Application method, device, terminal and storage medium of binomial capacity equation

Abstract

The embodiment of the present application discloses an application method, device, terminal and storage medium of a binomial production capacity equation, which belongs to the field of gas reservoir engineering for natural gas extraction. The application method of the binomial production capacity equation provided in the present embodiment is that the terminal can process the historical well test data according to the linear regression analysis method to obtain a binomial production capacity method, and at the same time obtain the binomial relationship that conforms to the physical environment parameters of the target gas well. Combining the above binomial production capacity method and the binomial relationship, the production capacity parameters of the target gas well can be obtained, wherein the parameters finally obtained can indicate the natural gas generation capacity of the target gas well. Since the above process can avoid the use of complex professional engineering software to calculate the production capacity parameters, the embodiment of the present application has the function of reducing the calculation complexity of the production capacity parameters, and at the same time can ensure the closeness of the production capacity parameters to the actual data.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of gas reservoir engineering for natural gas extraction, and in particular to an application method, device, terminal and storage medium of a binomial production capacity equation. Background Art

[0002] In gas reservoir engineering used in the field of natural gas extraction, the production capacity equation is required to calculate the production of gas wells.

[0003] In the related art, the binomial production capacity equation of gas wells introduces the characteristics of natural gas viscosity and deviation coefficient changing with pressure in order to obtain a scenario close to actual application. After the above processing, the binomial production capacity equation of gas wells can obtain data that is close to the actual scenario to a large extent. However, this calculation method requires professional engineering software to perform calculations, which limits ordinary engineers from knowing the production capacity of natural gas. Summary of the invention

[0004] The embodiment of the present application provides an application method, device, terminal and storage medium of the binomial capacity equation, which can enable ordinary engineering personnel to know the capacity of natural gas without installing professional engineering software on the terminal. The technical solution is as follows:

[0005] According to one aspect of the present application, a method for applying a binomial capacity equation is provided, the method comprising:

[0006] Process historical well test data using linear regression analysis to obtain a binomial production capacity equation;

[0007] Obtaining a binomial relationship that satisfies the physical environment parameters of the target gas well;

[0008] The binomial production capacity equation and the binomial relationship are combined to obtain the production capacity parameter of the target gas well, and the production capacity parameter is used to indicate the natural gas production capacity of the target gas well.

[0009] According to another aspect of the present application, there is provided an application device of a binomial energy production equation, the device comprising:

[0010] The first acquisition module is used to process the historical well test data according to the linear regression analysis method to obtain the binomial production capacity equation;

[0011] A second acquisition module is used to obtain a binomial relationship that meets the physical environment parameters of the target gas well;

[0012] The production capacity calculation module is used to combine the binomial production capacity equation and the binomial relationship to obtain the production capacity parameter of the target gas well, and the production capacity parameter is used to indicate the natural gas production capacity of the target gas well.

[0013] According to another aspect of the present application, a terminal is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement an application method of the binomial capacity equation as provided in various aspects of the present application.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement an application method of the binomial capacity equation as provided in various aspects of the present application.

[0015] According to one aspect of the present application, a computer program product is provided, the computer program product 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 the method provided in various optional implementations of the application of the above binomial capacity equation.

[0016] The beneficial effects brought by the technical solution provided by the embodiments of the present application may include:

[0017] In the embodiment of the present application, the terminal can process the historical well test data according to the linear regression analysis method to obtain a binomial production capacity method, and at the same time obtain a binomial relationship that conforms to the physical environment parameters of the target gas well. Combining the above binomial production capacity method and the binomial relationship, the production capacity parameters of the target gas well can be obtained, wherein the parameters finally obtained can indicate the natural gas generation capacity of the target gas well. Since the above process can avoid using complex professional engineering software to calculate the production capacity parameters, the embodiment of the present application has the ability to reduce the calculation complexity of the production capacity parameters, and at the same time can ensure the closeness of the production capacity parameters to the actual data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly introduce the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application 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 paying creative work.

[0019] Figure 1 is a structural block diagram of a terminal provided by an exemplary embodiment of the present application;

[0020] Figure 2 is a flow chart of an application method of a binomial capacity equation provided by an exemplary embodiment of the present application;

[0021] Figure 3 is a flow chart of another method for applying a binomial capacity equation provided by another exemplary embodiment of the present application;

[0022] Figure 4 It is a structural block diagram of an application device of a binomial capacity equation provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0023] 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.

