Review of Canadian Field Cases of Chemical Floods with Associative Polymer
Bibliographic record
Abstract
Abstract Polymer flooding is now a well-recognised and mature technology to increase hydrocarbon recovery, used in many parts of the world. Given its success, operators are looking at new opportunities for polymer and are trying to push the technical barriers even further. One of these barriers is high salinity which is detrimental to the economics of polymer floods with standard polymers, and thus requires other solutions. Associative polymers are polyacrylamide-based polymers well known for their good resistance to high salinity due to their structure and as a result they could be very promising for use in fields with high TDS. However, they have so far seen little use in field applications due to their perceived plugging tendency, high permeability and mobility reduction which make them more adapted to near-wellbore treatment. Most if not all of the field projects involving associative polymers have taken place in China and in Canada, but little has been published so far. Since public information is available for the Canadian projects, the aim of this paper is to present the field experience of associative polymers in these Canadian projects. The paper will focus on presenting four field cases, Bodo, Mooney and Suffield (2), all in Western Canada. Bodo is a polymer flood while Mooney and Suffield are both polymer and alkali-surfactant polymer projects. Although public information is not always complete, what is available provides some useful and much needed insight on the performances of associative polymers in the field. Our analysis of these four field cases suggests that associative polymers can be injected without special difficulty provided they are well chosen, that is they need to be sufficiently associative to outperform HPAM but not too much in order not to plug the reservoir. These results should comfort engineers who have so far been reluctant to use associative polymers due to lack of field experience. Very few field cases of polymer flood involving associative polymers have been published so far and this paper attempts to shed some light on the performances of associative polymer in some unpublished projects. These positive results may incite engineers working on projects where associative polymers could find a use to consider them.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".