Selection of the Right Demulsifier for Chemical Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract In this paper, the importance of five process variables (alkaline, surfactant, polymer, shear rate and oil cut) and their interactions that govern emulsion stability in chemical enhanced oil recovery (CEOR) was investigated. The surfactant, alkaline, and polymer decreased the size of oil droplets, increased the surface charge of oil droplets, and increased the film elasticity, making oil-water separation difficult. Selected cationic demulsifiers (patents pending) when added to a produced emulsion at ambient temperature for alkaline, surfactant, polymer (ASP) and surfactant, polymer (SP) processes yielded oil and water phases with greatly improved quality compared to emulsions treated with conventional nonionic demulsifier resins and polymeric cationic flocculants. Structure and performance relationships of alkyltrimethylammonium bromides and alkyldimethylbenzylammonium bromides (n=C8 to C18) were also studied. Octyltrimethylammonium bromide was the best demulsifier for SP flood and dodecyldimethylbenzylammonium bromide was the most effective for ASP flood. Di-alkyl quaternary ammonium bromides were more effective than mono-alkyl quaternary ammonium bromides of similar molecular weights. The zeta potential became less negative and the size of oil droplets remarkably increased when a cationic demulsifier was added to the emulsion. Application of this novel demulsifier resulted in the production of dry oil and clean water for a pilot field experiencing chemical breakthrough from an ASP flood
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".