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In this paper, a new multiphase flow metering device for real-time measurement of oil, gas, and water flow rates is presented. It is composed of several electrical and acoustic sensors whose signals are digitalized and processed by a multilayer neural network. This latest uses the physical models of multiphase fluids to reduce the complexity of the parameter space while improving its accuracy. Furthermore, to overcome the uncertainties of the electrical sensors in the range of 40%-60% and above 90% water-cut (i.e., ranges where most of the multiphase flow meter fail), two rings of high- and low-frequency ultrasonic sensors are used for low and high gas fractions, respectively. The results of experiments that have been conducted in an in-house laboratory-scale multiphase flow loop show that real-time classification for up to 90% gas fraction can be achieved with less than 10% relative error. [ABSTRACT FROM AUTHOR]

A new flow measurement system for real-time flow computation of a two-phase flow in an oil field is described. The system utilizes an array of ultrasound sensors together with capacitance and conductance sensors to accurately interpret the fluid composition in the whole water-cut range. The flow rate is determined using venturi and differential pressure techniques. A dedicated hierarchical neural network algorithm that relies on the various physical properties of the fluid was implemented and tested in a laboratory-scale flow loop. Experimental results demonstrated that real-time classification within a ±5% relative error in volumetric flow regardless of the flow regime or the fluid composition can be achieved. This is an improvement over traditional systems, where weak accuracy is usually obtained either within the 40%-60% water-cut range or in the case of a high water cut. [ABSTRACT FROM AUTHOR]