Mixed-phase flow online metering method and system based on light particles

Abstract

The invention relates to the technical field of mixed-phase fluid metering, and provides a mixed-phase flow online metering method and system based on light particles, and the method comprises the steps: collecting pressure data and density data at each moment in a period of time through a mixed-phase flowmeter; obtaining a flow pattern change coefficient at each moment to obtain a plurality of flow pattern change points; dividing to obtain a plurality of flow pattern time periods and interference time periods, and obtaining energy spectrum data at each moment in each flow pattern time period; acquiring a light particle counting sequence of the energy spectrum data at any moment in any flow pattern time period to obtain a light particle change sequence; obtaining the spectrum peak energy sum of the energy spectrum data at each moment, and obtaining an energy spectrum energy drift factor at each moment; energy spectrum reference weights at all moments are obtained, then comprehensive energy spectrum data of all flow pattern time periods are obtained, and metering of all phases of fluid in the mixed-phase flow is achieved. The invention aims to solve the problem that the flow state change of the mixed-phase flow in the oil and gas well pipeline interferes with the measurement effectiveness of the flowmeter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mixed phase fluid metering, in particular to a mixed phase flow online metering method and system based on light particles. BACKGROUND

[0002] The fluid of an oil and gas well generally exhibits a relatively complex mixed phase coexisting fluid, that is, a fluid containing at least two and more different phase state media such as oil, gas and water; taking oil well exploitation as an example, in the middle and later stages of the oil well development process, oil displacement by water is required, and the fluid extracted contains oil-water mixed fluid, and individual oil wells may also contain gas phase fluid. For this case, accurate metering of the mixed phase flow at the oil and gas wellhead is a problem to be solved, and therefore real-time online accurate metering and detection of the mixed phase flow of the oil and gas well has been a key research content in the oil and gas field.

[0003] In fluid metering and detection, the light particle metering method is a relatively advanced non-separation metering technology, which utilizes the difference in energy attenuation of light particles emitted by a light particle source when interacting with different phase state substances, and then captures the absorption signal data between the mixed phase flow and the light particles by an energy detector to determine the phase fraction (content parameter of different phase states) for metering. Compared with the separation metering, the light particle-based mixed phase flow metering and detection requires to place a flowmeter device at a key position (such as a throat diameter) of the oil and gas well, and the obtained original signal often contains a large amount of noise interference, and the flow state of the mixed phase flow in the oil and gas well pipe is variable. The complex flow state of the mixed phase flow greatly interferes with the measurement accuracy of the flowmeter, resulting in a decrease in the effectiveness of the obtained energy spectrum signal and affecting the accuracy of subsequent calculation of the phase fraction. SUMMARY

[0004] The present application provides a light particle-based mixed phase flow online metering method and system to solve the problem of interference of the flow state change of the mixed phase flow in the existing oil and gas well pipe with the measurement effectiveness of the flowmeter, and the technical scheme adopted is as follows:

[0005] The present application provides a light particle-based mixed phase flow online metering method and system to solve the problem of interference of the flow state change of the mixed phase flow in the existing oil and gas well pipe with the measurement effectiveness of the flowmeter, and the technical scheme adopted is as follows:

[0006] The pressure data and density data at each time point in a period of time are collected by the mixed phase flowmeter, and the light particle energy spectrum at each time point is obtained;

[0007] A neighborhood window is constructed for each time point, a flow pattern change coefficient of each time point is obtained based on the difference in pressure data and density data between each time point and its neighborhood window, and a plurality of flow pattern change points are obtained by screening; a plurality of flow pattern time periods and interference time periods are divided based on the time sequence distribution of the flow pattern change points, and the energy spectrum data of each time point in each flow pattern time period is obtained by the light particle energy spectrum;

[0008] The light particle count sequence of the energy spectrum data at any time in any flow pattern time period is obtained, and the light particle change sequence is obtained by first-order difference; the energy spectrum change difference factor of each time is obtained according to the similarity of the light particle change sequences at different times in the same flow pattern time period; the peak energy sum of the energy spectrum data at each time is obtained by analyzing the peak corresponding energy performance of the energy spectrum data at each time; and the energy spectrum energy drift factor of each time is obtained based on the performance of the peak energy sum of the energy spectrum data at different times in the same flow pattern time period.

[0009] Based on the energy spectrum change difference factor and the energy spectrum energy drift factor, the energy spectrum reference weight of each time is obtained, and then the comprehensive energy spectrum data of each flow pattern time period is obtained; and each phase fraction in the mixed phase flow is calculated, and each phase fluid metering in the mixed phase flow is realized.

