The impact of brine salinity on CO2 capillary trapping efficiency in sandstone saline aquifers using a two-dimensional micromodel
Bibliographic record
Abstract
Carbon dioxide (CO2) storage in deep saline aquifers is a promising strategy for mitigating global warming by reducing atmospheric CO2. Among various trapping mechanisms, capillary trapping offers a robust approach to immobilizing CO2 within the porous reservoir rock, ensuring its long-term containment. Previous studies focused on the effect of brine salinity on interfacial tension (IFT) and supercritical CO2 (ScCO2) solubility, but the published data clearly lacks in terms of ScCO2 capillary trapping in saline aquifers. Therefore, this study investigated the effect of brine salinity on ScCO2 capillary trapping efficiency and associated properties using a two-dimensional micromodel resembling heterogeneous sandstone. Five brine salinities—6000, 60 000, 100 000, 150 000, and 300 000 ppm—were tested under four flow rates, 96.41, 28.92, 2.89, and 0.29 μl/min, with a brine-alternate-CO2 (BAC) injection method. Results showed that reduced brine salinity improved capillary trapping efficiency, as larger ScCO2 clusters were observed at lower brine salinity. While higher brine salinity reduced capillary trapping efficiency, corresponding to changes in brine properties, increasing brine salinity led to increased density and viscosity, elevated IFT between ScCO2 and brine, and altered the wettability of the micromodel glass surface from water-wet to ScCO2-wet conditions. This study underscores the importance of optimizing the brine salinity to achieve enhanced capillary trapping of ScCO2 in sandstone reservoirs by using the BAC injection technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".