The effect of site characterization data on injection capacity and cap rock integrity modeling during carbon dioxide storage in the Nisku saline aquifer at the Wabamun Lake area, Canada
Bibliographic record
Abstract
ABSTRACT Geological storage of CO2 is an option for mitigating global climate change resulting from greenhouse gas emissions. Effective selection, design, and operation of storage sites require reliable models for predicting the response to CO2 injection. This paper revisits preliminary studies of CO2 storage in the Nisku saline aquifer in Alberta, Canada, which were conducted to assess CO2 injectivity, plume migration, and geomechanical response during 50 yr of injection, using model input parameters estimated from data available at the time. The new work presented here involved modeling of CO2 injection using the same tools but with input parameters obtained from data acquired in an evaluation well. The first series of new simulations modeled fluid flow using a commercial black-oil simulator and predicted a lower maximum injection rate (0.80 million t [Mt]/yr [0.88 million tons (Mtons)/yr] compared to 1.0 Mt/yr [1.1 Mtons/yr]) but a CO2 plume width nearly identical with the preliminary prediction (as a consequence of increases in some parameters that offset decreases in other parameters). The second series of new simulations was undertaken using a coupled thermo–hydro–mechanical simulator and predicted ground surface uplift approximately four times less than the preliminary study and (when injecting above the fracture pressure) fracture dimensions several times greater. As before, thermal effects resulting from cool CO2 injection were observed to promote lateral fracture growth in the aquifer and reduce (but not prevent) vertical growth into the cap rock. Use of the evaluation well data in this study enabled a more confident conclusion that injection above the fracturing pressure is not feasible for this site.
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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) | 0.001 | 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 | 0.000 |
| 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".