Prediction of oil flow rate through orifice flow meters: Optimized machine-learning techniques

Mohammad Farsi, Hossein Shojaei Barjouei, David A. Wood, Hamzeh Ghorbani, Nima Mohamadian, Shadfar Davoodi, Hamid Reza Nasriani, Mehdi Ahmadi Alvar

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Flow measurement is an essential requirement for monitoring and controlling oil movements through pipelines and facilities. However, delivering reliably accurate measurements through certain meters requires cumbersome calculations that can be simplified by using supervised machine learning techniques exploiting optimizers. In this study, a dataset of 6292 data records with seven input variables relating to oil flow through 40 pipelines plus processing facilities in southwestern Iran is evaluated with hybrid machine-learning-optimizer models to predict a wide range of oil flow rates (Qo) through orifice plate meters. Distance-weighted K-nearest-neighbor (DWKNN) and multi-layer perceptron (MLP) algorithms are coupled with artificial-bee colony (ABC) and firefly (FF) swarm-type optimizers. The two-stage ABC-DWKNN Plus MLP-FF model achieved the highest prediction accuracy (root mean square errors = 8.70 stock-tank barrels of oil per day) for oil flow rate through the orifice plates, thereby removing dependence on unreliable empirical formulas in such flow calculations.

Original languageEnglish
Article number108943
JournalMeasurement: Journal of the International Measurement Confederation
Volume174
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Beta ratios
  • Differential pressure
  • Discharge coefficients
  • Machine-learning-optimizer algorithms
  • Oil flow rate measurement
  • Optimized variable weights
  • Orifice plate meters

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Prediction of oil flow rate through orifice flow meters: Optimized machine-learning techniques'. Together they form a unique fingerprint.

Cite this