Microemulsion Phase-Behavior Equation-of-State Model Using Empirical Trends in Chemical Potentials
Bibliographic record
Abstract
Summary Surfactant-based enhanced oil recovery (EOR) is a promising technique because of surfactant's ability to mobilize previously trapped oil by significantly reducing capillary forces at the pore scale. However, the field-implementation of these techniques is challenged by the high cost of chemicals, which makes the margin of error for the deployment of such methods increasingly narrow. Some commonly recognized issues are surfactant adsorption, surfactant partitioning to the excess phases, thermal and physical degradation, and scale-representative phase behavior. Recent contributions to the petroleum-engineering literature have used the hydrophilic/lipophilic-difference net-average-curvature (HLD-NAC) model to develop a phase-behavior equation of state (EoS) to fit experimental data and predict phase behavior away from tuned data. The model currently assumes spherical micelles and constant three-phase correlation length, which may yield errors in the bicontinuous region where micelles transition into cylindrical and planar shapes. In this paper, we introduce a new empirical phase-behavior model that is based on chemical-potential (CP) trends and HLD that eliminates NAC so that spherical micelles and the constant three-phase correlation length are no longer assumed. The model is able to describe all two-phase regions, and is shown to represent accurately experimental data at fixed composition and changing HLD (e.g., a salinity scan) as well as variable-composition data at fixed HLD. Further, the model is extended to account for surfactant partitioning into the excess phases. The model is benchmarked against experimental data (considering both pure-alkane and crude-oil cases), showing excellent fits and predictions for a wide variety of experiments, and is compared to the recently developed HLD-NAC EoS model for reference.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".