CN113188613A  Multiphase flow measurement method and system based on uncertainty analysis  Google Patents
Multiphase flow measurement method and system based on uncertainty analysis Download PDFInfo
 Publication number
 CN113188613A CN113188613A CN202110244408.3A CN202110244408A CN113188613A CN 113188613 A CN113188613 A CN 113188613A CN 202110244408 A CN202110244408 A CN 202110244408A CN 113188613 A CN113188613 A CN 113188613A
 Authority
 CN
 China
 Prior art keywords
 uncertainty
 flow
 phase flow
 differential pressure
 gas
 Prior art date
 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
 Granted
Links
 238000013076 uncertainty analysis Methods 0.000 title claims abstract description 12
 238000000691 measurement method Methods 0.000 title claims description 5
 239000012071 phase Substances 0.000 claims abstract description 82
 239000007791 liquid phase Substances 0.000 claims abstract description 65
 238000000034 method Methods 0.000 claims abstract description 50
 238000005259 measurement Methods 0.000 claims abstract description 36
 239000007788 liquid Substances 0.000 claims abstract description 35
 238000012417 linear regression Methods 0.000 claims abstract description 13
 238000009530 blood pressure measurement Methods 0.000 claims abstract description 7
 230000001934 delay Effects 0.000 claims abstract description 3
 238000004364 calculation method Methods 0.000 claims description 29
 238000001514 detection method Methods 0.000 claims description 20
 230000005514 twophase flow Effects 0.000 claims description 16
 238000012545 processing Methods 0.000 claims description 10
 238000004590 computer program Methods 0.000 claims description 5
 238000004458 analytical method Methods 0.000 claims description 4
 238000003860 storage Methods 0.000 claims description 4
 239000012530 fluid Substances 0.000 claims description 3
 238000013179 statistical model Methods 0.000 claims description 3
 239000000126 substance Substances 0.000 claims 1
 238000012423 maintenance Methods 0.000 abstract description 2
 238000004422 calculation algorithm Methods 0.000 description 116
 238000009826 distribution Methods 0.000 description 46
 230000000694 effects Effects 0.000 description 25
 230000000875 corresponding effect Effects 0.000 description 16
 230000004927 fusion Effects 0.000 description 13
 238000010586 diagram Methods 0.000 description 11
 230000007423 decrease Effects 0.000 description 10
 230000003247 decreasing effect Effects 0.000 description 10
 238000011160 research Methods 0.000 description 8
 238000000342 Monte Carlo simulation Methods 0.000 description 7
 239000011159 matrix material Substances 0.000 description 7
 238000004088 simulation Methods 0.000 description 6
 230000001419 dependent effect Effects 0.000 description 5
 230000014509 gene expression Effects 0.000 description 5
 241000531897 Loma Species 0.000 description 4
 238000009795 derivation Methods 0.000 description 4
 238000011088 calibration curve Methods 0.000 description 3
 230000008859 change Effects 0.000 description 3
 238000013178 mathematical model Methods 0.000 description 3
 238000007500 overflow downdraw method Methods 0.000 description 3
 230000015572 biosynthetic process Effects 0.000 description 2
 238000005516 engineering process Methods 0.000 description 2
 230000008569 process Effects 0.000 description 2
 230000009467 reduction Effects 0.000 description 2
 230000035945 sensitivity Effects 0.000 description 2
 238000012935 Averaging Methods 0.000 description 1
 241000282376 Panthera tigris Species 0.000 description 1
 230000002596 correlated effect Effects 0.000 description 1
 238000010219 correlation analysis Methods 0.000 description 1
 230000008878 coupling Effects 0.000 description 1
 238000010168 coupling process Methods 0.000 description 1
 238000005859 coupling reaction Methods 0.000 description 1
 238000011161 development Methods 0.000 description 1
 238000011156 evaluation Methods 0.000 description 1
 238000002474 experimental method Methods 0.000 description 1
 230000004907 flux Effects 0.000 description 1
 238000004519 manufacturing process Methods 0.000 description 1
 238000012986 modification Methods 0.000 description 1
 230000004048 modification Effects 0.000 description 1
 230000003287 optical effect Effects 0.000 description 1
 230000000644 propagated effect Effects 0.000 description 1
 230000005855 radiation Effects 0.000 description 1
 238000005070 sampling Methods 0.000 description 1
 239000004065 semiconductor Substances 0.000 description 1
 238000000926 separation method Methods 0.000 description 1
 238000003786 synthesis reaction Methods 0.000 description 1
 238000012360 testing method Methods 0.000 description 1
 XLYOFNOQVPJJNPUHFFFAOYSAN water Substances O XLYOFNOQVPJJNPUHFFFAOYSAN 0.000 description 1
Images
Classifications

 G—PHYSICS
 G01—MEASURING; TESTING
 G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
 G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
 G01F1/05—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects
 G01F1/34—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure
 G01F1/36—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using mechanical effects by measuring pressure or differential pressure the pressure or differential pressure being created by the use of flow constriction
Abstract
The invention discloses a multiphase flow measuring method and system based on uncertainty analysis. The method comprises the following steps: performing multielement linear regression fitting on the equivalent flow and capacitance measurement values based on the crosscorrelation time delay to obtain liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof; obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof by multivariate linear regression fitting based on the capacitance measurement value and the differential pressure measurement value; obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof by performing multiple linear regression fitting on equivalent flow based on differential pressure measurement values and crosscorrelation time delays; and comparing the uncertainty obtained in different modes, and taking the gasliquid flow and the uncertainty corresponding to the minimum uncertainty as quality indexes for measuring flow measurement. The invention can reduce the error of the measuring result and help the operation and maintenance personnel to know the accuracy of the measuring result.
Description
Technical Field
The invention relates to the technical field of oilgaswater multiphase flow measurement in the oil and gas industry, in particular to a multiphase flow measurement method and a multiphase flow measurement system based on uncertainty analysis.
Background
During the research and application of multiphase flow testing, people tend to use the concept of error to assess the quality of a meter or an algorithm, and less deeply research is carried out on the uncertainty of measurement. This is mainly because the multiphase flow algorithm usually adopts empirical correlation and iterative algorithm, which results in complicated calculation of uncertainty. Meanwhile, the key parameters and specific algorithms necessary for uncertainty assessment are sometimes difficult to find from the open literature, which increases the difficulty of research to some extent. Because the measurement error can be calculated only under the condition that the true value is known, and the result has certain randomness, the source of the measurement error is difficult to trace, so that the research around the error has certain limitation, and the characteristics of different algorithms are difficult to compare deeply and meticulously.
