Valeriy S. Rukavishnikov

According to our database, Valeriy S. Rukavishnikov authored at least 8 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

Emerging applications of physics-informed and physics-guided machine learning in geoenergy science: A review.

Shadfar Davoodi, David A. Wood, Mohammed Al-Shargabi, Vladimir Vanovskiy, Valeriy S. Rukavishnikov, Evgeny Burnaev

Commun. Nonlinear Sci. Numer. Simul., 2026

A review of artificial intelligence techniques applied to subsurface oil and gas reservoir simulation.

Shadfar Davoodi, Nikita Makarov, David A. Wood, Mohammed Al-Shargabi, Vladimir Vanovskiy, Dmitry Koroteev, Valeriy S. Rukavishnikov, Evgeny Burnaev

Appl. Soft Comput., 2026

2025

A robust hybrid near-real-time model for prediction of drilling fluids filtration.

Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood, Mohammad Mehrad, Valeriy S. Rukavishnikov

Eng. Comput., August, 2025

2024

Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids.

Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, Mohammad Reza Hajsaeedi, Valeriy S. Rukavishnikov

J. Ind. Inf. Integr., 2024

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, Mohammad Mehrad, Mohammed Al-Shargabi, Valeriy S. Rukavishnikov

Expert Syst. Appl., 2024

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, Mohammad Mehrad, Valeriy S. Rukavishnikov

Appl. Soft Comput., August, 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, Mohammad Mehrad, Valeriy S. Rukavishnikov, Zhenxue Dai

Expert Syst. Appl., July, 2023

Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids.

Shadfar Davoodi, Mohammad Mehrad, David A. Wood, Hamzeh Ghorbani, Valeriy S. Rukavishnikov

Eng. Appl. Artif. Intell., 2023