Selection of the Right Demulsifier for Chemical Enhanced Oil Recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 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 it