Recent advances on carbon dioxide sequestration potentiality in salt caverns: A review
Bibliographic record
Abstract
Permanent CO 2 sequestration in the salt caverns seems to be one of the best geological storage options that can be used to reduce anthropogenic greenhouse gases emissions (GHGs) from the atmosphere. However, salt caverns are rarely used because other geological options are more available; hence, they are used for energy source storage for future use due to their high deliverability and ability to quickly switch an injection well to a production well. Nevertheless, salt caverns seem to have low leakage risk compared to other geological storage options due to low permeability, high ductility, and self-healing ability after deformation. In this review, recent advances in CO 2 sequestration in salt caverns have been presented. It has been revealed that salt caverns have great potential to store CO 2 permanently to help to mitigate global climatic change. Salt caverns built offshore in ultra-deep water in Brazil and Lotsberg salt formation, Alberta and Saskatchewan, Canada, have a great potential to store ∼108 million tons of CO 2 and 3500 megatons, respectively. Furthermore, from geochemical Modelling and simulation, it has been revealed that these caverns can store a substantial amount of CO 2 , specifically 4 billion sm 3 or 7.2 million tons, under conditions of 45 MPa pressure and a temperature of 42 °C. The identified research gaps in this study will motivate researchers and stakeholders to conduct more research on developing technology to sequestrate CO 2 into salt caverns as a reliable geological option to mitigate global climate change in places where other storage options are not available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".