Metering method, device and equipment for oil well yield and storage medium

Abstract

The invention discloses an oil well yield metering method, device and equipment and a storage medium, and belongs to the technical field of oil and gas development and production. According to the method, the mechanism model and data model mixed oil well metering model is built, after the real-time production data of the oil well is obtained, the target model is selected from the oil well metering model according to the model selection condition, the selected target model is used for processing the real-time production data, and the oil well yield is obtained. The target model for yield measurement can be selected according to the model selection condition, so that the problem that yield prediction cannot be performed due to the fact that a single model cannot meet the calculation condition is solved, and the application range of the oil well yield measurement model is expanded. On the other hand, the mechanism model is obtained after the initial mechanism model is corrected through the mechanism correction parameters, the mechanism correction parameters can be obtained after historical data are learned by the data model, and the problem that the mechanism model is large in correction difficulty is solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of oil and gas development and production, and in particular to a method, device, equipment and storage medium for measuring oil well production. Background Art

[0002] Oil well and oil field production measurement is one of the most important tasks in oil field production management. The measurement results of oil well production directly determine the subsequent oil field development decisions. However, due to the limitations of geological conditions, process level, development history, data quality and other conditions, oil well production changes over time. Therefore, it is necessary to provide a method for measuring oil well production. Summary of the invention

[0003] The present application provides a method, device, equipment and storage medium for measuring oil well production, which can be used to measure oil well production. The above technical solution is as follows:

[0004] In a first aspect, an embodiment of the present application provides a method for measuring oil well production, the method comprising: acquiring real-time production data of the oil well; selecting at least one model as a target model from the oil well metering model based on a model selection condition, the oil well metering model comprising a mechanism model and a data model, the model selection condition being determined by the real-time production data, the mechanism model being obtained by correcting an initial mechanism model by using a mechanism correction parameter, the mechanism correction parameter being obtained by learning historical production data by using the data model; and processing the real-time production data using the target model to obtain the oil well production.

[0005] In a second aspect, an embodiment of the present application provides an oil well production metering device, the device comprising: an acquisition module, used to acquire real-time production data of the oil well; a selection module, used to select at least one model as a target model from the oil well metering model based on a model selection condition, the oil well metering model comprising a mechanism model and a data model, the model selection condition is determined by the real-time production data, the mechanism model is obtained by correcting the initial mechanism model by using a mechanism correction parameter, and the mechanism correction parameter is obtained by learning the historical data by the data model; the metering module is used to process the real-time production data using the target model to obtain the oil well production.

[0006] In a third aspect, an embodiment of the present application provides an oil well production metering device, the oil well production metering device comprising a processor and a memory, the memory storing at least one computer program, and the at least one computer program being loaded and executed by the processor so that the oil well production metering device can implement any of the above-mentioned oil well production metering methods.

[0007] In a fourth aspect, an embodiment of the present application provides a storage medium, which may be a computer-readable storage medium, storing at least one computer program, and at least one computer program is loaded and executed by a processor so that the computer implements any of the above-mentioned methods for measuring oil well production.

[0008] In a fifth aspect, the embodiments of the present application further provide a computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes any of the above-mentioned methods for measuring oil well production.

[0009] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0010] In the embodiment of the present application, a mixed oil well metering model of a mechanism model and a data model is constructed, wherein the mechanism model can be obtained by correcting the initial mechanism model through the mechanism correction parameters, and the mechanism correction parameters can be obtained by learning the historical production data from the data model. In actual production, after obtaining the real-time production data of the oil well, at least one model is selected from the oil well metering model as the target model according to the model selection conditions, and the real-time production data is processed using the selected target model to obtain the oil well production. Since the target model for measuring the oil well production can be selected according to the model selection conditions, the problem that a single model cannot meet the calculation conditions and thus cannot perform production prediction in some special scenarios is solved, and the application scope of the metering model of the oil well production is expanded. On the other hand, the mechanism correction parameters for correcting the mechanism model are obtained by learning the historical production data from the data model, which solves the problem of difficulty in correcting the mechanism model. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present application;

[0013] Figure 2 It is a flow chart of another method for measuring oil well production provided by an embodiment of the present application;

[0014] Figure 3 It is a schematic diagram of a neural network architecture of a data model provided in an embodiment of the present application;

[0015] Figure 4 It is a schematic diagram of a flow chart of measuring oil well production according to a mechanism model provided in an embodiment of the present application;

[0016] Figure 5 It is a flow chart of the data model prediction mechanism correction parameters provided in the embodiment of the present application;

[0017] Figure 6 It is a schematic diagram of a flow chart of measuring oil well production using a data model provided in an embodiment of the present application;

[0018] Figure 7 It is a schematic diagram of a process of wellbore model correction provided by an embodiment of the present application;

[0019] Figure 8 is a gas correction coefficient diagram provided in an embodiment of the present application;

[0020] Fig. 9 It is a flow chart of another method for measuring oil well production provided in an embodiment of the present application;

[0021] Fig.10 It is a structural schematic diagram of an oil well production metering device provided in an embodiment of the present application;

[0022] Fig.11 It is a structural schematic diagram of an oil well production metering device provided in an embodiment of the present application;

[0023] Fig.12 It is a schematic diagram of the structure of another oil well production metering device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0025] This application embodiment provides a method for measuring oil well production. Figure 1 , which shows a schematic diagram of an implementation environment of the method provided in an embodiment of the present application. The implementation environment may include: a terminal 11 and a server 12.

[0026] Among them, the terminal 11 is installed with an application or webpage that can display information, and the information that can be displayed by the application or webpage includes the real-time production data and the measured oil well production obtained in the embodiment of the present application. The server 12 can store the information to be displayed, and the terminal 11 can obtain the information to be displayed from the server 12. Of course, the terminal 11 can also store the obtained display information.

[0027] Optionally, the terminal 11 may be a smart device such as a mobile phone, a tablet computer, a personal computer, etc. The server 12 may be a single server, or a server cluster consisting of multiple servers, or a cloud computing service center. The terminal 11 establishes a communication connection with the server 12 via a wired or wireless network.

