Water Enhancement Using Nanoparticles in Water Alternating Gas (WAG) Micromodel Experiments
Bibliographic record
Abstract
Abstract Nanotechnology has found widespread application in a diverse range of industries. Researchers are now investigating whether nanotechnology can be applied to enhance oil recovery (EOR). The goal of enhanced oil recovery is to manipulate the fluid-fluid properties (interfacial tension, viscosity), and fluid-rock properties (contact angle, relative permeability) between the injected fluid and the residual oil phase to improve pore scale recovery efficiency. Adding nanoparticles to the injected water has been shown to improve oil recovery. In this study, nanoparticles were added to the water phase of water alternating gas (WAG) and injected into waterflood residual oil in two dimensional glass micromodels to study the effect of the nanoparticles qualitatively at low pressures. Silicon oxide (SiO2) and aluminum oxide (Al2O3) nanoparticles, at different concentrations, were dispersed in the brine and injected as the water phase in WAG followed by air as the gas phase. Response Surface Methodology (RSM) was used to investigate the effect of the factors and interactions between the factors on oil recovery. The results from the micromodel studies indicate that adding a small amount of nanoparticles to the brine can enhance residual oil recovery.
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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 | 0.000 | 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 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".