Laboratory evaluation of an innovative polyfraction nanoemulsion for enhanced oil recovery in carbonate reservoirs
Bibliographic record
Abstract
Polymers and nanoemulsions are frequently employed to boost enhanced oil recovery (EOR) systems' performance. Several physical phenomena that are essential to the process can be used in a synergistic way when both of these additives are used. Laboratory core flooding investigations utilizing natural cores are one way to evaluate these processes. Carbonate rocks are displaced by oil under extreme heat and pressure in a variety of studies. Tests are conducted on polymer solutions and a recently created polyfraction nanoemulsion. The test findings show that these compounds are stable at high temperatures, high pressures, and in the presence of H 2 S, and they are useful for EOR operations. In the laboratory EOR simulation, the best results were obtained for polymer and nanoemulsion concentrations in diluted reservoir water of 0.05 % and 1 %, respectively. These concentrations were shown to be the most effective. The polymers continue to demonstrate a high level of effectiveness when it comes to the displacement of crude oil from carbonate rocks under these conditions. On the other hand, the nanoemulsion that was tested enhances the wettability of carbonate rocks and reduces interfacial tension, both of which are factors that promote the efficiency of oil displacement. When compared to the quantity that was accomplished with water that did not contain any additives, the oil recovery that was measured in this instance was 37.5 % higher. • Novel EOR Solution: Introduced polymer-nanoemulsion for improved oil recovery in carbonate reservoirs. • Improved Oil Recovery: 0.05% polymer and 1% nanoemulsion boosted oil recovery by 37.5%, reaching 77.5%. • Wettability & IFT: Nanoemulsion improved wettability and lowered IFT, enhancing oil displacement. • High Stability: System remained stable at 120 °C, high salinity, and H₂S presence. • Core Flooding: Core tests on Guelph Dolomite showed strong recovery improvement with the proposed EOR fluid.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".