Salinity‐buffered methane hydrate formation and dissociation in gas‐rich systems
Bibliographic record
Abstract
Abstract Methane hydrate formation and dissociation are buffered by salinity in a closed system. During hydrate formation, salt excluded from hydrate increases salinity, drives the system to three‐phase (gas, water, and hydrate phases) equilibrium, and limits further hydrate formation and dissociation. We developed a zero‐dimensional local thermodynamic equilibrium‐based model to explain this concept. We demonstrated this concept by forming and melting methane hydrate from a partially brine‐saturated sand sample in a controlled laboratory experiment by holding pressure constant (6.94 MPa) and changing temperature stepwise. The modeled methane gas consumptions and hydrate saturations agreed well with the experimental measurements after hydrate nucleation. Hydrate dissociation occurred synchronously with temperature increase. The exception to this behavior is that substantial subcooling (6.4°C in this study) was observed for hydrate nucleation. X‐ray computed tomography scanning images showed that core‐scale hydrate distribution was heterogeneous. This implied core‐scale water and salt transport induced by hydrate formation. Bulk resistivity increased sharply with initial hydrate formation and then decreased as the hydrate ripened. This study reproduced the salinity‐buffered hydrate behavior interpreted for natural gas‐rich hydrate systems by allowing methane gas to freely enter/leave the sample in response to volume changes associated with hydrate formation and dissociation. It provides insights into observations made at the core scale and log scale of salinity elevation to three‐phase equilibrium in natural hydrate systems.
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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) | 0.000 | 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".