ArticleScientific reports2025
A data driven predictive viscosity model for the microemulsion phase.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The changes in phase viscosity at the oil-brine interface due to surfactant addition are critical under practical reservoir conditions. This study develops a computational, data-driven model to accurately estimate and predict peak phase viscosity in microemulsion systems at dynamic environments. Using equilibrium molecular dynamics (MD) simulations, we investigate a decane-sodium dodecyl sulfate (SDS)-brine system, generating viscosity data as of temperatures, pressures, surfactant concentrations, and salinities. The data, computed via the Einstein relation and Green-Kubo formula, provides robust training and test datasets for model development. Various machine learning (ML) based regression algorithms are employed on our dataset to train and fit the model. This study aims to compare the accuracy and correlation coefficients of these models, selecting the most precise model for predicting microemulsion phase viscosity under diverse reservoir conditions. Support Vector Regression (SVR) outperformed other models with an R
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