The main document in the field of measurement uncertainty evaluation is "Guide to the expression of uncertainty in the measurement, GUM method" and its annex 1 "Propagation probability distribution by Monte Carlo method (MCM method). The above standards were jointly released in 2008 on behalf of 8 international organizations and adopted in 2012 by China. The starting point of the GUM method is that relevant information about the model input quantities is represented by their estimates and their associated standard uncertainties, which are "propagated" through the (linearized) model to provide estimates of the output quantities and their standard uncertainties. Furthermore, the GUM method is based on the central limit theorem, and the output quantity is assumed to be in a Gaussian distribution, so that the inclusion factor and the expansion uncertainty are obtained. Also, the GUM method takes into account the effect of correlation if the input quantities of the models are correlated.
The MCM method (monte carlo method), also called statistical simulation method, random sampling technique, is a method of solving problems using random numbers (or pseudorandom numbers). The monte carlo process operates as follows: fromA value is randomly generated in the probability density function for each input quantity and the corresponding output quantity value of the model at these input quantity values is calculated. This process is repeated a number of times, resulting in a total of M output magnitudes. According to the central limit theorem, the average value Y of the output magnitudes obtained in this way is in the order O (M) if a standard uncertainty of Y exists^{1/2}) Converges to the desired velocity of Y. Therefore, the Monte Carlo method has reasonable convergence.
However, the accuracy and applicability of the assessment for measuring multiphase flow uncertainty currently remains to be improved.
Disclosure of Invention
It is an object of the present invention to overcome the abovementioned drawbacks of the prior art and to provide a multiphase flow measurement method and system based on uncertainty analysis.
According to a first aspect of the present invention, a method of multiphase flow measurement based on uncertainty analysis is provided. The method comprises the following steps:
step S1: analyzing the oilgas twophase flow by using an online detection system, wherein the system is provided with a differential pressure type flowmeter and a highfrequency capacitance conductance detection element;
step S2: performing multielement linear regression fitting on the equivalent flow and capacitance measurement values based on the crosscorrelation time delay to obtain liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof;
step S3: obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof by multivariate linear regression fitting based on the capacitance measurement value and the differential pressure measurement value;
step S4: obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof by performing multiple linear regression fitting on equivalent flow based on differential pressure measurement values and crosscorrelation time delays;
step S5: and comparing the uncertainty obtained in the steps S1, S2 and S3, and taking the gasliquid flow corresponding to the minimum uncertainty and the corresponding uncertainty as quality indexes for measuring flow measurement.
According to a second aspect of the present invention, a multiphase flow measurement system based on uncertainty analysis is provided. The system comprises:
an online detection system: the system is used for analyzing oilgas twophase flow and is provided with a differential pressure type flowmeter and a highfrequency capacitance conductance detection element;
the first calculation unit: the method is used for obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof through linear fitting based on equivalent flow and capacitance measured values of crosscorrelation time delay;
a second calculation unit: the device is used for obtaining the liquid phase flow and the relative standard uncertainty thereof, and the gas phase flow and the standard uncertainty thereof through linear fitting based on the capacitance measured value and the differential pressure measured value;
a third calculation unit: the equivalent flow based on the differential pressure measurement value and the crosscorrelation time delay is subjected to linear fitting to obtain liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof;
a result output unit: and the uncertainty is used for comparing the uncertainty obtained by the first calculating unit, the second calculating unit and the third calculating unit, and the gasliquid flow corresponding to the minimum uncertainty and the corresponding uncertainty are used as quality indexes for measuring flow measurement.
Compared with the prior art, the invention has the advantages that the contribution proportion of the uncertainty is mastered by analyzing the uncertainty, and the contribution proportion changes along with the distribution change of the gasliquid twophase flow. In addition, the relationship between the relative error and the relative uncertainty was also studied, verifying that the uncertainty could be used to predict the error.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram of an oilgas twophase flow realtime online detection system according to one embodiment of the present invention;
FIG. 2 is a graph of experimental data distribution according to one embodiment of the present invention;
FIG. 3 is a flow chart of a capacitance + crosscorrelation algorithm calculation according to one embodiment of the present invention;
FIG. 4 is a diagram illustrating the fitting effect of key parameters of the capacitance + crosscorrelation algorithm according to an embodiment of the present invention, where FIG. 4(a) is a diagram illustrating the fitting effect of LVF and the distribution of standard uncertainty, and FIG. 4(b) is Q_{tot}Fitting effect schematic diagram and standard uncertainty distribution;
FIG. 5 is a graph showing the standard uncertainty and error distribution of experimental data in a capacitance + crosscorrelation algorithm, wherein FIG. 5(a) is a graph showing the standard uncertainty and error distribution of a liquid phase flow rate, and FIG. 5(b) is a graph showing the standard uncertainty and error distribution of a gas phase flow rate, according to an embodiment of the present invention;
FIG. 6 is a relative uncertainty composition and distribution plot of the liquid phase flow for the capacitance + crosscorrelation algorithm, wherein FIG. 6(a) is the composition plot and FIG. 6(b) is the distribution plot, in accordance with one embodiment of the present invention;
FIG. 7 is a graph and plot of the relative uncertainty composition and distribution of gas phase flow for the capacitance + crosscorrelation algorithm, where FIG. 7(a) is the composition graph and FIG. 7(b) is the distribution graph, according to one embodiment of the present invention;
FIG. 8 is a flow chart of a differential pressure + capacitance algorithm calculation according to one embodiment of the present invention;
FIG. 9 is a graph of gas phase virtual high flow Q in a differential pressure + capacitance algorithm, in accordance with one embodiment of the present invention_{tp}The fitting effect graph of (1);
FIG. 10 is a graph showing the standard uncertainty and error distribution of experimental data in a differential pressure + capacitance algorithm, wherein FIG. 10(a) is a graph showing the standard uncertainty and error distribution of a liquid phase flow rate, and FIG. 10(b) is a graph showing the standard uncertainty and error distribution of a gas phase flow rate, according to one embodiment of the present invention;
FIG. 11 is a relative uncertainty composition and distribution plot of the differential pressure + capacitance algorithm liquid phase flow, wherein FIG. 11(a) is the composition plot and FIG. 11(b) is the distribution plot, in accordance with one embodiment of the present invention;
FIG. 12 is a graph and plot of the relative standard uncertainty composition and plot of the differential pressure + capacitance algorithm gas phase flow, where 12(a) is the composition graph and 12(b) is the plot, according to one embodiment of the present invention;
FIG. 13 is a graph of the change in probability density distribution when the volumetric liquid fraction LVF is converted to a Luoman number X in the differential pressure + capacitance algorithm, in accordance with one embodiment of the present invention; wherein FIG. 13(a) is a correspondence between a volume liquid fraction LVF and a Loma number X and a slope of a correlation curve; FIG. 13(b) is a probability density distribution of the volume fraction LVF and the Loma number X at a certain operating point;
FIG. 14 is a flow chart of a crosscorrelation + differential pressure algorithm calculation according to one embodiment of the present invention;
FIG. 15 is a graph showing the standard uncertainty and error distribution of experimental data in the crosscorrelation + differential pressure algorithm, wherein FIG. 15(a) is a graph showing the standard uncertainty and error distribution of the liquid phase flow rate, and FIG. 15(b) is a graph showing the standard uncertainty and error distribution of the gas phase flow rate, in accordance with one embodiment of the present invention;
FIG. 16 is a composition diagram and profile of the liquid phase flow of the crosscorrelation + differential pressure algorithm according to one embodiment of the present invention, wherein FIG. 16(a) is the composition diagram and FIG. 16(b) is the profile;
FIG. 17 is a relative uncertainty composition and profile of the crosscorrelation + differential pressure algorithm gas phase flow, wherein FIG. 17(a) is the composition and FIG. 17(b) is the profile, in accordance with one embodiment of the present invention;
FIG. 18 is a flow chart of a multisensory fusion algorithm calculation according to one embodiment of the present invention;
FIG. 19 is a graph illustrating the standard uncertainty and error distribution of experimental data in a multisensing fusion algorithm, wherein FIG. 19(a) is a graph illustrating the standard uncertainty and error distribution of liquid phase flow, and FIG. 19(b) is a graph illustrating the standard uncertainty and error distribution of gas phase flow, according to one embodiment of the present invention;
FIG. 20 is a relative uncertainty composition and distribution plot of the liquid phase flux for a multisensor fusion algorithm, wherein FIG. 20(a) is the composition plot and FIG. 20(b) is the distribution plot, in accordance with one embodiment of the present invention.
