Feasibility Study and Pilot Test of Polymer Flooding in Third Class Reservoir of Daqing Oilfield
Bibliographic record
Abstract
Abstract This paper introduces laboratory and numerical simulation studies on selection of polymer injection parameters and project design optimization in a Third Class (Class III) reservoir of Daqing oilfield--the effective permeability is less than 100 md and the effective thickness is smaller than 1 m. Because of these physical properties, multiple producing layers, low permeability and highly dispersed residual oil, the recovery factor of water flooding is low. The amount of incremental producible oil is not large enough to further drill infill wells economically, so polymer flooding was proposed and a pilot test of polymer flooding was conducted. On the basis of the reservoir response to waterflooding, polymer flooding could substantially increase the amount of recoverable reserves. Results of numerical simulation show that the recovery factor using the technique of separate-layer injection with different molecular weight polymers is 8.60% higher than that of water flooding. Compared with common polymer flooding (polymer concentration 1,000 mg/L, viscosity 30 ~50 mPa·s), the incremental recovery efficiency of the technology is 3%. The polymer flooding technique of separate-layer injection with different molecular weight polymers will not only improve oil recovery, but is also more economic. Based on the results of these studies, a pilot test was conducted in March 2007. By the end of September 2007, the pilot test achieved desirable results: the allocated injection rate can be achieved, the injection pressure increased, and the watercut is decreasing, which indicate that polymer flooding can get good results in a Third Class reservoir with low permeability formations and thin pay zones.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".