Machine Learning Applied to Ultrasonic Flow Meters for measuring Dilute,
Turbulent Water-Bentonite Suspension Flow
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.