ArticleScientific reports2026
A robust machine learning framework for predicting contact angle in nano-assisted chemical EOR.
Article in Scientific reports, 2026. 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
Wettability alteration through nano-assisted chemical Enhanced Oil Recovery (nano-cEOR) critically governs capillary pressure and multiphase flow in porous media. Contact angle (CA) quantifies wettability, yet experimental measurement remains costly, inconsistent, and operationally impractical, creating nanofluid screening bottlenecks. Existing machine learning (ML) models lack nanoparticle (NP)-specific descriptors essential for nano-cEOR physics. This study introduces a comprehensive ML framework predicting CA from 418 experimental data points, integrating NP-specific descriptors (type, concentration, size, functionalization), fluid properties, rock mineralogy, and reservoir conditions. Six algorithms were benchmarked across independent training, testing, and validation datasets with hyperparameter optimization and tenfold nested cross-validation. Multi-method sensitivity analysis integrating Sobol indices, SHAP values, and partial dependence plots identified operational thresholds and lithology-specific strategies. XGBoost Regressor (XGBR) achieved superior validation performance: R
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