Flow pattern identification for gas-oil two-phase flow based on a virtual capacitance tomography sensor and numerical simulation

Gas-oil two-phase flow is widely encountered in oil exploitation and transportation pipelines. It's complex and transient changes of flow regimes present a great challenge for accurate and real-time measurement. As a non-invasion and real-time measuring method, electrical capacitance tomography (ECT) is suitable for the transient measurement of non-conductive gas-oil flow. However, the highly random and nonlinear nature of multiphase flow make it difficult and limited to investigate the flow parameters based on either static or dynamic measurement. In this research, the whole process of dynamic measurement of ECT applying in gas-oil two-phase flow is thoroughly studied, including simulation calculation, experimental validation and comprehensive data analysis. A simulation approach by coupling the flow and electrostatic field is proposed based on a virtual ECT sensor, in order to monitor the gas-oil two-phase flow characteristics. Based on FLUENT and COMSOL platform, the numerical simulation under six typical flow patterns in a horizontal pipe is carried out. Combining the visualized image generated by ECT measurement and the theory of flow pattern transition, the formation mechanism and structural characteristics of different gas-oil flow patterns are analyzed in detail. Furthermore, this research attempts to analyze the signal fluctuation characteristics caused by flow pattern change, in order to access more in-depth flow information implied in the original capacitance data, via time-series analysis as well as frequency domain analysis based on Flourier Transform. At last, a series of dynamic experiment is conducted to verify the feasibility of the simulation and data analysis approach. The experiment focuses on the flow pattern transition, gas-liquid dynamic characteristics and noise influence in the actual process. It can be concluded from the results of simulation and experiment tests, combining the visualized images and the dynamic characteristics of capacitance signals can make it more effective and intuitive for flow pattern identification, which might be used for the online measurement in real-industry process.