Investigation of Pore-Scale Evaporative Drying, Salt Precipitation and Crystallization Migration in CO2 Injection Process by a Lab-On-A-Chip System
Bibliographic record
Abstract
Abstract In this study, a self-designed microchip system was used to visually study the pore-scale salt crystallization and migration, and a high-performance optical microscope was used to dynamically observe the salt precipitation process and results. The results show that pore-scale salt crystals mainly precipitate in the residual water phase, and mainly present two forms of occurrence, large-grained salt crystals and small-grained aggregated crystals, respectively. In addition to growing in the brine phase, large-grained salt crystals also nucleate and grow at the gas-liquid interface, and the maximum salt crystal size can reach the order of the pore size. This phenomenon was discovered for the first time and has not been mentioned in the existing literature. In addition, this study also observed an interesting phenomenon. The salt crystals formed in the wetting brine film and the brine phase can migrate under the combined influence of displacement pressure and capillary force, and eventually accumulate and precipitate inside the pores. Injection flow rate and salinity have a strong influence on the pore-scale salt crystallization kinetics. There is a critical value for the injection flow rate, and the critical injection rate causes the salt precipitation to be significantly aggravated. Under the same injection flow rate, an increase in salinity leads to an increase in the amount of salt precipitation.
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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.
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".