Online metering system and method

Abstract

The invention provides an on-line metering system and method. The method comprises the steps that firstly, oil gas flow at different monitoring positions is measured; further performing dependency correlation on the fluctuation quantity of the temperature and the oil gas flow at each monitoring position to obtain correlation influence characteristics of the oil gas flow at each monitoring position, and further performing stability evaluation on each mass flow meter to obtain the stability margin of each mass flow meter; then measuring differences among the mass flow meters are determined; determining calibration constraint parameters of each mass flow meter according to the stability margin of each mass flow meter and the measurement difference between the mass flow meters; and then, confidence calibration is carried out on all the mass flow meters, and all the calibrated mass flow meters are obtained. By the adoption of the scheme, calibration of the mass flow meter under the complex oil gas flowing state can be achieved, and therefore the measurement precision of the mass flow meter is improved.

Description

Technical Field

[0001] The present application relates to the field of measurement and control technology, and more specifically, to an online metering system and method. Background Art

[0002] Measurement and control refers to ensuring the stability and safety of the production process by real-time monitoring and adjusting various physical quantities in the industrial process. In the oil and gas industry, measurement and control technology is particularly important, especially in oil and gas flow measurement. The accurate measurement of oil and gas flow is directly related to the rational use of resources and the safe operation of pipelines. By installing flow meters and data acquisition systems, it is possible to monitor oil and gas flow, pressure, temperature and other parameters in real time, and transmit data to the central control system for analysis and adjustment. Oil and gas flow measurement technology not only improves production efficiency, but also prevents risks such as leakage or overload through early warning mechanisms, optimizes the exploitation and transportation of oil and gas resources, ensures production safety and reduces environmental risks.

[0003] Existing online measurement of oil and gas flow is mainly measured by mass flow meters, turbine flow meters, differential pressure flow meters, ultrasonic flow meters and electromagnetic flow meters. These flow meters are often combined with temperature, pressure and density sensors and improve measurement accuracy through data fusion and calibration algorithms. However, in oil and gas production, especially in complex flow conditions such as oil-water-gas three-phase flow and bubble flow, traditional single-phase flow meters cannot accurately measure. In foam fluids, the ratio between gas foam and liquid often changes, the flow pattern is complex, and the presence of gas makes the flow more unstable, which will greatly affect the measurement accuracy of the flow meter. Therefore, how to achieve calibration of mass flow meters under complex oil and gas flow conditions, thereby improving the measurement accuracy of mass flow meters has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an online metering system and method, which can realize the calibration of a mass flow meter under complex oil and gas flow conditions, thereby improving the measurement accuracy of the mass flow meter.

[0005] In a first aspect, the present application provides an online calibration method for a mass flow meter, which is used to perform online calibration on a mass flow meter in an online metering system. The method comprises the following steps: Start online oil and gas metering and remotely control multiple mass flow meters to measure the oil and gas flow at different monitoring locations at the wellhead of the target oil field; The temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position are correlated to obtain the correlation influence characteristics of temperature fluctuation on the oil and gas flow at each monitoring position. Then, the stability of the mass flow meter at each monitoring position is evaluated based on all the correlation influence characteristics to obtain the stability margin of each mass flow meter when measuring the oil and gas flow. Calculate the gradient change of oil and gas flow at each monitoring position, and then determine the measurement difference between each mass flow meter based on all the gradient changes; Determine the calibration constraint parameter of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters; Confidence calibration is performed on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter.

[0006] In some embodiments, the temperature fluctuation at each monitoring position and the oil and gas flow rate at each monitoring position are correlated to obtain the correlation influence characteristics of the temperature fluctuation on the oil and gas flow rate at each monitoring position, which specifically include: Acquire temperature change data at each monitoring position, and then perform time series analysis on the temperature change data at each monitoring position to obtain the temperature fluctuation amount at each monitoring position; Determine the correlation coefficient between oil and gas flow and temperature change in online metering according to the fluctuation amount of temperature at each monitoring position and the fluctuation amplitude of oil and gas flow at each monitoring position; The correlation characteristics of temperature fluctuations on the measurement of oil and gas flow at each monitoring location are determined through the preset machine learning model and the correlation coefficient between oil and gas flow and temperature changes in online metering.

