The Key to Predicting Emulsion Stability: Solid Content
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 Chemical demulsifiers are routinely added in the oilfield 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 less 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 performing a standardized bottle test (as introduced in SPE 84610), 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 via partition trees shows that emulsion stability is most aptly described using several input parameters as opposed to a single factor. This statistical technique produces emulsion stability descriptions with Rsquare values on the order of 0.9.
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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 | 0.001 | 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