The Potential Applications in Heavy Oil EOR with the Nanoparticle and Surfactant Stabilized Solvent-Based Emulsion
Bibliographic record
Abstract
Abstract This study presents the results of nano-particle and surfactant-stabilized solvent-based emulsion bench test studies under laboratory conditions that investigate the recovery mechanisms of chemical flooding in a heavy oil reservoir. In the study, bench tests, including the phase behavior test, rheology studies and interfacial tension measurement are performed and reported for the optimum selecting method for the nano-emulsion which is going to be applied in the coreflooding experiments. Specifically, nanoemulsion systems have been mixed with Alaska North Slope West Sak heavy oil with 16 API°, which was dewatered in the laboratory condition, to investigate the rheology change during this process. The experiment results suggest that the potential application of this kind of emulsion flooding is a promising EOR process for some heavy oil reservoirs in Alaska, Canada and Venezuela after primary production. Heavy oil lacks mobility under reservoir conditions and is not suitable for the application of the thermal recovery method because of environmental issues or technical problems. The idea of nano-particle application in an EOR area has been recently raised by researchers who are interested in its feature- reaction catalysis-which could reduce in situ oil viscosity and generate emulsion without surfactant. Also, the nano-particle stabilized emulsions can long-distance drive oil in the reservoir, since the nano-particle size is 2-4 times smaller than the pore throat. In conclusion, the nano-emulsion can be an effective enhancement option for an oil recovery method in a heavy oil reservoir which is technically sensitive to the thermal recovery method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".