Emulsification of Heavy Oil in Aqueous Solutions of Poly(vinyl alcohol): A Method for Reducing Apparent Viscosity of Production Fluids
Bibliographic record
Abstract
Driven by the need to enhance heavy oil production, we have investigated the emulsification properties of poly(vinyl alcohol)s (PVAs) to generate oil-in-water (O/W) emulsions and achieve a significant viscosity reduction. O/W emulsions were successfully prepared using Canadian heavy oil with an American Petroleum Institute (API) gravity of 12° and a water cut of 25%. The effects of PVA molecular weight and degree of hydrolysis as well as emulsifier concentration and mixing method on emulsion stability and water quality were studied. For this purpose, phase separation kinetics was monitored by means of the Turbiscan Lab Expert particle dispersion analyzer, and the results were then correlated with interfacial tension, wettability, and droplet size measurements. For PVAs having comparable molecular weight, less hydrolyzed samples proved to induce more stable emulsions; this is in agreement with reduced particle size resulting from the increased reduction in interfacial tension. On the other hand, for a similar degree of hydrolysis, the increase of the molecular weight improved emulsion stability. These results, together with the measured droplet sizes and contact angles, indicated that a favorable adsorption of higher molecular weight PVAs at the oil–water interfaces occurs, thereby enhancing steric repulsions between oil droplets. Water quality showed a complex dependency upon the particle size, and the method of mixing was also demonstrated to be critical for emulsion stability. Of the PVAs tested, the PVA with the highest molecular weight (146 kg/mol) and lowest degree of hydrolysis (87%) was found to be the most effective.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".