A comparative study on enhancing oil recovery with partially hydrolyzed polyacrylamide: Emulsion versus powder
Bibliographic record
Abstract
Abstract In China, powders of partially hydrolyzed polyacrylamide (P‐HPAM) have been extensively used in chemically enhanced oil recovery (cEOR) processes. However, the relatively longer dissolution time of P‐HPAM and high investment in surface facilities diminish the profit. Here, we compared the properties between P‐HPAM and emulsion HPAM (E‐HPAM) with similar molecular weight of 1.5 × 10 7 g/mol at polymer concentration of 1000 mg/L under a simulated Daqing Oilfield reservoir. We found only 20 min was needed to completely dissolve E‐HPAM, 75% less time than for P‐HPAM. The apparent viscosity of E‐HPAM reached 69.6 mPa · s, 26% higher than that of P‐HPAM. Sheared at 7000 rpm for 30 s, 73.8% viscosity retention was maintained for E‐HPAM solution, 13.7% higher than that of P‐HPAM solution. After 90 days of thermal aging, E‐HPAM solution had 72.8% viscosity retention, 15.2% higher than P‐HPAM. After being adsorbed in a sand package, E‐HPAM and P‐HPAM solutions had 87.0% and 83.2% viscosity retention, respectively. The interfacial tension (IFT) between E‐HPAM solution and Daqing oil could reach 15.3 mN/m, 75.1% lower than that of the P‐HPAM solution with Daqing oil. After 1.2 PV of polymer solution was injected, E‐HPAM solution got an oil recovery factor of 24.1% (OOIP), 10.0% higher than that of P‐HPAM under identical conditions. Both polymer solutions could propagate smoothly in porous media without plugging. In the field trial of 63 wells, after 0.098 PV of E‐HPAM solution was injected within nine months, the injection pressure increased by 1.3 MPa, and an interim recovery factor of 0.58% was obtained.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".