Preliminary Screening and Formulation of New Generation Nanoparticles for Stable Pickering Emulsion in Cold and Hot Heavy-Oil Recovery
Bibliographic record
Abstract
Summary Injecting water with chemicals to generate emulsions in the reservoir is a promising method in the enhancement of heavy-oil recovery because the formation of oil-in-water (O/W) emulsions significantly reduces oil viscosity. Nanoparticles (NPs) (Pickering emulsions) can be used for this purpose as a cost-effective alternative to expensive surfactants; however, such Pickering emulsions need to be stable for successful applications. The objective of this study is to screen the effective emulsifier for O/W emulsions from a broad range of solid NPs and identify suitable Pickering emulsifying agents (e.g., adjusting pH or salt concentration) that can render emulsions stable at relevant conditions, and to investigate how a range of physical parameters, such as particle concentration, water/oil ratio (WOR), and temperature affect emulsion stability. Five NPs—including cellulose nanocrystals (CNCs), silica, alumina, magnetite, and zirconia—were tested on their capabilities of stabilizing O/W emulsions through glass vial screening tests under various pH and salinity conditions. The screening results showed that the CNC could become an effective emulsifier by either adjusting pH or salinity. In addition, zeta potential measurements were conducted to explain the observations. The stabilization mechanisms of CNCs were studied through an epifluorescent transmitted microscope showing that the formation of a dense particle layer around the oil droplets, as well as a network in the continuous phase, were the two main mechanisms accounting for the high stability of the emulsions stabilized by CNCs. The effects of particle concentrations on the emulsion stability were studied quantitatively by analyzing the droplet-size distributions calculated by the open-source ImageJ software, with the results showing a sharp decrease in droplet size, followed by a smooth change as the particle concentration increased. For the WOR effect, phase inversion from O/W to water-in-oil (W/O) emulsions was observed when the oil content was more than 0.6. The thermal stability of emulsions was studied both macroscopically by glass bottle tests and microscopically through a microscope, both of which show that the CNC-stabilized emulsions remained thermally stable up to 100°C. The rheological properties of both aqueous dispersions of CNCs and the corresponding O/W emulsions were also measured under various salinity conditions. The results showed that the salinity had a great impact on the viscosity of the CNC suspension and the typical shear-thinning behavior of Pickering emulsions. This study provides an option to enhance emulsion stability without surfactants, which will reduce the costs and facilitate field applications of emulsion flooding in heavy-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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".