Effect of a Natural Surfactant (Fenugreek Seeds) on Emulsification and Mobilization of Paraffins via Pore-Scale Micromodel Experiments
Bibliographic record
Abstract
The surface characteristics of minerals have been crucial in predicting the interactions between chemicals, particularly in chemical flooding. Thus, this paper evaluates the viability of natural surfactants derived from agricultural products for oil recovery studies using a micromodel filled with paraffinic oil. The study investigates the interfacial tension, viscosity, microscopic, dilution, and oil mobilization characteristics of the natural surfactants. The experimental setup involves conducting interfacial tension measurements between the surfactant solution and paraffinic oil using the Wilhelmy plate method and was found to be 14.2, 10.92, and 9.8 mN/m. Additionally, viscosity measurements and frequency sweep analysis were performed to assess the rheological properties of the prepared emulsion, which was stabilized using a natural surfactant. Microscopic evaluation depicts that, among the prepared emulsions, n -heptane emulsion seems more stable at both 30 and 90 °C. Moreover, dilution studies were conducted for each emulsion system, and the dilution ratio was varied from 1:5 to 1:1 (emulsion/saline solution). It was found that n -heptane emulsion possesses better stability at higher dilution (until a 3:5 ratio). Oil mobilization studies are conducted using a glass micromodel to simulate reservoir conditions and observe the displacement efficiency of the surfactant solutions. The results indicate that natural surfactants exhibit competitive interfacial tension reduction and viscosity modification properties compared to commercial surfactants. Furthermore, oil mobilization studies demonstrate the effectiveness of natural surfactants in enhancing oil recovery from paraffinic oil reservoirs. These findings suggest the potential of natural surfactants derived from agricultural products as sustainable alternatives for improving the oil recovery efficiency in petroleum reservoirs.
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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".