Water retention model for hydrate-bearing sediments considering pore structure evolution
Bibliographic record
Abstract
The water retention behaviour of hydrate-bearing sediments (HBS) is crucial for evaluating gas production efficiency and sediment response during methane hydrate exploitation from reservoirs. Effects of hydrate on the pore size distribution (PSD) are not explicitly considered in existing models, although the PSD governs the water retention behaviour of HBS. Nuclear magnetic resonance (NMR) data reveal that, with increasing hydrate saturation, the porosity fraction for larger pores decreases significantly, whereas the porosity fraction of smaller pores changes only slightly. Based on these observations, this study proposed a new equation for modelling the PSD evolution with increasing hydrate saturation. Subsequently, by incorporating this PSD evolution equation into the van Genuchten model, a new model was developed to describe the constitutive relationship between water saturation and suction across a wide range of hydrate saturation. Similarly, the proposed PSD evolution equation was applied to other water retention functions, such as those proposed by Fredlund–Xing, to simulate the water retention behaviour of HBS. Model validation against experimental data shows strong agreement between the calculated and measured results. The model successfully captures the key characteristics of water retention in HBS, including variations in air-entry pressure, adsorption/desorption rates, and residual water saturation with hydrate saturation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".