ANALYSIS OF PORE-LEVEL PHENOMENA OF DILUTE SURFACTANT FLOODING IN THE PRESENCE AND ABSENCE OF CONNATEWATER SATURATION
Bibliographic record
Abstract
There has been no fundamental work in the literature accentuating microscale behavior of different present phases in an oil reservoir and injected aqueous surfactant solutions and their mutual interactions both in the presence and absence of connate water saturation in dilute surfactant flooding. In this work, a glass micromodel was used to provide new insight into the fundamental issues associated with microscale interactions of oil and aqueous surfactant solutions with connate water. Surfactant flooding tests were conducted both in the presence and absence of connate water to better highlight the influence of connate water on microscopic phenomena as well as macroscopic behavior and performance of the surfactant flooding. This fundamental study revealed that the presence of connate water can decrease the active or working surfactant concentration and significantly modify the local and global microscopic displacement efficiencies, oil recovery efficiencies, and dominancy of the promoted surfactant solution imbibition in determining the oil recovery efficiency. The presence of connate water can also affect the extent of contribution of produced oil-in-surfactant solution emulsions to the improved oil recovery (especially after surfactant solution breakthrough) during the displacement of oil by aqueous surfactant solution in the porous medium. These phenomena occurred mainly due to the inalienable dilution of the surfactant solution by the connate water and possible promoted mass transfer between the mobilized connate water and surfactant solution, which could decrease the active or working surfactant concentration within the surfactant solution.
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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".