; 2) swelling potential or activity as indicated by the methylene blue index (MBI), and 3) clay mineral species and their abundance as well as the abundance of quartz. In Chapter two, hyperspectral time-series laboratory observations are collected from different tailings samples displaying variations in swelling potential and bitumen concentration. From these data effective spectral models were developed for the estimation of moisture content and normalized evaporation with best results achieved using the Normalized Soil Moisture Index (NSMI). Application of the predictive models to field setting would require a validation of initial results. In Chapter three shortwave and longwave infrared hyperspectral data are used to estimate the swelling potential or activity of oil sands soft tailings indicated by the Methylene Blue Index (MBI). Spectral features, in particular those attributed to the presence of clay and quartz minerals, are characterized and their correlation with MBI is investigated. In the SWIR, a band ratio of reflectance at 1.773 µm to 1.307 µm provided an estimation of MBI of tailings that could be applied to field settings. A water sensitivity analysis showed that the model based on these bands is robust against variations in the tailings moisture content for values less than 20 wt%. At moisture levels above 20 wt%, the MBI value was overestimated. The best MBI predictions were obtained in the LWIR using reflectance peaks at 9.67 μm and 11 μm attributed to total clays and kaolinite, respectively. The aim of Chapter four is to gain an understanding of the changes in reflectance response with change in mineralogy of oil sands and to investigate the effectiveness of hyperspectral sensing for the prediction of the mineral composition of oil sands ore and tailings, particularly clays. The results demonstrate that longwave infrared observations are of significant value for the prediction of clay and quartz content in oil sands ore and tailings. Also, spectral tools can be developed to monitor the ratio of swelling to non-swelling clays in ore and tailings. Overall, the results of this thesis offer a key enabling technology to remotely assess tailings characteristics related to establishing the geotechnical stability of a tailings deposit. Such observations and derived information would complement standard geotechnical testing and sampling campaigns (using manual techniques), which are expensive, time consuming, potentially hazardous to personnel, and necessarily limited to a small number of locations. In future efforts, the methods developed in this thesis can contribute in generating real-time maps of tailings properties for operational decision-making in predicting engineered tailings process performance.

  • Subjects / Keywords
  • Graduation date
    Spring 2016
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Wilson, Ward (Civil and Environmental Engineering)
    • Lipsett, Michael (Mechanical Engineering)
    • Richards, Jeremy (Earth and Atmospheric Sciences)
    • Rivard, Benoit (Earth and Atmospheric Sciences)
    • Niemann, Olaf (University of Victoria)