Pore-Scale Assessment of Nanoparticle-Stabilized CO<sub>2</sub> Foam for Enhanced Oil Recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it