A New Method for Enhanced Production of Gas Hydrate with CO2
Bibliographic record
Abstract
Presently, depressurization, thermal stimulation, inhibitor injection, or a combination of these methods have been considered as possible means of gas hydrate production. Depressuriza-tion is the most often considered method for commercial production of hydrates (SLOAN, 1998), but combined depressurization and thermal stimulation have been used recently to produce a small amount of gas from the Mallik 5L-38 research well located on the Mackenzie delta, Northwest Territories, Canada. Additional studies are needed to ascertain the economic viability of any of these methods for commercial production of gas hydrates. Another method that has been discussed for gas hydrate production involves the injection of CO2. The idea of swapping CO2 for CH4 in gas hydrates was first advanced by Ohgaki et al. (1996) and then for ethane hydrate by Nakano et al. (1998). Their concept involves injecting CO2 gas, which is then allowed to equilibrate with methane hydrate along the three-phase equilibrium boundary (SMITH et al., 2001). Because of the difference in chemical affinity for CO2 versus methane in the sI hydrate structure, the mole fraction of methane would be reduced to approxi-mately 0.48 in the hydrate and rise to a value of 0.7 in the gas phase at equilibrium. However, neither Ohgaki et al. (1996) or Nakano et al. (1998) addressed the important issue of the kinetics
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".