Stability of w/o emulsions for <scp>MgSO<sub>4</sub></scp> and <scp>Na<sub>2</sub>CO<sub>3</sub></scp> solutions under dynamic and static conditions
Bibliographic record
Abstract
Abstract Emulsions are widely used in various sectors of the oil industry. They can spontaneously be formed during drilling, production, transportation, and oil separation processes. The stability of these emulsions is closely related to species such as asphaltene and resin at the water/oil interface. In this study, emulsion stability was studied at static and dynamic conditions where w/o emulsions were prepared using different concentrations of MgSO4 and Na2CO3 solutions. The optimum concentration of MgSO4 and Na2CO3 solutions was then obtained by microscopic imaging of the samples and analyzing through the ImageJ software. Once the optimum concentrations was established, the stability of emulsions were examined in a 10‐day time period. Emulsion stability at dynamic condition was also examined through injecting the optimum aqueous solutions into a micromodel. Based on these findings, 50 000 ppm and 60 000 ppm were identified as optimum concentrations for the formation of stable w/o emulsions for MgSO4 and Na2O3, respectively. Asphaltene also showed distinct behaviour with pH in which higher polarization was observed for basic solutions. According to this finding, more stable emulsions have been observed in the optimum solution of MgSO4 (in comparison with the Na2O3 solution) under static and dynamic conditions.
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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 | 0.000 |
| 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".