An online metering system and method

Abstract

The present application provides an online metering system and method. First, the oil and gas flow rates at different monitoring positions are measured. Then, the fluctuations in temperature and the oil and gas flow rates at each monitoring position are dependently correlated to obtain the correlation influence characteristics of the oil and gas flow rates at each monitoring position. Further, the stability of each mass flowmeter is evaluated to obtain the stability margin of each mass flowmeter. Then, the measurement differences between the mass flowmeters are determined. Next, the calibration constraint parameters of each mass flowmeter are determined based on the stability margin of each mass flowmeter and the measurement differences between the mass flowmeters. Then, confidence calibration is performed on all mass flowmeters to obtain the calibrated mass flowmeters. By adopting the solution of the present application, calibration of the mass flowmeter under complex oil and gas flow conditions can be achieved, thereby improving the measurement accuracy of the mass flowmeter.

Description

Technical Field

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

[0002] Measurement control refers to ensuring the stability and safety of the production process by real-time monitoring and adjusting various physical quantities in industrial processes. In the oil and gas industry, measurement control technology is particularly important. Especially in the measurement of oil and gas flow, the accurate measurement of oil and gas flow is directly related to the rational utilization of resources and the safe operation of pipelines. By installing flow meters and data acquisition systems, parameters such as oil and gas flow, pressure, and temperature can be monitored in real time, and the data can be transmitted to the central control system for analysis and adjustment. The oil and gas flow measurement technology not only improves production efficiency but also can prevent risks such as leakage or overload through an early warning mechanism, optimize the exploitation and transportation process of oil and gas resources, ensure production safety, and reduce environmental risks.

[0003] Existing online metering of oil and gas flow mainly measures through mass flow meters, turbine flow meters, differential pressure flow meters, ultrasonic flow meters, and electromagnetic flow meters. These flow meters often combine temperature, pressure, and density sensors and use data fusion and calibration algorithms to improve measurement accuracy. However, in oil and gas production, especially in complex flow states 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 calibrate the mass flow meter under complex oil and gas flow states to improve the measurement accuracy of the mass flow meter has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides an online metering system and method, which can calibrate the mass flow meter under complex oil and gas flow states, thereby improving the measurement accuracy of the mass flow meter.

[0005] In a first aspect, this application provides an online calibration method for a mass flow meter, which is used to perform online calibration on the mass flow meter in an online metering system. The method includes the following steps:

[0006] Start online metering of oil and gas, and remotely control multiple mass flow meters to measure the oil and gas flow at different monitoring positions of the wellhead of the target oil field;

[0007] The fluctuation amount of the temperature at each monitoring position and the oil and gas flow rate at each monitoring position are dependently correlated to obtain the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position. Then, based on all the correlation influence characteristics, the stability of the mass flowmeter at each monitoring position is evaluated to obtain the stability margin when each mass flowmeter measures the oil and gas flow rate;

[0008] Calculate the gradient change amount of the oil and gas flow rate at each monitoring position, and then determine the measurement difference between each mass flowmeter based on all the gradient change amounts;

[0009] Determine the calibration constraint parameters of each mass flowmeter according to the stability margin when each mass flowmeter measures the oil and gas flow rate and the measurement difference between each mass flowmeter;

[0010] Perform confidence calibration on each mass flowmeter according to all the calibration constraint parameters to obtain each calibrated mass flowmeter.

[0011] In some embodiments, the dependently correlating the fluctuation amount of the temperature at each monitoring position and the oil and gas flow rate at each monitoring position to obtain the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position specifically includes:

[0012] Obtain the 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 fluctuation amount of the temperature at each monitoring position;

[0013] Determine the correlation coefficient between the oil and gas flow rate and the temperature change in on-line metering 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;

[0014] Determine the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position through a preset machine learning model and the correlation coefficient between the oil and gas flow rate and the temperature change in on-line metering.

[0015] In some embodiments, the stability of the mass flowmeter at each monitoring position is evaluated based on all the correlation influence characteristics to obtain the stability margin when each mass flowmeter measures the oil and gas flow rate, which specifically includes:

[0016] Determine the stable influence factor of the mass flowmeter at each monitoring position according to all the correlation influence characteristics;

[0017] Perform differential analysis on the oil and gas flow rates at all monitoring positions to obtain the difference values of the oil and gas flow rates at each monitoring position;

[0018] Determine the stability margin when each mass flowmeter measures the oil and gas flow rate through the stable influence factor of the mass flowmeter at each monitoring position and the difference value of the oil and gas flow rate at each monitoring position.