[0007] In some embodiments, the stability of the mass flowmeter at each monitoring position is evaluated according to all associated influencing features, and the stability margin of each mass flowmeter when measuring the oil and gas flow rate is obtained, which specifically includes: Determine the stability influence factor of the mass flow meter at each monitoring position based on all associated influence characteristics; Perform differential analysis on the oil and gas flow rates at all monitoring locations to obtain the differential value of the oil and gas flow rates at each monitoring location; The stability margin of each mass flowmeter when measuring the oil and gas flow is determined by the stability influencing factor of the mass flowmeter at each monitoring position and the difference value of the oil and gas flow at each monitoring position.

[0008] In some embodiments, determining the measurement difference between each mass flow meter according to all gradient changes specifically includes: Obtaining the oil-gas viscosity ratio at each monitoring location; Linear fitting is performed on the oil-gas viscosity ratio at all monitoring positions to obtain the variation curve of the oil-gas viscosity ratio; Linear fitting is performed on the gradient changes of oil and gas flow at all monitoring positions to obtain the gradient change curve of oil and gas flow; The measurement difference between the mass flow meters is determined according to the gradient change curve of the oil and gas flow rate and the change curve of the oil and gas viscosity ratio.

[0009] In some embodiments, determining the calibration constraint parameter of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters specifically includes: Selecting a monitoring location as a selected monitoring location; A measurement calibration model of the mass flowmeter at the selected monitoring location is constructed according to the stability margin when the mass flowmeter at the selected monitoring location measures the oil and gas flow rate, the oil and gas flow rate at the selected monitoring location, the oil and gas viscosity ratio at the selected monitoring location, and the measurement difference between each mass flowmeter; Setting constraint conditions for a measurement calibration model of a mass flow meter at a selected monitoring position, and then optimizing the measurement calibration model of the mass flow meter at the selected monitoring position to obtain calibration constraint parameters of the mass flow meter at the selected monitoring position; Continue to determine the calibration constraint parameters of the mass flow meters at the remaining monitoring locations.

[0010] In some embodiments, confidence calibration is performed on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter, specifically including: determining a gain calibration factor for each mass flow meter based on all calibration constraint parameters; The gain coefficient of each mass flow meter is calibrated by using the gain calibration factor of each mass flow meter, thereby obtaining each calibrated mass flow meter.

[0011] In some embodiments, the mass flow meter is a Coriolis mass flow meter.

[0012] In a second aspect, the present application provides an online metrology system, the system comprising an online calibration unit, the online calibration unit comprising: The measurement module is used to remotely control multiple mass flow meters to measure the oil and gas flow at different monitoring positions at the wellhead of the target oil field after starting the online oil and gas metering; A processing module is used to perform dependency correlation between the temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position, obtain the correlation influence characteristics of the temperature fluctuation on the oil and gas flow at each monitoring position, and then perform stability evaluation on the mass flow meter at each monitoring position according to all the correlation influence characteristics, and obtain the stability margin of each mass flow meter when measuring the oil and gas flow; The processing module is further used to calculate the gradient change of the oil and gas flow rate at each monitoring position, and then determine the measurement difference between each mass flow meter according to all the gradient changes; The processing module is further used to determine the calibration constraint parameters of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters; The calibration module is used to perform confidence calibration on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned online calibration method of the mass flow meter.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the online calibration method of the mass flow meter described above is implemented.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the online metering system and method provided by the present application, first, online oil and gas metering is started, and multiple mass flow meters are remotely controlled to measure the oil and gas flow at different monitoring positions at the wellhead of the target oil field; the temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position are dependently correlated to obtain the correlation influence characteristics of the temperature fluctuation on the measurement of the oil and gas flow at each monitoring position, and then the stability of the mass flow meter at each monitoring position is evaluated according to all the correlation influence characteristics to obtain the stability margin of each mass flow meter when measuring the oil and gas flow; the gradient change of the oil and gas flow at each monitoring position is calculated, and then the measurement difference between each mass flow meter is determined according to all the gradient changes; the calibration constraint parameters of each mass flow meter are determined according to the stability margin when each mass flow meter measures the oil and gas flow and the measurement difference between each mass flow meter; each mass flow meter is confidence calibrated according to all the calibration constraint parameters to obtain each calibrated mass flow meter.

