Visual Confirmation of the Elasticity Dependence of Unstable Secondary Polymer Floods
Bibliographic record
Abstract
The viscoelasticity of polymers is known to contribute significantly toward improved displacement efficiency in polymer flood operations. But the contribution of elasticity of viscoelastic polymers in enhanced oil recovery (EOR) still remains largely unexplored. The majority of literature available on polymer-aided EOR, in general, talks about the role played by viscoelasticity of polymers on improved oil recovery with little or no mention of the individual contribution of the elasticity of polymers on EOR. In this work, partially hydrolyzed polyacrylamide (HPAM) solutions, having identical shear viscosity but different elasticity, were flooded to investigate the individual effect of elasticity on improved oil recovery. A transparent, sand packed visual cell, initially saturated with mineral oil, was used for flooding with four different HPAM solutions. Because these polymer solutions differed only in terms of elasticity, a comparative study of the effect of elasticity on sweep efficiency was done. Images taken at regular intervals during the course of flooding were analyzed to study the frontal displacement patterns changing with the elasticity of different HPAM solutions. The mechanism of viscous fingering in immiscible two-phase flow in porous media at different polymer elasticity values was studied. Results from flooding experiments indicate that polymer solutions with higher elasticity not only yield higher oil recovery, but also require less polymer to produce a given amount of oil. These results were further bolstered by the visual analysis. HPAM solutions with lower elasticity showed a high degree of fingering, whereas solutions with higher elasticity produced more stable displacement fronts. Improved microscopic sweep efficiency, due to the greater flow resistance offered by polymer solution with higher elasticity, was visually confirmed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".