[0019] In some embodiments, determining the measurement differences between each mass flowmeter according to all the gradient change amounts specifically includes:

[0020] Obtain the oil-gas viscosity ratio at each monitoring position;

[0021] Perform linear fitting on the oil-gas viscosity ratios at all monitoring positions to obtain the change curve of the oil-gas viscosity ratio;

[0022] Perform linear fitting on the gradient change amounts of the oil-gas flow rates at all monitoring positions to obtain the gradient change curve of the oil-gas flow rate;

[0023] Determine the measurement differences between each mass flowmeter according to the gradient change curve of the oil-gas flow rate and the change curve of the oil-gas viscosity ratio.

[0024] In some embodiments, determining the calibration constraint parameters of each mass flowmeter according to the stability margin when each mass flowmeter measures the oil-gas flow rate and the measurement differences between each mass flowmeter specifically includes:

[0025] Select one monitoring position as the selected monitoring position;

[0026] Construct a measurement calibration model for the mass flowmeter at the selected monitoring position according to the stability margin when the mass flowmeter at the selected monitoring position measures the oil-gas flow rate, the oil-gas flow rate at the selected monitoring position, the oil-gas viscosity ratio at the selected monitoring position, and the measurement differences between each mass flowmeter;

[0027] Set constraint conditions for the measurement calibration model of the mass flowmeter at the selected monitoring position, and then optimize the measurement calibration model of the mass flowmeter at the selected monitoring position to obtain the calibration constraint parameters of the mass flowmeter at the selected monitoring position;

[0028] Continue to determine the calibration constraint parameters of the mass flowmeters at the remaining monitoring positions.

[0029] In some embodiments, performing confidence calibration on each mass flowmeter according to all the calibration constraint parameters to obtain each calibrated mass flowmeter specifically includes:

[0030] Determine the gain calibration factor of each mass flowmeter according to all the calibration constraint parameters;

[0031] Calibrate the gain coefficient of each mass flowmeter through the gain calibration factor of each mass flowmeter, and then obtain each calibrated mass flowmeter.

[0032] In some embodiments, the mass flowmeter is a Coriolis mass flowmeter.

[0033] Second aspect, the present application provides an on-line metering system, which includes an on-line calibration unit, and the on-line calibration unit includes:

[0034] A measurement module, configured to remotely control a plurality of mass flow meters to measure the oil and gas flow rates at different monitoring positions of the wellhead of the target oil field after starting on-line metering of oil and gas;

[0035] A processing module, configured to dependently correlate the fluctuation amount of the temperature at each monitoring position and the oil and gas flow rate at each monitoring position, obtain the correlation influence characteristics of the temperature fluctuation on the measurement of the oil and gas flow rate at each monitoring position, and then perform a stability evaluation on the mass flow meters at each monitoring position according to all the correlation influence characteristics to obtain the stability margin when each mass flow meter measures the oil and gas flow rate;

[0036] The processing module is further configured to calculate the gradient change amount 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 change amounts;

[0037] The processing module is further configured 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;

[0038] A calibration module, configured to perform confidence calibration on each mass flow meter according to all the calibration constraint parameters to obtain each calibrated mass flow meter.

[0039] Third aspect, the present application provides a computer device, which includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the on-line calibration method of the above-mentioned mass flow meter.

[0040] Fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the on-line calibration method of the above-mentioned mass flow meter.

[0041] The technical solutions provided by the embodiments disclosed in the present application have the following beneficial effects:

[0042] In the online metering system and method provided by the present application, first, start the online metering of oil and gas, and remotely control multiple mass flow meters to measure the oil and gas flow rates at different monitoring positions of the wellhead of the target oilfield; correlate the fluctuation amount of the temperature at each monitoring position with the oil and gas flow rate at each monitoring position to obtain the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position, and then evaluate the stability of the mass flow meters at each monitoring position according to all the correlation influence characteristics to obtain the stability margins of the mass flow meters when measuring the oil and gas flow rates; calculate the gradient change amount of the oil and gas flow rate at each monitoring position, and then determine the measurement differences between the mass flow meters according to all the gradient change amounts; determine the calibration constraint parameters of each mass flow meter according to the stability margins of the mass flow meters when measuring the oil and gas flow rates and the measurement differences between the mass flow meters; and perform confidence calibration on each mass flow meter according to all the calibration constraint parameters to obtain the calibrated mass flow meters.