[0028] Optionally, the terminal 11 can be any electronic product that can interact with the user through one or more methods such as a keyboard, a touch pad, a touch screen, a remote control, voice interaction or a handwriting device, such as a personal computer (PC), a mobile phone, a smart phone, a personal digital assistant (PDA), a wearable device, a pocket PC (PPC), a tablet computer, etc. The server 12 can be a server, or a server cluster composed of multiple servers, or a cloud computing service center. The terminal 11 establishes a communication connection with the server 12 through a wired or wireless network.

[0029] Those skilled in the art should understand that the above-mentioned terminal 11 and server 12 are only examples, and other existing or future terminals or servers that are applicable to the present application should also be included in the protection scope of the present application and are included here by reference.

[0030] Based on the above Figure 1 In the implementation environment shown, the present application embodiment provides a method for measuring oil well production, which can be executed by a terminal or a server. Figure 2 As shown, taking the method applied to a terminal as an example, the method includes S101-S103.

[0031] S101. Obtain real-time production data of oil wells.

[0032] Among them, real-time production data refers to the real-time data that can be obtained during the actual production process of the oil well. Among them, real-time production data includes real-time production data at the wellhead and real-time production data of the electric submersible pump under ideal conditions. Real-time production data at the wellhead includes at least one of the following: oil pressure, casing pressure, back pressure, real-time wellhead temperature and real-time temperature after the oil nozzle, etc. Real-time production data of the electric submersible pump includes at least one of the following: operating status of the electric submersible pump, real-time operating frequency, real-time motor voltage A, real-time motor voltage B, real-time motor voltage C, real-time motor current A, real-time motor current B, real-time motor current C, oil nozzle opening, minimum current, real-time pump outlet pressure, real-time pump outlet temperature, real-time pump inlet pressure, real-time pump inlet temperature and real-time motor temperature.

[0033] S102. Select at least one model from the oil well metering model as the target model based on the model selection condition. The oil well metering model includes a mechanism model and a data model. The model selection condition is determined by the real-time production data. The mechanism model is obtained by correcting the initial mechanism model with the mechanism correction parameter. The mechanism correction parameter is obtained by learning the historical production data with the data model.

[0034] Among them, the oil well metering model includes a mechanism model and a data model. The mechanism model refers to a flow and heat transfer physical model established for the flow process of the oil well flow system. The mechanism model simulates the actual production environment of the oil well by acquiring the fluid physical parameters and predicts the oil well production. Exemplarily, the oil well can be an electric submersible pump well. The data model is obtained by training the back propagation (BP) neural network with a data training set, and the data training set is determined by the historical production data of the oil well.

[0035] BP neural network is a multi-layer feedforward neural network trained according to the error back propagation algorithm. It is one of the most widely used neural networks. When using BP neural network to build a data model, the following steps need to be performed: First, sufficient historical production data needs to be collected for training and testing the model. Designing the network architecture Figure 3 As shown in the figure, it includes input layer, hidden layer and output layer. The input layer is used to input features, the hidden layer is used to learn the relationship between features, and the output layer is used to predict results. Use the training set to train the BP neural network, and use the back propagation algorithm to adjust the weights and biases. Use the test set to test the performance of the model, and adjust the network parameters to improve the accuracy.

[0036] The model selection condition can be determined based on the real-time production data. Furthermore, the model selection condition is determined by the parameter type included in the real-time production data. For example, when the mechanism model and the data model are used for production measurement, the parameter types used are different. When the parameter type included in the real-time production data is consistent with the parameter type required by the mechanism model, the mechanism model can be selected as the target model. When the parameter type included in the real-time production data is consistent with the parameter type required by the data model, the data model can be selected as the target model.

[0037] Optionally, the user may manually select a model from the mechanism model or the data model as the target model according to the application environment and usage requirements of the oil well metering model.

[0038] Furthermore, the mechanism model includes a nozzle model, a wellbore model and an electric submersible pump model, the nozzle model is connected to the wellbore model, and the wellbore model is connected to the electric submersible pump model. At least one model matching the parameter type included in the real-time production data is selected from the mechanism model as the target model.

[0039] The mechanism model realizes the calculation of production through the nozzle model, wellbore model and submersible pump model. The above three models can be used alone or in combination as needed. For example, when the wellbore model is used for production measurement, the production calculation result of the nozzle model can be used.

[0040] The use conditions of the mechanism model, in addition to user settings, are also related to the integrity of basic data and real-time production data. Figure 4 As shown, the operation process of the mechanism model mainly includes: obtaining basic data and real-time production data, selecting at least one model in the mechanism model as the target model based on user settings, basic data, and real-time production data, and using the target model to measure the real-time production data to obtain the oil well production.

[0041] Among them, the basic data include: black oil fluid physical property data, crude oil viscosity-temperature relationship parameters, submersible pump component parameters, submersible pump characteristic curve parameters, wellbore component parameters, wellbore trajectory data and nozzle component parameters. Black oil fluid physical property data include: production gas-oil ratio, production water content, fluid name, oil phase density, gas phase density, reverse phase water content and water phase density. Crude oil viscosity-temperature relationship parameters include: fluid name, viscosity measurement temperature and viscosity. Submersible pump component parameters include: pump name, pump stage number, minimum operating frequency, maximum operating frequency, maximum efficiency, pump installation length, cable cross-sectional area, cable resistivity, motor power factor, motor efficiency and pump depth. Submersible pump characteristic curve parameters include: pump name, displacement, head, power, efficiency and frequency. Wellbore component parameters include: Wellbore components: tubing depth, tubing inner diameter, tubing wall thickness, tubing inner wall roughness, tubing material; casing and annulus medium parameters include: casing level, casing depth, casing inner diameter, casing outer diameter, casing material, annulus medium end depth and annulus medium. Wellbore trajectory data include: depth, vertical depth and wellbore trajectory type. Nozzle component parameters include: valve type, valve name, nozzle diameter, opening and equivalent diameter.

