Improved Oil Recovery Potential by Using Emulsion Flooding
Bibliographic record
Abstract
Abstract The present paper deals with emulsion flooding laboratory experiments in porous media, which were performed to investigate the potential use of emulsions as IOR technologies in brownfields of the Gulf of Mexico. Until now, the method of chemical emulsion injections free of siloxanes or fluorinated compounds to promote the hydrophobization of sandstone rocks to reconfigure the phase flow paths, water and oil, generating changes in the flow patterns after the injection of water in order to improve and increase the oil recovery has not been described in the literature. Oil-in-water emulsions were used along with a sand pack and a sandstone core. The flooding tests were carried out with Ottawa sand and Berea sandstones, respectively. The diameter size of the 90 % dispersed phase (D90), which was represented by a hydrophobic chemical compound, was lower than 5 μm and characterized by laser diffraction; the continuous phase was water. Initially, the oil was displaced by water injection. At the end of this step, the oil production fell to zero; all the oil inside the pore space was immobile. An emulsion slug was then injected, followed by another cycle of water injection. During this process, the additional oil recovery was higher than 15 %. An oil-in-water emulsion based on a hydrophobic compound blocked a small, water-full pore and additionally, changed the pore surface wettability from water-wet to oil-wet, which modifies the preferential water routes, increasing the volumetric sweep, changing the residual oil saturation and finally, increasing the oil recovery factor. The results suggest the possibility of using this novel technology in IOR projects for mature oil fields that feature medium gravity (20 °API) and produce high water cut. In addition, this technology is not affected by water salinity and temperature.
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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".