Storage of CO<sub>2</sub> hydrate in shallow gas reservoirs: pre‐ and post‐injection periods
Bibliographic record
Abstract
Abstract With the growing concern about climate change, interest in reducing CO2 emissions has increased. Geological storage of CO2 is perceived to be one of the most promising methods that could provide significant reductions in CO2 emissions over the short and medium term. Since a major concern regarding geological storage is the possibility of leakage, trapping CO2 in a 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. Thermodynamic calculations suggest that CO2 hydrate is stable at temperatures that occur in a number of formations in Northern Alberta, in an area where there are significant CO2 emissions associated with the production of oil sands and bitumen. In this paper, we study storage of CO2 in hydrate form at conditions similar to those at depleted gas pools in Northern Alberta. Our numerical simulation results show that the CO2 storage capacity of such 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 from the oil sands operations in northeastern Alberta. In an earlier paper, we studied hydrate formation during a period of continued CO2 injection. In this paper, we extend the duration of the investigation to include the period after injection has stopped. In particular, we study the storage capacity of such depleted gas pools and the fate of the hydrate over long periods of time when the injection of CO2 has slowed down or ceased. We examine the effect of properties of the reservoir and cap/base rocks, as well as operating conditions. In particular, we investigate a shut‐in case as the most realistic condition in CO2 field sequestration. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 |
| 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".