Feasibility Study of Secondary Polymer Flooding in Henan Oilfield, China
Bibliographic record
Abstract
After polymer flooding, it is necessary to find relay technology to retain oil yield. In this paper, laboratory experiments were conducted to investigate feasibility of secondary polymer flooding, in which injecting more polymer with higher concentration and relative molecular mass. It is necessary to determine enhanced recovery range and optimum concentration. With microscopic visible glass physical models, further start up of oil drops with secondary polymer can be observed distinctly. In the mean time, macroscopic heterogeneous core tests were carried out with permeability range of 2, 5 and 8. Polymer concentration effective and economical for flooding is optimized. Ii is shown that 3%~8% of further enhanced recovery and 20% decreased water cut can be obtained, and water profile can be improved to some extend after secondary polymer flooding. Thence, it is proved that secondary polymer injection after primary polymer can indeed further improve recovery and the technology of secondary flooding is feasible. Moreover, laboratory optimum concentration of 2200mg/L is determined. On the basis of laboratory results, from 2007 to 2008, filed trial with above optimum parameters were implemented. Up to 2008.12, water cut decreased from 92% to 83%, and cumulative increased crude oil of 5.71×104t.The success of secondary polymer flooding technology provides reference for the development of oil fields after primary polymer flooding in China and other regions in the whole world. Key words: secondary polymer flooding; feasibility study; microscopic mechanism; polymer concentration optimization; after polymer flooding
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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 | 0.000 |
| 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) | 0.001 | 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".