Study of Carbon Capture in Oilsands Production and Upgrading
Bibliographic record
Abstract
Abstract Canada's oilsands production and upgrading industry has plans for a regional CO2 pipeline, enabling carbon capture and sequestration (CCS) solutions for reducing industry CO2 emissions. In order to evaluate the relative merits of carbon capture solutions, a case study is developed of three hypothetical carbon capture facilities: one post-combustion from a SAGD facility, a second post-combustion from an upgrader hydrogen plant, and a third pre-combustion from an upgrader hydrogen plant. All cases are based on process configurations of commercially proven technologies. Capital costs are developed for each of the cases based on Fluor process expertise and historical cost data for the oilsands region. Process and utility balances are developed to inform net carbon intensity reductions along with operating costs. The study includes a discussion of the influencing factors to CCS economics, including looking at the carbon footprint balance of production and upgrading operations, the existing utility profile, economies of scale, and carbon lifecycle impacts of choices. In addition to net carbon avoidance from a $CAD/ton CO2 perspective, the results also inform on relative merits of carbon intensity reduction of produced liquid fuels which generate carbon credits and revenue under the Canadian Clean Fuel Standard (CFS). Consideration of both carbon tax avoidance and fuel carbon intensity need to be considered to justify capital investment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".