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.