[0043] 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 margins of the mass flow meters when measuring the oil and gas flow rates and the measurement differences between the mass flow meters; among them, first, by remotely controlling multiple mass flow meters, the oil and gas flow rates at different monitoring positions of the oil and gas wellhead are measured in real time, and further, the temperature fluctuations at each monitoring position are analyzed, and according to the dependence relationship between the temperature fluctuations and the oil and gas flow rates, the correlation influence characteristics of the temperature fluctuations on the measurement results are extracted. Measuring the possible external factor influences on the mass flow meter under complex flow conditions through all the correlation influence characteristics helps to identify the error sources encountered by the mass flow meter under complex flow states. Next, by evaluating the stability of each mass flow meter, the stability margin for judging whether the flow meter can adapt to the changes in the oil and gas flow and continuously maintain a high measurement accuracy is obtained. The stability margin provides a quantitative basis for calibrating the mass flow meter; secondly, the measurement differences between the mass flow meters are obtained according to the gradient change amounts of the oil and gas flow rates at each monitoring position, and the measurement deviations between the mass flow meters under complex oil and gas flow states can be measured through the measurement differences; then, by quantifying the stability margins of the mass flow meters when measuring the oil and gas flow rates and the measurement differences between the mass flow meters, the calibration constraint parameters of each mass flow meter are obtained, where the calibration constraint parameters represent the parameter values for constraining the mass flow meter to maintain stability under complex oil and gas flow states during the calibration process 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 states, thereby improving the accuracy of the mass flow meter in the complex flow environment of the oil and gas wellhead; in summary, the solution of the present application can realize the calibration of the mass flow meter under complex oil and gas flow states, thereby improving the measurement accuracy of the mass flow meter. Description of the Drawings

[0044] Figure 1 is an exemplary flowchart of an on - line calibration method for a mass flowmeter according to some embodiments of the present application;

[0045] Figure 2 is a schematic flowchart of a process for determining associated influencing features according to some embodiments of the present application;

[0046] Figure 3 is a schematic flowchart of a process for determining the measurement differences between individual mass flowmeters according to some embodiments of the present application;

[0047] Figure 4 is a schematic structural diagram of an on - line calibration unit according to some embodiments of the present application;

[0048] Figure 5 is a schematic structural diagram of a computer device for implementing the on - line calibration method of a mass flowmeter according to some embodiments of the present application. Detailed implementation manners

[0049] 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 of the specification and specific implementation manners.

[0050] Refer to Figure 1 , which is an exemplary flowchart of an on - line calibration method for a mass flowmeter according to some embodiments of the present application. The on - line calibration method 100 of the mass flowmeter mainly includes the following steps:

[0051] In step 101, start on - line metering of oil and gas, and remotely control multiple mass flowmeters to measure the oil and gas flow rates at different monitoring positions of the wellhead of the target oilfield.

[0052] Specifically, first, a mass flowmeter is set at each monitoring position of the wellhead of the target oilfield. After starting on - line metering of oil and gas, then, remotely control the mass flowmeter at each monitoring position to collect the oil and gas flow rate at each monitoring position.

[0053] It should be noted that the mass flowmeter described in the present application is a Coriolis mass flowmeter. Additionally, it should 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 acquisition frequency of the oil and gas flow rate is once per second.

[0054] In step 102, the fluctuation amount of the temperature at each monitoring position and the oil and gas flow rate at each monitoring position are dependently correlated to obtain the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position. Then, based on all the correlation influence characteristics, the stability of the mass flowmeter at each monitoring position is evaluated to obtain the stability margin when each mass flowmeter measures the oil and gas flow rate.

