Machine learning and nanoparticles for enhancing condensate recovery in gas condensate reservoirs

Abstract Condensate buildup near the wellbore during gas production leads to reduced gas flow and decreased reservoir productivity. This study presents a hybrid approach that integrates experimental wettability alteration using nanoparticles (SiO 2 , CaCO 3 , Al 2 O 3 ) with advanced machine learning (ML) models to enhance condensate recovery in gas condensate reservoirs. Laboratory core flooding experiments were conducted under reservoir‐representative conditions, and the resulting datasets were analyzed using six ML algorithms: support vector machine (SVM), random forest (RF), artificial neural network (ANN), linear regression (LR), least squares boosting (LSBoost), and Bayesian methods. Among all models, SVM demonstrated the highest predictive performance across all nanoparticle types. For CaCO 3 , the model achieved R 2 = 0.998, RMSE = 0.473, and MAPE = 0.491% under residual condensate saturation (Re), and R 2 = 0.987, RMSE = 0.258, MAPE = 2.458% under residual oil saturation (Sor) conditions. For SiO 2 , SVM yielded R 2 = 0.997, RMSE = 0.569, MAPE = 0.551% (Re) and R 2 = 0.991, RMSE = 0.569, MAPE = 0.551% (Sor). For Al 2 O 3 , the model obtained R 2 = 0.978, RMSE = 1.776, MAPE = 1.682% (Re) and R 2 = 0.966, RMSE = 0.725, MAPE = 4.762% (Sor). Overall, CaCO 3 nanoparticles provided the highest recovery enhancement, improving condensate recovery by up to 18%. The integration of nanoparticle‐assisted EOR with ML‐based prediction offers a powerful strategy for optimizing recovery in complex gas condensate reservoirs.