The Key to Predicting Emulsion Stability: Solid Content
Bibliographic record
Abstract
Summary Chemical demulsifiers are routinely added in the oil field to effectively resolve water-in-crude-oil emulsions. As used in the common bottle test, demulsifiers, in effect, probe or interrogate emulsion stability strength. Emulsion stability, in turn, is defined by no fewer than three parameters: water drop, oil dryness, and interface quality. All three parameters are direct outputs of the bottle test, and, collectively, all three provide a more complete picture of emulsion stability, as opposed to the use of any singular parameter. By selecting a wide variety of demulsifiers and by performing a standardized bottle test, emulsion stability from a variety of sites can be quantified and compared. By coupling bottle test results with corresponding crude oil analytical data, fundamental questions concerning factors governing emulsion stability can be quantified. The results show that solid content, not asphaltene content or any other crude oil parameter investigated, is by far the best single predictor for gauging emulsion stability. Furthermore, statistical analysis through partition trees shows that emulsion stability is most aptly described using several crude oil parameters, as opposed to one single factor.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".