[0016] It can be seen that in the present application, the calibration constraint parameters of each mass flow meter can be determined according to the stability margin of each mass flow meter when measuring the oil and gas flow and the measurement difference between each mass flow meter; wherein, firstly, by remotely controlling multiple mass flow meters, the oil and gas flow at different monitoring positions of the oil and gas wellhead is measured in real time, and the temperature fluctuation of each monitoring position is further analyzed, and according to the dependency relationship between the temperature fluctuation and the oil and gas flow, the correlation influence characteristics of the temperature fluctuation on the measurement result are extracted, and the influence of external factors that the mass flow meter may be affected by under complex flow conditions is measured by all the correlation influence characteristics, which is helpful to identify the error sources encountered by the mass flow meter under complex flow conditions, and then by performing stability evaluation on each mass flow meter, the stability margin is obtained to judge whether the flow meter can adapt to the changes in oil and gas flow and continue to maintain a high measurement accuracy. The stability margin provides a quantitative basis for the calibration of the mass flow meter; secondly, according to The gradient change of the oil and gas flow rate at each monitoring position obtains the measurement difference between each mass flow meter, and the measurement difference can be used to measure the measurement deviation between each mass flow meter under the complex flow state of oil and gas; then, by quantifying the stability margin of each mass flow meter when measuring the oil and gas flow and the measurement difference between each mass flow meter, the calibration constraint parameter of each mass flow meter is obtained, wherein the calibration constraint parameter represents the parameter value that constrains the mass flow meter to maintain stability under the complex flow state of oil and gas during the calibration of the mass flow meter; finally, each mass flow meter is calibrated according to all the calibration constraint parameters, and the calibrated mass flow meter can maintain stable and high-precision measurement results under complex flow conditions, thereby improving the accuracy of the mass flow meter in the complex flow environment of the oil and gas wellhead; in summary, the scheme of the present application can realize the calibration of the mass flow meter under the complex flow state of oil and gas, thereby improving the measurement accuracy of the mass flow meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of an online calibration method of a mass flow meter according to some embodiments of the present application; Figure 2 is a schematic diagram of a process for determining associated impact features according to some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining measurement differences between mass flow meters according to some embodiments of the present application; Figure 4 is a schematic diagram of the structure of an online calibration unit according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing an online calibration method of a mass flow meter according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of an online calibration method for a mass flow meter according to some embodiments of the present application. The online calibration method 100 for a mass flow meter mainly includes the following steps: In step 101, online oil and gas metering is started, and multiple mass flow meters are remotely controlled to measure the oil and gas flow rates at different monitoring positions at the wellhead of the target oil field.

[0020] In the specific implementation, first, a mass flow meter is set at each monitoring position at the wellhead of the target oil field. After starting the online oil and gas metering, the mass flow meter at each monitoring position is remotely controlled to collect the oil and gas flow at each monitoring position.

[0021] It should be noted that the mass flowmeter described in the present application is a Coriolis mass flowmeter. It should also be noted that the oil and gas flow rate described in the present application represents the mass of the oil and gas mixture at the monitoring position per unit time, and the oil and gas flow rate is collected once per second.

[0022] In step 102, the temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position are dependently correlated to obtain the correlation influence characteristics of the temperature fluctuation on the measurement of the oil and gas flow at each monitoring position, and then the stability of the mass flow meter at each monitoring position is evaluated based on all the correlation influence characteristics to obtain the stability margin of each mass flow meter when measuring the oil and gas flow.