[0010] Optionally, the flow pattern change coefficient of each time is specifically obtained by the following method:

[0011] For the pressure data of each time and the pressure data of each time in the neighborhood window of the time, the mean and the standard deviation are calculated to obtain the variation coefficient as the flow pattern change coefficient of the pressure data of the time;

[0012] Based on the density data of each time and the density data of each time in the neighborhood window of the time, the mean and the standard deviation are calculated to obtain the variation coefficient as the flow pattern change coefficient of the density data of the time.

[0013] Optionally, the screening to obtain a plurality of flow pattern change points includes the following specific method:

[0014] Based on the mean and the standard deviation of the flow pattern change coefficients of the pressure data of all times, a neighborhood pressure threshold is constructed; and based on the mean and the standard deviation of the flow pattern change coefficients of the density data of all times, a neighborhood density threshold is constructed.

[0015] If the flow pattern change coefficient of the pressure data of any time is greater than the neighborhood pressure threshold, and the flow pattern change coefficient of the density data of the time is greater than the neighborhood density threshold, the time is taken as a flow pattern change point.

[0016] Optionally, the obtaining of a plurality of flow pattern time periods and interference time periods includes the following specific method:

[0017] The first time among all times is taken as a flow pattern change point, for all flow pattern change points, all times between adjacent two flow pattern change points and the former flow pattern change point among the adjacent two flow pattern change points constitute a time period, and the last time among all times is added to the last time period.

[0018] A preset time period base length, if the length of any time period is less than the time period base length, the time period is taken as an interference time period; otherwise, the time period is taken as a flow type time period.

[0019] Optionally, the light particle count sequence of the energy spectrum data of any time point in any flow type time period is obtained, and a first-order difference is used to obtain a light particle change sequence, including the following specific method:

[0020] For the energy spectrum data of any time point in any flow type time period, the abscissa is the number of energy points, and the ordinate is the number of light particles. The number of light particles is arranged in ascending order according to the number of energy points, and the obtained sequence is taken as the light particle count sequence of the energy spectrum data.

[0021] A first-order backward difference is performed on the light particle count sequence, and a difference value is obtained by subtracting the former from the latter of the adjacent two numbers of light particles, as the first-order backward difference value. All first-order backward difference values are arranged in order according to the corresponding number of light particles in the light particle count sequence, and the obtained sequence is taken as the light particle change sequence of the energy spectrum data.

[0022] Optionally, the energy spectrum change difference factor of each time point is obtained by the following specific method:

[0023] For the light particle change sequence of the energy spectrum data of any time point in any flow type time period, a DTW distance is obtained between the light particle change sequence and the light particle change sequence of the energy spectrum data of any time point other than the time point in the flow type time period. The mean value of the DTW distances between the light particle change sequence and the light particle change sequences of the energy spectrum data of all time points other than the time point in the flow type time period is taken as the energy spectrum change difference coefficient of the time point.

[0024] The energy spectrum change difference coefficients of all time points in all flow type time periods are obtained, and linear normalization processing is performed to obtain the result as the energy spectrum change difference factor of each time point.

[0025] Optionally, the spectral peak energy sum of the energy spectrum data of each time point is obtained by the following specific method:

[0026] For the light particle count sequence of the energy spectrum data of any time point in any flow type time period, an AMPD peak detection algorithm is used to obtain a plurality of peak points and corresponding energy point numbers, and the cumulative sum of all peak point numbers is taken as the spectral peak energy sum of the energy spectrum data of the time point.

[0027] Optionally, the energy spectrum energy drift factor of each time point is obtained by the following specific method:

[0028] For any flow pattern time period, the spectral peak energy sum of the energy spectrum data of any time point in the flow pattern time period is obtained, and the average of all spectral peak energy sums is taken as the spectral peak standard energy of the flow pattern time period.

[0029] The difference between the spectral peak energy sum of the energy spectrum data of the time point and the spectral peak standard energy is taken as a ratio of the spectral peak standard energy, and the ratio is taken as the energy spectrum energy drift factor of the time point.

[0030] Optionally, the specific method for obtaining the energy spectrum reference weight of each time point and then obtaining the comprehensive energy spectrum data of each flow pattern time period comprises the following steps.

[0031] Based on the energy spectrum change difference factor and the energy spectrum energy drift factor of any time point, the energy spectrum reference coefficient of the time point is obtained, and the energy spectrum reference coefficient is in negative correlation with the energy spectrum change difference factor and the energy spectrum energy drift factor.

