Applied Statistics: Crude Oil Emulsions and Demulsifiers
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 Water‐in‐crude oil emulsions are encountered at many oilfield production facilities. These emulsions are often inherently stable requiring the use of chemical treatment, heat, and residence time to effect resolution. The addition of chemical demulsifiers in small levels can greatly facilitate oil–water separation. Even with numerous demulsifier applications in place throughout the world, there still remains a great deal to understand regarding how to streamline demulsifier selection, how demulsifiers counter the indigenous crude oil components and properties that impart emulsion stability and which crude oil components and process variables are most critical in describing emulsion strength. Field studies were undertaken to address these concerns using two statistical methods—experimental design and cluster analysis. Experimental design was used to investigate the importance of four process variables, while cluster analysis used an ensemble of demulsifiers and crude oil characterization to build models describing emulsion stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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