Effect of Hydrophobic and Hydrophilic Metal Oxide Nanoparticles on the Performance of Xanthan Gum Solutions for Heavy Oil Recovery
Bibliographic record
Abstract
Recent studies revealed higher polymer flooding performance upon adding metal oxide nanoparticles (NPs) to acrylamide-based polymers during heavy oil recovery. The current study considers the effect of TiO₂, Al₂O₃, in-situ prepared Fe(OH)₃ and surface-modified SiO₂ NPs on the performance of xanthan gum (XG) solutions to enhance heavy oil recovery. Surface modification of the SiO₂ NPs was achieved by chemical grafting with 3-(methacryloyloxy)propyl]trimethoxysilane (MPS) and octyltriethoxysilane (OTES). The nanopolymer sols were characterized by their rheological properties and ζ-potential measurements. The efficiency of the nanopolymer sols in displacing oil was assessed using a linear sand-pack at 25 °C and two salinities (0.3 wt % and 1.0 wt % NaCl). The ζ-potential measurements showed that the NP dispersions in deionized (DI) water are unstable, but their colloidal stability improved in presence of XG. The addition of unmodified and modified SiO₂ NPs increased the viscosity of the XG solution at all salinities. However, the high XG adsorption onto the surface of Fe(OH)₃, Al₂O₃, and TiO₂ NPs reduced the viscosity of the XG solution. Also, the NPs increased the cumulative oil recovery between 3% and 9%, and between 1% and 5% at 0 wt % and 0.3 wt % NaCl, respectively. At 1.0 wt % NaCl, the NPs reduced oil recovery by XG solution between 5% and 12%, except for Fe(OH)₃ and TiO₂ NPs. These NPs increased the oil recovery between 2% and 3% by virtue of reduced polymer adsorption caused by the alkalinity of the Fe(OH)₃ and TiO₂ nanopolymer sols.
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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 | 0.000 |
| 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 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".