Application of Lauryl Betaine in enhanced oil recovery: A comparative study in micromodel
Bibliographic record
Abstract
Micromodel flooding is a cost-effective method to investigate enhanced oil recovery. In this study, we apply Lauryl Betaine as an amphoteric surfactant to the injected fluids into the micromodel and compare the results with conventional EOR techniques such as water flooding, solvent flooding, and microemulsion flooding. First, we determined the optimal flow rate of injected fluid into the micromodel to represent fluid flow in the formation. Next, we did water flooding with varying salinities. Next, we did solvent flooding with two different ratios of solvents. Condensate and hexane are the solvents we applied. Next, we did surfactant flooding using Lauryl Betaine. Surfactant flooding tests are conducted using different salinity and surfactant concentration (C s ). Finally, we did microemulsion flooding. The results show that surfactant flooding at high salinity using Lauryl Betaine leads to highest oil recovery among all tested EOR methods. Besides, the results indicate that addition of Lauryl Betaine to the injected brine leads to higher breakthrough time (BT).
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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 | 0.001 |
| 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".