loading page

Machine Learning Applied to Ultrasonic Flow Meters for measuring Dilute, Turbulent Water-Bentonite Suspension Flow
  • Thiam Wan,
  • Hon Chung Lau,
  • Wai Lam Loh
Thiam Wan
National University of Singapore
Author Profile
Hon Chung Lau
National University of Singapore
Author Profile
Wai Lam Loh
National University of Singapore
Author Profile

Abstract

An ultrasonic flow meter that is calibrated in single phase flow has inherent errors when applied to measure dilute water-bentonite mixture flow. This paper endeavors to use artificial intelligence for recalibration of an ultrasonic flow meter. A commercial ultrasonic transit time flow meter was tested for measuring dilute water-bentonite mixture flow of 0.1-1.0 vol% concentration at room temperature. Results show the test data had a systematic error of -8.3% and a random error of 20.3%. The machine learning LLS regression,2D interpolation and Gaussian Naïve Bayes methods were considered in this exercise. Finally, a combined 2D interpolation method and Gaussian Naïve Bayes classifier approach was preferred. It reduced the systematic error to -0.6% and random errors to ±13.7%. Our study shows a high accuracy ultrasonic flow meter with systematic errors smaller than 1% for oil and gas multiphase application is possible with the aid of artificial intelligence technology.