[0032] The energy spectrum reference coefficients of each time point in any flow pattern time period are subjected to weight normalization, and the obtained result is taken as the energy spectrum reference weight of each time point in the flow pattern time period.

[0033] Based on the energy spectrum reference weight of each time point in the flow pattern time period, the energy spectrum data of each time point is subjected to weighted fusion, and the obtained result is taken as the comprehensive energy spectrum data of the flow pattern time period.

[0034] The application further provides an online mixed-phase flow metering system based on light particles, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when running the computer program.

[0035] The application has the following beneficial effects: the application is based on light particle measurement of each phase fraction scene of mixed-phase flow, and the stable energy spectrum data is obtained by weighted average of multiple instantaneous energy spectrum data based on traditional light particle energy spectrum data acquisition, without considering the problems of deviation of energy spectrum data caused by flow pattern and other factors of mixed-phase flow in actual scenes, the flow pattern time period is divided according to the pressure and density data change of mixed-phase flow in the pipeline, and then the energy spectrum change difference factor and the energy spectrum energy drift factor are constructed according to the difference between the spectral shapes of each energy spectrum data in the same flow pattern time period and the drift performance between the corresponding energy points of the spectral peaks of each energy spectrum data, so as to obtain the energy spectrum reference weight of each time point in the flow pattern time period, eliminate the deviation problem of energy spectrum data caused by interference factors in the scene, improve the accuracy of the measured energy spectrum data, and facilitate subsequent metering of mixed-phase flow. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0037] Figure 1 A flow chart of an online measurement method of a mixed-phase flow based on light particles according to an embodiment of the present application is shown in

[0038] Figure 2 A structure diagram of a light particle mixed-phase flow meter product according to an embodiment of the present application is shown in DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of the present application.

[0040] Please refer to Figure 1 which shows a flow chart of an online measurement method of a mixed-phase flow based on light particles according to an embodiment of the present application. The method comprises the following steps:

[0041] In step S001, pressure data and density data at each time within a period of time are collected by a mixed-phase flow meter, and the light particle energy spectrum at each time is obtained.

[0042] The purpose of the embodiment is to collect relevant data of the mixed-phase flow by the mixed-phase flow meter, the relevant data including pressure and density data, to divide the flow pattern state of the mixed-phase flow by the relevant data, and to perform comprehensive analysis in the flow pattern time period according to the energy spectrum information of the light particles, so as to reduce the interference of noise and complex and changeable mixed-phase flow flow states. Therefore, it is necessary to collect the pressure data, the density data and the light particle energy spectrum by the mixed-phase flow meter.

[0043] Specifically, the mixed-phase flow meter is installed at a key position (such as a throat diameter) of an oil and gas well pipeline; as shown in Figure 2 Figure 2 ​A is a particle detector, B is a flow computer, C is a Venturi tube, D is a particle emitter, and E is a multi-parameter sensor. The mixed-phase flowmeter includes a light particle sensor (including a particle emitter and a particle detector), a differential pressure sensor, and a density sensor, which are used to obtain the light particle energy spectrum, pressure data, and density data of the mixed-phase flow, respectively, as well as a flow computer and a Venturi tube. The rays emitted by the light particle source in the light particle sensor are set by the implementer according to the implementation scenario, and the present embodiment does not limit the same. The sampling frequency of the light particle sensor, the differential pressure sensor, and the density sensor is 10 Hz, and the mixed-phase flow measurement statistical window length is 10 min, i.e., the actual flow rate of each phase in the statistical window is calculated according to the total volume of the mixed-phase flow passing through the pipeline within 10 min and related data. A period of time is 10 min, and each time is every 100 ms, so that the pressure data, density data, and light particle energy spectrum at each time are obtained.

[0044] It should be noted that, in the process of obtaining the energy spectrum data of the mixed-phase flow based on the light particle sensor, a certain amount of time is generally required to form a specific energy spectrum, and then the phase fraction in the mixed-phase flow is calculated according to the obtained energy spectrum data. However, in the later stage of oil and gas well exploitation, various displacement methods (such as water displacement) are used to displace oil, so that the fluid obtained at the oil and gas well exploitation port is a mixed-phase flow containing multiple different phases, and as the working condition of exploitation changes, the flow pattern of the mixed-phase flow changes and there may be problems such as oil-water emulsification in the mixed-phase flow, which interfere with the energy spectrum data of the mixed fluid collected by the light particle sensor. The traditional energy spectrum data is obtained by weighting and averaging the short-time energy spectrum under multiple counting windows, and the average value of the counts in the corresponding energy interval is counted as the final energy spectrum data. The different flow patterns of the mixed-phase flow and the noise interference in the scene are not considered, and the energy spectrum data obtained by directly using the weighted average has a large deviation under the influence of multiple factors, which affects the accuracy of the subsequent calculation of the phase fraction.

