Case Study of Polymer Flood Pilot in a Low Permeability Mannville Sand of the Western Canadian Sedimentary Basin Using Produced Water for Blending
Bibliographic record
Abstract
Abstract Polymer flooding is well established using fresh waters for polymer hydration. These waters can be difficult and expensive to acquire in some regions and environmental concerns make produced waters attractive alternatives. This case study will summarize the operation of the polyacrylamide polymer pilot using produced water in the Viking Kinsella Wainwright B pool. Particular focus will be put on the treatment of the produced water to make it suitable for polymer hydration. The pilot has clearly demonstrated that it is possible to generate a stable polymer fluid with the produced water given the high salinity and iron content. The paper will discuss the removal of undesirable elements, control of bacterial growth, compensation for the salinity and handling of the polymer injection fluid to preserve stability. The target reservoir while having high porosity has low permeability. The permeability to oil averages 95 md which is on the lower end of the traditionally accepted range for polymer flooding. The response of the flood is not definitive but directionally is encouraging. Injectivity challenges have delayed the formation of an oil bank. Production data suggests that the oil bank is starting to develop. The economic viability of the project is indeterminate at this time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".