Measurements of Molecular Diffusion Coefficients of Carbon Dioxide, Methane, and Propane in Heavy Oil under Reservoir Conditions
Bibliographic record
Abstract
In this paper, the so-called pressure decay method is applied to measure the molecular diffusivities of carbon dioxide, methane, and propane in heavy oil. In the experiment, a gaseous solvent is made in contact with a heavy oil, and thereby, the pressure in the solvent phase versus time data are accurately measured inside a closed high-pressure diffusion cell at a constant temperature while the solvent gradually dissolves into the heavy oil. In terms of the conservation law of mass and the equation of state for a real gas, the pressure in the solvent phase is calculated from the analytical solution to the diffusion equation for such a diffusion process. The equilibrium, quasi-equilibrium, and nonequilibrium boundary conditions are applied at the heavy oil−solvent interface, respectively. The solvent diffusivity in heavy oil is determined by finding the best match of the numerically calculated pressures with the experimentally measured data. It is found that the nonequilibrium boundary condition is the most applicable at the heavy oil−CO 2 interface at small diffusion times. In addition, the determined diffusivity for the heavy oil−CH 4 system is insensitive to the interface boundary condition. The mass transfer across the heavy oil−C 3 H 8 interface is best described by applying the quasi-equilibrium boundary condition. In particular, a new strategy is adopted to find the equilibrium pressure for each heavy oil−solvent system from its measured solubility versus pressure data. Thus, the diffusion coefficient of each solvent in heavy oil can be determined by measuring the pressure decay in a short duration.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".