Diffusion of Hydrocarbon Gases in Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract The use of gas hydrocarbon solvents in the recovery of heavy oil has been increased because of the advantages they have over the thermal methods under some reservoir conditions. The injection of a miscible solvent in the reservoir implies a mass transfer process which is governed by a diffusion coefficient. Consequently the measurement of the diffusion coefficient is extremely important. This, however, presents a significant amount of challenges in the laboratory and in the data analysis. In spite of the importance of the diffusion processes not much effort has been put in the understanding and calculation of the gas-liquid diffusion coefficient. In a recent work Guerrero-Aconcha and Kantzas (2008) used the "Slopes and Intercepts" analytical technique to successfully obtain the diffusion coefficient of liquid hydrocarbons in heavy oil. However the technique involves the previous knowledge of the composition density relationship. Here a non-iterative finite volume method was used to obtain the diffusion coefficient dependent on concentration without previous knowledge of relations for the diffusion coefficient and density with concentration. Computed Assisted Tomography (CAT) was used to obtain the density profiles and back calculate the concentration-dependent diffusion coefficients. The results agree very well with the theory of diffusion in binary mixtures. An empirical model was used to perform predictions on the studied systems. The results are the vital importance for VAPEX and cyclic solvent injection recovery processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".