Chemical EOR for Heavy Oil: The Canadian Experience
Bibliographic record
Abstract
Abstract Chemical EOR methods such as polymer flooding and ASP (Alkaline-Surfactant-Polymer) are generally not considered suitable for oil viscosities over one or two hundred cp (polymer) or even less (SP/ASP). However this perception is changing, in particular due to field results obtained from a number of chemical EOR pilots or full field floods implemented in Canada in higher viscosity oil in the past few years. Canada is a country well-known for its heavy oil production; recovery processes such as Cold Heavy Oil Production with Sand (CHOPS) and Steam Assisted Gravity Drainage (SAGD) have been invented there. However cold production is limited in terms of the level of recovery it can achieve and thermal techniques also have limitations in particular when reservoirs are thin. Thus Canadian companies have been pursuing chemical EOR to increase recovery in those types of reservoirs. The aim of this paper is to review some of the Canadian projects for which public information is available. Several mostly unpublished projects will be discussed in details, and conclusions will be drawn on the applicability of chemical EOR methods in heavy oil. The practical experience gained in Canada can be applied in other regions of the globe where chemical EOR has so far not been considered or has been screened out because of high viscosity.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".