FIG. 21 is a multisensor fusion algorithm gas phase flow relative uncertainty composition diagram and profile, wherein FIG. 21(a) is the composition diagram and FIG. 21(b) is the profile, in accordance with one embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The invention firstly simplifies three common gasoil twophase flow algorithms (capacitance + cross correlation, cross correlation + differential pressure and differential pressure + capacitance algorithms) in a laboratory properly, obtains an analytical expression of the uncertainty of the gas and liquid flow standards by a GUM method, and obtains a numerical solution of the uncertainty by simulating by using an MCM method. The analysis verifies that the results obtained by the two methods are basically the same, so that the correctness and feasibility of the two methods are theoretically verified. Subsequently, the invention traces the source of the uncertainty of each algorithm, and researches the contribution ratio of each sensor (such as Venturi differential pressure, ECT capacitance and electrical crosscorrelation time delay) to the final uncertainty and the change of the contribution ratio along with the distribution of gasliquid twophase flow. In addition, the invention also researches the relation between the relative error and the relative uncertainty, and verifies the useful conclusion that the uncertainty can be used for predicting the error. Finally, the invention provides a multisensor fusion method for multiphase flow measurement based on uncertainty analysis, and compared with any existing algorithm, the method has lower relative error and relative standard uncertainty, enables the distribution of the error and the uncertainty to be more uniform, and can effectively improve the accuracy and the application range of the multiphase flow measurement.
The uncertainty assessment of both GUM method and MCM method includes three stages of formula establishment, propagation and summarization. The establishment of the formula can be divided into two steps of establishing a mathematical model and analyzing uncertainty sources. Mathematical models of different flow algorithms will be described in detail below, while the source and spread of uncertainty generally follow the following laws.
1) Sources of uncertainty
The method of assessing the uncertainty of each input quantity criterion can be divided into a class a assessment based on repeated readings and a class B assessment based on other available information. The algorithm related to the embodiment of the invention is to obtain intermediate variables by performing least square fitting on the measurement signals, and then integrate different intermediate variables to obtain the gasliquid twophase flow. Thus, embodiments of the present invention are directed to algorithms where uncertainty in the input is derived from a multiple linear regression fit.
Standard statistical model of multiple linear regression specifies Y_{i}Is x_{ij}Is added to the random noise term, expressed as:
where ei is the random error, with: e (E)_{i})＝0，Var(e_{i})＝σ^{2}，Cov(e_{i}，e_{j})＝0，i≠j。
With matrix notation, equation (1) can be written as:
Y_{n×1}＝X_{n×p}β_{p×1}+e_{n×1}(2)
wherein: e (e) 0, Σ_{ee}＝σ^{2}I。
It can be shown that the least squares estimation of the coefficient betaAnd its covariance matrixRespectively as follows:
in the formula, σ^{2}Is the error e_{i}Square expectation of (a), can prove^{2}The unbiased estimate of (c) is:
in summary, the standard statistical model considers that the independent variable X is not random, but is a fixed variable that can be accurately controlled and observed, while the dependent variable Y has a constant variance σ due to the effect of the error e^{2}. Fitting coefficient beta and its standard uncertaintyThe specific expression of (2) can be derived from the formulas (3) to (5).
2) Propagation of uncertainty
The measurement model is expressed as:
Y＝f(X)＝f(X_{1}，…，X_{N})(6)
the estimated value of the output quantity is:
y＝f(x_{1}，…，x_{N})(7)
synthetic target of measured estimated value yQuasiuncertainty u_{c}(y) is calculated as:
equation (8) is the propagation rate of uncertainty, where u (x)_{i}，x_{j}) Is an input quantity x_{i}And x_{j}Covariance, Σ, between_{x}Is the corresponding covariance matrix. If the input quantity x_{i}And x_{j}All are coefficients obtained by multivariate linear fitting, then For sensitivity factors, it is usual to measure the function f at x_{i}The partial derivative of the position is obtained,is the corresponding sensitivity coefficient row vector. When the measurement model is complicated and inconvenient to calculate by partial derivatives,it can also be obtained by numerical calculation methods or experimental measurements.
The invention is described in further detail below with reference to the figures and the specific embodiments.
Referring to fig. 1, the oilgas twophase flow realtime online detection system of the embodiment includes a fluid mixing module 1, a highfrequency capacitance/conductance detection module 2, a differential pressure type flow meter module 3, a data processing module 4, and a display module 5. Oilgas twophase flow sequentially flows through a mixing module 1, a highfrequency capacitance/conductance detection module 2 and a differential pressure type flowmeter module 3. The data processing module 4 receives the signals of the highfrequency capacitance/conductance detection module 2 and the differential pressure type flowmeter module 3 for analysis and processing, and the respective flow of the oilgas phases is obtained through calculation. The display module 5 outputs and displays the result of the data acquisition processing module 4. The detection system utilizes the characteristic that the density of gas is far lower than that of oil, and the dielectric constant/conductivity of the oil is far higher than that of the gas, so that the volume gas fraction LVF is directly obtained as far as possible, and mutual coupling among errors is avoided; meanwhile, the oilgas twophase flow is detected on line in real time by using nonseparation, nonradiation and noninvasive detection technologies (such as time series crosscorrelation analysis, differential pressure type flow meters and capacitance/conductance detection elements).
