Suffield Area, Alberta, Canada – Caen Polymer Flood Pilot Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it