The use of an ultrasonic technique and neural networks for identification of the flow pattern and measurement of the gas volume fraction in multiphase flows

In the oil industry, the well stream often consists of a full range of hydrocarbons and a variety of non-wanted components such as water, carbon dioxide, salts, sulfur, and sand. The need for multiphase flow metering (MFM) arises when it is necessary or desirable to meter the flow upstream of the separators. The ultrasonic technique fulfils many of the requirements for MFM in the oil industry (mainly, non-invasive, non-radiative, robust, and relatively non-expensive) and has the capability to provide the information required. The drawback of current ultrasonic techniques, as is the case with other MFM methods, is the need for prior signal calibration. A broader solution to this issue could be the use of artificial neural networks (ANNs). ANNs provide a non-linear mapping between input and output variables and the cross-correlation among these variables and could be an alternative tool for automatic identification of flow patterns. In this context, the objectives of the current investigation are two-fold: (i) to present and analyze acoustic attenuation data for vertical, upward oil-continuous multiphase flows in 1-in. and 2-in. acrylic pipes and flow patterns ranging from bubbly flows to annular flows; (ii) to develop neural networks for flow pattern recognition and gas volume fraction (GVF) measurement using the ultrasonic attenuation data as input. The results shown testify to the ability of the neural networks and the ultrasonic technique to perform these tasks.