Effect of Temperature on Athabasca Type Heavy Oil – Water Relative Permeability Curves in Glass Bead Packs
Bibliographic record
Abstract
There have been a number of somehow contradictory reports in the literature on the effect of temperature on oil and water relative permeabilities. Although some authors have reported the dependence of relative permeability curves on temperature, others have attributed these dependencies to artifacts inherent in unsteady-state method of relative permeability measurement. In order to further investigate the impact of temperature changes on the relative permeability data, we have conducted laboratory core flooding experiments on heavy oil systems. The porous media used was glass bead packs, and the Athabasca type bitumen with varying viscosities was displaced by hot water. The history matching technique was conducted on production and pressure differential data to get the relative permeability curves. Results indicated that generally the increase in initial water saturation and the decrease in residual oil saturation are expected by increasing temperature. However, viscous instabilities can rule out the above mentioned trends. No temperature dependency of either oil or water relative permeability can be justified in our tests. The changes in relative permeabilities by temperature are probably related to experimental artifacts, viscous fingering and changes in oil to water viscosity ratio and not fundamental flow properties.
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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 | 0.000 | 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".