[0045] It needs to be further explained that, in the exploitation process of the oil and gas well, due to the influence of factors such as working condition fluctuation or displacement medium injection on the miscible phase flow in the underground reservoir during the exploitation process, the change of the phase material fraction of the miscible phase flow itself leads to the change of the flow pattern in a short time (such as slug flow and foam flow), and the flow state of the fluid changes after maintaining for a short time; such short-time flow pattern change will affect the accuracy of the measured data of the flowmeter, such as the significant difference in the distribution of the oil, gas and water phases in the pipeline under different flow patterns (such as the existence of oil-water emulsion in the foam flow, a large amount of scattering of light particles caused by the gas-liquid interface, and the influence of the light particle count in different energy intervals), the energy absorption attenuation coefficient, effective penetration length and other parameters of the light particles emitted by the light particle sensor on the propagation path will be affected by the difference in the distribution of the phases, which leads to a large difference in the spectral shape of the energy spectrum data under different flow patterns; if the instantaneous energy spectrum change caused by the difference is not considered for weighted average, the energy spectrum data obtained finally will be distorted, which will cause a large error in the subsequent phase fraction inversion calculation.

[0046] In step S002, a neighborhood window is constructed for each time, a flow pattern change coefficient of each time is obtained based on the difference between the pressure data and the density data in each time and the neighborhood window of the time, and a plurality of flow pattern change points are screened out; a plurality of flow pattern time periods and interference time periods are divided based on the time sequence distribution of the flow pattern change points, and energy spectrum data of each time in each flow pattern time period is obtained through the light particle energy spectrum.

[0047] It needs to be explained that, first, the data collected in the data statistical window is divided into flow pattern states, and due to the change of the phase fraction in the miscible phase flow in the oil and gas well pipeline, the flow state changes to obtain different flow patterns; the change of the phase fraction will also cause the fluctuation change of the measured pressure and density data at the throat diameter; for example, when the flow pattern is stable, the content of different phases in the fluid is close in a period of time, and the actually measured pressure and density data are close; when the flow pattern changes, for example, the increase of the gas phase leads to the decrease of the measured pressure and density.

[0048] Preferably, in an embodiment of the present application, a neighborhood window is constructed for each time, a flow pattern change coefficient of each time is obtained based on the difference between the pressure data and the density data in each time and the neighborhood window of the time, and a plurality of flow pattern change points are screened out, and the specific method includes:

[0049] The neighborhood window is constructed with any time point as the center, and the neighborhood window size is 3 to 11, and the embodiment adopts a neighborhood window size of 7 for description, and it is particularly pointed out that if the time point is close to the beginning or end of a period of time, the complete neighborhood window cannot be obtained, and the time points actually existing in the period of time are used to form the neighborhood window; for the pressure data of any time point and each time point in the neighborhood window, the mean value and standard deviation are calculated to obtain the variation coefficient, which is the flow pattern change coefficient of the pressure data of the time point; similarly, based on the density data of the time point and each time point in the neighborhood window, the mean value and standard deviation are calculated to obtain the variation coefficient, which is the flow pattern change coefficient of the density data of the time point, wherein the variation coefficient is the ratio of the standard deviation to the mean value.

[0050] Further, based on the mean value and standard deviation of the flow pattern change coefficients of the pressure data of all time points, a neighborhood pressure threshold is constructed, and the embodiment adopts a three-sigma method to obtain the neighborhood pressure threshold, that is, based on the normal distribution, the mean value of the flow pattern change coefficients of the pressure data is added to three times the standard deviation to obtain the result, which is used as the neighborhood pressure threshold; based on the mean value and standard deviation of the flow pattern change coefficients of the density data of all time points, a neighborhood density threshold is constructed, and the three-sigma method is used in the same way; if the flow pattern change coefficient of the pressure data of any time point is greater than the neighborhood pressure threshold, and the flow pattern change coefficient of the density data of the time point is greater than the neighborhood density threshold, the time point is regarded as a flow pattern change point; the flow pattern change coefficients of the pressure data and the density data of each time point are judged according to the above method, and then a plurality of flow pattern change points are obtained.

[0051] It needs to be further explained that in different flow patterns of the mixed phase flow, the duration of the general flow pattern is different, which is tens of seconds to tens of minutes, and if the flow pattern frequently changes in a short time, the mixed phase flow data in this period of time is unreliable, and it is difficult to provide a certain time to obtain stable energy spectrum data, then the period of time in which the flow pattern frequently changes in a short time is regarded as an interference period of time, and does not participate in subsequent statistical calculation.

