Water Permeability Reduction Under Flow-Induced Polymer Adsorption
Bibliographic record
Abstract
Proposal The influence of induced polymer adsorption for reducing the effective permeability to water in reservoirs was investigated. Results for the anionic polymer used in this study, show that polymer adsorption is unaffected by the ionic character of polymers, provided the polymer is hydrophilic. In this paper, we present experimental results that show at increased shear rates, there is improvement in the adsorbed polymer layer. This phenomenon is known as flow-induced adsorption. These experiments indicate that above a critical shear rate, there is a shift in permeability-reduction-mechanism from static to flow-induced adsorption, necessitating a sharp increase in adsorbed polymer layer. Different scenarios to investigate the effect of polymer residence time and increasing rate of brine flush were investigated. Results show that there is a marked difference on water permeability reduction at zero polymer residence time and when a polymer residence time is allowed. Under static adsorption, slight increase in adsorbed layer thickness at low shear rates is observed. This effect is induced during brine flush when polymer macromolecules are forced to penetrate the existing adsorbed polymer layer. This results in maximum conformance of the polymer macromolecules at low shear rates, contributing significantly to the influence of flow-induced adsorption, depending on the rate of brine flow. All the experimental results revealed that the critical shear rate for this polymer is between 400 to 500 s-1 in the absence of mechanical degradation. For comparison with the above experimental results, similar experiments were performed at increased polymer concentration and also carried out in a core. Results indicate that the permeability-reduction-mechanism is constrained by increased polymer concentration and low-permeability porous media.
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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 | 0.001 |
| 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".