Screening of Chemicals for Low Cost Heavy Oil Recovery through Microemulsion Stability Tests
Bibliographic record
Abstract
Abstract This paper reports the results of laboratory scale screening of different chemicals for their microeumlsion generation capabilities to be eventually recommended for non-thermal heavy oil recovery (chemical flooding). The study was performed through visualization of microemulsions generated using vials and microscopic images. The impact of salinities of brine on the emulsification was studied thoroughly in order to identify the synergy between the selected chemicals and the heavy oil. An alcohol propoxy sulfate surfactant from the Alfoterra series, Alfoterra S23-7S-90, a nonionic surfactant HORA-W10, gave good emulsion formation results at low salinity conditions (2.5 wt. %, 3.8 wt. %). Polysorbate-type nonionic surfactant Tween 20 gave good emulsion formation at high salinity conditions (6.35 wt. %, 7.6 wt.%). Their emulsion formation performance with a crude heavy oil of viscosity 4,812cP and 11.74 °API helped create an initial correlation of performance with the composition of crude oil and synthetic brine samples of various salinities. Attempts were also made to stabilize oil-in-water emulsions formed with Alfoterra S23-7S-90, HORA-W10, Tween 20 using nanofluids (metal oxides), sodium carbonate, and an anionic polyacrylamide-based polymer (PolyFlood MAX-165). Emulsions were visualized under the Axiostar plus transmitted-light microscope and their stability was studied in order to screen the most optimal chemical (or chemical combinations) available for low cost heavy 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".