[0052] Preferably, in an embodiment of the present application, based on the time sequence distribution of the flow pattern change points, a plurality of flow pattern time periods and interference time periods are divided, and the energy spectrum data of each time point in each flow pattern time period is obtained by the light particle energy spectrum, including the specific method:

[0053] The first time point among all time points is taken as a flow pattern change point. For all flow pattern change points, all time points between adjacent two flow pattern change points and the former flow pattern change point among the adjacent two flow pattern change points form a time period, and the last time point among all time points is added to the last time period. A preset time period basic length is adopted, and the time period basic length is 5 seconds in the embodiment. If the length of any time period is less than the time period basic length, the time period is taken as an interference time period. Otherwise, the time period is taken as a flow pattern time period. The light particle energy spectrum of any time point in each flow pattern time period is taken as the energy spectrum data of the time point. It should be noted that the time points in the interference time period are not involved in the calculation of the energy spectrum change difference factor and the energy spectrum energy drift factor of all subsequent time points.

[0054] Thus, a plurality of flow pattern time periods and the energy spectrum data of each time point in the flow pattern time periods are obtained.

[0055] In step S003, the energy spectrum data of any time point in any flow pattern time period is taken to obtain a light particle count sequence, and a first-order difference is taken to obtain a light particle change sequence. The energy spectrum change difference factor of each time point is obtained according to the similarity of the light particle change sequences of different time points in the same flow pattern time period. The spectral peak energy sum of the energy spectrum data of each time point is obtained by analyzing the peak value corresponding energy performance of the energy spectrum data of each time point. The energy spectrum energy drift factor of each time point is obtained based on the performance of the spectral peak energy sum of the energy spectrum data of different time points in the same flow pattern time period.

[0056] It should be noted that the obtained energy spectrum data of the miscible phase flow under the same flow pattern has relative stability in the macroscopic flow state. The miscible phase flow of the same flow pattern has close phase fractions in the fluid, the fluid state is relatively stable, the energy absorption effect of the emitted light particles is close, and the light particle count and the light particle distribution probability in the same energy interval in the energy spectrum data are similar, that is, the spectral peak of the energy spectrum data does not drift, and the overall peak shape is similar. However, in the actual measurement scene, the distribution of the phase medium in the miscible phase flow in the pipeline has uncertainty in the local space, and the randomness of the distribution of part of the phase medium is large (such as the size of the solid particles and the different aggregation degrees of the gas bubbles, which can cause serious local scattering), which causes the effective absorption length and the scattering probability of the light particles on the penetration path to fluctuate, and the energy spectrum data has a certain deviation drift in the spectral shape. Therefore, even under the same flow pattern condition, the spectral peak amplitude, the energy interval and the spectral shape stability of the energy spectrum data in the actual industrial measurement scene still show a certain dispersion type. If the weighted average statistics is not considered, a systematic deviation will be introduced.

[0057] It is further needed to be explained that the difference between the energy spectrum data under the same flow type and the variability of the spectrum peak in the single energy spectrum data are analyzed, so as to provide a basis for the subsequent construction of the energy spectrum reference weight for the energy spectrum data at each time, and eliminate the deviation of the energy spectrum data caused by the interference factors in the scene; in different energy spectrum data under the same flow type, there may be different volumes of fluid flowing through the pipeline, so that the spectrum peak of the energy spectrum data has differences in amplitude, but this difference is linearly changed with the volume, that is, the spectrum shape of the energy spectrum data as a whole is stably changed, and then the change of the light particle number under different energies in different energy spectrum data is analyzed to reflect the difference between the energy spectrum data.

[0058] Preferably, in an embodiment of the present application, the light particle count sequence of the energy spectrum data at any time in any flow type time period is obtained, and the light particle change sequence is obtained by first-order difference, and the specific method comprises:

[0059] For the energy spectrum data at any time in any flow type time period, the abscissa is the number of energy points, and the ordinate is the number of light particles. The number of light particles is arranged in ascending order according to the number of energy points, and the obtained sequence is taken as the light particle count sequence of the energy spectrum data. The first-order backward difference is performed on the light particle count sequence, and the difference value is obtained by subtracting the former from the latter of the adjacent two light particle numbers, which is taken as the first-order backward difference value. All the first-order backward difference values are arranged in order according to the corresponding light particle numbers in the light particle count sequence, and the obtained sequence is taken as the light particle change sequence of the energy spectrum data.

