Gryzlov, A., Mironova, L., Safonov, S. and Arsalan, M. (2021) Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates. SPE Symposium: Artificial Intelligence—Towards a Resilient and Efficient Energy Industry, Virtual, 18-19 October 2021, SPE-208648-MS.
https://doi.org/10.2118/208648-ms

has been cited by the following article:

• TITLE: Characterizing Trends in Metering Techniques for Wet Gas Measurement: A Critical Review

  AUTHORS: Ishigita Lucas Shunashu, Osmund Kaunde, Duncan Mwakipesile

  KEYWORDS: Wet Gas Metering Technologies, Wet Gas Flow Meters, Metrology, Machine Learning, Multiphase Flow

  JOURNAL NAME: Journal of Flow Control, Measurement & Visualization, Vol.12 No.1, July 14, 2025

  ABSTRACT: This review critically examines advancements, performance, and challenges in wet gas measurement technologies, emphasizing the need for innovation and adaptation in industrial applications. Key advancements include the integration of machine learning (ML) for real-time calibration, hybrid metering systems combining differential pressure, ultrasonic, gamma-ray, and optical sensors, and the development of high-sensitivity hardware for diverse operational scenarios. Performance evaluations demonstrate promising accuracy by ML algorithms and hybrid systems for gas flow rates (