[0023] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the correlation influence characteristics in some embodiments of the present application. In this embodiment, the fluctuation amount of the temperature at each monitoring position and the oil and gas flow at each monitoring position are dependently correlated to obtain the correlation influence characteristics of the temperature fluctuation on the oil and gas flow at each monitoring position. The following steps can be used to achieve: First, in step 1021, the temperature change data at each monitoring position is obtained, and then the temperature change data at each monitoring position is analyzed in time series to obtain the temperature fluctuation amount at each monitoring position; Secondly, in step 1022, the correlation coefficient between the oil and gas flow rate and the temperature change in the online metering is determined according to the fluctuation amount of the temperature at each monitoring position and the fluctuation amplitude of the oil and gas flow rate at each monitoring position; Then, in step 1023, the correlation influence characteristics of temperature fluctuation on the measurement of oil and gas flow at each monitoring position are determined through the preset machine learning model and the correlation coefficient between oil and gas flow and temperature change in online metering.

[0024] It should be noted that the temperature change data described in the present application is data consisting of temperature values ​​collected at one-second intervals at the monitoring location in the last hour.

[0025] In addition, it should be noted that the machine learning model preset in this application (for example, support vector machine) can be trained using historical oil and gas flow data, historical pressure data, historical oil and gas density data, and historical oil and gas viscosity ratio data at each monitoring location, and the trained model can be used as the preset machine learning model.

[0026] In the specific implementation, first, for the temperature change data at each monitoring position, the sliding window technology is used to process the temperature change data at the monitoring position, wherein the size of the sliding window is set to the value obtained by taking the square root of the total number of temperature values ​​in the temperature change data and rounding it down, and the sliding step is set to 1. In each sliding, the difference between the maximum temperature value and the minimum temperature value in the sliding window is calculated until the sliding window aligns with the last temperature value in the temperature change data and stops sliding, and then the mean of all the obtained differences is calculated, and the obtained mean is used as the temperature fluctuation at the monitoring position, thereby obtaining the temperature fluctuation at each monitoring position; secondly, for each monitoring position, the oil and gas flow rate of each monitoring position in the most recent hour is obtained, the maximum value of all oil and gas flows is subtracted from the minimum value, and the subtraction result is used as the fluctuation amplitude of the oil and gas flow rate at the monitoring position, thereby obtaining the temperature fluctuation at each monitoring position. The fluctuation amplitude of the oil and gas flow rate is set, and the Pearson correlation coefficient is further calculated for the fluctuation amplitude of the temperature at all monitoring positions and the fluctuation amplitude of the oil and gas flow rate at all monitoring positions, and the obtained Pearson correlation coefficient is used as the correlation coefficient between the oil and gas flow rate and temperature change in online metering; finally, for each monitoring position, the pressure value, oil and gas density, and oil and gas viscosity ratio at the monitoring position are input into the preset machine learning model, and the result output by the machine learning model (i.e., the predicted oil and gas flow rate) is subtracted from the oil and gas flow rate at the monitoring position, and the value obtained by subtraction is multiplied by the correlation coefficient between the oil and gas flow rate and temperature change in online metering, and the multiplied value is used as the correlation influence feature of the temperature fluctuation on the measurement of the oil and gas flow rate at the monitoring position, thereby obtaining the correlation influence feature of the temperature fluctuation on the measurement of the oil and gas flow rate at all monitoring positions. In other embodiments, other methods can also be used to achieve this, which will not be repeated here.

[0027] It should be noted that the associated influence characteristics described in the present application represent the characteristic values ​​of the degree of influence of temperature changes at the monitoring location on the oil and gas flow measurement results. By analyzing the associated influence characteristics, the measurement errors caused by the complex flow state of oil and gas can be identified and compensated, thereby improving the measurement accuracy of the flowmeter in these complex environments.

