Use of Nano Particles for Stable Pickering Emulsion in Heavy Oil Recovery
Bibliographic record
Abstract
Emulsion flooding and heavy oil recovery by in-situ emulsion formation have been reported to show great potential in enhancing heavy oil recovery. Emulsion stability is the key issue controlling the success of this process; conventionally, surfactants were used to facilitate emulsification and improve emulsion stability, increasing the cost remarkably. In this study, we explored using nanoparticles as a cost-effective alternative to expensive surfactants to generate stable Pickering emulsions and studied their effects on heavy oil recovery. Various types of nanoparticles (NPs) have been tested in this study, which can be classified into three main groups: (1) commercial NPs available in market, including cellulose nanocrystals (CNC), silica, alumina, magnetite, and zirconia, (2) natural clay particles existing naturally in the reservoir, including bentonite and kaolinite clays, and (3) natural asphaltenes in heavy crude oil. Heavy oil with a viscosity of 15,640 cP at 21°C from Western Saskatchewan was used in all the experiments except for the asphaltene study, where model oil composed of heavy mineral oil (653 cP) and toluene was used to get a good control over the asphaltene content in the oil. Effects of particle concentration, water salinity, pH, and water to oil ratio (WOR) on emulsion stability were systematically studied through glass vial tests. For clay particles and asphaltenes, sandpack flooding experiments were performed using the formulation which favored the formation of stable emulsions. The effluent samples were analyzed to determine both the emulsion type and the formation of a stable emulsion. The recoveries were monitored and cross-checked with produced effluent samples. In order to visualize the in-situ behavior of clay particles in the porous medium, glass bead micromodels with known amounts of clays were prepared and microscopy images were taken to monitor the movement of clay particles in pore space. The transparent mineral oil instead of opaque heavy oil was used in these micromodel tests for better visualization results. The results showed that for commercial NPs, CNC could become an effective O/W emulsifier by either adjusting pH (3 or 11) or salinity (0.1 wt.% NaCl). The salinity had a great impact on the viscosity of the CNC suspension and CNC-stabilized emulsions with an upper salinity limit of 1 wt.% NaCl; the required minimum concentration for CNC to stabilize O/W emulsion was 1 wt.%. Phase inversion from oil-in-water (O/W) to water-in-oil (W/O) emulsions occurred when the oil content was above 0.6. For clay particles, bentonite is more effective than kaolinite in terms of stabilizing O/W emulsions and the required bentonite concentration was 3 wt.%. The upper salinity limit for bentonite to stabilize O/W emulsion was 0.6 wt.% NaCl at neutral pH (i.e., 7) condition. High pH solutions (i.e., 12) further help the bentonite stabilize emulsions through a reduction of interfacial tension (IFT) between oil and water. Besides, the bentonite-stabilized O/W emulsion inverted to W/O emulsion when the oil content was 0.5 and above. The sandpack flooding test showed that the presence of bentonite further improved final oil recovery from 40%––where no clays were added in the sandpacks––to 49% where 5 wt.% of bentonites were present in the sandpack during the low salinity waterflood process (i.e., DIW). For asphaltenes, an optimal concentration of 0.5 wt.% asphaltenes in oil was found to stabilize W/O emulsions with the model oil composed of 70 vol.% heavy mineral oil (653 cP) and 30 vol.% toluene. The emulsion stability was further improved by adding 1 wt.% NaCl or adjusting the water pH to 10 and above. Additionally, in-situ emulsification of W/O emulsions by asphaltenes was observed in the sandpacks initially saturated with model oil containing 0.5 wt.% asphaltenes. This study promotes an alternative way to generate stable -Pickering- emulsions without using expensive surfactants, a method that will reduce the operational costs and thus favor the field application step of emulsification 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 | 0.000 | 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 | 0.001 |
| 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".