Fickian and Non-Fickian Diffusion in Heavy Oil + Light Hydrocarbon Mixtures
Bibliographic record
Abstract
Diffusive mass transfer is expected to play a key role in existing and proposed solvent-added processes for heavy oil production. Composition–distance profiles arising during free diffusion scale as a function of the joint variable (distance/time^ n w ). Simple fluids are governed by Fickian diffusion, where n w = 0.5. For nanostructured fluids, the value of n w can be as low as n w = 0.25, known as the single-file limit, but more typically, the value for the exponent falls between these two limits and is composition-dependent. In this work, five published data sets, comprising free diffusion composition profiles for Athabasca bitumen fractions and for Cold Lake bitumen + light hydrocarbons obtained using diverse apparatus, are probed from this perspective. Additional experimental results are provided for Athabasca bitumen + toluene mixtures over the temperature range of 273–313 K, and results from positive and negative control experiments for two well-defined mixtures—(0.25 mass fraction carbon nanotubes + polybutene) + toluene, and polybutene + toluene—are also provided. The value of n w for the negative control experiment remains at 0.50 ± 0.05 over the entire composition range, and for the positive control experiment, the value drops to n w = 0.30 ± 0.02 at low toluene mass fraction. Although the quality of the diffusion profile data in the data sets analyzed is variable, the values of the exponent n w are shown to be light-hydrocarbon-dependent and increase from n w ∼ 0.25 at low light-hydrocarbon mass fraction up to n w ∼ 0.50 at high light-hydrocarbon mass fraction. Secondary convective effects are also noted in free diffusion experiment outcomes at long times. The industrial applications of these findings are currently being evaluated, but it is clear that the time for light hydrocarbons to penetrate a fixed distance into nano- and micro-structured hydrocarbon resources is greater than the value anticipated for unstructured fluids.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".