Keys to improve the efficiency of nanoparticle‐stabilized foam injection based on influential mechanisms
Bibliographic record
Abstract
Abstract The effect of the existence of nanoparticles on foam stability, foamability, and the oil recovery factor (RF) has been studied experimentally, and influential phenomena and mechanisms have been examined. A sequence of experiments, including, ‘foam bulk‐static experiments’, ‘surface tension (ST) measurements,’ and ‘micromodel foam flood,’ were designed and then implemented to study the foam behaviour in two foam systems: (1) anionic‐nanoparticles + cationic‐surfactant and (2) anionic‐nanoparticles + anionic‐surfactant. This study provides a comprehensive insight into the mechanisms affecting the stability of nanoparticle‐stabilized foam. Also, despite previous studies, the effect of Marangoni flow on nanoparticle‐stabilized foam has been discussed briefly. Results show that the interactions of effective mechanisms work differently in the two series. In the like‐charge system, surfactant molecules accumulate in the interface of lamellas due to repulsive forces; therefore, stability and foamability improve as surface tension and molecular diffusion reduce. Additionally, Marangoni flow restitutes the negative impact of gravity drainage. In the unlike‐charge system, observations illustrate that nanoparticles reach the interface. The presence of nanoparticles at the interface increases detachment energy significantly, and as a result, the stability is boosted. The accumulation of nanoparticles in the interface changes it to a solid‐like surface with limited diffusibility and viscosity. Although Marangoni flow is lost, reducing molecular diffusion improves foam stability. Flooding tests show that foam stability increment improves sweep efficiency at near‐wellbore areas even when foamability is weak. Finally, it can be claimed that in the unlike‐charge system, the sweep efficiency and foam stability increase to a greater extent.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".