A Feasibility Study on Enhanced Oil Recovery of Modified Janus Nano Calcium Carbonate-Assisted Alkyl Polyglycoside to Form Nanofluids in Emulsification Flooding
Bibliographic record
Abstract
Emulsification flooding can effectively enhance crude oil recovery to solve the problem of petroleum shortage. In this work, a modified Janus Nano Calcium carbonate (JNC-12) with a particle size of 30–150 nm was synthesized, and an in situ emulsification nanofluid (ISEN) was prepared with JNC-12 and alkyl polyglycoside (APG). Scanning electron microscope (SEM) showed that the dispersion of JNC-12 in air or APG solution was better than Nano Calcium carbonate (Nano CaCO 3 ). The emulsification properties, interfacial tension, and expansion modulus of ISEN were studied, and the result showed that with the increase in salinity, the emulsification rate decreased, the water yield rate increased, the interfacial tension first decreased and then increased, and the expansion modulus first increased and then decreased. With the increase in temperature, the emulsification rate, emulsion viscosity, and interfacial tension decreased. With the increased oil–water volume, the water yield rate and the emulsion viscosity increased. With increase in the concentration of JNC-12, the water yield rate, the emulsion viscosity, and the interfacial tension decreased but the expansion modulus increased. The emulsion generated by emulsifying ISEN with crude oil was an O/W emulsion, the crude oil viscosity was 4–10 times that of emulsion, and the average particle size of emulsion was 1.107 μm. The addition of ISEN caused the decrease in interfacial tension of oil–water to 0.01–0.1 mN/m. The wettability alteration experiment found that ISEN could change the lipophilic rock to hydrophilic rock. Finally, the core displacement experiments showed that compared with the first water flooding, the oil recovery of the second water flooding after ISEN flooding enhanced by 17.6%. This research has important guiding significance for in situ emulsified nanofluid flooding to enhance oil recovery.
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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) | 0.000 | 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 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".