Tao Li

Non-separation measurement of gas–liquid two-phase flow is of great significance to industrial production and theoretical research on two-phase flow. In this paper, a soft measurement method of wet gas flow rate based on ultrasonic and differential pressure (DP) sensors is proposed. A total of 129 sets of experimental data are obtained in DN50 horizontal pipe, the superficial velocity of gas and liquid ranging from 5 m s⁻¹ to 33 m s⁻¹ and 0.015 m s⁻¹ to 0.6 m s⁻¹, respectively. Ten feature parameters of ultrasonic signals such as the time difference between two echoes and kurtosis factor are proposed from time and frequency domain. As well as the power spectral density, mean value and standard deviation value are analyzed based on DP signals. Furthermore, the Spearman correlation coefficient is introduced to evaluate these feature parameters quantitatively. And nine parameters which are highly correlated to gas–liquid flow rate are selected as the inputs of the soft measurement model. Finally, Decision Tree, least square support vector machines and artificial neural network (ANN) models are established through training with the experimental data. Comparison results show that ANN has the lowest prediction errors on the test set, with the mean absolute percentage errors of 1.49% and 3.2% for gas and liquid, respectively.

Faulty phase selection (FPS) methods are used to solve the problem of single phase to ground fault (SPGF) in the distribution network. When a false FPS (FFPS) issue occurs, the short-circuit current in the faulty line suddenly increases. It is crucial to diminish damages caused by the FFPS issue and rapidly re-select the faulty phase. Therefore, this paper proposes a fault-tolerant method based on an active-intervention-type arc suppression device with a soft switch mode (AASD-SSM) and a fast FPS method based on wavelet packet transform (WPT). To strengthen the fault-tolerant performance of FPS, the AASD-SSM is first developed with an intermediate resistance and a soft switch. Then the value of intermediate resistance is determined by the short-circuit current in AASD-SSM. To realize the accuracy and rapidity of FPS, a modified WPT (MWPT) method based on frequency band energy is presented. In different fault scenarios, the number of wavelet decomposition layer is updated by MWPT and the SPGF issues in the faulty lines can be monitored in real-time. Simulation results demonstrate that AASD-SSM improves the fault-tolerant performance of FPS. The phase selection accuracy of MWPT is better than that of WPT in all fault scenarios and the computational complexity of MWPT is reduced for more than 10%.

The swirl flow-meter is widely used in the wellhead measurement of low-permeability gas fields. However, the liquid loading in the measured wet gas will influence the precessing vortex signal. In this study, the MEMS three-axis acceleration sensor (MEMS-TAAS) replaced the traditional piezoelectric sensor to detect the frequency signal from the interference noise. It is found that within the range of type I wet gas, the vortex precession frequency has not been destroyed. Then through the Synchrosqueezed Wavelet Transform (SWT) method, the fluctuation of the instantaneous frequency of the vortex-precession signals was tracked in the time-frequency domain, which was found to be able to reflect the change of the liquid-phase flow rate while being less affected by the gas-phase flow rate. On this basis, the Relative Standard Deviation (RSD) parameter was proposed to represent and quantify this characteristic. Combining this with the main frequency which can reflect the gas phase flow rate, a wet gas two-phase flow measurement model was then set up. The results demonstrated that this model’s relative errors of gas and liquid volume flow rates were within ±4.0% and ±24.8 (Pc=90%), respectively. This method provides a new approach to the vortex precession signal processing and also gives an economical and practical solution for wet gas measurement, which has strong anti-interference ability and supports online measurement, and can achieve the accurate prediction of two-phase flow through only one swirl meter with a MEMS-TAAS.

In the process of natural gas exploitation, the complex pipe network coupled with changes in pressure and temperature can result in a variety of flow regimes, complicating the use of microwave technology for water content measurement. To overcome this challenge, this study introduces a Flow Regime Insensitivity-Microwave Resonant Cavity Sensor (FRI-MRCS). We achieved flow uniformity and regularity by converting the pipeline into a Venturi structure and introducing a swirler within the Venturi contraction section. This design induced a spiral annular flow in the gas-water two-phase flow, which mitigates the influence of the gas-water distribution on the water content measurement. The flow regime modified ability of the swirler is demonstrated by FLUENT simulation. The water content measurement characteristics of FRI-MRCS are investigated through experiments, and the water volume fraction prediction model (0-5%) is established. The confidence probability of relative error within ± 10% is 90.6%. The results show that the FRI-MRCS proposed in this study provides a reliable and effective solution for the measurement of water content in the oil and gas industry.

Swirlmeters are widely used in the wellhead metering of low permeability gas fields in China. However, the swirlmeter’s piezoelectric sensor has difficult distinguishing the vortex precession signal from fluid pulsation noise, pipeline mechanical vibration, and other interference. In this article, in order to detect the vortex precession frequency signal effectively and accurately from the interference noises, a signal detection method based on MEMS three-axis acceleration sensor was proposed. According to the difference between the force direction of vortex signal and interference signals, a dual-differential technique based on three-axis measurement is proposed to eliminate interference noises. The radiated interference of electromagnetic noise was eliminated by adding a notch circuit and radio frequency interference filter to the measuring circuit. Then, the sensitivity characteristics of vortex precession with different swirl angles were investigated by numerical simulation and the performance of the method was evaluated by experiments. The results of experiments show that the method proposed in this article can improve the sensor’s capability of anti-interference and widen the measurement range. Further wet gas experimental results show that the vortex precession frequency signal detected by MEMS-TAAS does not change its linear output law in spite of the presence of liquid phase. Finally, a correction model of the instrument coefficient of the swirlmeter for wet gas measurement was established. The relative errors of the fitting model are within ± 2.0%, the mean percentage error MPE is 0.17%, and the mean absolute percentage error MAPE is 0.81%. The method proposed in this article can improve the ratio of signal to noise of swirlmeter, overcome the influence of interference noise on the precession frequency, and broaden the gas flow measurement range. It realizes the efficient and accurate measurement of the gas flow rate for wet gas flow.

Accurate and real-time measurement of water cut (WC) and flowrate of oil-water flow is a critical factor in achieving intelligent digital oil field. In this paper, a dual-parameter (WC and water flowrate) measurement method for oil-water flow is proposed based on a Venturi embedded with microwave transmission line sensor (V&MS). Dynamic experiments of horizontal fine dispersed oil-water flow are conducted. A two-dimension finite element simulation model for V&MS under the fine dispersed flow is developed to simulate microwave phase outputs at 0-100% WC. The simulation results agree with the experiments, providing a low-cost alternative to expensive oil-water dispersed flow tests. Furthermore, it is found that the flow patterns for 0 - 10% WC and 40 - 100% WC are water-in-oil and oil-in-water flows, respectively, but for 10 - 40 % WC is dispersions of water-in-oil and oil-in-water flow, and 20 % WC is the transition point of the dominant continuous phase. When water phase is continuous, V&MS has a higher sensitivity of phase signals. Finally, a WC prediction model (40 - 100%) is developed by combining a modified Bruggeman model with microwave transmission line theory. Then a discharge coefficient model is developed and the water flowrate is obtained by combining the pressure drop and the predicted WC. Absolute average relative deviation of the predicted WC and water flowrate are 1.49 % and 1.54 %, respectively. The results suggest that appropriate sensing technology integration can offer a powerful solution for accurate and real-time determination of dual-parameter in oil-water flow systems.