Polymer Flooding: The Good, the Bad, and the Ugly - Lessons Learned from Field Practices
Bibliographic record
Abstract
Abstract It has been over 40 years since the publication of an early paper titled 'Polymer Flooding, Yesterday, Today, and Tomorrow' in the Journal of Petroleum Technology (Chang, 1978). Significant progress has been achieved since then, with successful commercial-scale applications in China (Daqing, Shengli, Xinjiang, Henan, and Bohai Bay offshore), Canada (Pelican Lake and Brintnell), India (Mangala), Oman (Marmul), the UK North Sea (Captain), and the USA (Yates, Vacuum, and Milne Point) since then. However, global polymer flooding (PF) production remains below expectations by the industry, particularly in the US (NPC, 1976 and 1984). The objective of this paper is to share our analyses and lessons learned to encourage more commercial-scale applications of PF worldwide. This paper reviews basic concepts, screening criteria, and mechanisms of polymer flooding and analyzes historical PF field activities from the early 1960s through 2023. It then presents reasons for the lower-than-forecast productions. Conventional wisdom holds that low crude oil prices are the roadblock to the commercialization of all chemical flooding. However, our analysis suggests that this is not the case, and there are other reasons for the lower-than-forecast results. Based on the progress made over the decades, we divide PF into three stages: the exploration stage from 1960 through 1980, the development stage from 1981 through 2000, and the commercialization stage from 2001 through 2023, including nine major commercial-scale polymer flooding projects worldwide. We analyzed key factors that impacted PF technology over the years, including the critical amount of polymer used, the impact of reservoir heterogeneity on-field performance, the issue of ineffective polymer recycling, the reversal of injection profile, injectivity and productivity problems, and difficulties in treating produced fluids. After these analyses, we propose a set of design criteria, including reservoir evaluation, polymer selection and slug design, laboratory and simulation studies, pre-commercial field tests, and surveillance/monitoring programs to ensure commercial success. We suggest areas for improvement in future operations, such as enhanced PF combined with other technologies. Future applications of polymer flooding in high-temperature and high-salinity, heavy oil, and carbonate reservoirs are also 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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".