Solubility and diffusivity of propane in heavy oil and its SARA fractions
Bibliographic record
Abstract
The design and modelling of solvent‐based heavy oil recovery requires information about the solubility and diffusivity of particular solvents in heavy oil and its fractions. In this study, the original Cactus Lake oil was first characterized into saturate, aromatic, resin, asphaltene, and maltene fractions. An intelligent gravimetric microbalance was used to measure the solubility of propane in the heavy oil and its saturate, aromatic, resin, asphaltene, and maltene fractions. The measurements were carried out at 288, 294, 299 and 303 K, and at pressures up to 600 kPa. From the experimental results, it was observed that the saturate fractions have the highest solubility followed by maltene, aromatic, heavy oil and resin fractions. Solubility data were also reported in the form of Henry's law constants. It was observed that the asphaltene content affects the propane solubility quite significantly in the heavy oil at the same equilibrium pressure. The Peng–Robinson equation of state was used to correlate the experimental results within acceptable deviations. The adsorbed amounts of propane in asphaltene were also measured at 288, 294, 299 and 303 K and at pressures up to 600 kPa to determine the adsorption capability of propane on asphaltene. Finally, time‐dependent concentration data were used to determine the diffusion coefficients of propane in heavy oil, saturate, aromatic and maltene fractions using a simple diffusion model. It was observed that the diffusion coefficient increases with pressure but decreases with the asphaltene content.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".