Oil Sands Technology Pathway Evaluation Using Life Cycle Assessment and Mathematical Optimization
Bibliographic record
Abstract
Oil sands producers must improve the environmental performance of their operations to remain competitive in the energy sector in a carbon constrained world. These improvements include both incremental changes in the existing operation to make it more efficient (e.g., by applying process integration techniques) or fundamental changes to the operation (e.g., by adopting emerging technologies). However, an individual company will consider regulatory requirements and economic feasibility prior to making decisions about investments in these technologies. This thesis investigates the potential improvements in oil sands operations through both incremental efficiency improvements (i.e., lower energy consumption per unit of energy produced) and fundamental changes in their operations. In the first part of the thesis, cost and energy savings opportunities in Steam Assisted Gravity Drainage (SAGD) (an oil sands extraction and recovery process) are assessed by applying process integration techniques through the sequential application of a water treatment system optimization followed by conventional energy pinch analysis (incremental improvement). In the second part of the thesis, the focus is on exploring fundamental improvements in the oil sands sector and identifying the optimal technology pathways for oil sands production and processing with respect to economic and environmental objectives. A comprehensive techno-economic framework is developed that considers all technological and economic input parameters that affect the performance of the oil sands supply chain in terms of total cost, total energy consumption and GHG emissions. This framework is used to: 1) find the technical, economic and policy conditions under which emerging oil sands technologies become competitive alternatives in global crude oil markets, and 2) investigate the prospect of reaching Canada’s climate goals (as it relates to the oil sands sector) by implementing available emission reduction solutions while maintaining oil sands production capacity at the current or increased level in the next three decades. The results of this study help oil sands producers to better understand the long-term effects associated with the use of existing and emerging oil sands technologies. In addition, the results inform short- and long-term investment decision making in oil sands sector under various scenarios with different combinations of input parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".