Impact of PAM-Grafted Nanoparticles on the Performance of Hydrolyzed Polyacrylamide Solutions for Heavy Oil Recovery at Different Salinities
Bibliographic record
Abstract
Nanoparticle (NP) inclusion is proposed to address polyacrylamides performance limitations at reservoir conditions when used for enhanced oil recovery. In this study, the effect of polyacrylamide (PAM)-grafted SiO2, TiO2, and Al2O3 NPs on the performance of hydrolyzed polyacrylamide (HPAM) and sodium dodecyl sulfate (SDS) solutions as a secondary recovery method is analyzed. The attachment of the polymer onto the surface of the NPs was confirmed by scanning electron microscopy (SEM–EDX), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). The properties of the nanopolymer sols were evaluated using viscosimetry, interfacial tension (IFT) and contact angle measurements. The oil displacement tests were performed in a linear sand-pack at 25 °C and different salinities (1.0, 2.0, and 3.0 wt % NaCl). According to the results, the surface properties of SiO2, TiO2, and Al2O3 NPs were improved by polymer grafting. The nanopolymer sols exhibited lower IFT and ability to alter the wettability of the glass substrate from oil-wet to intermediate-wet. The thickening behavior of the HPAM solution was improved by the addition of 0.2 and 0.4 wt % TiO2-PAM NPs at all salinities. Moreover, the TiO2-PAM NPs increased the cumulative oil recovery of the polymer solution between 5 and 7%, independent of the salinity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".