1.0 Introduction
Artificial Intelligence (AI) will reshape the future the flow metering
industry. The connectivity and flow of information between flow
measuring devices and sensors provide an abundance of available data.
The main goal behind the artificial intelligence research is the use of
technology and data to improve the flow meter accuracy and efficiency.
In recent years, artificial intelligence has been applied to recalibrate
utility ultrasonic flow meters (Yazdanshenashad et al. 2018). This paper
investigated the use of machine learning to address the accuracy problem
of ultrasonic flow meter in multiphase measurement. This includes the
use of available data and extracting only useful information for the
purpose of reducing costs and optimizing capacity.
According to a report by Berrebi et al. (2004), the maximum error of a
typical ultrasonic flow meter is 2% or 3% in turbulent flow rate (Re
>4000), and 5% at laminar (Re <2000) or
transient flow (20000<Re<4000). However, this kind
of accuracy is only valid in single phase flow assuming the flow is
homogeneous. In the case of water-bentonite suspension flow, the mixture
contains particles distributed randomly throughout the fluid. Due to the
nature of the processes, the particle distribution in the fluid may
randomly vary in time and space. As the ultrasound interacts with such
particles, some microscopic phenomena may take place between the waves,
particles and the flow (Eren 1998). For example, a water-bentonite
suspension up to 1 vol% consists of two or more substances of very
different acoustic impedance that alter the acoustic signal and the way
ultrasonic beam is transmitted through and reflected from multiphase
solid-solid, solid-fluid and fluid-fluid interfaces (Sirmurda et al.
2016). Therefore, the accuracy of ultrasonic flow meter in measuring
particulate flow such as water-bentonite mixture flow is still a
question that needs to be addressed.
In practice, transit time ultrasonic flow meters are sensitive to many
factors. They are sensitive the variation in velocity profile and the
installation effects. For example, all ultrasonic flow meters assume an
ideal flow profile before the disturbing geometry. However, the distance
between the flow meter and elbows in reality is typically not
sufficiently long to redevelop such an ideal profile. In a recent study
(Weissenbrunner et al. 2016), CFD simulation is used to quantify the
uncertainties of ultrasonic flow meter caused by variations of the
inflow profiles. The study revealed a bias of 1.5-4.5% as flow meter
was installed at a distance smaller than 40 pipe diameters to the double
elbow. Another experiment (Ma et al. 2012) showed that moving the
ultrasonic transducer 0.2 to 0.6 mm axially from the correct position
led to a velocity error as much as 4 - 10% in a transit time ultrasonic
flow meter. Changes in fluid density, flow viscosity and flow patterns
affected the dynamical characteristics in the ultrasonic flow meter
measurement (Catak and Ergan 2019). Therefore, re-calibration is often
necessary to reduce errors due to the possible change of the fluid
density, viscosity and flow patterns in multiphase flow.
In this study, we have examined the accuracy of transit time ultrasonic
flowmeters in measuring dilute water-bentonite suspension flow up to 1
vol% bentonite concentration. Error analysis has been conducted to
evaluate the meter performance in terms of systematic errors (indicated
by the mean relative error) and random errors (indicated by the standard
deviation value of relative errors). Next, the study attempted to close
the gap for both type of errors using multiple error reduction
algorithms including LLS regression method, 2D interpolation method, and
various Gaussian Naïve Bayes classifier algorithms. The goal is to
reduce the systematic error to less than 1% and improve random errors
as much as possible without compromising the resolution of data.
Typical results of the measurement errors are presented in section 4.2,
and the results of various machine learning error reduction exercise
will be presented in section 4.3. Finally, the best combination of
machine learning models is trained to reduce both systematic errors and
random errors as presented in section 5.0. This helps to re-calibrate
the transit time ultrasonic flow meters and improve its accuracy for
future use of ultrasonic flow meter measuring drilling fluid flow.
Further evaluation will be continued in higher bentonite concentrations
in the future. Another important issue in the accurate measurement of
multiphase flow is that of temperature compensation. Using machine
learning models for temperature compensation in an ultrasonic flow meter
will be investigated in future.