Diffusion Coefficient of n-alkanes in Heavy Oil
Bibliographic record
Abstract
Abstract The use of 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, mainly because of the viscous and opaque nature of the heavy oil and the dependence on concentration of the diffusion coefficient. The Matano-Boltzmann method has been used in the past to obtain the concentration dependency of the diffusion coefficient of solvents in heavy oil. Although the method successfully shows that such dependency exists, the results exhibit abnormal trends. In this work the concentration profiles of three n-alkanes in heavy oil were obtained in the laboratory using Computed Assisted Tomography (CAT), and the "Slopes and Intercepts" analytical technique was used to calculate the concentration-dependent diffusion coefficients. The results are in good agreement with the theory of diffusion in binary mixtures. In addition a comparison is presented with the Matano-Boltzmann method. Finally the Vignes model was successfully used to also perform predictions on the studied systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".