Synergistic exploitation of gas hydrates through surface seawater injection coupled with depressurization: Application and optimization in the South China Sea
Bibliographic record
Abstract
This study proposes and systematically evaluates an optimized integration of warm surface seawater injection with depressurization for the long-term exploitation of marine natural gas hydrates. By employing comprehensive multiphysics simulations guided by field data from hydrate production tests in the South China Sea, we pinpoint key operational parameters—such as injection rates, depths, and timings—that notably enhance production efficiency. The results indicate that a 3-phase hydrate reservoir transitions from a free-gas-dominated production stage to a hydrate-decomposition-dominated stage. Moderate warm seawater injection supplies additional heat during the hydrate decomposition phase, thereby enhancing stable production; however, excessively high injection rates can impede the depressurization process. Only injection at an appropriate depth simultaneously balances thermal supplementation and the pressure gradient, leading to higher overall productivity. A “depressurization-driven sensible-heat supply window” is introduced, highlighting that timely seawater injection following initial depressurization prolongs reservoir dissociation dynamics. In this study area, commencing seawater injection at 170 d of depressurization proved optimal. This optimized integration leverages clean and renewable thermal energy, providing essential insights into thermal supplementation strategies with significant implications for sustainable, economically feasible, and efficient commercial-scale hydrate production.
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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 | 0.001 |
| 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".