Carbon Capture and Storage in the Alberta Oil Sands
Bibliographic record
Abstract
An increasing number of countries worldwide have made commitments in recent years to reduce emissions with the goal of limiting global temperature increases to 1.5-2 °C. Carbon capture and storage (CCS) is capable of significantly reducing anthropogenic carbon dioxide (CO2) emissions and is an important tool in the effort to mitigate climate change. The ability of CCS to sequester emissions at a large scale makes it suitable to particularly emission-intensive sectors, such as the oil and gas sector in Canada. Many factors must be considered holistically to ensure the long-term success of large-scale CCS, such as the availability of emission sources, the design of a CO2 transportation network, the availability and capacity of suitable storage sites, the long-term fate of the injected CO2, the economic viability of the system, and the overall policy environment. Previous studies have considered these factors in demonstrating the viability of CCS in Alberta but have not done so holistically. We take a holistic approach in designing a large-scale integrated CCS system which includes CO2 capture from a hub of 10 large oil sands emitters, transport via a pipeline network, and permanent sequestration in the Nisku and Wabamun saline formations. We use a logistic model to forecast long-term oil sands hydrocarbon production and annual emissions, and ensure that all of the capturable emissions can be stored safely without exceeding pressure limits by modeling the long-term pressure evolution. The injected CO2 will be fully trapped in 6100-11000 years without migrating past the minimum storage depth. We calculate the capital expenditures for the pipeline and injection well components of the system and show that the amount of funding required is reasonable under the umbrella of federal infrastructure funding. This provides a comprehensive framework to ensure the long-term success of future CCS projects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".