Classifying Crude Oil Emulsions Using Chemical Demulsifiers and Statistical Analyses
Bibliographic record
Abstract
Abstract Crude oil emulsions are highly complex mixtures that can be stabilized by a number of naturally occurring species and conditions (e.g. asphaltenes, resins, acids, solids, solvency, viscosity, temperature, etc.). Emulsion resolution is often accomplished using chemicals. Optimization of chemical treatment is generally accomplished in the field by bottle testing a large number of potential chemical intermediates and their combinations. Successful chemical formulations are able to drop water rapidly, provide relatively clean interfaces, and produce dry, saleable oil. The very nature of bottle testing produces a large amount of data. Some of the data provides information regarding water drop, while part focuses on breaking the emulsion (i.e. producing dry oil and clean interfaces). To summarize results from multiple test sites, a highly structured bottle test was devised to make comparisons among different oilfield emulsions. Thirty-eight chemical intermediates, tested at two dosages, were evaluated at nine different sites. From the testing, ten bottle test performance parameters (four describing water drop, three describing oil dryness, and three describing the oil-water interface) were analyzed using several statistical methods: analysis of variance, multivariate correlations, cluster analysis, and principal component analysis. The analyses revealed a number of interesting trends. For example, the water drop and oil dryness parameters were found to be more independent of one another than the water drop and interface parameters. These results suggest that water drop and oil dryness are likely governed by two different mechanisms. This work has resulted in several emulsion "maps" where crudes can now be classified with regard to their ability to drop water and break emulsion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".