Determination of CO<sub>2</sub> storage density in a partially water‐saturated lab reservoir containing CH<sub>4</sub> from injection of captured flue gas by gas hydrate crystallization
Bibliographic record
Abstract
Abstract It has been demonstrated that CO2 storage in depleted natural gas reservoirs at gas hydrate formation conditions is an opportunity to mitigate the emission of CO2 from fossil fuel combustion or gasification sources. More than 100 depleted natural gas reservoirs located in Alberta, Canada were investigated and recognized as potential sites for CO2 storage using hydrate technology. Treated flue gas captured from large stationary sources is generally a gas mixture of CO2 with N2, O2, and other impurities. The depleted gas reservoirs still contain natural gas that was not economically recoverable. In this work CO2 and a CO2/N2 (90/10 mol%) gas mixture were used for injection in a laboratory reservoir that contained residual CH4. The experimental results indicated that about 80 % of the original water in the reservoir formed CO2 hydrate after 120 h. The total CO2 storage density (in hydrate, gaseous, and dissolved state) from the injection of the CO2/N2 mixture into a 500 kPa CH4 reservoir was found to be 118.6 kg/m3. The addition of a certain amount of tapioca starch in the reservoir delayed the onset of nucleation and improved the CO2 storage density (121 kg/m3). This storage density may also be achieved by compressing the gaseous CO2/N2 mixture to 5220 kPa at 285 K. This study shows that hydrate technology provides more CO2 storage density than other storage methods or the same storage but at much lower compression costs.
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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".