Application of Lauryl Betaine in enhanced oil recovery: A comparative study in micromodel
Why this work is in the frame
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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 (Cs). 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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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 | 0.000 | 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) | 0.000 | 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 it