Interaction of Foam and Microemulsion Components in Low-Tension-Gas Flooding
Bibliographic record
Abstract
Low-Tension-Foam (LTF) flooding is an emerging enhanced oil recovery technique for low-permeability carbonate reservoirs. Foam capacity is closely related to the salinity environment (or, equivalently, the phase behavior of the oil/water/surfactant system). Therefore, the interactions between microemulsion and foam components are of primary importance in the LTF process. In this study, the phase behavior of an oil/water/surfactant system under equilibrium is analyzed, firstly by assuming perfect mixing. Meanwhile, the formation kinetics of microemulsion are monitored through a novel low-field NMR technique, which is able to provide quantitative assessment on the microemulsion evolution characteristics. Then, foam stability is examined in the absence and in the presence of Winsor-I and Winsor-III type microemulsions. It is revealed that foam stability depends on the oil solubilization (oil swollen micelle size). A decrease in the oil swollen micelle size and micellar structure effectiveness, in conjunction with an increasing salinity, leads to lower foam stability in the presence of a Winsor-III type microemulsion.
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.
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) | 0.000 | 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 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".