Dominant Scaling Groups of Polymer Flooding for Enhanced Heavy Oil Recovery
Bibliographic record
Abstract
Polymer flooding of heavy oils on the laboratory scale shows appreciable incremental tertiary oil recovery. In reality, however, this high recovery efficiency usually cannot be achieved in the field due mainly to extremely unfavorable mobility ratio and reservoir heterogeneity. The former promotes viscous fingering while the latter induces channeling; hence both of these factors make the displacement process less efficient. This paper identifies the dominant scaling groups for polymer flooding currently conducted in western Canadian heavy oil reservoirs. Twenty-eight dimensionless scaling groups governing the process of polymer flooding for enhanced heavy oil recovery were derived using inspectional analysis, and a fully tuned numerical model for polymer flooding of a heavy oil sample in a two-dimensional sand pack was then developed to validate the effectiveness of these scaling groups. A good agreement among different cases with the same group values was observed, showing the validity of the scaling groups. The effect of each scaling group on oil recovery was examined by numerical sensitivity analysis. By doing so, nine scaling groups dominating polymer flooding enhanced heavy oil recovery were identified. These dominant scaling groups can be used to design scaled experiments to predict field-scale oil recovery by polymer flooding in heavy oil reservoirs.
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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".