Emulsion Characteristics and Novel Demulsifiers for Treating Chemical Induced Emulsions
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Bibliographic record
Abstract
Abstract In this paper, the stability of oil-in-water emulsions produced from chemical enhanced oil recovery processes was investigated over a wide range of parameters. These parameters are surfactant concentration, polymer concentration, mixing speed, asphaltene concentration, salinity concentration, water cut, temperature, and alkaline concentration. Emulsion stability decreased with an increase in temperature, salinity content, or water cut. Increasing surfactant concentration, polymer concentration, or shear rate enhanced emulsion stability. One of the main contributions for the tight emulsion from alkaline surfactant polymer (ASP) flood was the addition of alkaline. The surfactant, alkaline, and polymer decreased the size of oil droplets, increased the surface charge of oil droplets, and increased the film elasticity, thereby making oil-water separation difficult. Selected cationic surfactants (patents pending) proved much more effective than conventional nonionic resins and polymeric cationic flocculants in separating oil-in-water emulsions. We also studied the effect of alkyl chain length (C8 – C18) of benzyl and methyl quats on demulsifying efficiency and compared the performances of monoalkyl quat with dialkyl quat. As the surfactant concentration in the brine decreased, the concentration of the cationic demulsifier required to separate the emulsion decreased and the optimum chain length of the cationic demulsifier also changed. Particle video microscope and focused beam reflectance measurement probes showed significant increase of the size of oil droplets and reduction in the number of oil droplets in the presence of a cationic surfactant. This is in agreement of a decrease of the anionic charge on the surface of the oil droplets and a reduction of the film elasticity in the cationic system. Application of this novel demulsifier resulted in a much more effective oil/water separations process with the production of dry oil and clean water at a pilot ASP flood that was experiencing very stable emulsions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it