Potential of storing gas with high CO<sub>2</sub> content in salt caverns built in ultra‐deep water in Brazil
Bibliographic record
Abstract
Abstract Salt caverns have been identified as one of the best options for the underground storage of gases due to salt rock's excellent sealing capabilities and interesting mechanical properties, such as self‐healing when damaged or cracked. It is feasible to build salt caverns in the Brazilian pre‐salt ultra‐deep water environment for gas storage. However, the peculiar geology of the Brazilian province considered here is characterized by the stratification of thick layers of halite with intercalations of carnallite and tachyhydrite salt rock, whose creep strain rate is almost two orders of magnitude higher than halite's creep strain rate under the same conditions of temperature and pressure. Computational mechanics is being used for the design of offshore salt caverns opened by dissolution mining for the storage of natural gas. The challenge presented in this paper requires the storage of natural gas with high CO2 content offshore in ultra‐deep water (2140 m) in salt caverns. If the economics proves feasible, this offshore gas storage station will be the first of its kind in the world. A technical feasibility rock mechanics study of giant salt caverns, 450 m high by 150 m in diameter, has shown that one cavern can store 4 billion Sm3 or 7.2 million tons of CO2. The salt dome studied can accommodate the construction of 15 caverns, thus providing the confinement of approximately 108 million tons of gas. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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.
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".