[0042] The situations of selecting the target model from the mechanism model include but are not limited to the following 6 situations.

[0043] (1) Nozzle model as target model

[0044] In the case that the parameter types included in the real-time production data are the first nozzle parameter and the second nozzle parameter, the nozzle model is selected as the target model.

[0045] The first nozzle parameter refers to the nozzle post parameter, for example, the first nozzle parameter may include: real-time nozzle post pressure and real-time nozzle post temperature. The second nozzle parameter may be represented by the first wellbore parameter, for example, the second nozzle parameter includes real-time wellhead temperature and real-time wellhead pressure.

[0046] (2) Wellbore model as target model

[0047] In the case that the parameter types included in the real-time production data are the first wellbore parameter and the second wellbore parameter, the wellbore model is selected as the target model.

[0048] The first wellbore parameter may be a real-time wellhead parameter, for example, the first wellbore parameter includes a real-time wellhead temperature and a real-time wellhead pressure. The second wellbore parameter may be represented by a first electric submersible pump parameter, for example, the second wellbore parameter includes a real-time pump outlet temperature or a real-time bottom hole temperature, a real-time pump outlet pressure or a real-time bottom hole pressure.

[0049] (3) ESP model as target model

[0050] In the case that the parameter types included in the real-time production data are the first electric submersible pump parameter and the second electric submersible pump parameter, the electric submersible pump model is selected as the target model.

[0051] The first submersible pump parameter refers to the submersible pump outlet parameter, which includes: real-time pump outlet temperature or real-time bottom hole temperature, real-time pump outlet pressure or real-time bottom hole pressure; the second submersible pump parameter refers to the submersible pump inlet parameter, which includes: real-time pump inlet temperature and real-time pump inlet pressure.

[0052] Before using the electric submersible pump model for production metering, the standard characteristic curve of the electric submersible pump needs to be corrected. To correct the standard characteristic curve of the electric submersible pump, the following parameters need to be obtained: real-time motor voltage, real-time motor current, real-time motor frequency, and motor temperature.

[0053] (4) Nozzle model and wellbore model as target models

[0054] In the case where the parameter types included in the real-time production data are the first nozzle parameter and the second wellbore parameter, the nozzle model and the wellbore model are selected as the target model. The first nozzle parameter and the second wellbore parameter can refer to the above description. Since the nozzle model and the wellbore model are connected, the wellhead parameters can be calculated using the wellbore model, and the nozzle model uses the calculated wellhead parameters to measure the oil well production.

[0055] (5) Wellbore model and submersible pump model as target models

[0056] When the parameter types included in the real-time production data are the first wellbore parameters and the second submersible pump parameters, the wellbore model and the submersible pump model are selected as the target models. When the first wellbore parameters, the wellhead parameters, and the second submersible pump parameters, i.e., the submersible pump inlet parameters, are obtained, the wellbore model and the submersible pump model can be selected and used simultaneously to measure the oil well production.

[0057] Since the wellbore model is connected to the submersible pump model, the submersible pump outlet parameters can be calculated using the submersible pump model, and then the wellbore model uses the calculated submersible pump outlet parameters to measure the oil well production.

[0058] (6) Select the nozzle model, wellbore model and submersible pump model as the target model

[0059] In the case that the parameter types included in the real-time production data are the first choke parameter and the second submersible pump parameter, the choke model, the wellbore model and the submersible pump model are selected as the target models.

[0060] In actual scenarios, when the first nozzle parameter, i.e., the post-nozzle parameter, and the second submersible pump parameter, i.e., the submersible pump inlet parameter, are obtained, the nozzle model, the wellbore model, and the submersible pump model can be selected and used simultaneously to measure production. Since the wellbore model is connected to the submersible pump model, the submersible pump outlet parameters can be calculated using the submersible pump model, and the wellbore model uses the calculated submersible pump outlet parameters. Since the nozzle model is connected to the wellbore model, the wellhead parameters can be calculated using the wellbore model based on the submersible pump outlet parameters, and the nozzle model uses the calculated wellhead parameters and post-nozzle parameters to measure the oil well production.

[0061] On the basis of the above contents, the data model is used to periodically learn the historical production data of the oil well to obtain the mechanism correction parameters, and the mechanism correction parameters are used to correct the initial mechanism model to obtain the mechanism model.

[0062] The data model obtained after BP neural network training is used to obtain the mechanism correction coefficient. Figure 5 As shown, historical production data is obtained by integrating historical production parameters, calculation results of mechanism model and historical verified production data for a period of time, and data cleaning and data learning are performed on the historical production data to obtain a data training set. The BP neural network is trained using the data training set to obtain real-time production parameters, the calculation results of the latest mechanism model and the latest verified production, which are input into the BP neural network for mechanism correction parameter prediction to obtain mechanism correction parameters. Among them, the mechanism correction parameters are used to correct the initial mechanism model to obtain a mechanism model.

[0063] During the use of the mechanism model, the initial mechanism model can be modified periodically. The mechanism model before modification is called the initial mechanism model.

[0064] Historical production parameters include: nozzle back pressure, nozzle back temperature, nozzle opening, wellhead temperature, wellhead pressure, ESP outlet temperature or bottom hole temperature, ESP outlet pressure or bottom hole pressure, ESP inlet temperature, ESP inlet pressure, ESP motor voltage, ESP motor current, ESP motor frequency, ESP motor temperature. Historical verified production includes: gasoline ratio, water content, verified liquid production.

[0065] Mechanism correction parameters include: operating frequency, voltage, current, nozzle, pump outlet pressure, pump outlet temperature, pump suction pressure, pump inlet temperature, wellhead pressure and temperature, gasoline ratio, water content, etc. and production data.

[0066] S103. Use the target model to process the real-time production data to obtain the oil well production.

[0067] The use of target models for oil well production measurement includes but is not limited to the following:

[0068] (1) Using data models to measure oil well production

[0069] like Figure 6 As shown in the figure, the process of using the data model to measure the oil well production mainly includes: obtaining historical production parameters and historical verified production within a period of time as historical production data, and obtaining a data training set after data cleaning and data learning of the historical production data. The BP neural network is trained with the data training set to obtain a data model, and real-time production data is obtained. The real-time production data is input into the data model, and the real-time production data is predicted using the data model to obtain the predicted oil well production.