[0060] Preferably, in an embodiment of the present application, the energy spectrum change difference factor of each time is obtained according to the similarity of the light particle change sequences at different times in the same flow type time period, and the specific method comprises:

[0061] For the light particle change sequence of the energy spectrum data at any time in any flow type time period, the DTW distance is obtained between the light particle change sequence and the light particle change sequence of the energy spectrum data at any time other than the time in the flow type time period. The mean value of the DTW distances between the light particle change sequence and the light particle change sequences of the energy spectrum data at each time other than the time in the flow type time period is taken as the energy spectrum change difference coefficient of the time. The energy spectrum change difference coefficients of all times in all flow type time periods are obtained according to the above method, and linear normalization processing is performed, and the obtained result is taken as the energy spectrum change difference factor of each time.

[0062] It should be noted that different energy spectrum data may be affected by fluid volume, resulting in different sizes of light particle numbers, but in the absence of interference, the overall similarity of the spectrum peaks is high, that is, the light particle numbers at different energies in the energy spectrum data of the same flow pattern change similarly, and if there is interference, the spectrum peaks of a certain energy spectrum data will change locally, and the light particle numbers at different energies will change greatly. Through similarity analysis between light particle change sequences, the greater the average DTW distance, the more interference there is, resulting in local changes in spectrum peaks, and the greater the difference in spectrum shape from most energy spectrum data; on the contrary, the smaller the average DTW distance, the less interference it receives, and the more similar the spectrum shape to other energy spectrum data.

[0063] Preferably, in an embodiment of the present application, the peak value corresponding energy of the energy spectrum data at each time is analyzed to obtain the spectrum peak energy sum of the energy spectrum data at each time, and based on the performance of the spectrum peak energy sum of the energy spectrum data at different times in the same flow pattern time period, the energy spectrum energy drift factor at each time is obtained, including the specific method:

[0064] For the light particle count sequence of the energy spectrum data at any time in any flow pattern time period, the AMPD peak value detection algorithm is used to obtain a plurality of peak points and the corresponding energy point number, and the cumulative sum of the energy point number corresponding to all peak points is taken as the spectrum peak energy sum of the energy spectrum data at this time. The AMPD peak value detection algorithm is a known technology, and this embodiment will not be described again. The average of all spectrum peak energy sums is taken as the spectrum peak standard energy of the flow pattern time period. The difference between the spectrum peak energy sum of the energy spectrum data at this time and the spectrum peak standard energy is taken as the ratio of the spectrum peak standard energy, which is taken as the energy spectrum energy drift factor at this time.

[0065] It should be noted that in the same flow pattern time period, the energy spectrum data of the same flow pattern has small deviation without external interference, and the energy points corresponding to each spectrum peak remain the same. When external interference occurs, local phase interference in the fluid will cause the spectrum peak to drift, and the corresponding energy point will also increase or decrease. That is, the spectrum peak energy sum is obtained by extracting the peak point, and the greater the deviation from the average, the greater the corresponding energy spectrum energy drift factor.

[0066] At this point, the energy spectrum change difference factor and the energy spectrum energy drift factor at each time are obtained.

[0067] Step S004, based on the energy spectrum change difference factor and the energy spectrum energy drift factor, the energy spectrum reference weight at each time is obtained, and then the comprehensive energy spectrum data of each flow pattern time period is obtained; the phase fraction in the mixed phase flow is calculated in this way, and the phase fluid metering in the mixed phase flow is realized.

[0068] Specifically, based on the energy spectrum change difference factor and the energy spectrum energy drift factor at any moment, the energy spectrum reference coefficient at the moment is obtained, and the energy spectrum reference coefficient is negatively correlated with the energy spectrum change difference factor and the energy spectrum energy drift factor.

[0069] As an example, the energy spectrum change difference factor and the energy spectrum energy drift factor at the moment are inversely proportional to each other, and the embodiment adopts The model is used to present the inverse proportional processing, as the input of the model, The energy spectrum change difference factor and the energy spectrum energy drift factor are inversely proportional to each other, and the energy spectrum reference coefficient at the moment is obtained by taking the mean value of the inverse proportional results of the energy spectrum change difference factor and the energy spectrum energy drift factor as the energy spectrum reference coefficient. In this embodiment, it is considered that the energy spectrum change difference factor and the energy spectrum energy drift factor are equally important, so the energy spectrum reference coefficient is obtained by taking the mean value.

