Effect of Polymer Degradation on Polymer Flooding in Homogeneous Reservoirs
Bibliographic record
Abstract
In this paper, physical and numerical simulations were applied to investigate the polymer degradation performance and its effect on polymer enhanced oil recovery (EOR) efficiency in homogeneous reservoirs. Physical experiments were conducted to determine basic physicochemical properties of the polymer, including viscosity, rheology, and degradation. A new mathematical model was proposed, and an in-house simulator was designed to further explore polymer degradation. The results of the physical experiments illustrated that polymer could increase polymer solution viscosity significantly, and the relationship between polymer solution viscosity and polymer concentration exhibited a clear power law relationship. However, the viscosity of a polymer solution with the same polymer concentration decreased with an increase in the shear rate, showing shear thinning performance. Moreover, the viscosity decreased with an increase in time, which was caused by polymer degradation. The validation of the designed simulator was improved when compared to the simulation results using ECLIPSE V2013.1 software. The difference between 0 and 0.1 day-1 in the polymer degradation rate showed a decrease of 6% in oil recovery after 2,000 days, according to simulation results, which demonstrated that polymer degradation had an adverse effect on polymer flooding efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".