Pelican Lake Field: First Successful Application of Polymer Flooding In a Heavy-Oil Reservoir
Bibliographic record
Abstract
Summary The Pelican Lake heavy-oil field in northern Alberta (Canada) has had a remarkable history since its discovery in the early 1970s. Initial production by use of vertical wells was poor because of the thin (less than 5 m) reservoir formation and high oil viscosity (800–80,000-plus cp). The field began to reach its full potential with the introduction of horizontal drilling and was one of the first fields worldwide to be developed with horizontal wells. However, with primary recovery at less than 10% and 6.4 billion bbl of oil in place (OIP), the prize for enhanced oil recovery (EOR) is large. Initially, polymer flooding had not been considered as a viable EOR technology for Pelican Lake because of the high viscosity of the oil, until the idea came of combining it with horizontal wells. A first—unsuccessful—pilot was implemented in 1997, but the lessons drawn from that failure were learned and a second pilot was met with success in 2006. The response to polymer injection in this pilot was excellent, with oil rate increasing from 43 BOPD to more than 700 BOPD and remaining high for more than 6 years; the water cut has generally remained at less than 60%. Incremental recovery over primary production is variable but can reach as high as 25% of oil originally in place (OOIP) in places. This paper presents the history of the field and then focuses on the polymer-flooding aspects. It describes the preparation and results of the two polymer-flood pilots, as well as the extension of the flood to the rest of the field (currently in progress). Polymer flooding has generally been applied in light- or medium-gravity oil, and even currently, standard industry-screening criteria limit its use to viscosities up to 150 cp only. Pelican Lake is the first successful application of polymer flooding in much-higher-viscosity oil (more than 1,200 cp), and as such, it opens a new avenue for the development of heavy-oil resources that are not accessible by thermal methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".