[0055] In some embodiments, as shown in Figure 2 the figure which is a schematic flow chart for 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 rate at each monitoring position are dependently correlated, and the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position can be implemented by the following steps:

[0056] First, in step 1021, the temperature change data at each monitoring position is obtained, and then time series analysis is performed on the temperature change data at each monitoring position to obtain the fluctuation amount of the temperature at each monitoring position;

[0057] Second, in step 1022, the correlation coefficient between the oil and gas flow rate and the temperature change in on-line 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;

[0058] Then, in step 1023, the correlation influence characteristics of the temperature fluctuation on measuring the oil and gas flow rate at each monitoring position are determined through a preset machine learning model and the correlation coefficient between the oil and gas flow rate and the temperature change in on-line metering.

[0059] It should be noted that the temperature change data in the present application is the data composed of the temperature values collected at one-second intervals at the monitoring position in the most recent one hour.

[0060] In addition, it should also be noted that the preset machine learning model (for example: support vector machine) in the present application can be trained using the historical oil and gas flow rate data, historical pressure data, historical oil and gas density data, and historical oil and gas viscosity ratio data at each monitoring position, and the trained model is used as the preset machine learning model.

[0061] In specific implementation, first, for the temperature change data at each monitoring position, the sliding window technique is used to process the temperature change data at the monitoring position. Among them, the size of the sliding window is set to the value obtained by taking the integer part of the arithmetic square root of the total number of temperature values in the temperature change data, and the sliding step is set to 1. In each sliding, the difference between the maximum temperature value and the minimum temperature value within the sliding window is calculated until the sliding window aligns with the last temperature value in the temperature change data and stops sliding. Then, the mean value of all the obtained differences is calculated, and the obtained mean value is used as the fluctuation amount of the temperature at the monitoring position, so as to obtain the fluctuation amount of the temperature at each monitoring position. Secondly, for each monitoring position, the oil and gas flow rate in the most recent hour at each monitoring position is obtained. The maximum value among all the oil and gas flow rates is subtracted from the minimum value, and the obtained difference is used as the fluctuation amplitude of the oil and gas flow rate at the monitoring position, so as to obtain the fluctuation amplitude of the oil and gas flow rate at each monitoring position. Further, the Pearson correlation coefficient is calculated for the fluctuation amount of the temperature at all the monitoring positions and the fluctuation amplitude of the oil and gas flow rate at all the monitoring positions, and the obtained Pearson correlation coefficient is used as the correlation coefficient between the oil and gas flow rate and the temperature change in on-line 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 a preset machine learning model. 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 then the obtained difference value is multiplied by the correlation coefficient between the oil and gas flow rate and the temperature change in on-line metering. The obtained multiplied value is used as the correlation influence feature of the temperature fluctuation on the measured oil and gas flow rate at the monitoring position, so as to obtain the correlation influence feature of the temperature fluctuation on the measured oil and gas flow rate at all the monitoring positions. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0062] It should be noted that the correlation influence feature described in this application represents the characteristic value of the influence degree of the temperature change at the monitoring position on the measurement result of the oil and gas flow rate. By analyzing the correlation influence feature, the measurement error caused by the complex flow state of the oil and gas can be identified and compensated, so as to improve the measurement accuracy of the flowmeter in these complex environments.

[0063] In some embodiments, the stability assessment of the mass flowmeter at each monitoring position is carried out according to all the correlation influence features, and the stability margin of each mass flowmeter when measuring the oil and gas flow rate can be realized by the following steps:

[0064] Determine the stability influence factor of the mass flowmeter at each monitoring position according to all the correlation influence features;

[0065] Perform differential analysis on the oil and gas flow rates at all the monitoring positions to obtain the difference values of the oil and gas flow rates at each monitoring position;

[0066] The stability margin of each mass flowmeter when measuring oil-gas flow is determined by the stable influence factor of the mass flowmeter at each monitoring position and the difference value of the oil-gas flow at each monitoring position.