[0070] Among them, it is recommended to select data from a recent stable production period for learning. If a well has been repaired recently, it is recommended to select historical production data after the repair. For example, the period of time may be 3 months.

[0071] Historical production parameters mainly include: nozzle back pressure, nozzle back temperature, nozzle opening, wellhead temperature, wellhead pressure, ESP outlet temperature or bottom hole temperature, ESP outlet pressure or bottom hole pressure, ESP inlet temperature, ESP inlet pressure, ESP motor voltage, ESP motor current, ESP motor frequency and ESP motor temperature. Historical verified production includes: gasoline ratio, water content and actual production.

[0072] (2) Using wellbore models to measure oil well production

[0073] When the target model is a wellbore model, the first production of the oil well is obtained; the first production, the real-time wellhead pressure, and the real-time wellhead temperature are processed using the wellbore model to obtain the predicted pump outlet pressure; the predicted pump outlet pressure is compared with the real-time pump outlet pressure; when the difference between the predicted pump outlet pressure and the real-time pump outlet pressure is less than the allowable error, the first production is used as the oil well production.

[0074] The wellbore model is a hydraulic calculation of the wellbore flow process. Through the lifting flow characteristics of the wellbore, the corresponding flow rate is calculated based on the changes in the wellbore pressure and temperature as well as the changes in the density and viscosity of the fluid. Figure 7 As shown in the figure, under the premise of obtaining the real-time wellhead pressure P0, the real-time wellhead temperature T0, and the first production Q, the real-time wellhead pressure P0, the real-time wellhead temperature T0, and the first production Q are processed by the wellbore multiphase flow model to obtain the predicted pump outlet pressure Pl. The predicted pump outlet pressure Pl is compared with the real-time pump outlet pressure Pc in the real-time production data. If the difference between the predicted pump outlet pressure Pl and the real-time pump outlet pressure Pc is less than the allowable error △ε, the first production Q is taken as the oil well production.

[0075] If there is a difference, it means that the basic data used in the wellbore multiphase flow model has errors, and the wellbore multiphase flow model is iteratively corrected. After observing for a period of time, the gas-liquid ratio, water content, and crude oil viscosity of a single electric submersible pump well do not change much, so it is believed that production is the main factor causing the error. The wellbore model will be corrected through an iterative method, and the corrected wellbore model will be used to measure the oil well production.

[0076] like Figure 7 As shown, the correction by the iterative method includes: if the difference between the predicted pump outlet pressure Pl and the real-time pump outlet pressure Pc is greater than the allowable error △ε, the sum of the first production Q and the preset value is used as the new first production Q, and the step of processing by the real-time wellhead pressure P0, the real-time wellhead temperature T0, and the first production Q of the wellbore multiphase flow model is returned until the difference between the predicted pump outlet pressure Pl and the real-time pump outlet pressure Pc is less than the allowable error △ε, and the wellbore model correction is completed. If the difference between the real-time pump outlet pressure Pc and the predicted pump outlet pressure Pl is greater than the allowable error △ε, the difference between the first production Q and the preset value is used as the new first production Q, and the step of processing by the real-time wellhead pressure P0, the real-time wellhead temperature T0, and the first production Q of the wellbore multiphase flow model is returned until the difference between the predicted pump outlet pressure Pl and the real-time pump outlet pressure Pc is less than the allowable error △ε, and the wellbore model correction is completed. Wherein, the preset value can be set according to the actual situation, for example: the preset value is 0.1.

[0077] The multiphase flow model includes at least one of a Beggs-Brill method, a Hagedron-Brown method, and an Orkiszewski method.

[0078] (3) Using the nozzle model to measure oil well production

[0079] The nozzle model is implemented using a standard flow meter model that is less dependent on the gas-liquid ratio. This standard flow meter model applies the Bernoulli equation and the principle of flow continuity, conforms to physical principles, and its correction fitting coefficient has been improved many times and has a high accuracy.

[0080] When the target model is the nozzle model, the density of the oil upstream of the throttling device is calculated based on the real-time saturation pressure, the real-time pressure behind the nozzle, the real-time gasoline ratio, the crude oil density and the water content; the oil well production is calculated based on the density of the oil upstream of the throttling device, the real-time pressure behind the nozzle, the real-time wellhead pressure and the nozzle structure parameters.

[0081] The oil well production includes the oil volume flow rate of the oil well; the calculation formula of the oil volume flow rate is formula (1)

[0082]

[0083] Among them, q v is the oil volume flow rate under working conditions, in cubic meters per second m 3 / s; c is the outflow coefficient; ε=1, β is the diameter ratio, which is the ratio of the nozzle diameter to the pipe diameter; d is the nozzle diameter, in millimeters (mm); △p is the pressure difference, in megapascals (MPa), which is equal to the difference between the real-time nozzle back pressure and the real-time wellhead pressure; ρ l is the density of the oil upstream of the throttle, in kilograms per cubic meter (kg / m 3 );η is the calibration coefficient.

[0084] Density of oil upstream of throttling element ρ l It changes with the gas-liquid ratio and oil pressure of the oil well, and is calculated by formula (2):

[0085]

[0086] Among them, f w is the moisture content, decimal; γ 油 is the density of crude oil (g / cm 3 ); density coefficient Calculated by formula (3):

[0087]

[0088] Among them, Pb is the real-time saturation pressure, in MPa; P l is the oil pressure, the unit is MPa; R S It is the real-time gas-oil ratio, where the production gasoline ratio can be replaced by the block dissolved gas-oil ratio.

[0089] (3) Using the electric submersible pump model to measure oil well production

[0090] Generally, only the standard characteristic curve of the ESP based on clean water can be obtained, which is quite different from the actual working state of the ESP in oil well production. Therefore, before using the ESP model to measure the oil well production, it is necessary to make viscosity correction and gas correction for the standard characteristic curve of the ESP model. At the same time, the influence of frequency conversion on the characteristic curve of the ESP can also be considered to obtain the corrected characteristic curve of the ESP.