[0024] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0025] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0026] In this embodiment, natural gas extraction is a complex and important project. Among many tasks, gas well productivity evaluation is an important task in natural gas exploration and development. Schematically, establishing a gas well productivity equation, estimating the absolute unimpeded flow rate of the gas well, and predicting the production of the gas well under different conditions are the core of the quantitative analysis of gas well productivity evaluation.

[0027] After long-term research by technicians in this field, a large category of gas well productivity equation characterization methods has been formed in this field, including binomial, exponential, IPR relationship and other special forms.

[0028] Among the above-mentioned gas well productivity characterization methods, the binomial productivity equation of gas wells should be the most widely used in the actual work of gas field development. It should be noted that the analysis theory of the binomial productivity equation in the square form of gas well pressure has a long history of application. Although this method is simple and easy to use, the error is relatively large. It is particularly prominent under higher pressure conditions. At the same time, the pseudo-pressure analysis method of gas well testing has a long history of proposal and application. Because the pseudo-pressure analysis method of gas well testing takes into account the characteristics of natural gas viscosity and deviation coefficient changing with pressure. Therefore, the pseudo-pressure analysis method of gas well testing can obtain more accurate results.

[0029] The same is true for the analysis of binomial productivity equations for gas wells, which is a consensus in the gas reservoir engineering and technology community. The pseudo-pressure analysis method requires the use of computers for numerical integration calculations. The analysis of the binomial productivity equation in the form of pseudo-pressure for gas wells is basically completed by well test analysis professionals. It is not difficult to calculate pseudo-pressure when equipped with professional well test analysis software. However, many personnel in gas field development technology and management need to frequently use the established binomial productivity equation for gas wells. It is difficult to calculate pseudo-pressure when not equipped with professional well test analysis software. Expensive professional well test analysis software cannot be equipped by everyone, which results in that even if the binomial productivity equation for pseudo-pressure for gas wells is established, its application scope is severely limited and its application efficiency is low.

[0030] It can be seen that although gas reservoir engineering researchers have done a lot of exploratory research on improving gas well productivity equations in the past, no other method has yet emerged that surpasses the accuracy of the pseudo-pressure binomial productivity equation, nor has it fundamentally solved the problem of cumbersome and inefficient application of the pseudo-pressure method. It is a long-term expectation of gas field developers to maintain the accuracy of the gas well pseudo-pressure binomial productivity equation analysis method and learn from the simplicity of the traditional gas well pressure square binomial productivity equation analysis method to improve it into a method with good universality and high accuracy. However, this restrictive problem has not been fundamentally solved at the theoretical research level before, and the scattered empirical knowledge obtained based on a small number of case studies has a narrow scope of application and is not universally instructive.

[0031] Based on the above analysis, an embodiment of the present application provides a simplified method for using a pseudo-pressure binomial production capacity equation for a gas well, which includes two steps, including step (1) and step (2).

[0032] Step (1), based on the mathematical approximate equivalent transformation of the gas well pseudo-pressure binomial productivity equation, the deformed formula of the pressure square binomial productivity equation is obtained:

[0033]

[0034] In formula (1): p R is the formation pressure, MPa; p wfis the bottom hole flowing pressure, MPa; A is the first-order coefficient of the gas well pseudo-pressure binomial production capacity equation, MPa 2 ·(mPa·s) -1 ·(10 4 m 3 ·d -1 ) -1 ; B is the quadratic coefficient of the gas well pseudo-pressure binomial productivity equation, MPa 2 ·(mPa·s) -1 ·(10 4 m 3 ·d -1 ) -2 ; q is the gas well production, 10 4 m 3 ·d -1 ; For the meanings of other symbols, see the symbol description of formula (3).

[0035] Under the theoretical assumption that the bottom hole flow pressure is equal to the standard atmospheric pressure, the equation that characterizes the relationship between the absolute zero flow rate and pressure of the gas well is obtained from equation (1):

[0036]

[0037] In formula (2): q AOF is the absolute unobstructed flow rate of the gas well, 10 4 m 3 ·d -1 ;p SC is standard atmospheric pressure, MPa.

