Supplying Synthetic Crude Oil from Canadian Oil Sands: A Comparative Study of the Costs and CO2 Emissions of Mining and In-Situ Recovery
Bibliographic record
Abstract
High crude oil prices and the eventual decline of conventional oil production raise the issue of alternative fuels such as non-conventional oil. The paper describes a simple probabilistic model of the costs of synthetic crude oil (SCO) produced from Canadian oil sands. Synthetic crude oil is obtained by upgrading bitumen that is first produced through mining or in-situ recovery techniques. This forward-looking analysis quantifies the effects of learning and production constraints on the costs of supplying synthetic crude oil from Canadian bitumen deposits. The results show the uncertainties associated with the future costs of synthetic crude oil. Carbon costs have a large impact of the total costs of synthetic crude oil, in particular in the case of synthetic crude oil from in-situ bitumen, due to the carbon-intensity of the recovery techniques. The influence of each parameter on the supply costs is examined. In the case of mined SCO, the maximum production rate, the ultimate recovery rate and the depletion parameters show the largest influence on the results, while learning parameters dominate in the case of in-situ SCO.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".