Diffusivity of Gas Into Bitumen: Part I—Analysis of Pressure-Decay Data With Swelling
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. The gas diffusivities required to model these processes are determined indirectly from models of mass-transfer experiments. However, the data in the literature are scattered partly because different methods and model assumptions are used in each case. In this work, the pressure-decay method is examined with a focus on accounting for swelling and the dependence of the diffusivity on the solvent content. To assess these issues, the diffusion of gaseous propane into bitumen is measured at conditions where significant swelling occurs. A numerical model is developed for the pressure-decay experiment that accounts for swelling (including excess volumes of mixing) and variable diffusivity. For gases, such as propane, with a relatively high solubility in bitumen, the error in the calculated diffusivity reached 25% when swelling was not included in the model. The error in the height of the gas/oil interface reached 15%. Nonideal mixing had no effect on the calculated diffusivity and only a small effect on the height of the interface. It was found that the diffusion data from a single experiment could be modeled equally well with a constant or a solvent-concentration-dependent (or viscosity-dependent) diffusivity. However, the apparent constant diffusivities for each experiment were different, confirming their dependence on the solvent content. The constant diffusivity approximately correlated to the viscosity of the oil. A larger data set is required to fully develop and test a correlation, and this work will be presented in Part II of this study (Richardson et al. 2019).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".