The raw data used in the present invention are from gasoil twophase experiments of 9, 12 and 2020, which are 20 groups, and have a gauge pressure of 0.3MPa, and the distribution is shown by the small circle in fig. 2. The solid line on the right side of fig. 2 represents a contour of capacitance, the dotted line represents a contour of crosscorrelation equivalent flow rate, and the dotted line represents a contour of differential pressure equivalent flow rate. It can be seen that by finding the intersection point of any two contour lines, the corresponding gasliquid twophase flow can be found from fig. 2. Therefore, theoretically, three algorithms of capacitance + cross correlation, differential pressure + capacitance, cross correlation and differential pressure can be selected, and the calculation flows of the three algorithms are described in detail below.
1) Capacitance + crosscorrelation algorithm
The basic calculation flow of the capacitance + cross correlation algorithm is shown in fig. 3. As can be seen from FIG. 3, the total volume flow Q required for calculating the gas and liquid flow rates_{tot}And the volume fraction LVF were both obtained by unary linear fitting.
Wherein the average capacitanceThe capacitance between four groups of counter electrodes of ECT is selected to be averaged to obtain:and the average time delay is obtained by averaging the time delay signals between the two adjacent electrodes through the doublelayer ECT eight pairs:then the equivalent flow rate Q_{th}Calculated by the following formula:a represents the transverse section of the ductCrosssectional area, L, represents the spacing between the two layers of ECT.
Then, the average capacitances are respectively measuredPositive equivalent flow rate Q_{th}As dependent variable y, LVF and Q are respectively used at the same time_{tot}Is used as an independent variable x to perform unary linear fitting:
y＝β_{0}+β_{1}x(9)
the effect of fitting to LVF is shown in the left panel of FIG. 4(a), Q_{th}And Q_{tot}The fitting effect of (c) is shown in the left graph of fig. 4 (b). Coefficient of performanceCovariance matrixAnd the standard uncertainty u (y) of the dependent variable y may be calculated according to equation (3), equation (4), and equation (5), respectively.
According to the calculation flow of the capacitance + crosscorrelation algorithm in fig. 3 and the formula (9), the estimated value LVF of the volume liquid fraction LVF_{0}Need to pass through intermediate variablesCalculated to obtain the total volume flow Q_{tot}Estimated value of Q_{tot0}Need to pass through an intermediate variable Q_{th0}And (4) calculating according to the following calculation formulas:
when dependent variable y_{0}When known, x can be derived_{0}The standard uncertainty of (c) is:
simplifying to obtain:
Volume liquid fraction LVF_{0}The distribution of the standard uncertainty is shown in the right graph of FIG. 4(a), and the total volume flow Q_{tot0}The standard uncertainty distribution of (a) is shown in the right graph of FIG. 4 (b). As can be seen from FIG. 4 and equation (12), the calibration curve is obtained by unary linear fitting, and the uncertainty of the fitted value obtained on the calibration curve is related to the difference in addition to the parameters n and pIt is related. When fitting point x_{0}Andthe closer together, the less measurement uncertainty. This, in turn, can be used as a basis for selecting measurement points when initially fitting the calibration curve, i.e. selecting the mean value of the Xcoordinates of the respective measurement pointsShould be as close as possible to the fitting point x to be measured in the future_{0}。
Obtaining an estimated value LVF of the volume liquid content LVF_{0}And standard uncertainty u (LVF)_{0}) And total volumetric flow rate Q_{tot}Estimated value of Q_{tot0}And standard uncertainty u (Q)_{tot0}) Then, the liquid phase flow rate Q_{l0}And its relative standard uncertainty u_{rel}(Q_{l0}) The calculation can be made as follows:
q_{l0}＝Q_{tot0}LVF_{0}(13)
the gas phase flow rate and its corresponding standard uncertainty can be calculated with reference to equations (13) and (14), and will not be described herein.
The gas and liquid flow prediction effect of the capacitance + crosscorrelation algorithm is shown in fig. 5. The figure respectively shows the numerical simulation result of the MCM method (Monte Carlo method) and the theoretical derivation result of the GUM method, the length of a line segment represents the standard uncertainty of an estimated value, and the vertical distance from a point to a central black line reflects the error of the point. As can be seen from fig. 5, the capacitancecrosscorrelation algorithm has a poor prediction effect on the liquid phase flow rate and a good prediction effect on the gas phase flow rate, and is particularly obvious under the working condition of the atmospheric air volume. As can also be seen in fig. 5, there is a certain link between error and uncertainty: the longer the line segment corresponding to the uncertainty is, the greater the possibility that the point deviates from the central black line is; meanwhile, if the average value of the uncertainty is larger, the average value of the error is generally larger. Therefore, the standard uncertainty corresponding to the estimated value can predict the point error to a certain extent, thereby providing guidance for practical application.
The relative uncertainty composition and distribution of the liquid phase flow for the capacitancecross correlation algorithm is shown in fig. 6. As can be seen from fig. 6(a), in the relative uncertainty of the liquid phase flow of the capacitancecrosscorrelation algorithm, the component introduced by crosscorrelation and the component introduced by capacitance are similar. At the same time, the liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}The component introduced by the crosscorrelation gradually decreases and the component introduced by the capacitance gradually increases, resulting in a relative uncertainty u in the liquid phase flow_{rel}(Q_{l}) Showing a tendency to decrease first and then increase. And with Q_{l}Increase of (2) relative uncertainty u of the liquid phase flow_{rel}(Q_{l}) Presenting a monotonically decreasing trend. Thus, the relative uncertainty u of the liquid phase flow of the capacitancecross correlation algorithm_{rel}(Q_{l}) The contour lines of (a) are shown in FIG. 6 (b).
Relative uncertainty in gas phase flow for capacitancecross correlation algorithmThe degree of formation and distribution is shown in fig. 7. As can be seen from fig. 7(a), in the relative uncertainty of the gasphase flow of the capacitancecrosscorrelation algorithm, the component introduced by the crosscorrelation is much larger than the component introduced by the capacitance, so the gasphase flow uncertainty u_{rel}(Q_{g}) Is dominated by the crosscorrelation component. At the same time, the liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}Is increased, the components introduced by the crosscorrelation and by the capacitance are both gradually reduced, resulting in a relative uncertainty u of the gasphase flow_{rel}(Q_{g}) Presenting a monotonically decreasing trend. And with Q_{l}Increase of gas phase flow relative uncertainty u_{rel}(Q_{g}) But also exhibits a monotonically decreasing trend. Thus, the relative uncertainty u of the gas phase flow of the capacitancecross correlation algorithm_{rel}(Q_{g}) At a low liquid phase flow rate Q_{l}Low gas phase flow rate Q_{g}The contour line of the maximum value (c) is shown in FIG. 7 (b).
