A Systematic Study to Assess Displacement Performance of a Naturally-Derived Surfactant in Flow Porous Systems
Bibliographic record
Abstract
For the first time, the present work assesses the feasibility of using Korean red ginseng root extract, a non-ionic surfactant, for the purposes of enhanced oil recovery (EOR). The surfactant is characterized by Fourier-transform infrared spectroscopy (FT-IR) analysis. Pendant drop and sessile drop techniques are employed to study the oil–water interfacial tension (IFT) and wettability nature of the sandstone rock, respectively. In addition, oil recovery enhancement is investigated using micromodel and core floods. In the salt-free system, IFT measurements indicate that the surfactant carries a critical micelle concentration of 5 g/L. In a saline medium (up to 50 g/L), the addition of a surfactant with different concentrations leads to an IFT reduction of 47.28–84.21%. In a constant surfactant concentration, a contact angle reduction is observed in the range of 5.61–9.30°, depending on salinity rate, revealing a wettability alteration toward more water-wet. Surfactant flooding in the glass micromodel provides a more uniform sweeping, which leads to an oil recovery enhancement of 3.02–11.19%, depending on the extent of salinity. An optimal salt concentration equal to 30 g/L can be recognized according to the results of previous tests. Surfactant flooding (10 g/L) in optimal salt concentration achieves an additional oil recovery of 7.52% after conventional water flooding.
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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 | 0.001 |
| 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 | 0.000 | 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".