Suffield Area, Alberta, Canada – Caen Polymer Flood Pilot Project
Bibliographic record
Abstract
Abstract The Suffield Caen reservoir contains 17°API heavy oil and the pool has been under waterflooding since 1996 with water cut of 96%. Primary and secondary oil recovery is 15 – 20% of OOIP. A major problem encountered in waterflood was poor sweep efficiency and high water cut caused by high water/oil mobility ratio, as water quickly broke through the reservoir owing to fingering effects. It is known that sweep efficiency during waterflood can be improved significantly by increasing the viscosity of injected water by use of polymer solution, thus generating a more favorable mobility ratio and enhancing oil recovery. The results of reservoir simulation studies suggested that polymer flood would achieve incremental recovery factor of 7 – 12%, and coreflood results indicated that 29 –32% of incremental recovery is achievable by 0.5 pore volume (PV) of polymer injection. Core floods including polymer, surfactant/polymer(S/P) and alkali/surfactant/polymer (A/S/P) were conducted through lab experiments and eventually polymer flood was selected as a pilot project to improve oil recovery for the Caen reservoir on the basis of polymer, S/P and A/S/P core flood results and project economic evaluation. Polymer injection started in the reservoir 15 months ago and a very positive response has been seen as oil cut has increased to 10% from 5% and oil production rose to 600 bbl/d from 400 bbl/d. Therefore, the polymer flood pilot project is continually implemented and the polymer flood is planned to extend to similar reservoirs in the Suffield area. There is a large amount of conventional heavy oil resaves in the West Canada Basin, so far the primary recovery factor is only 10%, there is a big potential to enhance oil recovery by polymer flood. This polymer flood pilot project provides valuable experiences and guidance to field application.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".