New efficient tool diagnoses asphaltene stability: Utilization of refractive index
Bibliographic record
Abstract
Abstract Asphaltene precipitation/deposition is considered a critical issue in the petroleum industry as it causes damages and blockage in various process equipment (e.g., pipe, pump, and vessel) and porous formation. To prevent this obstruction, it seems necessary to attain a reasonable estimate of the precipitation time, in other words, the time of asphaltene stability in crude oils. The gene expression programming (GEP) technique is utilized in this study to develop a correlation for the prediction of this important parameter using real refractive index (RI) data. The independent variables considered for the development of the correlation are the composition of aromatics, saturates, and resins in weight percentage. The predicted outputs show a better match with the real data, compared to the results obtained from other available predictive tools and/or correlations. Besides its simplicity, the developed correlation can have a broad range of applications in oil production plants in order to ensure that proper strategies are determined to avoid asphaltene precipitation/deposition throughout various stages of oil production and processing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".