Heavy Oil Mobilization by Cold Solvent, Hot Solvent, and Heat - A Comparative Pore Level Evaluation
Bibliographic record
Abstract
Abstract The first commercial test of solvents took place in California in 1960 where solvent stimulation was used to increase production of heavy oil. Currently, successful performance of steam injection in horizontal wells suggests the idea of using hot solvent alone or in conjunction with steam to reduce bitumen viscosity. The present study compares the performance of heat, cold solvent, and hot solvent for reducing bitumen viscosity at the pore scale using typical field data. The governing equations were derived for heat transport by conduction and convection and solvent diffusion and dispersion. The equations were solved in spherical geometry for a droplet of bitumen at different flow rates. In addition to solvent and steam together, equations were derived for a hot solvent. The mass and heat balance equations were solved simultaneously and the viscosity profile was obtained. The performance of different solvents at different temperatures was compared with heat under the same conditions. The results indicated that hot solvent is much more effective than solvent alone due to the effect of temperature on oil viscosity. It was found that the effect of solvent is much less than that of heat and that the high recovery by heated solvent is directly related to the heat. The time required for cold solvent to reduce bitumen viscosity was much longer compared to conduction-convective heating even at high solvent rates. Hot solvent shows promise compared with conduction-convective heating as a result of the heat contribution. In spite of the benefits of using solvent, the economics must be considered. This study improves our understanding of the mechanistic behavior of solvent assisted recovery processes and modelling approaches at the pore scale.
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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".