[0067] When specifically implemented, first, calculate the mean value of all associated influence characteristics, and use the obtained mean value as the global associated equilibrium value. For each monitoring position, subtract the associated influence characteristic of the oil-gas flow at the monitoring position from the global associated equilibrium value, and use the subtracted value as the stable influence factor of the mass flowmeter at the monitoring position, thereby obtaining the stable influence factors of the mass flowmeters at all monitoring positions; secondly, sort the oil-gas flows at all monitoring positions in descending order, and then perform a first-order difference processing on the obtained sequence to obtain the difference values of the oil-gas flows at each monitoring position; then, calculate the negative exponential function with the natural logarithm e as the base for the stable influence factor of the mass flowmeter at each monitoring position, and then multiply the obtained value after calculating the negative exponential function with the natural logarithm e as the base by the difference value of the oil-gas flow at each monitoring position, and use the multiplied value as the stability margin of the mass flowmeter at each monitoring position when measuring oil-gas flow. In other embodiments, other methods can also be used to implement, which is not limited here.

[0068] It should be noted that in this application, the stability margin represents the stability degree of maintaining the measurement accuracy when the mass flowmeter measures the oil-gas flow under the complex oil-gas flow state. The larger the stability margin, the higher the stability degree of maintaining the measurement accuracy when the mass flowmeter measures the oil-gas flow under the complex oil-gas flow state, and vice versa.

[0069] In step 103, calculate the gradient change amount of the oil-gas flow at each monitoring position, and then determine the measurement differences between the mass flowmeters according to all the gradient change amounts.

[0070] When specifically implemented, for each monitoring position, the oil-gas flow in the most recent hour at the monitoring position can be collected, the oil-gas flow in the most recent hour is differenced, then calculate the mean value of all the values obtained by the difference, and use the obtained mean value as the gradient change amount of the oil-gas flow at the monitoring position, thereby obtaining the gradient change amounts of the oil-gas flows at all monitoring positions.

[0071] In some embodiments, as shown in Figure 3 This figure is a schematic flowchart of determining the measurement differences between the mass flowmeters in some embodiments of this application. In this embodiment, the measurement differences between the mass flowmeters can be determined according to all the gradient change amounts by the following steps:

[0072] Obtain the oil-gas viscosity ratio at each monitoring position;

[0073] Perform a linear fit on the oil-gas viscosity ratios at all monitoring positions to obtain the change curve of the oil-gas viscosity ratio;

[0074] Perform a linear fit on the gradient change amounts of the oil-gas flow rates at all monitoring positions to obtain the gradient change curve of the oil-gas flow rate;

[0075] Determine the measurement differences between each mass flowmeter according to the gradient change curve of the oil-gas flow rate and the change curve of the oil-gas viscosity ratio.

[0076] When specifically implemented, first, obtain the oil-gas viscosity ratio at each monitoring position online through viscometers arranged at each monitoring position, perform a linear fit on the oil-gas viscosity ratios at all monitoring positions using an existing linear fitting algorithm (such as the least squares support vector machine algorithm), and take the fitted curve as the change curve of the oil-gas viscosity ratio. Among them, the values on the change curve of the oil-gas viscosity ratio are all used as the oil-gas viscosity ratio fitting values, and at the same time, each oil-gas viscosity ratio fitting value corresponds to the oil-gas viscosity ratio at a monitoring position. Secondly, perform a linear fit on the gradient change amounts of the oil-gas flow rates at all monitoring positions using an existing linear fitting algorithm (such as the least squares support vector machine algorithm), and take the fitted curve as the gradient change curve of the oil-gas flow rate. Among them, the values on the gradient change curve of the oil-gas flow rate are all used as the oil-gas flow rate gradient fitting values, and at the same time, each oil-gas flow rate gradient fitting value corresponds to the gradient change amount of the oil-gas flow rate at a monitoring position. Then, subtract the gradient change amount of the oil-gas flow rate at the monitoring position corresponding to each oil-gas flow rate gradient fitting value on the gradient change curve of the oil-gas flow rate, take the absolute value, sum all the obtained absolute values, and take the sum value as the oil-gas flow rate gradient offset value. Then, subtract the gradient change amount of the oil-gas viscosity ratio at the monitoring position corresponding to each oil-gas viscosity ratio fitting value on the change curve of the oil-gas viscosity ratio, take the absolute value, sum all the obtained absolute values, and take the sum value as the oil-gas viscosity ratio offset value. Further, divide the oil-gas flow rate gradient offset value by the oil-gas viscosity ratio offset value, and take the obtained division value as the measurement difference between each mass flowmeter. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0077] It should be noted that the measurement difference described in this application represents a parameter for measuring the deviation between each mass flowmeter under the complex flow state of oil and gas.