2) Differential pressure + capacitance algorithm
The calculation flow of the differential pressure + capacitance algorithm is shown in fig. 8, and the estimated value LVF of the volume liquid fraction LVF thereof_{0}And its standard uncertainty u (LVF)_{0}) The calculation method of (2) is completely the same as the capacitance + crosscorrelation algorithm. After obtaining LVF_{0}The algorithm then transforms it to X as follows_{0}：
The formula of uncertainty of the synthetic standard according to the linear measurement model includes:
according to the research results of Murdock, Bizon and forest tiger, etc., the gas phase imaginary high coefficientAnd Loma numberThere is a linear relationship between:
φ_{g}＝β_{0}+β_{1}X(17)
the left and right sides of the above formula are multiplied by Q_{g}The method comprises the following steps:
in the formula Q_{tp}The gas phase virtual high flow can be calculated by the venturi differential pressure Δ p according to the following formula:
in the above formula, A represents the crosssectional area of the pipe, ρ_{g}Denotes the gas phase density, p_{l}Denotes the density of the liquid phase, C_{d}Representing the outflow coefficient of the venturi, epsilon representing the coefficient of expansion of the compressible fluid, C_{d}And the value of epsilon can be chosen by ISO 51674.
The invention utilizes the formula (18) to correct the gas phase virtual high flow Q_{tp}The fitting was performed and the effect of the fitting is shown in fig. 9. The fitting method considers the gas phase virtual high flow Q_{tp}Standard uncertainty u (Q) of_{tp}) Is constant, the value thereof can be calculated with reference to the formula (5), and the coefficientAnd its corresponding covariance matrixThe calculation may be performed with reference to equation (3) and equation (4), respectively. These parameters will be used in the calculation of the gasliquid twophase flow and its standard uncertainty.
Note that equation (18) can be written as Q_{tp}＝Q_{g}(β_{0}+β_{1}X)＝Q_{g}φ_{g}Can also be written asSo that the gas phase virtual height coefficient phi is obtained_{g}And liquid phase imaginary height coefficient phi_{l}And its corresponding standard uncertainty u (phi)_{g}) And u (phi)_{l}) According to the formula Q_{g}＝Q_{tp}/φ_{g}Andand calculating the volume flow of the gas phase and the liquid phase.
Wherein the gas phase virtual height coefficient phi_{g}The uncertainty of (d) can be calculated according to the following equation:
if Q is ignored_{tp}And phi_{g}Correlation between, relative uncertainty u of gas phase flow_{rel}(Q_{g}) The calculation can be performed according to the uncertainty synthesis formula of the linear measurement model:
relative uncertainty u of liquid phase flow_{rel}(Q_{l}) Similar processing can be performed with reference to formula (20) and formula (21).
The gas and liquid flow prediction effect of the differential pressure + capacitance algorithm is shown in fig. 10, which respectively shows the numerical simulation result of the MCM method (monte carlo method) and the theoretical derivation result of the GUM method. It can be seen from fig. 10 that the MCM method result and the GUM method result of the differential pressure + capacitance algorithm substantially coincide with each other, but there is still a certain difference, and the reason for this difference will be finally analyzed below. As can be seen from fig. 10, the differential pressure + capacitance algorithm has a better prediction effect on the liquid flow rate, and is particularly obvious under the working condition of large liquid amount. Meanwhile, the prediction effect of the algorithm on the gas phase flow is stable and ideal, but the prediction effect of the algorithm under the working condition of large air volume is poor.
The relative uncertainty makeup and distribution of the differential pressure + capacitance algorithm liquid phase flow is shown in fig. 11. As can be seen in fig. 11(a), the components introduced by differential pressure and capacitance are substantially similar in the relative uncertainty of the liquid phase flow for the differential pressure + capacitance algorithm. At the same time, the liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}The component introduced by differential pressure gradually decreases and the component introduced by capacitance gradually increases, resulting in a relative uncertainty u of the liquid phase flow_{rel}(Q_{l}) Showing a tendency to decrease slightly before increasing. And with Q_{l}Increase of (2) relative uncertainty u of the liquid phase flow_{rel}(Q_{l}) Presenting a monotonically decreasing trend. Thus, the relative uncertainty u of the liquid phase flow of the differential pressure + capacitance algorithm_{rel}(Q_{l}) The flow rate is maximum at a low liquid phase flow rate and a high gas phase flow rate, and the contour lines thereof are as shown in FIG. 11 (b).
Differential pressure + capacitance algorithm the relative uncertainty makeup and distribution of gas phase flow is shown in fig. 12. As can be seen from FIG. 12(a), in the relative uncertainty of the differential pressure + capacitance algorithm gas phase flow, the component introduced by the capacitance is much larger than the component introduced by the differential pressure, so the gas phase flow uncertainty u_{rel}(Q_{g}) Is dominated by the capacitance component. At the same time, at a liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}The component introduced by the capacitance decreases slightly and then increases gradually, while the component introduced by the differential pressure decreases gradually, resulting in a relative uncertainty u of the gasphase flow_{rel}(Q_{g}) It also shows a tendency of slightly decreasing and then increasing. And with Q_{l}Increase of gas phase flow relative uncertainty u_{rel}(Q_{g}) But also exhibits a substantially monotonically decreasing trend. Thus, the differential pressure + capacitance algorithm relative uncertainty u of the gas phase flow_{rel}(Q_{g}) At a low liquid phase flow rate Q_{l}High gas phase flow rate Q_{g}The contour line of the maximum value (c) is shown in FIG. 12 (b).
As can be seen from fig. 11(b) and 12(b), the matching effect of the GUM and MCM results in the differential pressure + capacitance algorithm is not very good. After the analysis, it can be found that this is caused by the fact that when the formula (15) is used for converting the volume liquid content LVF into the loma number X, the probability density distribution of X deviates from the gaussian distribution, and the effect is shown in fig. 13 (b). The relationship between the volume liquid fraction LVF and the logma number X and the slope of the correlation curve are shown in fig. 13(a), and it can be seen that the LVF ranges from 0 to 1, and X ranges from 0 to + ∞. Therefore, the larger the LVF is, the larger the corresponding curve slope dX/dLVF is, and the increase of X caused by positive disturbance of the LVF is always larger than the decrease of X caused by negative disturbance of the LVF, so that X has the characteristic of right deviation. Meanwhile, the larger the LVF is, the more serious the X right deviation is, and it is obvious from FIG. 13(b) that the expected MCM result of X is larger than the GUM result thereof. A right shift in X will further cause the gas and liquid flow to deviate from the Gaussian distribution, resulting in MCM results deviating from GUM results. The inclusion interval in this case shall be subject to the results given in the MCM method.
