Pelican Lake: Learning from the Largest Polymer Flood Expansion in a Heavy Oil Field
Bibliographic record
Abstract
Summary Polymer has been injected continuously since 2005-06 in the Pelican Lake field in Canada, starting with a pilot rapidly followed by an expansion. At some point, 900 horizontal wells were injecting 300,000 bbl/d of polymer solution and oil production related to polymer injection reached 65,000 bopd. As a result, the Pelican Lake polymer flood is the largest polymer flood in heavy oil in the world and the largest polymer flood using horizontal wells. Although some papers have already been devoted to the initial polymer flood pilots, very little has been published on the expansion of the polymer flood and this is what this paper will focus on. The paper will describe the various phases of the polymer flood expansion and their respective performances as well as discuss the specific challenges in the field including strong variations in oil viscosity (from 800 to over 10,000 cp), how irregular legacy well patterns were dealt with, and how primary, secondary and tertiary polymer injection compare. It will also show the performances of polymer injection in combination with multi-lateral wells and touch upon the surface issues including the facilities. The availability of field and production data (which are public in Canada) combined with the variability in the field properties provide us with a wealth of data to better understand the performances of polymer flooding in heavy oil. This case study will benefit engineers and companies that are interested in polymer flood, in particular in heavy oil. The paper will be a significant addition to the literature where few large scale chemical EOR expansions are described.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".