Pelican Lake Field: First Successful Application of Polymer Flooding in a Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract The Pelican Lake heavy oil field located in northern Alberta (Canada) has had a remarkable history since its discovery in the early 1970s. Initial production using vertical wells was poor because of the thin (less than 5m) reservoir formation and high oil viscosity (600 to over 40,000cp). 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. Still, with primary recovery less than 10% and several billion barrels of oil in place, the prize for EOR is large. Initially, polymer flooding had not been considered as a viable EOR technology for Pelican Lake due to 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 learnt and a second pilot met with success in 2006. The response to polymer injection in this pilot was excellent, oil rate climbing from 43bopd to over 700bopd and remaining high for over 6 years now; the water-cut has generally remained below 60%. This paper presents the history of the field 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 today, standard industry screening criteria limit its use to viscosities up to 150cp only. Pelican Lake is the first successful application of polymer flooding in much higher viscosity oil (1,000-2,500cp) and as such, it opens a new avenue for the development of heavy oil resources that are not accessible to thermal methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".