Modelling of petroleum multiphase flow in electrical submersible pumps with shallow artificial neural networks

Morteza Zaheri, Reza Tafreshi, Zurwa Khan, Mojatba Ghodsi, Mathew Franchek, Karolos Grigoriadis

Research output: Contribution to journalArticle

1 Citation (Scopus)


This paper first investigates existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical submersible pumps (ESPs); then, proposes an alternative model, a shallow artificial neural network (ANN) for the same purpose. Empirical models of ESP are widely used; whereas, analytical models are still unappealing due to their reliance on over-simplified assumptions, need to excessive extent of information or lack of accuracy. The proposed shallow ANN is trained and cross-validated with the same data used in developing a number of empirical models; however, the ANN evidently outperforms those empirical models in terms of accuracy in the entire operating area. Mean of absolute prediction error of the ANN, for the experimental data not used in its training, is 69% less than the most accurate existing empirical model.

Original languageEnglish
JournalShips and Offshore Structures
Publication statusPublished - Jan 1 2019



  • Electrical submersible pump
  • empirical model
  • multiphase petroleum fluid
  • shallow artificial neural network

ASJC Scopus subject areas

  • Ocean Engineering
  • Mechanical Engineering

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