Abstract
Summary Asphaltenes create severe problems in light crude oils (LO) and heavy crude oils (HO) production; therefore, understanding the proper asphaltenes adsorption is a demanding topic to circumvent asphaltene deposition and reconfigure asphaltene viscoelastic networks. The aim of this work is to develop several artificial intelligence (AI) agents that accurately predict the asphaltene adsorption produced by different types of nanoparticles. More than 35 experimental data points were used including different types of crude oils (LO, HO, and extraheavy oils) combined with different types of nanoparticles including silica and alumina. This work presents a general AI agent that predicts the adsorption isotherms of asphaltene exclusively for silica and alumina nanoparticles.
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