Comparative investigation of the static and dynamic properties of CO<sub>2</sub> foam and N<sub>2</sub> foam
Bibliographic record
Abstract
Abstract Foam flood using CO2 can both enhance oil recovery and achieve geological storage of CO2, which has attracted increasing attention in the last two decades. This work systematically investigates the behaviours of CO2 and N2 foams both in bulk and porous media by static foam tests and core flood experiments. The stability and foamability of CO2 and N2 foams were compared in static foam tests. The foam texture showed that CO2 foam bubbles were relatively larger and exhibited a wider size distribution than N2 foam bubbles, which was detrimental to foam stability. Furthermore, steady‐state foam flows of CO2 and N2 foams at a fixed liquid flow rates were comparatively investigated with a foam quality range of 30–98 %. The difference between CO2 foam stability and N2 foam stability in porous media was relatively smaller, compared with the difference in bulk. Additionally, the analysis of foam rheology showed that foam exhibited different non‐Newtonian flow characteristics at different foam qualities, and the resistance to shearing of CO2 foam was lower than that of N2 foam. Finally, the results of foam flood experiments demonstrated that the oil recoveries of CO2 foam were lower than those of N2 foam with a co‐injection strategy under both subcritical and supercritical conditions. Compared with results in the atmosphere environment, the oil recovery of CO2 foam flood was higher under supercritical conditions.
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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".