Online Validation of Multiphase Flowmeters Using Simple Capacitance Sensors

Abou-Arkoub, Ahmed A.; Thorn, Richard; Bousbaine, Amar

doi:10.1109/tim.2010.2045554

Cited by 22 publications

(8 citation statements)

References 15 publications

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“…5, a large range of effective permittivity is undetermined, except in two small regions on top and bottom, when the sensor is filled up with the lowest and highest permittivity components. Such conclusions have also been shown elsewhere [40].…”

Section: B Continuous Phase Inversionsupporting

confidence: 86%

New Algorithm to Discriminate Phase Distribution of Gas-Oil-Water Pipe Flow With Dual-Modality Wire-Mesh Sensor

et al. 2020

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Three-phase gas-oil-water flow is an important type of flow present in petroleum extraction and processing. This paper reports a novel threshold-based method to visualize and estimate the crosssectional phase fraction of gas-oil-water mixtures. A 16x16 dual-modality wire-mesh sensor (WMS) was employed to simultaneously determine the conductive and capacitive components of the impedance of fluid. Then, both electrical parameters are used to classify readings of WMS into either pure substance (gas, oil or water) or two-phase oil-water mixtures (foam is neglected in this work). Since the wire-mesh sensor interrogates small regions of the flow domain, we assume that the three-phase mixture can be segmented according to the spatial sensor resolution (typically 2-3 mm). Hence, the proposed method simplifies a complex three-phase system in several segments of single or two-phase mixtures. In addition to flow visualization, the novel approach can also be applied to estimate quantitative volume fractions of flowing gas-oil-water mixtures. The proposed method was tested in a horizontal air-oil-water flow loop in different flow conditions. Experimental results suggest that the threshold-based method is able to capture transient three-phase flows with high temporal and spatial resolution even in the presence of water-oil dispersion regardless of the continuous phase.

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Section: B Continuous Phase Inversionsupporting

confidence: 86%

New Algorithm to Discriminate Phase Distribution of Gas-Oil-Water Pipe Flow With Dual-Modality Wire-Mesh Sensor

et al. 2020

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“…If the mean flow velocity profiles can be reconstructed with a high sampling rate, the flow diagnostic parameters can indicate the flow conditions with real-time. This can lead to on-line monitoring in the measuring stations via a wireless sensor network, as suggested in the literature [9][10][11]. However, the present experiment relies on the manual rotation of an ultrasonic flow meter, which hinders the fast reconstruction of flow images.…”

Section: Resultsmentioning

confidence: 99%

“…Flow diagnostic parameters are derived from mean flow velocity distribution to verify the flow conditions as to whether the velocity profile is fully-developed turbulent or not [8][9][10][11]. While most of the previous works have focused on the selfvalidation of flow metering in a metering station in applications with wireless sensor networks, some other works have suggested flow diagnostic parameters to investigate flow conditions by examining the mean flow velocity profile.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic flow metering using ultrasound tomography

Chun

Yoon

Lee³

2011

J Mech Sci Technol

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For an accurate flow metering without considering the influences of flow control devices such as valves and elbows in closed conduits, velocity distribution in the cross-sectional area must be integrated. However, most flow meters, including multi-path ultrasonic, electromagnetic or Coriolis mass flow meters, require assumptions on the fully-developed turbulent flows to calculate flow rates from physical quantities of their own concern. Therefore, a long straight pipe has been a necessary element for accurate flow metering because the straight pipe can reduce flow disturbances caused by flow control devices. To reduce costs due to the installation of long straight pipes, another flow metering technique is required. For example, flow rates can be estimated by integrating velocity distributions in the crosssection of conduits. In the present study, ultrasound tomography was used to find the velocity distribution in the cross-section of a closed conduit where flow was disturbed by a Coriolis mass flow meter or a butterfly valve. A commercial multi-path ultrasonic flow meter was installed in the pipeline to measure the line-averaged velocity distribution in the pipe flow. The ultrasonic flow meter was rotated 180° at intervals of 10° to construct line-averaged velocity distributions in Radon space. Flow images were reconstructed by using a backprojection algorithm (inverse Radon transform). Flow diagnostic parameters were defined by calculating statistical moments, i.e., average, standard deviation, skewness, and kurtosis, based on the normalized velocity distribution. The flow diagnostic parameters were applied to flow images to find whether the parameters could discern flow disturbances in the reconstructed velocity distribution.

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“…Chiang and Huang [21] proposed a semi-cylindrical capacitance sensor with a structure similar to concave capacitance sensor and found that the gap between two electrodes was the major factor for output characteristics. Ahmed et al [22] proposed a new capacitance sensor with the electrodes closely placed and attached to the pipe wall, and used it to validate the performance of multiphase flowmeters by identifying flow patterns. Huang et al [23] used a single wire-capacitance probe to measure water layer thickness in horizontal oil-water two-phase flow.…”

Section: Introductionmentioning

confidence: 99%