[0028] In some embodiments, the stability of the mass flowmeter at each monitoring position is evaluated according to all associated influencing features, and the stability margin of each mass flowmeter when measuring the oil and gas flow can be obtained by the following steps: Determine the stability influence factor of the mass flow meter at each monitoring position based on all associated influence characteristics; Perform differential analysis on the oil and gas flow rates at all monitoring locations to obtain the differential value of the oil and gas flow rates at each monitoring location; The stability margin of each mass flowmeter when measuring the oil and gas flow is determined by the stability influencing factor of the mass flowmeter at each monitoring position and the difference value of the oil and gas flow at each monitoring position.

[0029] In the specific implementation, first, the mean of all the associated influence characteristics is calculated, and the obtained mean is used as the global associated equilibrium value. For each monitoring position, the associated influence characteristics of the oil and gas flow at the monitoring position are subtracted from the global associated equilibrium value, and the value obtained by subtraction is used as the stable influence factor of the mass flowmeter at the monitoring position, thereby obtaining the stable influence factor of the mass flowmeter at all monitoring positions; secondly, the oil and gas flow at all monitoring positions are sorted in descending order, and the obtained sequence is processed by first-order difference to obtain the differential value of the oil and gas flow at each monitoring position; then, a negative exponential function with the natural logarithm e as the base is calculated for the stable influence factor of the mass flowmeter at each monitoring position, and the value obtained after calculating the negative exponential function with the natural logarithm e as the base is multiplied by the differential value of the oil and gas flow at each monitoring position, and the multiplied value is used as the stability margin of the mass flowmeter at each monitoring position when measuring the oil and gas flow. Other methods can also be used in other embodiments, which are not limited here.

[0030] It should be noted that the stability margin described in the present application indicates the degree of stability in maintaining measurement accuracy when the mass flowmeter measures the oil and gas flow rate under complex oil and gas flow conditions. The larger the stability margin, the higher the degree of stability in maintaining measurement accuracy when the mass flowmeter measures the oil and gas flow rate under complex oil and gas flow conditions, and vice versa.

[0031] In step 103, the gradient change of the oil and gas flow rate at each monitoring position is calculated, and then the measurement difference between each mass flow meter is determined according to all the gradient changes.

[0032] In specific implementation, for each monitoring location, the oil and gas flow rate in the most recent hour at the monitoring location can be collected, the oil and gas flow rate in the most recent hour can be differentiated, and then the mean of all the values ​​obtained by the difference can be calculated, and the mean value obtained is used as the gradient change of the oil and gas flow rate at the monitoring location, thereby obtaining the gradient change of the oil and gas flow rate at all monitoring locations.

[0033] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of a process for determining the measurement difference between each mass flow meter in some embodiments of the present application. In this embodiment, the measurement difference between each mass flow meter can be determined according to all gradient changes by the following steps: Obtaining the oil-gas viscosity ratio at each monitoring location; Linear fitting is performed on the oil-gas viscosity ratio at all monitoring positions to obtain the variation curve of the oil-gas viscosity ratio; Linear fitting is performed on the gradient changes of oil and gas flow at all monitoring positions to obtain the gradient change curve of oil and gas flow; The measurement difference between the mass flow meters is determined according to the gradient change curve of the oil and gas flow rate and the change curve of the oil and gas viscosity ratio.

