Application of a Regular Solution-Based Model to Asphaltene Precipitation from Live Oils
Bibliographic record
Abstract
A previously developed regular solution approach was adapted to model asphaltene precipitation caused by compositional changes and depressurization. The model inputs are the mass fraction, molecular weight, density, and solubility parameters for each component. A Gulf of Mexico crude oil was characterized into components, and mass fractions were assigned on the basis of gas chromatographic and saturates, aromatics, resins, and asphaltenes (SARA) analysis. Densities for pentane plus and SARA fractions were obtained from published data. For lighter components, effective densities were determined from extrapolated n -alkane data. The density of the live oil from 80 to 120 °C and pressures from 10 to 100 MPa was predicted to within the error of the data assuming ideal mixing. Solubility parameters of each component were determined as a function of the temperature and pressure. The only unknown was the average molar mass of the asphaltene nano-aggregates in the oil, which was used to fit the measured precipitation onset pressure data. The model successfully predicted asphaltene yield data below the onset pressure for the live oil as well as yields for the dead oil diluted with n -heptane. The results indicate that a common characterization can be used to model both solvent- and pressure-induced precipitation. However, the pressure-induced precipitation is very sensitive to the average aggregate molar mass. Thus, the predictive capability of this approach is limited.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".