[0038] Considering the different natural gas components, formation pressures, and formation temperatures, after a large number of calculations and analyses, it was found that the relationship between the natural gas pseudo-pressure and the pressure change can be approximately expressed by a binomial function. This application uses the following formula to simplify the gas well pseudo-pressure binomial production capacity equation:

[0039] ψ(p)=ap 2 +bp+c (3)

[0040] In formula (3), p is the pressure, MPa; ψ(p) is the pseudo-pressure corresponding to pressure p, MPa 2 ·(mPa·s) -1 ; a is the quadratic coefficient of the binomial function that characterizes the relationship between the pseudo-pressure of natural gas and the pressure change, (mPa·s) -1 ; b is the linear coefficient of the binomial function that characterizes the relationship between the pseudo-pressure of natural gas and the pressure, MPa·(mPa·s) -1 ; c is the constant term of the binomial function that characterizes the relationship between the pseudo-pressure of natural gas and the pressure, MPa 2 ·(mPa·s) -1 .

[0041] Step (2) is similar in simplicity to the traditional gas well pressure square binomial productivity equation analysis method, but the accuracy of calculating the gas well production or absolute unobstructed flow is close to that of the pseudo-pressure binomial productivity equation analysis method:

[0042]

[0043] Under the theoretical assumption that the bottom hole flow pressure is equal to the standard atmospheric pressure, the formula for calculating the absolute unobstructed flow rate of the gas well is obtained from formula (4):

[0044]

[0045] Optionally, based on the above calculations, the present application also provides a simplified method for using the following gas well pseudo-pressure binomial capacity equation. Among them, the quadratic term coefficient value and the linear term coefficient value of the binomial function relationship are obtained based on the regression analysis of the pseudo-pressure versus pressure change data. This process is an approximate approach in a strict mathematical sense, but it has a sufficiently high accuracy for application; then the above binomial function relationship is substituted into the existing gas well pseudo-pressure binomial capacity equation, and in a strict mathematical sense, it is simplified to obtain a deformed formula similar to the traditional pressure square binomial capacity equation, the main difference of which is the addition of Additional items.

[0046] Optionally, the deformed formula of the gas well pressure square binomial production capacity equation obtained in the study does not require the calculation of pseudo-pressure when applied, and has the ease of application similar to the traditional gas well pressure square binomial production capacity equation, avoiding the cumbersome and inefficient problem of calculating pseudo-pressure each time the gas well pseudo-pressure binomial production capacity equation is applied. The deformed formula is mathematically derived from the gas well pseudo-pressure binomial production capacity equation, so it can ensure that its error is within an acceptable range for actual work.

[0047] In order to facilitate the understanding of the scheme shown in the embodiments of the present application, several nouns appearing in the embodiments of the present application are introduced below.

[0048] Target gas well: used to indicate the natural gas well being tested. In the actual natural gas generation process, the target gas well is usually a gas well.

[0049] Historical well test data: well test data of the target gas well that has been tested.

[0050] In one possible manner, the historical well test data is gas well stability well test data.

[0051] In another possible manner, the historical well test data is modified isochronous well test data.

[0052] Production capacity parameter: In this application, it is used to indicate the natural gas production capacity of the target gas well. Optionally, the production capacity parameter can be the gas well production. Optionally, the production capacity parameter can also be the absolute unimpeded flow rate of the gas well.

[0053] Exemplarily, the application method of the binomial production capacity equation shown in the embodiment of the present application can be applied in a terminal, which has a display screen and has the application function of the binomial production capacity equation. The terminal may include a mobile phone, a tablet computer, a laptop, a desktop computer, an all-in-one computer, a server, a workstation or a vehicle-mounted terminal, etc. It should be noted that since the calculation amount of the solution provided in the embodiment of the present application is small and the data processing steps are relatively small. Therefore, the embodiment of the present application can be normally applied in the above-mentioned devices to obtain the production capacity parameters of the target gas well required by the technician, thereby knowing the natural gas production capacity of the current target gas well.

[0054] Please refer to Figure 1 , Figure 1 is a structural block diagram of a terminal provided by an exemplary embodiment of the present application, such as Figure 1 As shown, the terminal includes a processor 120 and a memory 140, wherein the memory 140 stores at least one instruction, and the instruction is loaded and executed by the processor 120 to implement the application method of the binomial capacity equation as described in each method embodiment of the present application.

[0055] In the present application, the terminal 100 is an electronic device having the function of calculating and applying the binomial production capacity equation. The terminal 100 processes the historical well test data according to the linear regression analysis method to obtain the binomial production capacity equation; obtains the binomial relationship that meets the physical environment parameters of the target gas well; combines the binomial production capacity equation and the binomial relationship to obtain the production capacity parameter of the target gas well, and the production capacity parameter is used to indicate the natural gas production capacity of the target gas well.

[0056] The processor 120 may include one or more processing cores. The processor 120 uses various interfaces and lines to connect various parts of the entire terminal 100, and executes various functions and processes data of the terminal 100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 140, and calling data stored in the memory 140. Optionally, the processor 120 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 120 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 120, but may be implemented separately through a chip.

