An assessment of the long-term water, greenhouse gas, and cost impacts of low-carbon in situ oil sands technologies
Bibliographic record
Abstract
The oil sands sector is a significant emitter of greenhouse gases, accounting for 11.3 % of Canada’s greenhouse gas emissions. In the next 30 years, bitumen production is expected to increase by 1.2 million cubic meters per year, representing a 42 % increase from the 2020 production level; therefore, advancing low-carbon oil sands extraction technologies is critical. While many strategies to mitigate greenhouse gas emissions from the oil sands sector have been proposed, there are few assessments of associated water-use impacts. To fill this knowledge gap, this research builds on a novel data-intensive and technology-specific model of the in situ bitumen extraction sector in Canada developed to determine the long-term water and greenhouse gas footprints of the penetration of emerging low-carbon oil sands recovery technologies. The market penetration of seven novel low-carbon and three conventional in situ bitumen extraction techniques through four different technology mix scenarios between 2020 and 2050 were considered. The results show maximum water savings and GHG abatement potential in 2050 of 7 % and 17 %, respectively, at a $59/cubic meter water savings cost and a $32/tonne carbon dioxide equivalent greenhouse gas abatement cost in a high carbon tax scenario. Total water consumption and greenhouse gas emissions are projected to reach 43.8 million cubic meters and 49.9 million tonnes in 2050 under the scenario that best reduces water use and emissions. Although freshwater use from in situ recovery is 0.05 % of the Athabasca River flow, projected annual emissions from the oil sands industry are significant, thus further efforts are needed to meet Canada’s net-zero emissions target by 2050.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".