Synthetic Rock Analogue for Permeability Studies of Rock Salt with Mudstone
Bibliographic record
Abstract
Knowledge about the permeability of surrounding rock (salt rock and mudstone interlayer) is an important topic, which acts as a key parameter to characterize the tightness of gas storage. The goal of experiments that test the permeability of gas storage facilities in rock salt is to develop a synthetic analogue to use as a permeability model. To address the permeability of a mudstone/salt layered and mixed rock mass in Jintan, Jiangsu Province, synthetic mixed and layered specimens using the mudstone and the salt were fabricated for permeability testing. Because of the gas “slippage effect”, test results are corrected by the Klinkenberg method, and the permeability of specimens is obtained by regression fitting. The results show that the permeability of synthetic pure rock salt is 6.9 × 10−20 m2, and its porosity is 3.8%. The permeability of synthetic mudstone rock is 2.97 × 10−18 m2, with a porosity 17.8%. These results are close to those obtained from intact natural specimens. We also find that with the same mudstone content, the permeability of mixed specimens is about 40% higher than for the layered specimens, and with an increase in the mudstone content, the Klinkenberg permeability increases for both types of specimens. The permeability and mudstone content have a strong exponential relationship. When the mudstone content is below 40%, the permeability increases only slightly with mudstone content, whereas above this threshold, the permeability increases rapidly with mudstone content. The results of the study are of use in the assessment of the tightness of natural gas storage facilities in mudstone-rich rock salt formations in China.
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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 | 0.000 |
| Research integrity | 0.000 | 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.
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