[0091] Viscosity correction is mainly achieved through viscosity correction coefficient, which mainly includes displacement viscosity correction coefficient, head viscosity correction coefficient and shaft power viscosity correction coefficient.

[0092] The displacement viscosity correction coefficient is calculated by formula (4):

[0093] k Qμ =K g +10(K q -K g )(η b -0.6) (4)

[0094] If 50<μ s <4000, then:

[0095]

[0096]

[0097] If 0<μ s ≤50, then:

[0098] K q =K g =1

[0099] in,

[0100] Among them, k Qμ is the displacement viscosity correction coefficient; K q , K g is the intermediate parameter for calculation; μ is the viscosity of the well fluid at the temperature and pressure in the electric submersible pump, in megapascal seconds (mPa.s); μ s is the universal viscosity of Sebolin, in s; ρ is the density of the well fluid in the electric submersible pump, in g / cm 3 ; ηb It is the highest pump efficiency of the selected pump, a decimal.

[0101] The head viscosity correction coefficient is given by formula (5):

[0102] k Hμ =K H1 +10(K H2 -K H1 )(η b -0.6) (5)

[0103] If 50<μ s <4000, then:

[0104]

[0105]

[0106] If 0<μ s ≤50, then:

[0107] K H1 =K H2 =1

[0108] Among them, k Hμ K is the head viscosity correction coefficient; H1 , K H2 is the intermediate parameter for calculation; μ is the viscosity of the well fluid at the temperature and pressure in the electric submersible pump, the unit is mPa.s; μ s It is the universal viscosity of Sepolin, the unit is s.

[0109] The shaft power viscosity correction coefficient is given by formula (6):

[0110] k Nμ =K N1 +10(K N2 -K N1 )(η b -0.6) (6)

[0111] If 50<μ s <4000, then:

[0112]

[0113]

[0114] If 0<μ s ≤50, then:

[0115] K N1 =K N2 =1

[0116] Where, kNμ is the head viscosity correction coefficient; KN1 , K N2 is the intermediate parameter for calculation; μ is the viscosity of the well fluid at the temperature and pressure in the electric submersible pump, the unit is mPa.s; μ s It is the universal viscosity of Sepolin, the unit is s.

[0117] The gas correction factor is calculated using the difference method based on the experimental results, such as Figure 8 As shown, K QG is the pump displacement gas correction factor, K HEG is the correction factor for head and efficiency.

[0118] For submersible pump wells with variable frequency production, it is necessary to calculate the variable frequency correction factor. The calculation formula for the variable frequency correction factor is K Hz =Hz / 50. Among them, K HZ is the frequency conversion correction coefficient; Hz is the current production frequency of the submersible pump well, the unit is Hertz (HZ).

[0119] By considering the influence of viscosity, gas and frequency conversion on pump characteristics, the pump characteristic parameter correction formula includes head correction formula (7), displacement correction formula (8), pump efficiency correction formula (9) and power correction formula (10):

[0120] H(I)=H·K Hμ ·K HEG ·K Hz (7)

[0121] Q(I)=Q·K Qμ ·K QG (8)

[0122] η(I)=η·K Qμ ·K Hμ ·K HEG / K Nμ (9)

[0123]

[0124] Where, I is the first-stage pump; H is the head of the first-stage pump before correction, m; H(I) is the head of the first-stage pump after correction, m; Q is the displacement before correction of the first-stage pump, m 3 / d; Q(I) is the corrected displacement of the first-stage pump, m 3 / d; η is the pump efficiency of the first-stage pump before correction, a decimal; η(I) is the pump efficiency of the first-stage pump after correction, a decimal; ρ(I) is the well fluid density in the first-stage pump, g / cm 3 ; N(I) is the power of the first stage pump, kW.

[0125] The ESP model is based on the real-time monitoring of the surface and downhole production parameters of the ESP well, and uses the energy conservation principle to calculate the oil well production. It is assumed that the water content and gas-oil ratio of the oil well remain unchanged over a certain period of time. During the oil well production process, the input power of the pump is equal to the output power of the motor. Since the torque and speed of the multi-stage centrifugal pump and the motor in the ESP production system are always equal, formula (11) is obtained:

[0126]

[0127] After transformation, the calculation formula of output and energy efficiency ratio is obtained (12):

[0128]

[0129] in, is the ratio of output to pump efficiency, Δp is the pump pressure difference, which is the difference between the real-time pump outlet pressure and the real-time pump inlet pressure, and the unit is MPa; Q P is the oil well production, in cubic meters per day. 3 / d;η p is the pump efficiency; U m is the real-time motor voltage, in volts V; I m is the real-time motor current in ampere A; η m is the real-time motor efficiency; cosψ is the real-time motor active power factor.

[0130] By calculating the data on the right side of the equal sign in formula (12), we can get the ratio of production to pump efficiency: The value of the yield-pump efficiency curve in the correction characteristic curve is used to calculate the ratio between the yield and the pump efficiency in turn, and the ratio array is obtained to find the value corresponding to the yield-pump efficiency ratio. The corresponding numerical point can get the corresponding Q P (yield) and η P (Pump efficiency). Therefore, it is only necessary to calculate the result on the right side of formula (12).

[0131] The sources of the parameters in formula (12) are as follows: Pump pressure difference Δp = real-time pump outlet pressure - real-time pump inlet pressure, where the real-time pump outlet pressure and the real-time pump inlet pressure are both derived from real-time production data; real-time motor current I m From real-time production data; real-time motor voltage U m From real-time production data, it can also be obtained by subtracting the cable voltage drop loss from the ground measured voltage. The voltage loss can be estimated based on the cable impedance characteristics and the measured current; real-time motor efficiency η m The real-time motor active power factor cosψ will change with the load factor and can be calculated using an empirical formula.

