Optimization of synthesis parameters for petroleum sulphonate and evaluation of oil displacement performance
Bibliographic record
Abstract
Abstract As conventional oil declines, enhanced oil recovery (EOR) technologies like surfactant flooding gain importance. Petroleum sulphonate (PS) enhances recovery by reducing interfacial tension and altering wettability in low‐permeability reservoirs. In this study, we employed response surface methodology to systematically optimize the synthesis parameters of PS, with a focus on the effects of reaction temperature, time, and oleic acid molar ratio on the active substance content. The results showed that the optimal synthesis conditions were: reaction temperature of 45°C, reaction time of 45 min, and oleic acid molar ratio of 1.5:1, achieving an active substance content of 46%. Furthermore, the synthesized PS demonstrated exceptional interfacial activity and salt tolerance: at 0.3% concentration and 20,000 mg/L salinity, it achieved an ultra‐low interfacial tension of 0.112 mN/m and a high emulsification rate of 76.8%. Additionally, 0.1% PS reduced the contact angle by 45.7% (from 116 to 63°) at 10,000 mg/L salinity, indicating significant wettability alteration. Core flooding experiments confirmed that PS enhanced oil recovery by 12.86% compared to conventional surfactant SDBS. This study not only establishes an optimized synthesis process for PS but also elaborates on its mechanism of EOR, offering a practical and efficient solution for low‐permeability reservoirs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".