Oil Characterization from Simulation of Experimental Distillation Data
Bibliographic record
Abstract
The characterization of crude oil involves dividing the oil into pseudocomponents and allocating mole fractions, molar mass, specific gravity, average boiling point, and critical properties to each component. The characterization is typically based on distillation data reported in terms of true boiling points. Standard assay types such as the ASTM D86 or ASTM D1160 vacuum distillation do not provide well established saturated bubble temperatures and require empirical interconversion curves to convert the assay data into true boiling point (TBP) data. Recently developed assays such as the ASTM D5236 and Bruno’s new distillation assay methodology do provide well-defined saturated bubble temperatures that correspond to actual thermodynamic state points but lack an established interconversion method to a TBP, that is, a method to determine the TBP of the fluid based on the measured temperatures of the assay. In this work, a methodology is presented to determine pseudocomponent mole fractions that match the boiling point data from these new assays. The fluid is divided into pseudocomponents of different average boiling point, and the molar mass and other physical properties of each component are determined using established correlations. A simulation of the distillation is optimized to match the assay data by adjusting the mole fraction of each pseudocomponent. The characterization can also be constrained to match other data such as the bulk density and molar mass of the fluid. The proposed methodology is tested on naphtha and Alaska crude oil and then verified through three heavy oil case studies. The methodology is entirely general and can be applied to a compositional analysis from a distillation of any material.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".