[0057] The memory 140 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 140 includes a non-transitory computer-readable storage medium. The memory 140 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 140 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc.; the data storage area may store data involved in the following various method embodiments, etc.

[0058] Please refer to Figure 2 , Figure 2 is a flowchart of an application method of a binomial capacity equation provided by an exemplary embodiment of the present application. The application method of the binomial capacity equation can be applied to the above Figure 1 in the terminal shown. Figure 2 In the application of binomial capacity equation, the following methods are used:

[0059] Step 210, the historical well test data is processed according to the linear regression analysis method to obtain the binomial production capacity equation.

[0060] In the embodiment of the present application, the terminal can process the historical well test data by a preset linear regression analysis method. Optionally, the terminal will derive a binomial production capacity equation.

[0061] Step 220, obtaining a binomial relationship that satisfies the physical environment parameters of the target gas well.

[0062] Step 230, combining the binomial production capacity equation and the binomial relationship to obtain a production capacity parameter of the target gas well, where the production capacity parameter is used to indicate the natural gas production capacity of the target gas well.

[0063] In the embodiment of the present application, the terminal can process according to the following points:

[0064] Point 1: In the processing method of this application, different natural gas components, different formation pressures and different formation temperatures are considered. By calculating the relationship data of the natural gas pseudo-pressure with pressure in the interval from 0MPa to the gas well formation pressure, it is confirmed that the binomial function can be used to approximate the law of the pseudo-pressure with pressure change, and it is used to simplify the binomial production capacity equation of the gas well pseudo-pressure form:

[0065] ψ(p)=ap 2 +bp+c (1-1)

[0066] In formula (1-1): p is pressure, MPa; ψ(p) is the pseudo pressure corresponding to p, MPa 2 ·(mPa·s) -1 ; a is the coefficient of the quadratic term of the binomial relationship, (mPa·s) -1 ; b is the coefficient of the first term of the binomial relationship, MPa·(mPa·s) -1 ; c is the constant term of the binomial relationship, MPa 2 ·(mPa·s) -1 .

[0067] Based on the calculated data of the change of pseudo-pressure with pressure in specific gas wells, the binomial regression analysis method can be used to determine the values ​​of coefficients a, b, and c in formula (1-1). For gas wells with similar natural gas composition, formation pressure, and formation temperature, the coefficient values ​​of the binomial relationship between pseudo-pressure and pressure of other gas wells can be borrowed.

[0068] Point 2: Based on the quasi-pressure binomial capacity equation, a deformed formula similar to the pressure square binomial capacity equation is derived:

[0069] First, the gas well pseudo-pressure binomial production capacity equation is taken as the starting point, and the invention of this application is to improve the formula on this basis:

[0070] ψ(p R )-ψ(p wf )=Aq+Bq 2 (1-2)

[0071] In formula (1-2): p R is the formation pressure, MPa; p wf is the bottom hole flowing pressure, MPa; A is the first-order coefficient of the gas well pseudo-pressure binomial production capacity equation, MPa 2 ·(mPa·s) -1 ·(10 4 m 3 ·d -1 ) -1 ; B is the quadratic coefficient of the gas well pseudo-pressure binomial productivity equation, MPa 2 ·(mPa·s) -1 ·(10 4 m 3 ·d -1 ) -2 ; q is the gas well production, 10 4 m 3 ·d -1 .

[0072] Substituting equation (1-1) into equation (1-2) and simplifying it, the gas well pseudo-pressure binomial production capacity equation is mathematically equivalently transformed into a deformed formula similar to the pressure square binomial production capacity equation, which characterizes the relationship between gas well production and pressure:

[0073]

[0074] Under the theoretical assumption that the bottom hole flow pressure is equal to the standard atmospheric pressure, the formula that characterizes the relationship between the absolute zero flow rate and pressure of the gas well is obtained from formula (1-3):

[0075]

[0076] In formula (1-4): q AOF is the absolute unobstructed flow rate of the gas well, 10 4 m 3 ·d -1 ;p SC is standard atmospheric pressure, MPa.

[0077] Point 3: Based on equations (1-3) and (1-4), we obtain modified formulas for calculating gas well production and absolute unobstructed flow, which maintain the accuracy of the gas well pseudo-pressure binomial production capacity equation analysis method but have the simplicity of the traditional pressure square analysis method.