[0070] As another example, the difference value obtained by subtracting the energy spectrum change difference factor at the moment from 1 is obtained, and the ratio of the difference value to the energy spectrum energy drift factor at the moment is taken as the energy spectrum reference coefficient at the moment. In the ratio calculation process, in order to avoid the denominator being 0, an over parameter is added to the denominator and the numerator at the same time, and the over parameter in this embodiment is 0.001.

[0071] It should be noted that since the energy spectrum change difference factor and the energy spectrum energy drift factor both reflect that the energy spectrum data at the corresponding moment does not conform to the overall change in the corresponding flow type time period, that is, the larger the energy spectrum change difference factor and the energy spectrum energy drift factor, the greater the interference of the energy spectrum data, and the smaller the corresponding energy spectrum reference coefficient.

[0072] Further, the energy spectrum reference coefficients at each moment in any flow type time period are weight normalized to obtain the energy spectrum reference weight at each moment in the flow type time period; based on the energy spectrum reference weight at each moment in the flow type time period, the energy spectrum data at each moment is weighted and fused to obtain the comprehensive energy spectrum data of the flow type time period, wherein the weighted fusion is to weight and sum the number of light particles in the same energy point in multiple energy spectrum data to obtain the weighted sum value of the number of light particles at each energy point, and to form the comprehensive energy spectrum data.

[0073] Further, the comprehensive energy spectrum data of each flow type time period is acquired according to the method, and the phase fraction of the multiphase flow is acquired for the comprehensive energy spectrum data of any flow type time period. The phase fraction of different phase states (oil, gas and water phases) is solved under the conditions that the activity of the emitted rays, the transmission activity of rays of different energy levels, the penetration length and the linear absorption coefficient of gamma rays of different phase states (oil, gas and water phases) at different energy levels of the optical particle sensor are known. The penetration length is a fixed parameter of the optical particle sensor, and the linear absorption coefficient of gamma rays of different phase states (oil, gas and water phases) at different energy levels is a physical property of the material and is directly acquired. The activity of the emitted rays and the transmission activity of rays of different energy levels are obtained by integral calculation of the energy spectrum data, and the detailed description is omitted. The phase fraction is acquired based on the comprehensive energy spectrum data of each flow type time period, and the fluid measurement of each phase state medium in the multiphase flow can be realized by combining the comprehensive energy spectrum data.

[0074] Thus, the embodiment is completed.

[0075] Another embodiment of the present application provides an optical particle-based online measurement system for multiphase flow, which comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the above-mentioned method steps S001 to S004 are realized.

[0076] The above description is only the preferred embodiment of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for online metering of a mixed phase flow based on light particles, characterized in that, The method comprises the following steps: Collecting pressure data and density data of each time point in a period of time by a mixed phase flowmeter, and obtaining light particle energy spectrum of each time point; Constructing a neighborhood window for each time point, obtaining a flow pattern change coefficient of each time point based on the difference between the pressure data and the density data of each time point and its neighborhood window, and screening a plurality of flow pattern change points; obtaining a plurality of flow pattern time periods and interference time periods based on the time sequence distribution of the flow pattern change points, obtaining energy spectrum data of each time point in each flow pattern time period through the light particle energy spectrum; Obtaining a light particle count sequence of the energy spectrum data of each time point in any flow pattern time period, and obtaining a light particle change sequence through first-order difference; obtaining a spectrum change difference factor of each time point according to the similarity of the light particle change sequences of different time points in the same flow pattern time period; analyzing the peak value corresponding energy in the energy spectrum data of each time point, obtaining a spectrum peak energy sum of the energy spectrum data of each time point, and obtaining a spectrum energy drift factor of each time point based on the spectrum peak energy sum of the energy spectrum data of different time points in the same flow pattern time period; Obtaining a spectrum reference weight of each time point based on the spectrum change difference factor and the spectrum energy drift factor, and then obtaining comprehensive energy spectrum data of each flow pattern time period; and calculating the phase fraction in the mixed phase flow, thereby realizing the metering of each phase fluid in the mixed phase flow.

2. A method for online metering of a mixed phase flow based on light particles according to claim 1, characterized in that, The flow pattern change coefficient of each time point is obtained by: For the pressure data of each time point in any time point and its neighborhood window, calculating the mean and standard deviation to obtain the variation coefficient as the flow pattern change coefficient of the pressure data of the time point; For the density data of each time point in the time point and its neighborhood window, calculating the mean and standard deviation to obtain the variation coefficient as the flow pattern change coefficient of the density data of the time point.

