Evolutionary automated radial basis function neural network for multiphase flowing bottom-hole pressure prediction

• Main Authors: Campos, Deivid, Wayo, Dennis Delali Kwesi, De Santis, Rodrigo Barbosa, Martyushev, Dmitriy A., Yaseen, Zaher Mundher, Duru, Ugochukwu Ilozurike, Saporetti, Camila M., Goliatt, Leonardo
• Format: Article
• Language: English; English
• Published: Elsevier 2024
• Subjects: TP Chemical technology
• Online Access: http://umpir.ump.edu.my/id/eprint/43134/1/Evolutionary%20automated%20radial%20basis%20function%20neural%20network_ABST.pdf; http://umpir.ump.edu.my/id/eprint/43134/2/Evolutionary%20automated%20radial%20basis%20function%20neural%20network.pdf; http://umpir.ump.edu.my/id/eprint/43134/; https://doi.org/10.1016/j.fuel.2024.132666; https://doi.org/10.1016/j.fuel.2024.132666

Accurate multiphase flowing bottom-hole pressure prediction within wellbores is a critical requirement to improve tube design and production optimization. Existing models often struggle to achieve reliable accuracy across the full range of operational conditions encountered in oil and gas wells. Thi...