[0132] The motor power factor is calculated by formula (13):

[0133] cosψ=1-0.9945(1-cosψ e )β -1.095 (β≧0.25) (13)

[0134] in,

[0135]

[0136] I0=I e cosψ e (2.26-ξcosψ e )

[0137] Among them, cosψ e is the rated power factor of the motor, dimensionless; β is the motor load factor, dimensionless; I e is the rated current of the motor, A; I0 is the no-load current, A; ξ is the coefficient, cosψ e ≤0.85,ξ=2.1;cosψ e When >0.85, ξ=2.15.

[0138] Under different load conditions, the motor efficiency is calculated using formula (14):

[0139]

[0140] Where β is the motor load factor, and the calculation method has been given in the previous article; A and B are coefficients, A is calculated by formula (15), and B is calculated by formula (16).

[0141] A=P0 / P N (15)

[0142]

[0143] Where P0 is the no-load magnetization loss (can be obtained from no-load test and load test), kW; P N is the load loss at rated load (the load of the submersible pump can be used to determine the load loss at rated load. KN value), kW; P Cu1 is the stator copper loss, kW; P Cu2 is the rotor copper loss, kW; P S is the stray loss (calculated as 0.5% of the input power), kW.

[0144] like Fig. 9As shown in the figure, the measurement model of oil well production mainly includes a mechanism model and a data model. The mechanism model includes a nozzle model, a wellbore model, and an electric submersible pump (ESP) model. In the process of establishing and using the measurement model of oil well production, four types of data need to be obtained, namely basic data, real-time production data, historical production data, and data training set. The neural network model is trained using the basic data and the data training set to obtain a data model. The data model predicts the real-time production data to obtain the oil well production. The mechanism model is corrected using the mechanism correction parameters. Among them, the mechanism correction parameters can be obtained from the data training set or generated by the data model. The mechanism model is constructed using the basic data. The mechanism model includes a nozzle model, a wellbore model, and an ESP model. At least one model in the mechanism model is selected as the target model, and the real-time production data is predicted to obtain the oil well production.

[0145] The oil well metering model provided in the embodiment of the present application is a hybrid model of a mechanism model (nozzle model, submersible pump model, wellbore model) and a data model, which solves the problem that the single mechanism model cannot perform production calculation because the calculation conditions are not met due to the different quantity and quality of the production measurement parameters that can be obtained for submersible pump wells. It can automatically select a suitable mechanism model based on the actual situation of the field parameters, and can also use a single machine learning method to predict the production, so that the submersible pump wells that can meet the wellhead virtual metering conditions are increased from the original 40% to more than 90%; the mechanism model is corrected to a certain extent by the machine learning method, which not only reduces the manual maintenance workload of the model, but also greatly reduces the error in calculating the production.

[0146] In one possible implementation, 10 submersible pump wells in an oil field are selected and the oil well metering model of the present application is used to perform a comparative analysis with the real-time production as follows. Table 1 is a result table of a comparative analysis of the oil well production predicted by the oil well metering model provided in an embodiment of the present application and the real-time production.

[0147] Table 1

[0148]

[0149] By comparing the above Table 1, the oil well production metering model can be used to calculate the production of all electric submersible pump wells, and the more real-time parameters there are, the smaller the relative error of the liquid production. Using the oil well production metering model, the average error of the liquid production of a single well is 7.44%, and the total liquid volume error is reduced from 5.16% to 3.36%, and the calculation accuracy is significantly improved.

[0150] See also Fig.10The embodiment of the present application provides a metering device for oil well production, and the metering device 100 for oil well production includes: an acquisition module 101, which is used to acquire real-time production data of the oil well; a selection module 102, which is used to select at least one model from the oil well metering model as a target model based on a model selection condition, the oil well metering model includes a mechanism model and a data model, the model selection condition is determined by the real-time production data, the mechanism model is obtained by correcting the initial mechanism model by the mechanism correction parameter, and the mechanism correction parameter is obtained by learning the historical data by the data model; the metering module 103, which is used to process the real-time production data using the target model to obtain the oil well production.

[0151] In one possible implementation, the mechanism model includes a nozzle model, a wellbore model and an electric submersible pump model, the nozzle model is connected to the wellbore model, and the wellbore model is connected to the electric submersible pump model; the selection module 102 is specifically used to select at least one model that matches the parameter type included in the real-time production data from the mechanism model as the target model.

[0152] In one possible implementation, the real-time production data includes: real-time wellhead pressure, real-time wellhead temperature, and real-time pump outlet pressure; the metering module 103 is used to obtain the first production of the oil well when the target model is a wellbore model; the first production, the real-time wellhead pressure, and the real-time wellhead temperature are processed using the wellbore model to obtain a predicted pump outlet pressure; the predicted pump outlet pressure is compared with the real-time pump outlet pressure; when the difference between the predicted pump outlet pressure and the real-time pump outlet pressure is less than the allowable error, the first production is used as the oil well production.

[0153] In a possible implementation, the real-time production data includes: real-time motor efficiency, real-time motor voltage, real-time motor current, real-time motor active power factor, real-time pump outlet pressure, and real-time pump inlet pressure; the metering module 103 is used to correct the standard characteristic curve of the electric submersible pump using the characteristic parameters when the target model is the electric submersible pump model, so as to obtain a corrected characteristic curve of the electric submersible pump, wherein the corrected characteristic curve includes a corresponding relationship between the production and the pump efficiency; the real-time motor efficiency, real-time motor voltage, real-time motor current, real-time motor active power factor, real-time pump outlet pressure, and real-time pump inlet pressure are used to determine the ratio of production to pump efficiency; a numerical point matching the ratio of production to pump efficiency is selected in the corrected characteristic curve; and the production corresponding to the numerical point is used as the production of the oil well.

[0154] In a possible implementation, the calculation formula of the output and pump efficiency ratio is as follows:

[0155]

[0156] in, is the ratio of output to pump efficiency, Δp is the pump pressure difference, which is the difference between the real-time pump outlet pressure and the real-time pump inlet pressure, and the unit is MPa; Q P is the oil well production, in cubic meters per day. 3 / d;η p is the pump efficiency; U m is the real-time motor voltage, in volts V; I m is the real-time motor current in ampere A; η m is the real-time motor efficiency; cosψ is the real-time motor active power factor.

