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

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 Naive Bayes methods were considered in this exercise. Finally, a combined 2D interpolation method and Gaussian Naive 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.