Molecular interactions of surfactants with other chemicals in chemical flooding processes: A comprehensive review on molecular dynamics simulation studies
Bibliographic record
Abstract
Due to the growing demand for fossil fuels and the transition of many oil fields into a high water-cut stage, enhanced oil recovery (EOR) techniques have become more prevalent to meet this rising demand. Among these techniques, chemical flooding stands out as an effective method, supported by numerous experimental and simulation studies. However, the complexity of a chemical slug composition under harsh reservoir conditions makes the physicochemical phenomena involved in a chemical flooding process highly intricate. To comprehensively understand the microscopic mechanisms governing the phase behavior of complex fluid systems underground, molecular dynamics (MD) simulations have been increasingly employed in recent years to investigate the molecular interactions between various chemicals involved in chemical flooding processes. In this work, we have comprehensively reviewed the recent MD studies focusing on the molecular interactions between surfactants and other chemicals in the chemical flooding processes. Based on the molecular interactions within different chemicals, various nanoscale mechanisms have been proposed to shed light on the physicochemical flow in porous media. Additionally, the MD techniques used in these studies have been summarized, and challenges in the application of MD simulations in the field of chemical flooding have been identified for improving the quality of future MD studies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".