Bibliographic record
Abstract
Primary production of heavy oil in general only achieves a recovery of less than 10% OOIP. Waterflooding has been applied for a number of years in heavy oil pools and can yield much higher recovery but the efficiency of the process diminishes when viscosity is above a few hundreds cp with high water-cuts and the need to recycle significant volumes of water; in addition, significant quantities of oil are still left behind. To increase recovery beyond that, Enhanced Oil Recovery methods are needed. Thermal methods such as steam injection or Steam-Assisted Gravity Drainage (SAGD) are not always applicable, in particular when the pay is thin and in that case chemical EOR can be an alternative. The two main chemical EOR processes are polymer and Alkali-Surfactant-Polymer (ASP) flooding. The earlier records of field application of polymer injection in heavy oil fields date from the 1970’s however; the process had seen very few applications until recently. ASP in heavy oil has seen even fewer applications. A major specificity of chemical EOR in heavy oil is that the highly viscous oil bank is difficult to displace and that injectivity with vertical wells can be limited, particularly in thin reservoirs which are the prime target for chemical EOR. This situation has changed with the development of horizontal drilling and as a result, several chemical floods in heavy oil have been implemented in the past 10 years, using horizontal wells. The goal of this paper is to present some of the best documented field cases. The most successful and largest of these is the Pelican Lake polymer flood in Canada, operated by CNRL and Cenovus which is currently producing over 60,000 bbl/d. The Patos Marinza polymer flood by Bankers Petroleum in Albania and the Mooney project (polymer, ASP) by BlackPearl (again in Canada) are also worthy of discussion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".