Pelican Lake Polymer Flood - First Successful Application in a High Viscosity Reservoir
Bibliographic record
Abstract
The Pelican Lake heavy oil field located in northern Alberta (Canada) has had a remarkable history since its discovery in the early 1970s. The reservoir formation is thin (less than 5m) and as the oil is viscous (from 600 to over 40,000cp), initial production using vertical wells was poor. Several methods were used in order to improve production and recovery, including an air injection scheme in the 1990’s. However it is only with the introduction of horizontal drilling that the field began to reach its full potential; indeed Pelican Lake was one of the first fields worldwide to be developed with horizontal then multi-lateral wells. With primary recovery around 5-7% and several billion barrels OOIP, the prize for EOR is large; polymer flood had never been considered in such high viscosity oil until 1995, when the idea of combining polymer flood and horizontal wells gave way to a polymer flood pilot in 1997. This was the first step on the way, and today the field is in the process of being fully converted to polymer flood, with several hundred injection wells already in action. Polymer flooding has the potential to increase recovery to over 20%OOIP at relatively low cost. Pelican Lake is the first successful application of polymer flood in a high viscosity oil reservoir (1,000-2,500cp). This paper presents the history of the field then focuses on the polymer flooding aspects. It describes the preparation and results of the first polymer flood pilots as well as the extension to the field.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".