3) Crosscorrelation + differential pressure algorithm
The flow of calculation of the crosscorrelation + differential pressure algorithm is shown in FIG. 14, with a volume flow Q_{tot}Estimated value of Q_{tot0}And its standard uncertainty u (Q)_{tot0}) The calculation method of (1) is identical to the capacitance + crosscorrelation algorithm, and the virtual high flow Q is_{tp}Coefficient of (2)Covariance matrixAnd u (Q)_{tp}) The calculation method is completely the same as the differential pressure + capacitance algorithm. Obtaining an estimate Q of the total volumetric flow_{tot0}The algorithm then transforms Q_{l}＝Q_{tot}Q_{g}Substituting into equation (18), the reduction is:
in the same way, if Q is equal_{g}＝Q_{tot}Q_{l}Substituting into equation (18) can obtain a table of liquid phase flowThe expression is as follows:
The standard uncertainties referred to in equations (24) and (25) may both be derived from a multivariate linear fit to obtain a covariance matrixAnd the standard uncertainty u (y) of the dependent variable y.
In solving for gas phase flow Q_{g}Standard uncertainty u (Q) of_{g}) When, special attention should be paid toAndthe correlation between them.
In the formula:
standard uncertainty u (Q) of liquid phase flow_{l}) Similar processing can be performed with reference to equations (24) to (27) based on equation (23).
The gas and liquid flow prediction effect of the crosscorrelation + differential pressure algorithm is shown in fig. 15, and as can be seen from fig. 15, the crosscorrelation + differential pressure algorithm has a very stable prediction effect on the liquid flow, has very high prediction precision, and is particularly obvious under the working condition of large liquid amount. However, the prediction effect of the algorithm on the gas flow is very unstable, and the prediction accuracy of the algorithm is rapidly reduced along with the reduction of the gas flow, so that the prediction effect of the algorithm is very good under the working condition of large gas flow, and the performance of the algorithm is very poor under the working condition of small gas flow. Therefore, the algorithm is more suitable for running under the working condition of large flow.
The relative uncertainty makeup and distribution of the liquid phase flow for the crosscorrelation + differential pressure algorithm is shown in fig. 16. As can be seen in fig. 16(a), the components introduced by differential pressure and the components introduced by crosscorrelation are similar in the standard uncertainty of the liquid phase flow for the crosscorrelation + differential pressure algorithm. At the same time, at a liquid phase flow rate Q_{l}Under certain conditions, the components introduced by differential pressure and crosscorrelation do not follow the gas phase flow Q_{g}Is varied to result in a relative uncertainty u of the liquid phase flow_{rel}(Q_{l}) Substantially independent of gas phase flow Q_{g}And (4) changing. And with Q_{l}Increase of (2) relative uncertainty u of the liquid phase flow_{rel}(Q_{l}) Presenting a monotonically decreasing trend. And with Q_{l}The relative uncertainty of the liquid phase flow rate shows a monotonically decreasing trend. Thus, the relative uncertainty u of the liquid phase flow of the crosscorrelation + differential pressure algorithm_{rel}(Q_{l}) The contour lines of (a) are shown in FIG. 16 (b).
The crosscorrelation + differential pressure algorithm gas phase flow relative uncertainty makeup and distribution is shown in fig. 17. As can be seen from fig. 17(a), in the relative uncertainty of the gasphase flow rate in the crosscorrelation + differential pressure algorithm, the component introduced by the crosscorrelation is much larger than the component introduced by the capacitance, and therefore the gasphase flow rate uncertainty u_{rel}(Q_{g}) Is dominated by the crosscorrelation componentAnd (4) leading. At the same time, the liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}Both the components introduced by the crosscorrelation and the differential pressure decrease rapidly, resulting in a relative uncertainty u of the gas phase flow_{rel}(Q_{g}) Showing a tendency to decrease rapidly. And with Q_{l}Increase of gas phase flow relative uncertainty u_{rel}(Q_{g}) And is substantially invariant. Thus, the relative uncertainty u of the gas phase flow for the crosscorrelation + differential pressure algorithm_{rel}(Q_{g}) The contour lines of (a) are shown in FIG. 17 (b).
4) Multisensor fusion method
The calculation flow of the multisensor fusion algorithm is shown in FIG. 18, and the basic idea is to calculate the uncertainty and flow rate of the three algorithms respectively, then take the serial number of the minimum uncertainty in the three algorithms, and then take the gasliquid flow rate Q corresponding to the serial number_{tp1}And the corresponding uncertainty is output as a final result.
The gas and liquid flow prediction effect of the multisensing fusion algorithm is shown in fig. 19, and as can be seen from fig. 19, the multisensing fusion algorithm has a very stable prediction effect on the liquid flow and very ideal precision, especially under the working condition of large liquid amount. Meanwhile, the prediction effect of the algorithm on the gas phase flow is very stable, the accuracy is ideal, and especially under the working condition of large air volume. Therefore, the multisensing fusion algorithm has lower relative uncertainty than the three conventional algorithms and lower relative error than the three conventional algorithms, so that the conclusion that the uncertainty analysis can be used for guiding the error of the improved algorithm is proved to a certain extent. Thus, multisensing fusion methods based on uncertainty analysis can be used in practice to improve the accuracy of the meter.
The relative uncertainty composition and distribution of the multisensory fusion algorithm is shown in fig. 20. As can be seen in fig. 20(a), the uncertainty of the differential pressure + capacitance algorithm and the crosscorrelation + differential pressure algorithm are similar in magnitude for the relative uncertainty of the liquid phase flow, whereas the uncertainty of the capacitance + crosscorrelation algorithm is not advantageous. At the same time, the liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}The uncertainty of the differential pressure + capacitance algorithm is gradually increased, and the uncertainty of the differential pressurecross correlation algorithm is gradually reduced, so that the liquid phase uncertainty of the multisensor fusion algorithm is essentially an organic combination of the differential pressure + capacitance algorithm under a lowflow working condition and the cross correlation + differential pressure algorithm under a highflow working condition. And with Q_{l}The liquid phase flow relative uncertainty of all three algorithms shows a monotonically decreasing trend, and therefore the contour of the uncertainty of the liquid phase flow is shown in fig. 20 (b).
