Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids. - Shadfar Davoodi; Mohammad Mehrad; David A. Wood 003; Hamzeh Ghorbani; Valeriy S. Rukavishnikov (Eng. Appl. Artif. Intell., 2023)

Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites. - Shadfar Davoodi; Hung Vo Thanh; David A. Wood 003; Mohammad Mehrad; Valeriy S. Rukavishnikov; Zhenxue Dai (Expert Syst. Appl., 2023)

Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations. - Shadfar Davoodi; Hung Vo Thanh; David A. Wood 003; Mohammad Mehrad; Valeriy S. Rukavishnikov (Appl. Soft Comput., 2023)

A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms. - Milad Zamanzadeh Talkhouncheh; Shadfar Davoodi; Babak Larki; Mohammad Mehrad; Sina Rashidi; Maher Vasfi (Earth Sci. Informatics, 2023)

Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern - a theoretical study. - Shadfar Davoodi; Hung Vo Thanh; David A. Wood 003; Mohammad Mehrad; Mohammed Al-Shargabi; Valeriy S. Rukavishnikov (Expert Syst. Appl., 2024)

Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids. - Shadfar Davoodi; Hung Vo Thanh; David A. Wood 003; Mohammad Mehrad; Mohammad Reza Hajsaeedi; Valeriy S. Rukavishnikov (J. Ind. Inf. Integr., 2024)

A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data. - Shadfar Davoodi; Mohammad Mehrad; David A. Wood 003; Mohammed Al-Shargabi; Grachik Eremyan; Tamara Shulgina (Earth Sci. Informatics, 2024)

Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review. - Shadfar Davoodi; Mohammed Al-Shargabi; David A. Wood 003; Mohammad Mehrad (Appl. Soft Comput., 2025)