Polymer Screening Criteria for EOR Application - A Rheological Characterization Approach
Bibliographic record
Abstract
Abstract Polymer flooding has been the most widely used enhanced oil recovery technique in both sandstone and carbonate reservoirs. Ample polymer flooding projects have been conducted with different level of success ever since the technique was introduced 50 years ago. It is a usual practice to select a polymer based on viscosity range, concentration and molecular weight without getting into rheological characterization and its effect on oil recovery. However, in recent times the application of polymer flood has gained critical mechanistic insight with advancement in understanding the role of elasticity on sweep efficiency. Therefore, selecting the type of polymer and understanding how its fluid rheology affects oil recovery are probably among the most critical factors involved in designing a successful polymer flood job. To deal with this, a systematic approach for screening a polymer based on rheological characterization was adapted. Three different polymers, partially hydrolyzed polyacrylamide (HPAM) and Polyoxyethylene (PEO) were first used for fluid rheology study using a cone and plate rheometer and then for oil recovery through a special core holder designed to simulate radial flow through a sand pack – saturated with mineral oil. Effects of various rheological parameters such as; a) Newtonian vs. non-Newtonian rheology (constant shear viscosity vs. shear thinning), b) shear viscosity vs. elasticity, and c) average molecular weight vs. molecular weight distribution (polydispersivity) on oil recovery were investigated. Finally, a parametric characterization study was performed to develop a screening criteria and to correlate oil recovery prediction. The approach could lead to a successful screening process of polymer based on various characteristic parameters such as average molecular weight, polydispersity and Trouton ratio.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".