Replacing Natural Gas in Alberta’s Oil Sands: Trade-Offs Associated with Alternative Fossil Fuels
Bibliographic record
Abstract
Concerns regarding resource availability and price volatility have prompted industries to consider replacing natural gas (NG) with an alternative fuel. The oil sands industry utilizes large amounts of NG for the production of steam, electricity, and hydrogen, and several “replacement fuels” are currently being considered. A life cycle framework is developed and applied to two generic oil sands projects as a case study (mining with upgrading and in situ with upgrading) to examine the energy, greenhouse gas, and financial implications of replacing NG with four fossil fuels: asphaltenes, coke, bitumen, and coal. Key trade-offs are identified among the fuels, as well as those associated with applying carbon capture and storage (CCS) to the systems. The analysis indicates that there is no vector dominant alternative to NG among the fuels investigated, although asphaltenes appear to offer the most potential. The analysis confirms that CCS can reduce life cycle emissions to 25% of those of current systems but will not be implemented for oil sands energy systems without a financial incentive or regulatory requirement. Under the analysis’ base conditions, the CO 2 avoidance cost is $66/tonne CO 2 equivalent and $87/tonne for the mining and in situ asphaltenes cases, respectively. However, the impact of compounding uncertainties is demonstrated and shown to be critical for appropriate interpretation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".