3. A method for online metering of a mixed phase flow based on light particles according to claim 2, characterized in that, The specific method for screening a plurality of flow pattern change points comprises: Based on the mean and standard deviation of the flow pattern change coefficients of the pressure data of all time points, a neighborhood pressure threshold is constructed; based on the mean and standard deviation of the flow pattern change coefficients of the density data of all time points, a neighborhood density threshold is constructed; If the flow pattern change coefficient of the pressure data of any time point is greater than the neighborhood pressure threshold, and the flow pattern change coefficient of the density data of the time point is greater than the neighborhood density threshold, the time point is taken as a flow pattern change point.

4. The method of claim 1, wherein the method is an on-line method. The specific method for obtaining a plurality of flow pattern time periods and interference time periods comprises: The first time point in all time points is taken as a flow pattern change point, for all flow pattern change points, all time points between adjacent two flow pattern change points and the former flow pattern change point in adjacent two flow pattern change points constitute a time period, and the last time point in all time points is added to the last time period; A preset time period basic length, if the length of any time period is less than the time period basic length, the time period is taken as an interference time period; otherwise, the time period is taken as a flow pattern time period.

5. The method of claim 1, wherein the method is an on-line method. The specific method for obtaining a light particle count sequence of the energy spectrum data of each time point in any flow pattern time period, and obtaining a light particle change sequence through first-order difference comprises: For any energy spectrum data of any time in any flow pattern time period, the horizontal coordinate is the energy point number, the vertical coordinate is the light particle number, the light particle number is arranged in ascending order according to the energy point number, and the sequence obtained is the light particle count sequence of the energy spectrum data; The first-order backward difference is performed on the light particle count sequence, the difference value is obtained by subtracting the former from the latter of the adjacent two light particle numbers, and the sequence obtained by arranging all the first-order backward difference values in the order of the corresponding light particle numbers in the light particle count sequence is the light particle change sequence of the energy spectrum data.

6. A method for online metering of a mixed phase flow based on light particles according to claim 1, characterized in that, The specific method for obtaining the energy spectrum change difference factor of each time is as follows: For the light particle change sequence of the energy spectrum data of any time in any flow pattern time period, the DTW distance is obtained between the light particle change sequence and the light particle change sequence of the energy spectrum data of any time other than the time in the flow pattern time period, and the average of the DTW distances between the light particle change sequence and the light particle change sequences of the energy spectrum data of all times other than the time in the flow pattern time period is taken as the energy spectrum change difference coefficient of the time; The energy spectrum change difference coefficients of all times in all flow pattern time periods are obtained, and linear normalization processing is performed to obtain the result as the energy spectrum change difference factor of each time.

7. A method for online metering of a mixed phase flow based on light particles according to claim 1, characterized in that, The specific method for obtaining the spectral peak energy sum of the energy spectrum data of each time is as follows: For the light particle count sequence of the energy spectrum data of any time in any flow pattern time period, the AMPD peak detection algorithm is used to obtain a plurality of peak points and their corresponding energy point numbers, and the cumulative sum of the energy point numbers corresponding to all peak points is taken as the spectral peak energy sum of the energy spectrum data of the time.

8. The method of claim 1, wherein the method is an on-line method. The specific method for obtaining the energy spectrum energy drift factor of each time is as follows: For the energy spectrum data of any time in any flow pattern time period, the spectral peak energy sum of the energy spectrum data of each time in the flow pattern time period is obtained, the average of all spectral peak energy sums is taken as the spectral peak standard energy of the flow pattern time period; The absolute value of the difference between the spectral peak energy sum of the energy spectrum data of the time and the spectral peak standard energy is divided by the spectral peak standard energy to obtain a ratio, which is taken as the energy spectrum energy drift factor of the time.

9. A method for online metering of a mixed phase flow based on light particles according to claim 1, characterized in that, The specific method for obtaining the energy spectrum reference weight of each time and then obtaining the comprehensive energy spectrum data of each flow pattern time period includes: Based on the energy spectrum change difference factor and the energy spectrum energy drift factor of any time, the energy spectrum reference coefficient of the time is obtained, and the energy spectrum reference coefficient is negatively correlated with the energy spectrum change difference factor and the energy spectrum energy drift factor; The energy spectrum reference coefficients of each time in any flow pattern time period are subjected to weight normalization to obtain the energy spectrum reference weight of each time in the flow pattern time period; Based on the energy spectrum reference weight of each time in the flow pattern time period, the energy spectrum data of each time is weighted and fused to obtain the comprehensive energy spectrum data of the flow pattern time period.

10. An online optical particle based multiphase flow metering system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to implement the steps of the light particle-based online metering method of mixed-phase flow according to any one of claims 1-9.

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