Using Infrastructure Optimization to Reduce Greenhouse Gas Emissions from Oil Sands Extraction and Processing
Bibliographic record
Abstract
The Alberta oil sands are a significant source of oil production and greenhouse gas emissions, and their importance will grow as the region is poised for decades of growth. We present an integrated framework that simultaneously considers economic and engineering decisions for the capture, transport, and storage of oil sands CO(2) emissions. The model optimizes CO(2) management infrastructure at a variety of carbon prices for the oil sands industry. Our study reveals several key findings. We find that the oil sands industry lends itself well to development of CO(2) trunk lines due to geographic coincidence of sources and sinks. This reduces the relative importance of transport costs compared to nonintegrated transport systems. Also, the amount of managed oil sands CO(2) emissions, and therefore the CCS infrastructure, is very sensitive to the carbon price; significant capture and storage occurs only above 110$/tonne CO(2) in our simulations. Deployment of infrastructure is also sensitive to CO(2) capture decisions and technology, particularly the fraction of capturable CO(2) from oil sands upgrading and steam generation facilities. The framework will help stakeholders and policy makers understand how CCS infrastructure, including an extensive pipeline system, can be safely and cost-effectively deployed.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".