The Synergistic Effect of Branched-Preformed Particle Gel and Hydrolyzed Polyacrylamide on Further-Enhanced Oil Recovery after Polymer Flooding
Bibliographic record
Abstract
Polymer flooding is widely and successfully used to enhance oil recovery. How to further enhance oil recovery after polymer flooding becomes an important question that needs to be solved to stabilize oil production. Branched-preformed particle gel (B-PPG) is a newly developed chemical agent to enhance the oil recovery from heterogeneous reservoirs. Here, laboratory experiments were performed to investigate the enhanced oil recovery of B-PPG/hydrolyzed polyacrylamide (HPAM) mixed solutions through heterogeneous porous media by the core flood test. The results show that the B-PPG/HPAM mixed solutions have a higher oil recovery than solutions containing HPAM or B-PPG alone because of the synergistic effect between B-PPG and HPAM. B-PPG can adjust flows in different permeability zones by its properties of blocking, deforming, and passing through the throat during flow, which can be proved by the fractional flow behaviors in the parallel-sandpack displacement test. HPAM can not only increase the viscosity of the flooding but also enhance the sustained effect of B-PPG. The resistance factors during the flow of B-PPG/HPAM mixed solutions through heterogeneous porous media were also measured. The B-PPG/HPAM mixed solutions have higher resistance factors and residual factors than the solutions of HPAM or B-PPG alone. The relationships between oil recovery and resistance factor show that B-PPG/HPAM mixed solutions have better abilities to enhance oil recovery because of the synergistic effect. Furthermore, the microscopic displacement behaviors in the heterogeneous microscopic model were investigated. The results are consistent with the core flood test and confirm that B-PPG and HPAM have the synergistic effect on further-enhanced oil recovery after polymer flooding.
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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) | 0.001 | 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".