Effect of Elasticity During Viscoelastic Polymer Flooding: A Possible Mechanism of Increasing the Sweep Efficiency
Bibliographic record
Abstract
Summary It has been long believed that the viscoelasticity of polymer solution improves the displacement efficiency in polymer flood operations, but the individual effect of elasticity has not been clearly distilled for a single viscoelastic polymer. In this study, the effect of elasticity of polymer-based fluids on the microscopic sweep efficiency is investigated by injecting two polymer solutions with similar shear viscosity, but significantly different elastic characteristics. Blends of various grades of polyethylene oxide (PEO) with similar average molecular weight and different molecular weight distribution (MWD) were prepared by dissolving in deionized water. The polymer solutions exhibited identical shear viscosity, but different elasticity. A series of experiments were performed by injecting the polymer solutions through a sandpack saturated with mineral oil. The experiments were performed using a special core holder designed to simulate radial flow. Injection was done through a perforated injection line located at the centre of the cell and fluids were produced through two production lines located at the periphery. The experiments were conducted within a shear rate range of field applications. Because both polymer solutions had similar shear viscosity behaviour, but different elastic properties, it was possible to see the effect of elasticity on the sweep efficiency alone. Results of the polymer flooding experiments indicated that the sweep efficiency of a polymeric fluid could be effectively improved by adjusting the MWD of the solution at constant shear viscosity and polymer concentration. The polymer solution with higher elasticity exhibited considerably higher resistance to flow through porous media than the one with lower elasticity, resulting in higher sweep efficiency and lower residual oil saturation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".