[0157] In one possible implementation, the real-time production data includes: real-time nozzle back pressure, real-time wellhead pressure, real-time saturation pressure and real-time gasoline ratio; the metering module 103 is used to calculate the density of the oil upstream of the throttling device based on the real-time saturation pressure, real-time nozzle back pressure, real-time gasoline ratio, crude oil density and water content when the target model is the nozzle model; and calculate the oil well production based on the density of the oil upstream of the throttling device, the real-time nozzle back pressure, the real-time wellhead pressure and the nozzle structure parameters.

[0158] In a possible implementation, when the target model is a nozzle model, the oil well production includes the oil volume flow rate of the oil well; wherein the calculation formula of the oil volume flow rate is:

[0159]

[0160] Among them, q v is the oil volume flow rate under working conditions, in cubic meters per second m 3 / s; c is the outflow coefficient; ε=1, β is the diameter ratio, which is the ratio of the nozzle diameter to the pipe diameter; d is the nozzle diameter, in millimeters (mm); △p is the pressure difference, in megapascals (MPa), which is equal to the difference between the real-time nozzle back pressure and the real-time wellhead pressure; ρ l is the density of the oil upstream of the throttle, in kilograms per cubic meter (kg / m 3 );η is the calibration coefficient.

[0161] It should be noted that the device provided in the above embodiment only uses the division of the above functional modules as an example to implement its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0162] Fig.11It is a schematic diagram of the structure of a metering device for oil well production provided in an embodiment of the present application. The metering device for oil well production may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 601 and one or more memories 602, wherein at least one computer program is stored in the one or more memories 602, and the at least one computer program is loaded and executed by the one or more processors 601, so that the device implements the metering method for oil well production provided in the above-mentioned various method embodiments. Of course, the device may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the device may also include other components for realizing the functions of the device, which will not be described in detail here.

[0163] Fig.12 1 is a schematic diagram of a metering device for oil well production provided in an embodiment of the present application. The device may be a terminal, such as a smart phone, a tablet computer, a laptop computer, or a desktop computer. The terminal may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal, or other names.

[0164] Typically, the terminal includes: a processor 701 and a memory 702 .

[0165] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (central processing unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 701 may be integrated with a GPU (graphics processing unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (artificial intelligence) processor, which is used to process computing operations related to machine learning.

[0166] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is used to be executed by the processor 701 so that the terminal implements the oil well production measurement method provided in the method embodiment of the present application.

[0167] In some embodiments, the terminal may further optionally include: a peripheral device interface 703 and at least one peripheral device. The processor 701, the memory 702 and the peripheral device interface 703 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 703 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707 and a power supply 708.

[0168] The peripheral device interface 703 may be used to connect at least one peripheral device related to I / O (input / output) to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0169] The radio frequency circuit 704 is used to receive and transmit RF (radio frequency) signals, also known as electromagnetic signals. The radio frequency circuit 704 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 704 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (wireless fidelity) network. In some embodiments, the radio frequency circuit 704 may also include circuits related to NFC (near fField communication), which is not limited in this application.

[0170] The display screen 705 is used to display a UI (user interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 705 is a touch display screen, the display screen 705 also has the ability to collect touch signals on the surface or above the surface of the display screen 705. The touch signal can be input to the processor 701 as a control signal for processing. At this time, the display screen 705 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 705 can be one, which is set on the front panel of the terminal; in other embodiments, the display screen 705 can be at least two, which are respectively set on different surfaces of the terminal or are folded; in other embodiments, the display screen 705 can be a flexible display screen, which is set on the curved surface or folded surface of the terminal. Even, the display screen 705 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 705 can be made of materials such as LCD (liquid crystal display), OLED (organic light-emitting diode), etc.

[0171] The camera assembly 706 is used to capture images or videos. Optionally, the camera assembly 706 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and virtual reality (VR) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0172] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 701 for processing, or input them into the radio frequency circuit 704 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0173] The power supply 708 is used to power various components in the terminal. The power supply 708 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 708 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0174] In some embodiments, the terminal further includes one or more sensors 709 , including but not limited to: an acceleration sensor 710 , a gyroscope sensor 711 , a pressure sensor 712 , an optical sensor 713 , and a proximity sensor 714 .

[0175] The acceleration sensor 710 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal. For example, the acceleration sensor 710 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 701 can control the display screen 705 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 710. The acceleration sensor 710 can also be used for collecting game or user motion data.

[0176] The gyroscope sensor 711 can detect the body direction and rotation angle of the terminal, and the gyroscope sensor 711 can cooperate with the acceleration sensor 710 to collect the user's 3D actions on the terminal. The processor 701 can implement the following functions based on the data collected by the gyroscope sensor 711: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0177] The pressure sensor 712 can be set in the side frame of the terminal and / or the lower layer of the display screen 705. When the pressure sensor 712 is set in the side frame of the terminal, it can detect the user's holding signal of the terminal, and the processor 701 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 712. When the pressure sensor 712 is set in the lower layer of the display screen 705, the processor 701 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 705. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0178] The optical sensor 713 is used to collect the ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 according to the ambient light intensity collected by the optical sensor 713. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is reduced. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 according to the ambient light intensity collected by the optical sensor 713.

[0179] The proximity sensor 714, also known as a distance sensor, is usually arranged on the front panel of the terminal. The proximity sensor 714 is used to collect the distance between the user and the front of the terminal. In one embodiment, when the proximity sensor 714 detects that the distance between the user and the front of the terminal is gradually decreasing, the processor 701 controls the display screen 705 to switch from the screen-on state to the screen-off state; when the proximity sensor 714 detects that the distance between the user and the front of the terminal is gradually increasing, the processor 701 controls the display screen 705 to switch from the screen-off state to the screen-on state.

