Bibliographic record
Abstract
Abstract Polymer flooding is becoming increasingly more common and more successful in western Canada. In the year 2011, western Canada produced over 1,600,000 m3 of oil using polymer flooding. In the statistical study reported here, production, injection, reservoir, and operating data were gathered from 32 polymer floods of heavy and medium oil in western Canada. Success was highly variable. Incremental recovery ranged from 0.5 to 14% of the original oil in place over periods lasting between 1 and 9 years. Half of the polymer floods in the project showed a decline in water cut. Oils of very high viscosity and low gravity—as high as 5,000 mPa-s for dead oil and as low as 15°API gravity — were successfully flooded using a polymer solution. Operational factors that were most significant to flooding success were injection volumes and rates; inclusion of horizontal wells, in particular injection wells; and water quality. Water quality was a major issue, suggesting that the success of many projects comes down to handling operational issues rather than project concept or design. Polymer integrity and injectivity led to many of the operational difficulties.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".