[0034] In the specific implementation, firstly, the oil-gas viscosity ratio at each monitoring position is obtained online by using the viscometer arranged at each monitoring position, and the existing linear fitting algorithm (such as the least squares support vector machine algorithm) is used to perform linear fitting on the oil-gas viscosity ratios at all monitoring positions, and the fitted curve is used as the oil-gas viscosity ratio change curve, wherein the values ​​on the oil-gas viscosity ratio change curve are all used as the oil-gas viscosity ratio fitting values, and each oil-gas viscosity ratio fitting value corresponds to the oil-gas viscosity ratio at a monitoring position; secondly, the existing linear fitting algorithm (such as the least squares support vector machine algorithm) is used to perform linear fitting on the gradient change of the oil-gas flow rate at all monitoring positions, and the fitted curve is used as the oil-gas flow rate gradient change curve, wherein the values ​​on the oil-gas flow rate gradient change curve are all used as the oil-gas flow gradient fitting values, and each oil-gas flow gradient fitting value is used for The gradient change of the oil and gas flow rate at a monitoring position should be obtained; then, the absolute value is taken after subtracting the gradient change of the oil and gas flow rate at the monitoring position corresponding to each oil and gas flow gradient fitting value from each oil and gas flow gradient fitting value on the oil and gas flow gradient change curve, and then the absolute value is taken after summing up all the values ​​obtained after taking the absolute values, and the value obtained by summing up is used as the oil and gas flow gradient bias value; then, the absolute value is taken after subtracting the gradient change of the oil and gas viscosity ratio at the monitoring position corresponding to each oil and gas viscosity ratio fitting value from each oil and gas viscosity ratio fitting value on the oil and gas viscosity ratio change curve, and then the absolute value is taken after summing up all the values ​​obtained after taking the absolute values, and the value obtained by summing up is used as the oil and gas viscosity ratio bias value; further, the oil and gas flow gradient bias value is divided by the oil and gas viscosity ratio bias value, and the value obtained by the division is used as the measurement difference between each mass flow meter. Other methods can also be used to achieve this in other embodiments, which is not limited here.

[0035] It should be noted that the measurement difference described in the present application represents the parameters that cause measurement deviations between various mass flow meters under complex oil and gas flow conditions.

[0036] In step 104, the calibration constraint parameters of each mass flow meter are determined according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters.

[0037] In some embodiments, determining the calibration constraint parameter of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters can be implemented by the following steps: Selecting a monitoring location as a selected monitoring location; A measurement calibration model of the mass flowmeter at the selected monitoring location is constructed according to the stability margin when the mass flowmeter at the selected monitoring location measures the oil and gas flow rate, the oil and gas flow rate at the selected monitoring location, the oil and gas viscosity ratio at the selected monitoring location, and the measurement difference between each mass flowmeter; Setting constraint conditions for a measurement calibration model of a mass flow meter at a selected monitoring position, and then optimizing the measurement calibration model of the mass flow meter at the selected monitoring position to obtain calibration constraint parameters of the mass flow meter at the selected monitoring position; Continue to determine the calibration constraint parameters of the mass flow meters at the remaining monitoring locations.

[0038] In specific implementation, first, random variables are set for the stability margin when the mass flowmeter at the selected monitoring position measures the oil and gas flow, the oil and gas flow at the selected monitoring position, and the oil and gas viscosity ratio at the selected monitoring position, and the set random variables are used as the stability margin weight coefficient, the oil and gas flow weight coefficient, and the oil and gas viscosity ratio weight coefficient, respectively. Then, the stability margin when the mass flowmeter at the selected monitoring position measures the oil and gas flow, the oil and gas flow at the selected monitoring position, and the oil and gas viscosity at the selected monitoring position are multiplied by the corresponding weight coefficients, and then all the multiplied values ​​are added to the measurement differences between the mass flowmeters, and the added variable objective function is used as the measurement calibration model of the mass flowmeter at the selected monitoring position; secondly, the range and measurement accuracy error of the mass flowmeter at the selected monitoring position are set as the constraint conditions of the mass flowmeter at the selected monitoring position, wherein the measurement accuracy error is less than 1%; then, an optimization algorithm (such as a genetic algorithm) is used to optimize the measurement calibration model of the mass flowmeter at the selected monitoring position, and the optimized value is used as the calibration constraint parameter of the mass flowmeter at the selected monitoring position. In other embodiments, other methods can also be used for implementation, which will not be repeated here.

[0039] It should be noted that the measurement calibration model in the present application represents the objective function model for calculating the calibration constraint parameters of the mass flow meter; in addition, it should also be noted that the calibration constraint parameters described in the present application represent the parameter values ​​that constrain the mass flow meter to remain stable under complex oil and gas flow conditions during the calibration process of the mass flow meter.

[0040] In step 105, confidence calibration is performed on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter.

