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International Society for Industrial Process Tomography

8th World Congress on Industrial Process Tomography

Experimental Evaluation of Dual-Modality Electrical Tomographic Systems on Gas-Oil-Water Flow in Horizontal Pipeline

Qiang Wang1, Jiri Polansky1, Mi Wang1, Kent Wei2, Changhua Qiu2, Asaad Kenbar3, and David Millington3

1 University of Leeds, Leeds LS2 9JT, UK

2 Industrial Tomography System plc, Manchester M3 3JZ, UK

3 NEL, Glasgow, G75 0QF, UK



A variety of dual-modality tomographic systems have been proposed for the characterisation of multiphase flow, but the evaluation of such systems are generally carried out under simplified flow conditions, such as stratified flow and slug flow. This paper reports the evaluation results of a dual- modality electrical tomographic system in an industry-scale gas-oil-water three-phase flow. Experimental conditions include water-to-liquid ratio (WLR) from 0% to 100% in parallel with gas volume fractions from 0% to 100%, which produces a variety of flow patterns, such as stratified-wavy flow, slug flow, plug flow, bubbly flow, and annular flow. Commercialised ITS M3C (ECT) and V5R (ERT) dual-modality systems were applied to perform the measurement. A threshold-based multi- dimensional data fused approach was implemented for the data fusion process. The results demonstrated that the ERT system is able to measure water continuous flow with WLR higher than 40%, which is in good agreement with previous reports. The ECT system is able to measure from 0% to 100% WLR, far beyond its conventional capabilities. Even though the tomograms are distorted when WLR is higher than 90%, this result is much better than the reported 40% limit. Visualisation and mean concentration derived from the tomograms by advanced data fusion verify the capability of the system in the application of gas-oil-water flow characterisation.

Keywords electrical resistance tomography (ERT), electrical capacitance tomography (ECT), dual-modality tomographic system, gas-oil-water flow visualisation, multi-dimensional data fusion

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