Numerical Simulation of Gas Leakage in Bedded Salt Rock Storage Cavern
Bibliographic record
Abstract
Storage of hydrocarbons in underground salt caverns created by solution mining has been commonly and successfully used for several years in developed country. Sealing performance is an important technique and safety index for salt rock gas storage. Considering the unique stratigraphic characteristics of the bedded salt rocks in China, interlayer has great influence on the tightness of salt carven. It is likely that stored natural gas will escape along the interface of interlayer. It is the key problem to establish the permeability model and to provide corresponding numerical simulation which is suitable for bedded salt rock. In the paper, based on seepage theory in porous media, evolvement equation of gas permeability is established. The salt cavern in Jintan salt mines is taken as engineering background; during the operating period, the pressure distribution and gas leak distance at interface and layers with different operating pressures are studied. It is demonstrated that the gas infiltration velocity along the damaged interface is much faster than the salt rock and mudstone interlayer, and damaged interface is the main gas leakage path. Moreover, with the bigger operation pressure, natural gas spreads much faster at the beginning years. The simulation results prove scientific basis for the design and reasonable control of operating pressure and tightness evaluation of bedded salt rock storage cavern.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".