학술논문

Measurement of Gas-Phase Velocities in Two-Phase Flow Using Distributed Acoustic Sensing
Document Type
Periodical
Source
IEEE Sensors Journal IEEE Sensors J. Sensors Journal, IEEE. 23(4):3597-3608 Feb, 2023
Subject
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Robotics and Control Systems
Sensors
Optical fiber sensors
Fluid flow measurement
Acoustic measurements
Acoustics
Spatial resolution
Optical fiber amplifiers
Distributed acoustic sensing (DAS)
multiphase flow characterization
two-phase flow measurement
Language
ISSN
1530-437X
1558-1748
2379-9153
Abstract
From wells to refining plants and distribution networks, operations in the oil and gas industry involve the transport of multiphase mixtures through long pipelines. One of the most representative patterns is the so-called slug flow, which is characterized by the intermittent passage of gaseous and liquid structures. The monitoring of slug flows allows for proper control and safety in many operations in the oil and gas industry. In recent years, distributed acoustic sensing (DAS) has emerged as a promising technology for industrial use and is applied here for flow monitoring. Namely, this article introduces the use of DAS for the measurement of slug flow. A number of flow configurations under controlled conditions are investigated. Measurements were carried out in a 9-m horizontal pipeline Section in a flow-loop laboratory. Fiber optics were helically wrapped along the outer surface of the pipeline to promote higher sensitivity at a higher spatial resolution space. The velocity of the gas phase given by the elongated bubbles velocity is estimated from DAS data through radial integration in ${k}$ – ${f}$ space and distributed cross correlation methods. Using measurements from a commercial conductance-based sensor as a reference, the results showed the root-mean-square deviation in % (RMSD%) of 8.34% and 12.18%. The cross correlation method has the clear advantage of being able to yield (spatial) distributed data.