Surfactant and nanoparticle synergy: Towards improved foam stability
Bibliographic record
Abstract
Surfactant foam stability gets a lot of interest while posing a significant obstacle to many industrial operations. One of the viable solutions for addressing gas mobility concerns and boosting reservoir fluid sweep efficiency during solvent-based enhanced heavy oil recovery processes is foam formation. The synergistic effect of nanoparticles and surfactants in a porous reservoir media can help create a more durable and sturdier foam. This study aims to see how well a combination of the nanoparticles (NPs) and surfactant can generate foam for controlling gas mobility and improving oil recovery. This research looked at the effects of silicon and aluminum oxide nanoparticles on the bulk and dynamic stability of sodium dodecyl surfactant (SDS)-foam in the presence and absence of oil. Normalized foam height, liquid drainage, half-decay life, nanoparticle deposition, and bubble size distribution of the generated foams with time were used to assess static foam stability in the bulk phase, while dynamic stability was studied in the micromodel. To understand the processes of foam stabilization by nanoparticles, the microscopic images of foam and the shape of bubbles were examined. When nanoparticles were applied in foamability testing in bulk and dynamic phase, the foam generation and stability were improved by 23% and 17%, respectively. In comparison to surfactant alone, adding nanoparticles to surfactant solutions leads to a more significant pressure drop of 17.34 psi for SiO2 and 14.86 psi for Al2O3 NPs and, as a result, a higher reduction in gas mobility which ultimately assists in enhancing 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".