Diffusivity of Gas Into Bitumen: Part II—Data Set and Correlation
Bibliographic record
Abstract
Summary The oil-production rate of in-situ heavy-oil-recovery processes involving the injection of gaseous hydrocarbons partly depends on the diffusivity of the gas in the bitumen. Data for gas diffusivities, particularly above ambient temperature, are relatively scarce because they are time consuming to measure. In this study, the diffusion and solubilities of gaseous methane, ethane, propane, and n-butane in a Western Canadian bitumen were measured from 40 to 90°C and pressures from 300 to 2300 kPa, using a pressure-decay method. The diffusivities were determined from a numerical model of the experiments that accounted for the swelling of the oil. In Part I of this study (Richardson et al. 2019), it was found that both constant and viscosity-dependent diffusivities could be used to model the mass of gas diffused and the gas-concentration profile in the bitumen; however, the constant diffusivity was different for each experiment and mainly depended on the oil viscosity. In this study, a correlation for the constant diffusivity to the oil viscosity is developed as a tool to quickly estimate the gas diffusivity. A correlation of diffusivity to the mixture viscosity is also developed for use in more-rigorous diffusion models. The maximum deviations in the mass diffused over time predicted with the constant and viscosity-dependent (mixture viscosity) correlations at each condition are on average 7.4 and 8.7%, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".