Nait Amar, MenadGhriga, Mohammed AbdelfetahOuaer, HocineBen Seghier, Mohamed El AmineThai Pham, Binh2021-01-112021-01-1120201875-5100https://doi.org/10.1016/j.jngse.2020.103271https://www.sciencedirect.com/science/article/pii/S1875510020301256#!https://dspace.univ-boumerdes.dz/handle/123456789/6105The present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.enCO2ViscosityData-drivenCorrelationsMLPGEPArticle