Abstract
During the research and development of multiphase flowmeters, errors are often used to evaluate the advantages and disadvantages of different devices and algorithms, whilst an in-depth uncertainty analysis is seldom carried out. However, limited information is sometimes revealed from the errors, especially when the test data are scant, and this makes an in-depth comparison of different algorithms impossible. In response to this problem, three combinations of sensing methods are implemented, which are the “capacitance and cross-correlation”, the “cross-correlation and differential pressure” and the “differential pressure and capacitance” respectively. The analytical expressions of the gas/liquid flowrate and the associated standard uncertainty have been derived, and Monte Carlo simulations are carried out to determine the desired probability density function. The results obtained through these two approaches are basically the same. Thereafter, the sources of uncertainty for each combination are traced and their respective variations with flowrates are analyzed. Further, the relationship between errors and uncertainty is studied, which demonstrates that the two uncertainty analysis approaches can be a powerful tool for error prediction. Finally, a novel multi-sensor fusion algorithm based on the uncertainty analysis is proposed. This algorithm can minimize the standard uncertainty over the whole flowrate range and thus reduces the measurement error.
Highlights
Oil and nature gas are critical strategic resources that support the national economy and people’s livelihood, and their exploration, extraction, transportation and processing all involve the measurement of multiphase flow [1]
1 − β4 s where A denotes the cross-sectional area of the Venturi throat, dp is the measured pressure difference, β is the diameter ratio, β = d/D, Cd and ε are the discharge coefficient and the expansion factor respectively, and both of them can be determined by ISO 5167-4 [36]
Provide a compreIt is worth mentioning that the intention of this paper is not to provide a comprehenhensive and detailed uncertainty analysis for all algorithms, but to use uncertainty analsive as and uncertainty analysis for all algorithms, but to use uncertainty ysis a detailed tool for composition and distribution analysis, and measurement erroranalysis reducas a tool for composition and distribution analysis, and measurement error reduction, tion, as emphasized in the introduction
Summary
Oil and nature gas are critical strategic resources that support the national economy and people’s livelihood, and their exploration, extraction, transportation and processing all involve the measurement of multiphase flow [1]. The commonly used metering method is to separate the multiphase flow into oil, gas and water first and measure their respective flowrates with single-phase flowmeters [3]. If it is already known that the uncertainties of flowrates are dominated by the densitometer rather than the Venturi tube, replacing the differential pressure sensor with a more accurate but more expensive one will not help improve the system performance significantly. A novel multi-sensor fusion algorithm based on uncertainty analysis is proposed This algorithm can minimize the standard uncertainty over the whole range and reduces measurement errors, as well as making their distribution more even
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