Effects of Displacement Efficiency of Surfactant Flooding in High Salinity Reservoir: Interfacial Tension, Emulsification, Adsorption
Bibliographic record
Abstract
Surfactant could be used for enhancing oil recovery by enlarging displacement efficiency. The commercial anionic-nonionic polyoxyethylene alkyl sulfonate surfactants (ANS1 & ANS2) were used in high salinity reservoir(277g/L) and the performances were evaluated as well as the affect of salinity on performances in the range of 197g/L to 277g/L was studied. Interfacial tension was one of the most important factors. And the ultra interfacial tension could be obtained in the surfactant ANS1 concentration beyond 0.2% and ANS2 concentration beyond 0.3%. And interfacial tension had the trend of first decreasing and then increasing with increasing salinity. Emulsification can be represented by the unstability index (USI). The emulsification become better with increasing surfactant concentration and was not affected by salinity. The adsorption of surfactant on washed sands was much higher than that on oil sands. At the same time, with salinity increasing the adsorption increased. Displacement efficiency was not the result of single-factor, but was the representative of multi-factor of surfactant. It might be higher with ultra interfacial tension, better emulsification and lower adsorption. Key words: Key words: Surfactant; Interfacial tension; Emulsification; Adsorption; Displacement efficiency; Salinity
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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".