[0041] In some embodiments, confidence calibration is performed on each mass flow meter according to all calibration constraint parameters, and each calibrated mass flow meter can be obtained by the following steps: determining a gain calibration factor for each mass flow meter based on all calibration constraint parameters; The gain coefficient of each mass flow meter is calibrated by using the gain calibration factor of each mass flow meter, thereby obtaining each calibrated mass flow meter.

[0042] In the specific implementation, first, the mean of all the calibration constraint parameters is calculated, and the obtained mean is used as the global calibration constraint parameter, and then the calibration constraint parameter of each mass flow meter is divided by the global calibration constraint parameter, and the values ​​obtained by the division are used as the gain calibration factor of each mass flow meter; then, the gain coefficient of each mass flow meter is multiplied by the gain calibration factor of each mass flow meter, and all the obtained values ​​are used as the new gain coefficient of each mass flow meter, thereby completing the confidence calibration of each mass flow meter. In other embodiments, other methods can also be used to implement it, which will not be repeated here.

[0043] It should be noted that the gain calibration factor described in the present application represents a parameter for calibrating the gain coefficient of the mass flow meter under complex oil and gas flow conditions.

[0044] In addition, in another aspect of the present application, in some embodiments, the present application provides an online measurement system, the system includes an online calibration unit, reference Figure 4 , which is a schematic diagram of the structure of an online calibration unit according to some embodiments of the present application, the online calibration unit 400 includes: a measurement module 401, a processing module 402 and a calibration module 403, which are described as follows: The measurement module 401 in this application is mainly used to remotely control multiple mass flow meters to measure the oil and gas flow at different monitoring positions at the wellhead of the target oil field after starting the online oil and gas metering; Processing module 402, in the present application, is used to perform dependency correlation between the temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position, obtain the correlation influence characteristics of the temperature fluctuation on the oil and gas flow at each monitoring position, and then perform stability evaluation on the mass flow meter at each monitoring position according to all the correlation influence characteristics, and obtain the stability margin of each mass flow meter when measuring the oil and gas flow; It should be noted that the processing module 402 in the present application is also used to calculate the gradient change of the oil and gas flow rate at each monitoring position, and then determine the measurement difference between each mass flow meter according to all the gradient changes; In addition, the processing module 402 in the present application is also used to determine the calibration constraint parameters of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between each mass flow meter; The calibration module 403, in the present application, the execution module 403 is mainly used to perform confidence calibration on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter.

[0045] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned online calibration method for the mass flow meter.

[0046] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing an online calibration method for a mass flow meter according to some embodiments of the present application. The online calibration method for a mass flow meter in the above embodiment can be performed by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0047] The processor 501 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of the online calibration method of the mass flow meter in the present application.

[0048] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0049] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0050] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method described in the above method embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0051] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0052] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0053] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0054] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the online calibration method of the mass flow meter described above is implemented.

[0055] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An online calibration method for a mass flow meter, used for online calibration of a mass flow meter in an online metering system, characterized in that: The method comprises the following steps: Start online oil and gas metering and remotely control multiple mass flow meters to measure the oil and gas flow at different monitoring locations at the wellhead of the target oil field; The temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position are correlated to obtain the correlation influence characteristics of temperature fluctuation on the oil and gas flow at each monitoring position. Then, the stability of the mass flow meter at each monitoring position is evaluated based on all the correlation influence characteristics to obtain the stability margin of each mass flow meter when measuring the oil and gas flow. Calculate the gradient change of oil and gas flow at each monitoring position, and then determine the measurement difference between each mass flow meter based on all the gradient changes; Determine the calibration constraint parameter of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters; Confidence calibration is performed on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter.

2. The method according to claim 1, characterized in that The temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position are correlated to obtain the correlation influence characteristics of the temperature fluctuation on the oil and gas flow at each monitoring position, which specifically include: Acquire temperature change data at each monitoring position, and then perform time series analysis on the temperature change data at each monitoring position to obtain the temperature fluctuation amount at each monitoring position; Determine the correlation coefficient between oil and gas flow and temperature change in online metering according to the fluctuation amount of temperature at each monitoring position and the fluctuation amplitude of oil and gas flow at each monitoring position; The correlation characteristics of temperature fluctuations on the measurement of oil and gas flow at each monitoring location are determined through the preset machine learning model and the correlation coefficient between oil and gas flow and temperature changes in online metering.

