CO2 foam structure and displacement dynamics in a Hele–Shaw cell
Bibliographic record
Abstract
The substitution of CO 2 gas with CO 2 foam for improved mobility control has recently attracted research attentions in enhanced oil recovery (EOR) as well as aquifer and soil remediation. However, the interplay between CO 2 foam properties and oil displacement remains less investigated. In this work, using clear visualizations, we systematically investigate the dynamic advection-dominated foam morphology, its corresponding viscosity, and the efficiency and dynamics of oil displacement process by CO 2 foam in a Hele–Shaw cell under the effects of gas ratio ( R g ), fluid injection rates ( Q t ), and surfactant type. Clear visualization enables particle image velocimetry to calculate actual, rather than nominal, foam velocity that significantly affects the estimated foam viscosity. Our results demonstrate that at elevated gas ratios under constant total injection rate, larger and more uniform bubbles with less number density and greater interfacial area are obtained. Increasing the injection rates leads to finer foam texture at a constant gas ratio. Different foam structures have an impact on the foam viscosity, with a general increasing trend of viscosity for larger bubbles as R g increases from 0.5 to 0.85. The higher the foam viscosity, the more stable displacement interfaces with less viscous fingers are observed, leading to improved sweeping rates. The green surfactants (saponin + Cellulose NanoFibers) provide foams with higher viscosity and, thus, more stable displacement interfaces. These findings highlight the important effect of R g –dependent foam structure on its viscosity, which in turn is crucial for controlling the mobility of CO 2 foam to maximize oil recovery rate during EOR processes.
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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".