On the Effect of Polymer Elasticity on Secondary and Tertiary Oil Recovery
Bibliographic record
Abstract
Typically, a polymer for enhanced oil recovery (EOR) is selected on the basis of the viscosity range or average molecular weight, concentration, and brine composition, besides other reservoir properties. There is not much emphasis given on how the elasticity of polymers could enhance the oil recovery. In this study, in an effort to find a systematic approach for selecting the best polymer for water flooding, the effect of molecular weight distribution (MWD), a direct measure of a polymer’s elasticity, was studied on oil recovery performance. The individual effect of the elasticity of polymers on oil recovery, breakthrough and overall recovery, and residual resistance factor (RRF) was determined by keeping the viscosity constant and varying the elasticity during secondary and tertiary recovery experiments. Within two different groups of polymers each with similar average molecular weight studied here, nearly 10% higher recovery for the highest elastic polymer was observed during secondary recovery, whereas for tertiary flood ∼6% higher recovery with ∼5 times higher RRF value was observed for the highest elastic polymer solution studied here. Results have shown that average molecular weight by itself might not be the best criterion to select the optimum polymer fluid composition for polymer flooding operations. Polymer elasticity should be weighted more than the average molecular weight, as it could correspond to higher sweep efficiency due to the stretching of polymer along the pores. Considering the polymer elasticity or MWD together with average molecular weight seems to be a better approach for achieving higher oil recovery performance at lower polymer concentrations.
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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) | 0.002 | 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".