Determination of Concentration Dependent Diffusivity Coefficient in Solvent Gas Heavy Oil System
Bibliographic record
Abstract
Abstract Molecular diffusion coefficient is an important parameter in modeling mass-transfer based reservoir processes. However, experimentally measured diffusivities for heavy oil systems are relatively scarce and no standardized method exists for measurements of this important parameter. The available measurement techniques are tedious, expensive and often not very reliable. There is an obvious need for developing improved methods for measuring diffusivity of gases in heavy oils. It is well known that as a gas dissolves into heavy oil, the oil viscosity drops considerably and this affects the diffusivity. The objective of this work is to measure the diffusivity of highly soluble gaseous solvents in heavy oils at different concentration levels. We have developed a modified pressure decay method that maintains constant concentration at the gas-liquid interface and measures the amount of gas transferred to the liquid as a function of time. An analytical solution has been developed for finding diffusion coefficient and the equilibrium solubility of gas in the oil at the test pressure. To study the concentration dependence of diffusivity, a stepwise increase in pressure is used starting from a low pressure. Through this stepwise procedure, a diffusion coefficient is measured for each gas saturation pressure (concentration), going from low pressure to near gas dew point pressure in 5 to 6 steps. The bitumen height in our cell is updated at each pressure to account for bitumen swelling. Propane was used as vapor solvents and diffusion cell was kept at constant temperature.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".