Nanoparticle Stablized CO2 in Water Foam for Mobility Control in Enhanced Oil Recovery via Microfluidic Method
Bibliographic record
Abstract
Abstract Nanoparticle stabilized CO2 in water foam can overcome the low stability challenges facing surfactant foams in reservoir conditions. Foams are effective in mobility control against viscous fingering during gas injection in enhanced oil recovery. This study presents a microfluidic approach to image and quantify the stability of foam at pore scale and the dynamics of the oil recovery process during water flooding, CO2 gas flooding, and nanoparticle foam flooding. In addition to chip scale flooding visualization, micro-scale imaging reveals the mechanisms of the viscous fingering in gas flooding and the high sweep efficiency of foam; micro-emulsion size and distribution in gas and foam flooding. Coated silica nanoparticle CO2 foam is significantly more stable than sodium dodecyl sulfate (SDS) foam at both pore scale and bulk foam. Nanoparticle foam can improve oil recovery an additional 17% IOIP after water flooding, this is 10% IOIP more efficient than CO2 gas flooding as a result of high sweep efficiency and increase in effective viscosity.
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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".