Modelling of electrical submersible pumps for petroleum multiphase fluids, an intelligent approach supported by a critical review and experimental results

Morteza Zaheri*, R. Tafreshi, Z. Khan, H. Ziaiefar, M. Ghodsi, M. Franchek, K. Grigoriadis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper initially reviews 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 fully connected cascade (FCC in short) artificial neural network to serve the same purpose. Empirical models of ESP are extensively in use; while analytical models are yet to be vastly employed in practice due to their complexity, reliance on over-simplified assumptions or lack of accuracy. The proposed FCC is trained and cross-validated with the same data used in developing a number of empirical models; however, the developed model presents higher accuracy than the aforementioned empirical models. The mean of absolute prediction error of the FCC for the experimental data not used in its training, is 68% less than the most accurate existing empirical model.

Original languageEnglish
Pages (from-to)77-86
Number of pages10
JournalJournal of Engineering Research
Volume16
Issue number2
DOIs
Publication statusPublished - Jan 1 2019

Keywords

  • Cascade artificial neural networks
  • Electrical submersible pumps
  • Empirical models
  • Multiphase petroleum fluid

ASJC Scopus subject areas

  • Engineering(all)

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