Numerical Simulation on the Influence of Underlying Free Gas on Hydraulic Fracturing in Low-Permeability Gas Hydrate Deposits
Bibliographic record
Abstract
With the continuous growth of world energy demand and the necessity of energy conservation and carbon reduction in the current environment, the efficient development of clean energy is urgently needed. As one of the most promising alternative energy sources, low-permeability hydrate deposits with different occurrence characteristics are widely distributed around the world. Reservoir stimulation exhibits different adaptabilities to various classes of hydrate deposits. Among them, there is a free gas layer under the hydrate layer in the Class I hydrate reservoir. Unlike the Class II and Class III hydrate deposits, there is a lower free gas layer during the depressurization development of the Class I hydrate deposit, which provides a second gas source for methane extraction. Based on identical basic geological parameters, this study uses an embedded discrete fracture model combined with complex fracture morphology to conduct numerical simulation research on reservoir stimulation. The influence and corresponding mechanism of the underlying free gas and water layers on the reservoir stimulation depressurization development were revealed. The sensitivity of various geological and fracture parameters to reservoir stimulation depressurization was clarified. Therefore, the feasibility and economy of different classes of hydrate reservoir stimulation were evaluated, and the advances in Class I hydrate reservoir stimulation were clarified. The results provide a theoretical basis and technical support for the field selection and scheme design of hydrate reservoir stimulation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".