Synthesis and Evaluation of a Low Molecular Weight Amphiphilic Polymer for Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract In this work, a novel, low molecular weight amphiphilic polymer (LAP) with high content of surface‐active functional groups was developed, and its effectiveness for emulsion‐based enhanced oil recovery (EOR) in relatively low permeability reservoirs was evaluated. The LAP polymer can potentially be synthesized on an industrial scale through a free radical polymerization reaction between acrylamide (AM), methyl acrylate (MAA), and the unsaturated surfactant monomer 2‐(acylamido)‐dodecane sulfonic acid (C12AMPS). The interfacial tension between 1000 mg L−1 LAP water solution and Bohai crude oil at 50 °C was reduced to a low value of around 0.2 mN m−1. Core flooding tests were also conducted to study the EOR potential and migration properties of LAP in porous media. The resistance and residual resistant factors of 1500 mg L−1 LAP solution were measured to be 51.0 ± 0.1 and 16.5 ± 0.1, respectively, suggesting LAP's great potential to be used for reservoir profile control. In addition, oil recovered from a three‐layer heterogeneous core by LAP solution was found to be comparable to that recovered using alkaline‐surfactant‐polymer (ASP) flooding. It is also worth mentioning that to accommodate different reservoir conditions, the molecular weight of LAP is adjustable by modifying the concentration of chain transfer agent, surfactant monomer, or initiator.
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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) | 0.000 | 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 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".