[0180] Those skilled in the art will understand that Fig.12 The structure shown in the figure does not constitute a limitation on the terminal, and may include more or less components than those shown in the figure, or combine certain components, or adopt a different component arrangement.

[0181] In an exemplary embodiment, a device for measuring oil well production is also provided, the device comprising a processor and a memory, wherein at least one computer program is stored in the memory. The at least one computer program is loaded and executed by one or more processors, so that the computer device implements any of the above-mentioned methods for measuring oil well production.

[0182] In an exemplary embodiment, a storage medium is also provided, which may be a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is loaded and executed by a processor of a computer device so that the computer implements any of the above-mentioned methods for measuring oil well production.

[0183] In one possible implementation, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0184] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes any of the above-mentioned methods for measuring oil well production.

[0185] It should be noted that the data (including but not limited to real-time production data, historical production data, basic data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions. For example, the real-time production data and historical production data involved in this application are obtained with full authorization.

[0186] It should be understood that the "plurality" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0187] It should be noted that the terms "first", "second", etc. (if any) in the specification and claims of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0188] The above are merely exemplary embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for measuring oil well production, characterized in that: The method comprises: Get real-time production data of oil wells; Selecting at least one model from the oil well metering model as a target model based on a model selection condition, the oil well metering model includes a mechanism model and a data model, the model selection condition is determined by the real-time production data, the mechanism model is obtained by correcting the initial mechanism model by a mechanism correction parameter, and the mechanism correction parameter is obtained by learning the historical production data by the data model; The real-time production data is processed using the target model to obtain the oil well production.

2. The method according to claim 1, characterized in that The mechanism model includes a nozzle model, a wellbore model and an electric submersible pump model, wherein the nozzle model is connected to the wellbore model, and the wellbore model is connected to the electric submersible pump model; The step of selecting at least one model from the oil well metering models as the target model based on the model selection condition includes: At least one model matching the parameter type included in the real-time production data is selected from the mechanism models as a target model.

3. The method according to claim 2, characterized in that The real-time production data includes: real-time wellhead pressure, real-time wellhead temperature and real-time pump outlet pressure; the real-time production data is processed by the target model to obtain the oil well production, including: When the target model is the wellbore model, obtaining a first production rate of the oil well; Processing the first production rate, the real-time wellhead pressure and the real-time wellhead temperature using the wellbore model to obtain a predicted pump outlet pressure; comparing the predicted pump outlet pressure with the real-time pump outlet pressure; When the difference between the predicted pump outlet pressure and the real-time pump outlet pressure is less than the allowable error, the first production is used as the oil well production.

4. The method according to claim 2, characterized in that: The real-time production data includes: real-time motor efficiency, real-time motor voltage, real-time motor current, real-time motor active power factor, real-time pump outlet pressure and real-time pump inlet pressure; the real-time production data is processed by the target model to obtain the oil well production, including: In the case where the target model is the electric submersible pump model, the standard characteristic curve of the electric submersible pump is corrected by using the characteristic parameters to obtain a corrected characteristic curve of the electric submersible pump, wherein the corrected characteristic curve includes a corresponding relationship between the output and the pump efficiency; Determine the output and pump efficiency ratio using the real-time motor efficiency, the real-time motor voltage, the real-time motor current, the real-time motor active power factor, the real-time pump outlet pressure and the real-time pump inlet pressure; Selecting a numerical point in the correction characteristic curve that matches the output and pump efficiency ratio; The production corresponding to the numerical point is taken as the production of the oil well.

5. The method according to claim 4, characterized in that The calculation formula of the output and pump efficiency ratio is as follows: in, is the ratio of the output to the pump efficiency, Δp is the pump pressure difference, and the pump pressure difference is the difference between the real-time pump outlet pressure and the real-time pump inlet pressure, and the unit is MPa; Q P is the oil well production, in cubic meters per day m 3 / d;η p is the pump efficiency; U m is the real-time motor voltage, in volts V; m is the real-time motor current, in ampere A; η m is the real-time motor efficiency; cosψ is the real-time motor active power factor.

6. The method according to claim 2, characterized in that The real-time production data includes: real-time nozzle back pressure, real-time wellhead pressure, real-time saturation pressure and real-time gasoline ratio; the real-time production data is processed by the target model to obtain the oil well production, including: In the case where the target model is the nozzle model, the density of the oil at the upstream of the throttling element is calculated based on the real-time saturation pressure, the real-time nozzle back pressure, the real-time gasoline ratio, the crude oil density and the water content; The oil well production is calculated based on the density of the oil at the upstream of the throttling member, the real-time pressure after the oil nozzle, the real-time wellhead pressure and the oil nozzle structure parameters.

7. The method according to claim 6, characterized in that In the case where the target model is the nozzle model, the oil well production includes the oil volume flow rate of the oil well; wherein the calculation formula of the oil volume flow rate is: Among them, q v is the oil volume flow rate under the working condition, the unit is cubic meter per second m 3 / s; c is the outflow coefficient; ε=1, β is the diameter ratio, which is the ratio of the nozzle diameter to the pipe diameter; d is the nozzle diameter, in millimeters (mm); △p is the pressure difference, in megapascals (MPa), and △p is equal to the difference between the real-time nozzle back pressure and the real-time wellhead pressure; ρ l is the density of the oil upstream of the throttle, in kilograms per cubic meter (kg / m 3 ), η is the calibration coefficient.

8. A device for measuring oil well production, characterized in that: The device comprises: An acquisition module is used to obtain real-time production data of oil wells; A selection module, used for selecting at least one model from the oil well metering model as a target model based on a model selection condition, wherein the oil well metering model includes a mechanism model and a data model, wherein the model selection condition is determined by the real-time production data, and the mechanism model is obtained by correcting the initial mechanism model by a mechanism correction parameter, and the mechanism correction parameter is obtained by learning the historical data by the data model; The metering module is used to process the real-time production data using the target model to obtain the oil well production.

9. An oil well production metering device, characterized in that: The oil well production metering device includes a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the oil well production metering device can implement the oil well production metering method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the computer implements the oil well production measurement method as described in any one of claims 1 to 7.

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