Demulsification of steam-assisted gravity drainage (SAGD) emulsions under high temperature and high pressure: Effects of emulsion breaker and reverse emulsion breaker dosages
Bibliographic record
Abstract
Effective treatment of complex emulsions formed during steam-assisted gravity drainage (SAGD) production is crucial for oil/water separation and subsequent processing. However, it remains challenging due to the complexity of SAGD emulsions, and there are only very limited studies on demulsification under simulated SAGD conditions. This study systematically characterized the comprehensive properties of a fresh field SAGD emulsion with about 84.6 % water and 15.4 % bitumen, and then achieved effective demulsification under a simulated SAGD condition at 135 °C and 60 psi via adding a poly(ethylene oxide)-poly(propylene oxide) (PEO-PPO) copolymer (as a model emulsion breaker or EB) combined with a branched polyethylenimine (PEI, as a model reverse emulsion breaker or REB) in a bench-scale bottle test setup. The effects of EB and REB dosages on the demulsification of the SAGD emulsion were evaluated based on the properties of the separated phases. Dynamic interfacial tension, zeta potential, and coalescence time were measured to reveal the demulsification mechanisms of EB and REB. The highly interface-active EB facilitated oil dehydration by displacing the natural interface-active species at the interfaces, disrupting the rigid interfacial films and promoting water droplet coalescence, while the cationic REB aided water de-oiling by neutralizing oil droplet surface charges, disrupting the rigid interfacial films and promoting the approach and coalescence of oil droplets in aqueous media. This work has improved the understanding of SAGD emulsion characteristics, the effects of EB and REB dosages on the demulsification performance, and the underlying demulsification mechanisms in SAGD operations. The results provide useful insights for developing effective and cost-efficient chemical approaches to address challenging emulsion issues in oil production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".