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Related Concept Videos

Contact Angle01:13

Contact Angle

When a solid is dipped inside a liquid, the liquid surface becomes curved near the contact. For some solid–liquid interfaces, the liquid is pulled up along the solid, while for others, the liquid surface is convex or depressed near the solid surface. This phenomenon can be explained using the concept of cohesive and adhesive forces.
The adhesive force is the molecular force between molecules of different materials, that is, between the molecules of the solid and the liquid. The cohesive force...

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A robust machine learning framework for predicting contact angle in nano-assisted chemical EOR.

Youssef E Kandiel1, Omar Mahmoud2, Ahmed Farid Ibrahim3,4

  • 1Department of Petroleum and Energy Engineering, School of Sciences and Engineering, American University in Cairo (AUC), Cairo, Egypt. youssef.kandiel@aucegypt.edu.

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|May 8, 2026
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Summary

This study developed a machine learning model to predict wettability for nano-assisted chemical Enhanced Oil Recovery (nano-cEOR). The model accurately forecasts contact angles, optimizing nanofluid selection and improving oil recovery efficiency.

Keywords:
Enhanced oil recoveryMachine learningNanoparticlesRock-type optimizationSHAPWettability

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Area of Science:

  • Petroleum Engineering
  • Materials Science
  • Computational Science

Background:

  • Wettability alteration via nano-assisted chemical Enhanced Oil Recovery (nano-cEOR) is crucial for controlling fluid flow in porous media.
  • Experimental contact angle measurements for wettability are costly and impractical, hindering nanofluid screening for nano-cEOR.
  • Existing machine learning models lack essential nanoparticle-specific descriptors for accurate nano-cEOR predictions.

Purpose of the Study:

  • To develop a comprehensive machine learning framework for predicting contact angles (CA) in nano-cEOR.
  • To integrate nanoparticle-specific descriptors, fluid properties, rock mineralogy, and reservoir conditions into the ML model.
  • To identify operational thresholds and lithology-specific strategies for effective nano-cEOR.

Main Methods:

  • Collected and utilized 418 experimental data points for model training and validation.
  • Developed a machine learning framework incorporating 418 experimental data points, including NP-specific descriptors, fluid properties, rock mineralogy, and reservoir conditions.
  • Benchmarked six algorithms using hyperparameter optimization and tenfold nested cross-validation, followed by multi-method sensitivity analysis (Sobol indices, SHAP values, PDPs).

Main Results:

  • The XGBoost Regressor (XGBR) model achieved high accuracy (R²=0.953, RMSE=9.24°, MAE=5.87°).
  • Identified key operational parameters: minimum permeability (0.1 mD), optimal salinity (30,000-80,000 ppm), and NP-to-chemical ratios (1:1 to 1.5:1).
  • Discovered lithology-dependent optimal nanoparticle formulations for carbonates (ZrO₂, TiO₂) and sandstones (Fe₃O₄, CuO).

Conclusions:

  • The developed ML framework significantly enhances nanofluid screening and reservoir-specific formulation design for nano-cEOR.
  • Provides crucial decision-support guidelines for efficient field-scale nano-cEOR deployment.
  • Advances the understanding of wettability alteration mechanisms in nano-cEOR through data-driven insights.