Polymer Flooding Dynamics in Enhanced Oil Recovery: A Pore‐Scale Study of the Influence of Shear‐Thinning Rheology on Flow Dynamics and Recovery Efficiency
Bibliographic record
Abstract
ABSTRACT This study addresses the limited understanding of how shear‐thinning polymer rheology influences enhanced oil recovery (EOR) at the pore scale. Using a pore network model and the Carreau rheological model, the impact of shear thinning under varying wettability, dilution, flow rates, and mobility ratios is examined. Results show that shear thinning strongly affects displacement patterns, with significant viscous fingering and reduced recovery efficiency at high shear rates, as viscosity declines within pore spaces. In contrast, minimal shear‐thinning effects lead to stable displacement fronts, resembling a shear‐independent flood with improved recovery. Higher oil viscosities exacerbate the impact of shear thinning, with reduced oil recovery in the presence of more severe shear‐thinning polymers. In oil‐wet systems, capillary forces counteract shear‐thinning effects, promoting uniform displacement. The results also show that higher injection rates do not guarantee better recovery when shear thinning is present, as excessive shear may reduce polymer viscosity. Optimal recovery occurs at lower flow rates, where the polymer maintains higher viscosity and displacement fronts remain stable. This work highlights the importance of incorporating realistic shear‐thinning behavior in polymer flooding models to enhance the predictive accuracy of EOR simulations and improve understanding of how polymer rheology influences pore‐scale mechanisms in oil recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".