Improvement of Microemulsion Generation and Stability Using New Generation Chemicals and Nano Materials During Waterflooding as a Cost-Efficient Heavy-Oil Recovery Method
Bibliographic record
Abstract
Abstract This study focuses on the ability of complex colloidal solution to stabilize a heavy oil-brine Pickering emulsion by changing the activity at the interface between heavy oil and brine. After testing many different combinations of anionic and cationic surfactants and nano-particles, we formulated the best stability options and created oil-in-water Pickering emulsions stabilized by silica, a cationic surfactant [dodecyltrimethylammonium bromide (DTAB)], and an anionic surfactant [alcohol propoxy sulfate (Alfoterra S23-7S-90)]. Then, various core flooding experiments were conducted in order to demonstrate the practical ability of the created emulsion system and observe its capacity to enhance oil recovery. Rate-dependency flooding tests were also conducted to determine the optimal flow rate required for heavy oil production through emulsification for different permeability media. Ultimately, slim tube sandpack flooding experiment at the optimal rate was conducted to confirm in-situ emulsion generation and to support the potential use of the chemical combination in the heavy oil industry.
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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".