Application of artificial neural network to multiphase flow metering: A review

Abstract

Multiphase flow has many applications, such as oil and gas industries. Flow meter devices must be calibrated with field or laboratory data. One of the best methods to calibrate the devices and determine flow parameters is artificial neural networks. In addition, due to development of multiphase flow sensors and computer systems, artificial neural networks are employed to determine the flow regime and phase volume fractions along the pipeline. Up until now, different methods of measurement such as flow pressure signal, radioactive, ultrasonic, impedance, and their combination with neural networks have been presented. In this paper, a review of these works is performed. The type of neural network, measurement method, neural network inputs and development of each method are investigated. Studies show that the use of the gamma-ray method is the most prevalent among the papers presented. Also, examining the flow regime detection and flow rate measurement over time has moved to the use of flow parameters, including pressure, temperature, velocity, viscosity, etc. This simple method (using flow parameters) has the potential to gradually replace more advanced techniques like ultrasonic and electrical, along with expensive techniques like gamma ray. The results of this review indicate that employing intricate neural networks with many layers and neurons slightly increases the accuracy of the results. It can also be said that the use of a combination of several techniques has led to improved results.