Nanoparticle-Fortified Emulsification of Heavy Oil
Bibliographic record
Abstract
Abstract Chemical flooding has been suggested as an efficient conformance control technique to develop many of thin post-CHOPS heavy oil reservoirs in Western Canada. In-situ formation of oil in water emulsions due to the effect of surfactant/natural soap has been reported as the main mechanism behind chemical EOR. In this work, the effect of surface-modified silica NPs to enhance the efficiency of surfactant to emulsify heavy oil (14,850 mPa.s and 980 kg/m3 at 25 °C, from the Luseland field) in water has been investigated. Bulk fluid screening experiments were conducted using different surfactants and surface-modified silica NPs for selecting the best heavy oil emulsifier. Complementary experiments such as interfacial/surface tension, NP zeta potential and size measurements, and elemental analysis were conducted to understand the interactions between NPs and surfactant molecules. In the absence of NPs, concentration of both anionic and cationic surfactants should be tuned within a narrow window, near CMC, to create stable heavy oil in water emulsions. It was found that there is a threshold for IFT, obtained at the CMC, which should be met to have stable oil in water emulsions. The created oil in water emulsions break easily at surfactant concentrations higher than the CMC, yielding IFTs higher than the threshold. This observation was also seen in a system containing dodecane. At the CMC of both anionic and cationic surfactants, the IFT between dodecane and an aqueous phase is negative, producing stable dodecane in water emulsions for over three months. In the presence of surface-modified silica NPs heavy oil emulsification is achieved at surfactant concentrations much lower than the CMC. In this case, IFT is remarkably (54 %) reduced, well below the threshold value, due to the combined effect of 2 wt. % negatively-charged silica NPs and only 0.1 wt. % anionic surfactant. These results suggest that the repulsive interaction between negatively-charged NPs and anionic surfactant may result in pushing the surfactant molecules back towards the oil-water interface to enhance IFT reduction.
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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".