Comparison of Primary, Secondary and Tertiary Polymer Flood in Heavy Oil - Field Results
Bibliographic record
Abstract
Abstract Polymer flooding is an Enhanced Oil Recovery process usually employed in tertiary mode, after a waterflood. However, in heavy oil reservoirs the process is also commonly employed either in primary or secondary conditions because waterflooding is not considered to be economic. For new projects the question often arises as to whether it is better to start by injecting water or to go straight to polymer injection. Injecting water first provides a baseline for comparison with the polymer flood and in some cases allows an earlier start-up of the project due to the construction and commissioning of polymer mixing facilities. On the other hand, the risk is that water channeling could occur and could be irreversible, thus reducing the potential recovery during polymer injection. Some authors have reported laboratory studies comparing the different methods but so far there has been no field results published that could be used for validation or guidance. Although several polymer flood projects in heavy oil have been reported in the past few years they are still relatively few and no detailed field results have been reported to date on that issue. The largest polymer flood in heavy oil is currently being implemented in the Pelican Lake field in Canada, with several hundreds of horizontal wells injecting polymer. All the methods - primary, secondary and tertiary - have been tested and as a result the field provides a large database that allows to compare recovery and other parameters for each method. Although such a comparison is mostly focused on a single field and is thus inherently biased, it should provide useful guidelines for companies looking to start new pilots or field projects. Relevant literature on this topic will also be reviewed to present a complete review of the issue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".