[0078] In step 104, determine the calibration constraint parameters of each mass flowmeter according to the stability margin when each mass flowmeter measures the oil-gas flow rate and the measurement differences between each mass flowmeter.

[0079] In some embodiments, determining the calibration constraint parameters for each mass flowmeter according to the stability margin when each mass flowmeter measures the oil-gas flow rate and the measurement differences between the mass flowmeters can be implemented by the following steps:

[0080] Select a monitoring position as the selected monitoring position;

[0081] Construct a measurement calibration model for the mass flowmeter at the selected monitoring position according to the stability margin when the mass flowmeter at the selected monitoring position measures the oil-gas flow rate, the oil-gas flow rate at the selected monitoring position, the oil-gas viscosity ratio at the selected monitoring position, and the measurement differences between the mass flowmeters;

[0082] Set constraint conditions for the measurement calibration model of the mass flowmeter at the selected monitoring position, and then optimize the measurement calibration model of the mass flowmeter at the selected monitoring position to obtain the calibration constraint parameters for the mass flowmeter at the selected monitoring position;

[0083] Continue to determine the calibration constraint parameters for the mass flowmeters at the remaining monitoring positions.

[0084] Specifically, in implementation, first, set random variables for the stability margin when the mass flowmeter at the selected monitoring position measures the oil-gas flow rate, the oil-gas flow rate at the selected monitoring position, and the oil-gas viscosity ratio at the selected monitoring position respectively, and use the set random variables as the stability margin weight coefficient, the oil-gas flow rate weight coefficient, and the oil-gas viscosity ratio weight coefficient respectively. Then, multiply the stability margin when the mass flowmeter at the selected monitoring position measures the oil-gas flow rate, the oil-gas flow rate at the selected monitoring position, and the oil-gas viscosity at the selected monitoring position by the corresponding weight coefficients respectively, and then add the measurement differences between the mass flowmeters to all the multiplied values, and use the obtained changing objective function as the measurement calibration model for the mass flowmeter at the selected monitoring position. Second, set the range and measurement accuracy error of the mass flowmeter at the selected monitoring position as the constraint conditions for the mass flowmeter at the selected monitoring position, where the measurement accuracy error is less than 1%. Then, use an optimization algorithm (such as a genetic algorithm) to optimize the measurement calibration model of the mass flowmeter at the selected monitoring position, and use the obtained value after optimization as the calibration constraint parameter for the mass flowmeter at the selected monitoring position. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0085] It should be noted that in this application, the measurement calibration model represents the objective function model for calculating the calibration constraint parameters of the mass flowmeter; in addition, it should also be noted that the calibration constraint parameters in this application represent the parameter values that constrain the mass flowmeter to remain stable under the complex oil-gas flow state during the calibration of the mass flowmeter.

[0086] In step 105, confidence calibration is performed on each mass flowmeter according to all calibration constraint parameters, and each calibrated mass flowmeter is obtained.

[0087] In some embodiments, confidence calibration is performed on each mass flowmeter according to all calibration constraint parameters, and each calibrated mass flowmeter can be implemented by the following steps:

[0088] Determine the gain calibration factor of each mass flowmeter according to all calibration constraint parameters;

[0089] Calibrate the gain coefficient of each mass flowmeter through the gain calibration factor of each mass flowmeter, and then each calibrated mass flowmeter is obtained.

[0090] When specifically implemented, first, calculate the mean value of all calibration constraint parameters, and use the obtained mean value as the global calibration constraint parameter. Then, divide the calibration constraint parameter of each mass flowmeter by the global calibration constraint parameter, and use the obtained division values as the gain calibration factor of each mass flowmeter. Then, multiply the gain coefficient of each mass flowmeter by the gain calibration factor of each mass flowmeter, and use all the obtained values as the new gain coefficient of each mass flowmeter, thereby completing the confidence calibration of each mass flowmeter. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0091] It should be noted that the gain calibration factor described in this application represents a parameter for calibrating the gain coefficient of a mass flowmeter under complex oil-gas flow conditions.

