Current Status of Polymer Flooding in Heavy Oil Fields: When Performances Beat Theory
Bibliographic record
Abstract
Abstract In the past few years many new (including large-scale) polymer flood projects in heavy oil fields have been implemented and have shown very good success; the Milne Point project in Alaska is just a case in point. The goal of this paper is to provide a review of the current situation for polymer flood in heavy oil fields and explore the potential reasons for their success. This paper is based on a review of the ongoing polymer floods projects in heavy oil fields to obtain a good understanding of the performances that can be expected and indeed have been achieved in the field, and to explore the reasons for the differences in responses. Not only do polymer floods in heavy oil achieve recovery factors that are much higher than expected - up to 50% OOIP @ 1 PV injected in some Canadian cases, but they also provide significant reductions in water consumption and a better carbon footprint than steam-based projects. Moreover, thanks to generations of new polymers, those projects can use any kind of water quality without need for water softening, as opposed to steam-based projects. Polymer injection in heavy oil fields should thus have a bright future. The reasons for the high recoveries are not completely clear and are even more puzzling when considering that these results are achieved with relatively low polymer viscosity - in most cases only 25-50 cP which still corresponds to unfavorable Mobility Ratios. One potential explanation put forward by some authors involves some elements of viscous crossflow and field experience in some cases will be compared to theory to better appreciate the validity of this potential mechanism. A complete review of polymer flood field cases in heavy oil will be presented together with an analysis of the high recoveries achieved with relatively low injected polymer viscosities and some hypotheses for these good performances will be discussed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".