Morteza Mohammadzaher¹, Mojataba Ghodsi¹ and Abdullah AlQallaf², ¹Sultan Qaboos University, Oman and ²Kuwait University, Kuwait

This paper proposes radial basis function network (RBFN) models to estimate the head of gaseous petroleum fluids (GPFs) in electrical submersible pumps (ESPs) as an alternative to widely used empirical models. Both exact and efficient RBFN modelling approaches were employed. RBFN modelling essentially tend to minimise the modelling error, the discrepancy of estimated and real outputs within the modelling data. This may lead to overfitting and lack of model generality for operating conditions not reflected in modelling data. This critical matter was addressed in RBFN design process, and highly accurate RBFNs were developed and cross validated.

Electrical Submersible Pump(ESP), Radial Basis Function Network (RBFN), Model, Petroleum, Gaseous, Head Estimation