Abstract
An ultrasonic tomographic array system was developed to measure oil/water/gas phase fraction in multiphase flows. This system interrogates fluids with a ring-array of ultrasonic transducers to generate tomographic images and sound speed profiles of the fluid. Gas entrained within the flow presents a signal transmission challenge for this system, due to reflections at liquid/gas interfaces and absorption of ultrasonic energy. Techniques were developed using physical models and machine learning based algorithms to extend the operating envelope of this system in multiphase fluid flows containing gas entrained in the flow. The array system measures the travel time of the ultrasonic pulses between transceiver pairs. As the signal-to-noise (SNR) at the receiver decreases due to the absorption of the signal due to the presence of gas in the fluid, the variance of the travel time measurement increases. Travel time measurements may also be distorted by reflections at liquid/gas phases leading to longer ray paths between transducer pairs. The upper limit of gas phase measurements is evaluated using physical models for the travel time of pulses between transceiver pairs. Machine learning techniques are applied to determine the gas phase fraction and increase accuracy of the liquids-phase fraction measurements of the system.
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