An overview of the Wabamun Area CO2 Sequestration Project (WASP)
Bibliographic record
Abstract
Large stationary CO2 emitters are located in central Alberta with cumulative annual emissions in the order of 30 Mt CO2. This includes four coal-fired power plants in the Wabamun Lake area, southwest of Edmonton with emissions between 3 to 6 Mt/year each. The study will perform a comprehensive characterization of large-scale CO2 storage opportunities in the Wabamun area and analyze any potential risks. As a benchmark, the project will examine the feasibility of storing 20 Mt- CO2/year for 50 years within 30 km of Wabamun. This gigaton-scale storage assessment project is one to two orders of magnitude larger than the commercial projects now under study. It will fill a gap between Canadian province-wide capacity estimates (which do not involve site specific studies of flow and geomechanics etc.) and the detailed commercial studies of small CO2 storage projects currently underway. Unlike the commercial projects, this project is planned as a public non-confidential project lead by the University of Calgary (U of C). The study will first assess the possible injection formations within the area based on storage capacity, ease of injectivity, leakage likelihood, and interference with current petroleum production. Then a few (1–3) specific targets will be selected for more detailed studies. The detailed studies will evaluate how the injected CO2 moves and reacts within the reservoir, the storage integrity of the over and underlying shaly aquitard, leakage risks of CO2 along existing wells and a preliminary well injection design. Finally, the study will do a preliminary assessment of currently available options for monitoring such large scale injection of CO2. Since the project is planned to develop a realistic scenario we will add an economic evaluation of the total project costs downstream of the capture and pipeline transportation components. Furthermore, the study will outline the necessary next steps to close any remaining knowledge gaps before planning and conducting the actual injection phase of the project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".