CO2 -hydrate formation in depleted gas reservoirs–A methodology for CO2 storage
Bibliographic record
Abstract
With the growing concern about climate change, interest towards reducing CO2 emissions has increased. Geological storage of CO2 is perceived to be one of the most promising methods that could provide significant reduction in CO2 emissions over the short and medium term. Since a major concern regarding geological storage is the possibility of leakage, trapping CO2 in the solid form is quite attractive. Unlike mineral trapping, the kinetics of CO2-hydrate formation is quite fast, providing the opportunity for long-term storage of CO2. In this paper, we study storage of CO2 at conditions similar to those at depleted gas pools of Northern Alberta. Thermodynamic calculations suggest that CO2 hydrate is stable at temperatures that occur in a number of formations in Northern Alberta, in an area where significant CO2 emissions are associated with production of oil sands and bitumen. Numerical simulation results presented in this paper suggest that, upon CO2 injection into such depleted gas reservoirs, pressure would initially rise until conditions are appropriate for hydrate formation, enabling storage of large volumes of CO2 in solid form. These results show that, because of tight packing of CO2 molecules in the solid (hydrate), the CO2 storage capacity of these pools is many times greater than their original gas-in-place. This provides a local option for storage of a portion of the CO2 emissions there.
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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 | 0.000 | 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 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".