[0092] In addition, on the other hand of this application, in some embodiments, this application provides an on-line metering system, which includes an on-line calibration unit. Refer to Figure 4 , this figure is a schematic structural diagram of the on-line calibration unit shown in some embodiments of this application. The on-line calibration unit 400 includes: a measurement module 401, a processing module 402, and a calibration module 403, which are described as follows:

[0093] Measurement module 401. In this application, the acquisition module 401 is mainly used to remotely control multiple mass flowmeters to measure the oil-gas flow at different monitoring positions of the wellhead of the target oilfield after starting on-line oil-gas metering;

[0094] Processing module 402. In this application, the processing module 402 is used to dependently associate the temperature fluctuation amount at each monitoring position with the oil-gas flow at each monitoring position, obtain the correlation influence characteristics of temperature fluctuation on measuring the oil-gas flow at each monitoring position, and then perform a stability assessment on the mass flowmeter at each monitoring position according to all the correlation influence characteristics, and obtain the stability margin when each mass flowmeter measures the oil-gas flow.

[0095] It should be noted that the processing module 402 in the present application is further configured to calculate the gradient change amount of the oil and gas flow rate at each monitoring position, and then determine the measurement difference between each mass flowmeter according to all the gradient change amounts;

[0096] In addition, the processing module 402 in the present application is further configured to determine the calibration constraint parameters of each mass flowmeter according to the stability margin when each mass flowmeter measures the oil and gas flow rate and the measurement difference between each mass flowmeter;

[0097] Calibration module 403. In the present application, the execution module 403 is mainly configured to perform confidence calibration on each mass flowmeter according to all the calibration constraint parameters to obtain each calibrated mass flowmeter.

[0098] In addition, the present application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the online calibration method of the mass flowmeter described above.

[0099] In some embodiments, refer to Figure 5 , this figure is a schematic structural diagram of a computer device for implementing the online calibration method of a mass flowmeter according to some embodiments of the present application. The online calibration method of the mass flowmeter in the above embodiments can be implemented by Figure 5 The computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0100] The processor 501 can be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more for controlling the execution of the online calibration method of the mass flowmeter in the present application.

[0101] The communication bus 502 can be used to transmit information between the above components.

[0102] The memory 503 can 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, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0103] Among them, the memory 503 is used to store the program code for implementing the solution of this application and is controlled by the processor 501 to execute. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The methods described in the above method embodiments can be implemented by one or more software modules in the program code in the processor 501 and the memory 503.

[0104] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0105] In a specific implementation, as an embodiment, the computer device can include multiple processors, and each of these processors can be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0106] The computer device described above 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 laptop 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 embodiments of the present application do not limit the type of the computer device.

[0107] In addition, the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the online calibration method of the mass flowmeter described above is implemented.

[0108] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0109] 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 equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. An online calibration method for a mass flowmeter, which is used to perform online calibration on the mass flowmeter in an online metering system, characterized in that, The method includes the following steps: Start on-line metering of oil and gas, and remotely control multiple mass flow meters to measure the oil and gas flow rates at different monitoring positions at the wellhead of the target oilfield; Perform a dependency correlation on the temperature fluctuation amount at each monitoring position and the oil and gas flow rate at each monitoring position to obtain the correlation influence characteristics of temperature fluctuation on measuring the oil and gas flow rate at each monitoring position. Furthermore, perform a stability evaluation on the mass flow meters at each monitoring position based on all the correlation influence characteristics to obtain the stability margins of each mass flow meter when measuring the oil and gas flow rate; Calculate the gradient change amount of the oil and gas flow rate at each monitoring position, and then determine the measurement differences between each mass flow meter based on all the gradient change amounts; Determine the calibration constraint parameters of each mass flow meter based on the stability margin of each mass flow meter when measuring the oil and gas flow rate and the measurement differences between each mass flow meter; Perform confidence calibration on each mass flow meter based on all the calibration constraint parameters to obtain each calibrated mass flow meter; Among them, determining the measurement differences between each mass flow meter based on all the gradient change amounts specifically includes: Obtain the oil and gas viscosity ratio at each monitoring position; Perform linear fitting on the oil and gas viscosity ratios at all monitoring positions to obtain the change curve of the oil and gas viscosity ratio; Perform linear fitting on the gradient change amounts of the oil and gas flow rates at all monitoring positions to obtain the gradient change curve of the oil and gas flow rate; Determine the measurement differences between each mass flow meter based on the gradient change curve of the oil and gas flow rate and the change curve of the oil and gas viscosity ratio.

