Evolutionary automated radial basis function neural network for multiphase flowing bottom-hole pressure prediction
Campos D. Wayo D.D.K. De Santis R.B. Martyushev D.A. Yaseen Z.M. Duru U.I. Saporetti C.M. Goliatt L.
1 December 2024Elsevier Ltd
Fuel
2024#377
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. This can lead to misallocating resources during well design, inefficient production strategies resulting in lost revenue, increased risk of wellbore damage, and poorly informed investment decisions. This research presents a data-driven hybrid approach that uses a Radial Basis Function Neural Network and a Particle Swarm Optimization algorithm to construct an automated hybrid machine learning model. The proposed model was compared with several well-established machine learning models in the literature using the same computational framework. The modeling results demonstrated the superiority of the hybrid approach. The model achieved superior performance with lower errors, as evidenced by a Relative Root Mean Squared Error (RRMSE) of 0.055. Furthermore, the model exhibited a low level of uncertainty throughout the analysis, indicating its high degree of reliability. These findings suggest the proposed data-driven approach offers a robust and practical solution for FBHP prediction in oil and gas wells.
Evolutionary optimization , Flowing bottom-hole pressure , Machine learning , Neural networks
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Computational Modeling Program, Engineering Faculty, Federal University of Juiz de Fora, Juiz de, Fora, 36036-900, Brazil
Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana, 010000, Kazakhstan
Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, 26300, Malaysia
Graduate Program in Industrial Engineering, Federal University of Minas Gerais, Belo, Horizonte, 31270-901, Brazil
Department of Oil and Gas Technologies, Perm National Research Polytechnic University, Perm, 614990, Russian Federation
Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
Department of Petroleum Engineering, Federal University of Technology, Owerri, Imo State, PMB, 1526, Nigeria
Department of Computational Modeling, Polytechnic Institute, Rio de Janeiro State University, Nova, Friburgo, 22000-900, Brazil
Department of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de, Fora, 36036-900, Brazil
Computational Modeling Program
Department of Petroleum Engineering
Faculty of Chemical and Process Engineering Technology
Graduate Program in Industrial Engineering
Department of Oil and Gas Technologies
Civil and Environmental Engineering Department
Department of Petroleum Engineering
Department of Computational Modeling
Department of Computational and Applied Mechanics
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