The relative uncertainty makeup and distribution of the multisensing fusion algorithm gas phase flow is shown in FIG. 21. As can be seen from fig. 21(a), the uncertainty of the differential pressure + capacitance algorithm and the capacitance + crosscorrelation algorithm are similar in magnitude for the relative uncertainty of the gas phase flow, while the uncertainty of the crosscorrelation + differential pressure algorithm is not advantageous. At the same time, the liquid phase flow rate Q_{l}Under certain conditions, along with the gas phase flow rate Q_{g}The uncertainty of the differential pressure + capacitance algorithm is gradually increased, and the uncertainty of the capacitance + crosscorrelation algorithm is gradually decreased, so that the gas phase uncertainty of the multisensor fusion algorithm is essentially an organic combination of the differential pressure + capacitance algorithm under a lowflow working condition and the capacitance + crosscorrelation algorithm under a highflow working condition. And with Q_{l}The gas phase flow relative uncertainty of all three algorithms shows a tendency to decrease monotonically to some extent, so the contour of the uncertainty of the liquid phase flow is shown in fig. 21 (b).
In this context, uncertainty means the degree of uncertainty in the measured value, due to the presence of measurement errors, which is an indicator for characterizing the quality of the measurement result. The smaller the uncertainty is, the closer the measurement result is to the measured true value is, the higher the quality is, the higher the level is, and the higher the use value is; the greater the uncertainty, the lower the quality of the measurement and the lower the level, the lower its value of use.
In summary, the invention firstly simplifies three common gasoil twophase flow algorithms of a laboratory multiphase flow measuring device, and obtains a mathematical model between the input quantity and the output quantity of the laboratory multiphase flow measuring device through theoretical derivation. Meanwhile, uncertainty of different conventional flow algorithms is analyzed by using a GUM method and an MCM method respectively, and correctness and feasibility of the two methods are verified theoretically. Meanwhile, the invention also obtains the following important conclusions:
1) the flow uncertainty is obviously different under different gas and liquid flow combinations, and the uncertainty given by different algorithms has obvious difference in both value and distribution. This difference in distribution can provide the basis for multisensory fusion: if the multiphase flowmeter is provided with a plurality of flow calculation methods, the flow prediction result of the algorithm with the minimum uncertainty is only required to be given under a certain gas and liquid flow.
2) The uncertainty contribution ratios of the individual sensors differ significantly from one algorithm to the next. The conclusion helps to reveal the deep differences among different algorithms and provides guidance for the development of the algorithms and the selection of sensors in the future.
3) The uncertainty may predict to some extent the magnitude of the error. Therefore, the research uncertainty can also help the operation and maintenance personnel to know the accuracy of the metering result, thereby providing help for production decision.
4) An uncertainty analytic solution obtained through theoretical derivation is basically the same as an uncertainty numerical solution obtained through Monte Carlo method simulation, and therefore correctness and feasibility of the two methods are theoretically verified. With the help of statistical and simulation software, the Monte Carlo method can be directly used for solving more complex nonlinear fitting and iterative algorithm in practical problems, thereby ensuring the expandability and practicability of the algorithm. The analytical solution can be used for verifying the numerical simulation result under specific or simplified conditions, so that the reliability of the simulation result is ensured.
The present invention may be a system, method and/or computer program product. The computer program product may include a computerreadable storage medium having computerreadable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computerreadable program instructions.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. A multiphase flow measurement method based on uncertainty analysis comprises the following steps:
step S1: analyzing the oilgas twophase flow by using an online detection system, wherein the system is provided with a differential pressure type flowmeter and a highfrequency capacitance conductance detection element;
step S2: performing multielement linear regression fitting on the equivalent flow and capacitance measurement values based on the crosscorrelation time delay to obtain liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof;
step S3: obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof by multivariate linear regression fitting based on the capacitance measurement value and the differential pressure measurement value;
step S4: obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof by performing multiple linear regression fitting on equivalent flow based on differential pressure measurement values and crosscorrelation time delays;
step S5: and comparing the uncertainty obtained in the steps S2, S3 and S4, and taking the gasliquid flow corresponding to the minimum uncertainty and the corresponding uncertainty as quality indexes for measuring flow measurement.
2. The method of claim 1, wherein in step S2, the liquid phase flow rate Q_{l0}And its relative standard uncertainty u_{rel}(Q_{l0}) Calculated according to the following formula:
Q_{l0}＝Q_{tot0}LVF_{0}
wherein, LVF_{0}Is an estimate of the volume liquid fraction LVF, Q_{tot0}Is the total volume flow Q_{tot}Estimated value of u_{rel}(.) represent the corresponding relative standard uncertainty.
3. The method according to claim 1, wherein in step S3, the gas phase flow rate Q_{g}And its relative standard uncertainty u_{rel}(Q_{g}) Expressed as:
Q_{g}＝Q_{tp}/φ_{g}
wherein Q is_{tp}Denotes a gas phase virtual high flow, u_{rel}(Q_{tp}) Is Q_{tp}The relative standard uncertainty of (a), the value of which is obtained by linear fitting; phi is a_{g}Is the gas phase imaginary high coefficient, u_{rel}(φ_{g}) Is phi_{g}Relative standard uncertainty of (d).
4. The method of claim 3, wherein in step S4, the gas phase streamQuantity Q_{g}And its standard uncertainty u (Q)_{g}) Expressed as:
where ρ is_{g}Denotes the gas phase density, p_{l}Denotes the density of the liquid phase, beta_{1}And beta_{0}Is a coefficient of multiple linear regression, Q_{tp}Representing a gas phase virtual high flow.
6. The method of claim 1, wherein the differential pressure flow meter is a venturi differential pressure flow meter.
7. The method according to claim 1, wherein the online detection system comprises a fluid mixing module, a highfrequency capacitance/conductance detection module, a differential pressure type flowmeter module, a data processing module and a display module, wherein the oilgas twophase flow sequentially flows through the mixing module, the highfrequency capacitance conductance detection module and the differential pressure type flowmeter module, the data processing module is used for receiving signals of the highfrequency capacitance conductance detection module and the differential pressure type flowmeter module for analysis and processing, the respective flow rates of the oilgas two phases are obtained through calculation, and the display module is used for outputting and displaying the result of the data acquisition and processing module.
8. A multiphase flow measurement system based on uncertainty analysis, comprising:
an online detection system: the system is used for analyzing oilgas twophase flow and is provided with a differential pressure type flowmeter and a capacitance conductance detection element;
the first calculation unit: the device is used for obtaining the liquid phase flow and the relative standard uncertainty thereof, and the gas phase flow and the standard uncertainty thereof through linear fitting based on the capacitance measured value;
a second calculation unit: the device is used for obtaining the liquid phase flow and the relative standard uncertainty thereof, and the gas phase flow and the standard uncertainty thereof through linear fitting based on the capacitance measured value and the differential pressure measured value;
a third calculation unit: the method is used for obtaining liquid phase flow and relative standard uncertainty thereof, and gas phase flow and standard uncertainty thereof through linear fitting based on equivalent flow and differential pressure measured values of crosscorrelation time delay;
a result output unit: and the uncertainty is used for comparing the uncertainty obtained by the first calculating unit, the second calculating unit and the third calculating unit, and the gasliquid flow corresponding to the minimum uncertainty and the corresponding uncertainty are used as quality indexes for measuring flow measurement.
