History Matching of a Successful Polymer Flood Pilot in the Pelican Lake Heavy Oil Field, Canada
Bibliographic record
Abstract
Abstract A successful polymer flood is being implemented in the Pelican Lake heavy oil field located in Northern Alberta (Canada). With primary recovery around 5-7 % and several billion barrels OOIP, the field offered a big target for EOR but polymer flooding had never been considered in such high viscosity oil (600 to 80,000cp) until the idea of using horizontal wells gave way to a very successful 5 horizontal wells polymer flood pilot in 2005, followed by a progressive extension to the rest of the field. This paper provides a brief description of the polymer flood pilot then focuses on the various steps involved to generate a realistic reservoir model to history match the pilot. Polymer flooding in heavy oil reservoirs (1500 cp oil in the pilot area) using horizontal wells is really new and the response of the pilot was not totally expected. The oil rate has increased beyond expectations but more surprisingly, the water-cut has increased very slowly and is only in the 50-60 % range after 7 years of operations. History matching the pilot history was important in order to understand what was really taking place in the reservoir; it was performed using up to date Assisted History Matching techniques. Good results have been obtained in terms of history matching. The model can therefore be used to investigate the influence on oil recovery of many parameters such as well length and spacing, injection rate, polymer concentration, slug size, and to evaluate additional recovery compared to continued primary recovery or waterflood. History matching the pilot performance opens the door to a better understanding of polymer flooding in heavy oil reservoirs and to increasing the number of potential application cases.
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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) | 0.000 | 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".