Exploring the Upper Limit of Oil Viscosity for Polymer Flood in Heavy Oil
Bibliographic record
Abstract
Abstract Large scale polymer flooding projects in heavy oil are now ongoing in several countries and numerous other projects are at the pilot or design stages. However, there is currently no guideline for the maximum acceptable oil viscosity, one of the important parameters in the screening of new projects. Standard screening criteria do not take the latest field results into account and more recent guidelines rely mostly on viscosity averages whereas they should focus on the extreme values instead. Since the laboratory can only provide little help to settle this issue we propose to examine current field projects for guidance. To the best of the author's knowledge, the Pelican Lake and the Seal polymer floods, both in Canada, are operating in the highest oil viscosity ranges; moreover, the data is public and can easily be accessed. We have therefore examined the performances of polymer injection in the highest ranges of oil viscosity in both fields to get an understanding of the limits. This involved first the identification of the highest oil viscosity patterns, then the estimation of the live oil viscosity during the polymer flood in these patterns and finally the performances of the polymer flood. Viscosity measurements are notoriously difficult and not always very reliable in heavy oil and the evaluation of in-situ viscosity is even more difficult; therefore, we used ranges of viscosity rather than definite values. The observations from Pelican Lake and Seal seem in good agreement and suggest that polymer flood is still feasible and can provide an acceleration in production for live oil viscosities up to 10,000-12,000 cp. There is little experience beyond these values, but it appears that for higher ranges of viscosity polymer injection becomes much more difficult; in Seal polymer flood does not appear to be working satisfactorily in oil viscosities above 14,000 cp. To the best of the author's knowledge, this is the first time that the issue of maximum oil viscosity is investigated in such a manner. Although these results are preliminary and would require further confirmation from other field cases, this paper will provide guidance to engineers screening heavy oil reservoirs for potential application of polymer flood.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".