3. The method according to claim 1, characterized in that According to all the associated influencing characteristics, the stability of the mass flow meter at each monitoring position is evaluated, and the stability margin of each mass flow meter when measuring oil and gas flow is obtained, including: Determine the stability influence factor of the mass flow meter at each monitoring position based on all associated influence characteristics; Perform differential analysis on the oil and gas flow rates at all monitoring locations to obtain the differential value of the oil and gas flow rates at each monitoring location; The stability margin of each mass flowmeter when measuring the oil and gas flow is determined by the stability influencing factor of the mass flowmeter at each monitoring position and the difference value of the oil and gas flow at each monitoring position.

4. The method according to claim 1, characterized in that Determining the measurement differences between the mass flow meters based on all gradient changes specifically includes: Obtaining the oil-gas viscosity ratio at each monitoring location; Linear fitting is performed on the oil-gas viscosity ratio at all monitoring positions to obtain the variation curve of the oil-gas viscosity ratio; Linear fitting is performed on the gradient changes of oil and gas flow at all monitoring positions to obtain the gradient change curve of oil and gas flow; The measurement difference between the mass flow meters is determined according to the gradient change curve of the oil and gas flow rate and the change curve of the oil and gas viscosity ratio.

5. The method according to claim 1, characterized in that Determining the calibration constraint parameters of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters specifically includes: Selecting a monitoring location as a selected monitoring location; A measurement calibration model of the mass flowmeter at the selected monitoring location is constructed according to the stability margin when the mass flowmeter at the selected monitoring location measures the oil and gas flow rate, the oil and gas flow rate at the selected monitoring location, the oil and gas viscosity ratio at the selected monitoring location, and the measurement difference between each mass flowmeter; Setting constraint conditions for a measurement calibration model of a mass flow meter at a selected monitoring position, and then optimizing the measurement calibration model of the mass flow meter at the selected monitoring position to obtain calibration constraint parameters of the mass flow meter at the selected monitoring position; Continue to determine the calibration constraint parameters of the mass flow meters at the remaining monitoring locations.

6. The method according to claim 1, characterized in that The confidence calibration is performed on each mass flow meter according to all the calibration constraint parameters, and the calibrated mass flow meters specifically include: determining a gain calibration factor for each mass flow meter based on all calibration constraint parameters; The gain coefficient of each mass flow meter is calibrated by using the gain calibration factor of each mass flow meter, thereby obtaining each calibrated mass flow meter.

7. The method according to claim 1, characterized in that The mass flow meter is a Coriolis mass flow meter.

8. An online metrology system, comprising an online calibration unit, characterized in that: The online calibration unit comprises: The measurement module is used to remotely control multiple mass flow meters to measure the oil and gas flow at different monitoring positions at the wellhead of the target oil field after starting the online oil and gas metering; A processing module is used to perform dependency correlation between the temperature fluctuation at each monitoring position and the oil and gas flow at each monitoring position, obtain the correlation influence characteristics of the temperature fluctuation on the oil and gas flow at each monitoring position, and then perform stability evaluation on the mass flow meter at each monitoring position according to all the correlation influence characteristics, and obtain the stability margin of each mass flow meter when measuring the oil and gas flow; The processing module is further used to calculate the gradient change of the oil and gas flow rate at each monitoring position, and then determine the measurement difference between each mass flow meter according to all the gradient changes; The processing module is further used to determine the calibration constraint parameters of each mass flow meter according to the stability margin when each mass flow meter measures the oil and gas flow rate and the measurement difference between the mass flow meters; The calibration module is used to perform confidence calibration on each mass flow meter according to all calibration constraint parameters to obtain each calibrated mass flow meter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the online calibration method for the mass flow meter according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the online calibration method for a mass flow meter according to any one of claims 1 to 7 is implemented.

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