9. A computerreadable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. A computer device comprising a memory and a processor, on which memory a computer program is stored which is executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the processor executes the program.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN202110244408.3A CN113188613B (en)  20210305  Multiphase flow measurement method and system based on uncertainty analysis 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN202110244408.3A CN113188613B (en)  20210305  Multiphase flow measurement method and system based on uncertainty analysis 
Publications (2)
Publication Number  Publication Date 

CN113188613A true CN113188613A (en)  20210730 
CN113188613B CN113188613B (en)  20240405 
Family
ID=
Cited By (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN113670384A (en) *  20210819  20211119  海默潘多拉数据科技(深圳)有限公司  Multivariable timing diagram convolution multiphase flow virtual metering method and system 
CN115495939A (en) *  20221117  20221220  国网江西省电力有限公司电力科学研究院  Steam turbine set throttle flow characteristic diagnosis method based on equivalent flow difference amplitude 
CN115628783A (en) *  20221021  20230120  深圳市联恒星科技有限公司  Gasliquid twophase flow metering system based on multiple sensors 
Citations (4)
Publication number  Priority date  Publication date  Assignee  Title 

EP1409966A1 (en) *  19990922  20040421  Bechtel BWXT Idaho, LLC  Improved method and system for measuring multiphase flow using multiple pressure differentials 
US20150204706A1 (en) *  20120724  20150723  Lanzhou Haimo Technologies, Co. Ltd.  Method for Accurately Measuring Gas Flow and Liquid Flow in a Gas and Liquid Mixed Fluid 
CN105181040A (en) *  20150925  20151223  辽宁聚焦科技有限公司  Method for digitized calibration and optimization of difference pressure flow meter 
CN107179289A (en) *  20170428  20170919  河南省计量科学研究院  Uncertainty analysis method based on gas concentration lwevel in infrared spectroscopic determination industrial smoke 
Patent Citations (4)
Publication number  Priority date  Publication date  Assignee  Title 

EP1409966A1 (en) *  19990922  20040421  Bechtel BWXT Idaho, LLC  Improved method and system for measuring multiphase flow using multiple pressure differentials 
US20150204706A1 (en) *  20120724  20150723  Lanzhou Haimo Technologies, Co. Ltd.  Method for Accurately Measuring Gas Flow and Liquid Flow in a Gas and Liquid Mixed Fluid 
CN105181040A (en) *  20150925  20151223  辽宁聚焦科技有限公司  Method for digitized calibration and optimization of difference pressure flow meter 
CN107179289A (en) *  20170428  20170919  河南省计量科学研究院  Uncertainty analysis method based on gas concentration lwevel in infrared spectroscopic determination industrial smoke 
NonPatent Citations (1)
Title 

张翔: "基于确定性抽样的过冷沸腾边界条件不确定性分析", 《原子能科学技术》, vol. 54, no. 11 * 
Cited By (5)
Publication number  Priority date  Publication date  Assignee  Title 

CN113670384A (en) *  20210819  20211119  海默潘多拉数据科技(深圳)有限公司  Multivariable timing diagram convolution multiphase flow virtual metering method and system 
CN113670384B (en) *  20210819  20230908  海默潘多拉数据科技(深圳)有限公司  Multivariable time sequence diagram convolution multiphase flow virtual metering method and system 
CN115628783A (en) *  20221021  20230120  深圳市联恒星科技有限公司  Gasliquid twophase flow metering system based on multiple sensors 
CN115628783B (en) *  20221021  20230912  深圳市联恒星科技有限公司  Gasliquid twophase flow metering system based on multiple sensors 
CN115495939A (en) *  20221117  20221220  国网江西省电力有限公司电力科学研究院  Steam turbine set throttle flow characteristic diagnosis method based on equivalent flow difference amplitude 
Similar Documents
Publication  Publication Date  Title 

Cantrell  Review of methods for linear leastsquares fitting of data and application to atmospheric chemistry problems  
US7654151B2 (en)  Method and apparatus for measuring multistreams and multiphase flow  
Yıldırım  Computerbased analysis of explicit approximations to the implicit Colebrook–White equation in turbulent flow friction factor calculation  
Cohn et al.  Estimating discharge measurement uncertainty using the interpolated variance estimator  
Larour et al.  Sensitivity Analysis of Pine Island Glacier ice flow using ISSM and DAKOTA  
EP2843376A1 (en)  Gas flow meter program of constriction device and flow measurement method and flow measurement device using same  
GolijanekJędrzejczyk et al.  Uncertainty of mass flow measurement using centric and eccentric orifice for Reynolds number in the range 10,000≤ Re≤ 20,000  
Pirouzpanah et al.  Multiphase flow measurements using coupled slotted orifice plate and swirl flow meter  
Andrade et al.  Analysis of uncertainties and convergence of the statistical quantities in turbulent wallbounded flows by means of a physically based criterion  
Fuest et al.  Scalar dissipation rates in a turbulent partiallypremixed dimethyl ether/air jet flame  
Miller et al.  Evaluation of hotwire spatial filtering corrections for wall turbulence and correction for endconduction effects  
FarzanehGord et al.  An intelligent approach for calculating natural gas compressibility factor and its application in ultrasonic flow meters  
EP2504696A1 (en)  Composition of multiphase flow  
Muzzo et al.  Uncertainty of pipe flow friction factor equations  
Zhang et al.  Mathematical model of time difference for Coriolis flow sensor output signals under gasliquid twophase flow  
Meng et al.  Application of principal component analysis in measurement of flow fluctuation  
CN113188613A (en)  Multiphase flow measurement method and system based on uncertainty analysis  
Alfredsson et al.  Turbulent boundary layers over flat plates and rotating disks—The legacy of von Kármán: A Stockholm perspective  
Dayev et al.  Modeling of coefficient of contraction of differential pressure flowmeters  
CN113188613B (en)  Multiphase flow measurement method and system based on uncertainty analysis  
US6560562B2 (en)  Method of estimating precision of apparatus  
Dayev  Theoretical modelling of natural gas unsteady flow rate measurement using variable differential pressure method  
Cremona et al.  Application of the Kragten method in order to evaluate the uncertainty of the heat release rate determination using of the cone calorimeter  
D’Elia et al.  A Data Assimilation technique for including noisy measurements of the velocity field into NavierStokes simulations  
Merle et al.  Robust prediction of dense gas flows under uncertain thermodynamic models 
Legal Events
Date  Code  Title  Description 

PB01  Publication  
PB01  Publication  
SE01  Entry into force of request for substantive examination  
SE01  Entry into force of request for substantive examination  
GR01  Patent grant 