Impact of formation and dissociation conditions on stiffness of a hydrate-bearing sand
Bibliographic record
Abstract
Methane gas recovery from gas hydrate–bearing sands requires dissociation of the hydrate. Understanding changes in the stiffness of the sand is essential if future production scenarios are to be modelled realistically. This paper reports the results of resonant column tests conducted to measure changes in shear and flexural Young’s modulus (stiffness) of sand specimens during the formation and dissociation of hydrate within the pore space. Factors such as hydrate saturation, effective stress, and dissociation method (thermal stimulation and depressurization) were evaluated. Results show a nonlinear relationship between stiffness and hydrate volume, with hydrate formation and dissociation giving markedly different changes in stiffness. Stiffness increases more slowly during the initial stages of hydrate formation, compared to later stages, with the eventual stiffness being independent of the effective stress applied at the start of formation. In contrast, the onset of dissociation leads to a rapid reduction in stiffness, with thermal stimulation giving a greater reduction compared to depressurization for similar changes in hydrate volume. These results highlight the impact of hydrate morphology on changes in stiffness during the hydrate formation process or its dissociation. We present and discuss a conceptual model to explain the differences observed.
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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 | 0.001 |
| 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) | 0.002 | 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".