Pore-Scale Assessment of Nanoparticle-Stabilized CO<sub>2</sub> Foam for Enhanced Oil Recovery
Bibliographic record
Abstract
In this paper, we evaluate nanoparticle-stabilized CO 2 foam stability and effectiveness in enhanced oil recovery at the pore and micromodel scales. The nanoparticle-stabilized CO 2 gas-in-brine foams maintain excellent stability within microconfined media and continue to be stable after 10 days, as compared to less than 1 day for surfactant foam. The nanoparticle-stabilized CO 2 foams are shown to generate a 3-fold increase in oil recovery (an additional 15% initial oil in place), as compared to an otherwise similar CO 2 gas flood. Fluorescence imaging is applied to quantify emulsion size distribution (down to 1 μm) in both CO 2 and nanoparticle-stabilized CO 2 foam flood cases. Nanoparticle-stabilized CO 2 foam flooding results in significantly smaller oil-in-water emulsion sizes with an average size of 1.7 μm (∼80% smaller than a CO 2 gas flood), with negligible impact on water-in-oil emulsions. The effectiveness of nanoparticle-stabilized CO 2 foam is compared for representative light, medium, and heavy oils. All three oils show substantial additional oil recovery and a potentially valuable reservoir homogenization effect. Collectively, these results highlight the pore-scale dynamics, effectiveness, and potential for nanoparticle-stabilized foams in enhanced oil recovery.
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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 | 0.001 |
| 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".