Measurement of Heavy Oil and Bitumen Vapor Pressure for Fluid Characterization
Bibliographic record
Abstract
The prediction of heavy oil phase behavior, particularly with solvents, is sensitive to the characterization of the middle and heavy boiling point components of the oil. These components are typically characterized based on an extrapolation of distillation data. One method to test the extrapolated characterization is to model the vapor pressures of these fractions or residues containing these fractions. Unfortunately, the vapor pressures are too low to be reliably measured with conventional techniques. A new high vacuum static apparatus was designed and constructed for the measurement of vapor pressure of heavy oil and bitumen samples. The apparatus is capable of measuring pressures from 100 down to 0.1 Pa and temperatures in the range of 293.15–473.15 K. New procedures were developed to degas samples and obtain accurate vapor pressures at vacuum conditions. The apparatus was tested on n -hexadecane and naphthalene at temperatures between 303.15 and 363.15 K. The measured vapor pressures were, on average, all within 13% of the literature data. The vapor pressures of a Western Canadian bitumen sample (WC_BIT_B1) and three of its fractions were measured using the apparatus. The WC_BIT_B1 bitumen was modeled using the Advanced Peng–Robinson equation of state using a Gaussian extrapolation of its distillation curve for the maltene fraction and a Gamma molecular distribution for its asphaltene fraction. The measured vapor pressures were all predicted to within 3.5%.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".