Salt Cavern Dissolution Mining: Lessons Learned from Simulations
Bibliographic record
Abstract
ABSTRACT: Salt caverns are usually developed by circulating fresh water through a well in a salt formation and extracting brine. Due to the difference in density (buoyancy), fresh water quickly rises to the top of the cavern and promotes dissolution, so the shape of the cavern can be controlled in part by an air pad at the top of the cavern. Relatively high fluid velocities and buoyancy lead to the creation of vortices that are shown to play a critical role in cavern development. Vortices induce higher localized rates of dissolution, leading to complexity which has traditionally been thought to be exclusively due to heterogeneity of the salt formation. Using a combination of simulation based on a novel method involving full CFD and physical-driven dissolution front evolution, we demonstrate how flow complexities combined with the non-reversible nature of salt dissolution drive cavern shape complexity. We discuss and compare mechanisms observed in terms of concentration distributions, brine flow patterns, dissolution rates, and the non-linear impact of injection rate and insoluble interlayer on cavern shape and size. 1 INTRODUCTION The increases in renewable energy generation promote a high demand for large energy storage facilities. Compressed Air Energy Storage (CAES) is considered one of the best options because it has the advantage of possessing a large storage capacity, small energy loss rate, relatively high cycle efficiency, and is environmentally friendly (Li et al., 2018). A large compressed air storage reservoir is key to ensure the CAES system performs large-scale energy storage, and cavern stability is needed for storage security. Salt formations provide favourable conditions for large-scale cavities because the salt rock has low permeability and excellent self-healing characteristics (Chen et al., 2013; Ozarslan, 2012), ensuring air tightness. However, during the construction of the salt cavern by dissolution mining, overhanging blocks will develop, and the failure of these blocks can threaten the utility of the cavern (Wang et al., 2017). Although different technologies, such as blanket application with sonar monitoring, are used to control roof dissolution and help optimize cavern shape, caverns with high irregularity, as sketched in Fig. 1 are still commonly constructed. So, the key factors that affect the dissolution of solid minerals should be investigated in order to guide and execute effective cavern shape control. Numerical simulation is an efficient way to investigate the factors, especially for large cavern dissolution, which is challenging to explore through laboratory or field experiments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".