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Modeling Viscosity of CO~2~–N~2~ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM by Haimin Zheng; Atena Mahmoudzadeh; Behnam Amiri-Ramsheh; Abdolhossein Hemmati-Sarapardeh is a Engineering article available to read on EtoBox.

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Carbon dioxide (CO 2 ) has an essential role in most enhanced oil recovery (EOR) methods in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an immiscible CO 2 -EOR process. Besides numerous CO 2 applications in EOR, most oil reservoirs do not have access to natural CO 2 , and capturing it from flue gas and other sources is costly. Flue gases are available in huge quantities at a significantly lower price and can be considered economically viable agents for EOR operations. In this work, four powerful machine learning algorithms, namely, extra tree (ET), random forest (RF), gradient boosting (GBoost), and light gradient boosted machine (LightGBM) were utilized to accurately estimate the viscosity of CO 2 −N 2 mixtures. To this aim, a databank was employed, containing 3036 data points over wide ranges of pressures and temperatures. Temperature, pressure, and CO 2 mole fraction were applied as input parameters, and the viscosity of the CO 2 −N 2 mixture was the output. The RF smart model had the highest precision with the lowest average absolute percent relative error (AAPRE) of 1.58%, root mean square error (RMSE) of 2.221, and determination coeff

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Author

Haimin Zheng; Atena Mahmoudzadeh; Behnam Amiri-Ramsheh; Abdolhossein Hemmati-Sarapardeh

Publisher

American Chemical Society

Published

2023

Language

EN

Field

Engineering (Physical Sciences)