2. The method according to claim 1, wherein Performing a dependency correlation on the temperature fluctuation amount at each monitoring position and the oil and gas flow rate at each monitoring position to obtain the correlation influence characteristics of temperature fluctuation on measuring the oil and gas flow rate at each monitoring position specifically includes: Obtain the 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 the oil and gas flow rate and the temperature change in on-line metering based on the temperature fluctuation amount at each monitoring position and the fluctuation amplitude of the oil and gas flow rate at each monitoring position; Determine the correlation influence characteristics of temperature fluctuation on measuring the oil and gas flow rate at each monitoring position through a preset machine learning model and the correlation coefficient between the oil and gas flow rate and the temperature change in on-line metering.

3. The method according to claim 1, characterized in that, Performing a stability evaluation on the mass flow meters at each monitoring position based on all the correlation influence characteristics to obtain the stability margins of each mass flow meter when measuring the oil and gas flow rate specifically includes: Determine the stable influence factor of the mass flow meter at each monitoring position based on all the correlation influence characteristics; Perform differential analysis on the oil and gas flow rates at all monitoring positions to obtain the difference values of the oil and gas flow rates at each monitoring position; Determine the stability margins of each mass flow meter when measuring the oil and gas flow rate through the stable influence factor of the mass flow meter at each monitoring position and the difference value of the oil and gas flow rate at each monitoring position.

4. The method according to claim 1, wherein Determining the calibration constraint parameters of each mass flow meter based on the stability margin of each mass flow meter when measuring the oil and gas flow rate and the measurement differences between each mass flow meter specifically includes: Select one monitoring position as the selected monitoring position; Construct a measurement calibration model for the mass flowmeter at the selected monitoring location based on the stability margin when measuring the oil-gas flow by the mass flowmeter at the selected monitoring location, the oil-gas flow rate at the selected monitoring location, the oil-gas viscosity ratio at the selected monitoring location, and the measurement differences between each mass flowmeter. Set constraint conditions for the measurement calibration model of the mass flowmeter at the selected monitoring location, and then optimize the measurement calibration model of the mass flowmeter at the selected monitoring location to obtain the calibration constraint parameters of the mass flowmeter at the selected monitoring location. Continue to determine the calibration constraint parameters of the mass flowmeters at the remaining monitoring locations.

5. The method according to claim 1, characterized in that, Perform confidence calibration on each mass flowmeter according to all the calibration constraint parameters to obtain each calibrated mass flowmeter, specifically including: Determine the gain calibration factor of each mass flowmeter according to all the calibration constraint parameters. Calibrate the gain coefficient of each mass flowmeter through the gain calibration factor of each mass flowmeter, and then obtain each calibrated mass flowmeter.

6. The method according to claim 1, characterized in that, The mass flowmeter is a Coriolis mass flowmeter.

7. An online metering system that performs online calibration of a mass flowmeter by using the method according to any one of claims 1 to 6. The system includes an online calibration unit, and is characterized in that, The online calibration unit includes: A measurement module, which is used to remotely control multiple mass flowmeters to measure the oil-gas flow rates at different monitoring locations of the wellhead of the target oilfield after starting the online metering of oil and gas. A processing module, which is used to perform a dependency association on the fluctuation amount of the temperature at each monitoring location and the oil-gas flow rate at each monitoring location to obtain the associated influence characteristics of the temperature fluctuation on measuring the oil-gas flow rate at each monitoring location, and then perform a stability assessment on the mass flowmeter at each monitoring location according to all the associated influence characteristics to obtain the stability margin when each mass flowmeter measures the oil-gas flow rate. The processing module is also used to calculate the gradient change amount of the oil-gas flow rate at each monitoring location, and then determine the measurement differences between each mass flowmeter according to all the gradient change amounts. The processing module is also used to determine the calibration constraint parameters of each mass flowmeter according to the stability margin when each mass flowmeter measures the oil-gas flow rate and the measurement differences between each mass flowmeter. A calibration module, which is used to perform confidence calibration on each mass flowmeter according to all the calibration constraint parameters to obtain each calibrated mass flowmeter.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the online calibration method of the mass flowmeter according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the online calibration method of the mass flowmeter according to any one of claims 1 to 6.

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