[0078] Deformed formula for calculating gas well production:

[0079]

[0080] In one possible beneficial effect, the present application can avoid the cumbersome and inefficient process of frequently relying on computers to calculate pseudo-pressures when applying a pseudo-pressure binomial capacity equation with higher accuracy. With the help of easily available regression analysis results of the relationship between pseudo-pressure and pressure binomial, a new method is established that has the simplicity of the traditional pressure square binomial capacity equation analysis method and maintains the accuracy of the pseudo-pressure binomial capacity equation analysis method, so that the pseudo-pressure binomial capacity equation for gas wells with higher accuracy can be easily used widely and efficiently.

[0081] In summary, the application method of the binomial production capacity equation provided in this embodiment is that the terminal can process the historical well test data according to the linear regression analysis method to obtain a binomial production capacity method, and at the same time obtain the binomial relationship that meets the physical environment parameters of the target gas well. Combining the above binomial production capacity method and the binomial relationship, the production capacity parameters of the target gas well can be obtained, wherein the parameters finally obtained can indicate the natural gas generation capacity of the target gas well. Since the above process can avoid using complex professional engineering software to calculate the production capacity parameters, the embodiment of the present application has the ability to reduce the calculation complexity of the production capacity parameters, and at the same time can ensure the closeness of the production capacity parameters to the actual data.

[0082] See also Figure 3 , Figure 3 is a flowchart of another method for applying the binomial capacity equation provided by another exemplary embodiment of the present application. The method for applying the binomial capacity equation can be applied to the above Figure 1 in the terminal shown. Figure 3 In the application of the binomial capacity equation, the following methods are used:

[0083] Step 311, drawing a scatter plot based on historical well test data, the scatter plot is used to indicate the correlation between the production of the target gas well and the intermediate parameters, and the intermediate parameters are calculated by the formation pressure, the bottom hole flowing pressure and the production of the target gas well.

[0084] Optionally, the historical well test data includes stable well test data of gas wells, or modified isochronous well test data.

[0085] Step 312, for the scatter plot, determine the linear coefficient of the binomial relationship and the quadratic coefficient of the binomial relationship by linear regression analysis.

[0086] Step 320, obtaining a binomial relationship that satisfies the physical environment parameters of the target gas well.

[0087] Step 331, substituting the binomial production capacity equation into the binomial relationship to obtain a gas well production calculation formula, which is used to indicate the relationship between the gas well production and the formation pressure and the bottom hole flowing pressure.

[0088] Among them, the production capacity parameter is the gas well production.

[0089] Step 332, under the second condition, combining the binomial production capacity equation and the binomial relationship, obtains a calculation formula for the absolute unobstructed flow rate of the gas well.

[0090] Among them, the second condition is that the bottom hole flowing pressure is equal to the standard atmospheric pressure, and the absolute open-flow flow calculation formula of the gas well is used to calculate the absolute open-flow flow of the gas well.

[0091] In an embodiment of the present application, the terminal can also obtain the production capacity parameters of the target gas well through steps (a), (b) and (c).

[0092] Step (a), obtain the binomial production capacity equation of the gas well in pseudo-pressure form according to the conventional method.

[0093] Indicatively, the terminal uses the gas well stable well test or modified isochronous well test data, selects the highly accurate pseudo-pressure binomial production capacity equation analysis method, and makes Figure 2 shows that linear regression analysis is used to determine the first-order coefficient A and the second-order coefficient B, and the pseudo-pressure binomial production capacity equation of the gas well is obtained: ψ(p R )-ψ(p wf )=Aq+Bq 2 This step is common knowledge in the gas reservoir engineering field, and the invention of this application is to use it in subsequent improved formulas.

[0094] Step (b) uses an existing binomial relationship describing the change of pseudo-pressure of a gas well with pressure, or obtains the relationship through computational analysis.

[0095] Illustratively, for an example gas well, if the coefficient value of the binomial relationship between the pseudo-pressure and pressure variation of the gas well has been previously calculated and analyzed, or there are coefficient values ​​of the binomial relationship between the pseudo-pressure and pressure variation of other gas wells with similar natural gas composition, formation pressure, and formation temperature conditions to the gas well, they can be directly used in subsequent deformation formula calculation and analysis.

[0096] Otherwise, calculate the pseudo-pressure variation data in the interval from 0 MPa to the formation pressure of the gas well. It is required that the pressure interval of the calculated data points should not be greater than one data point per 1 MPa, and the total number of calculated points in the interval from 0 MPa to the formation pressure of the gas well should not be less than 50.

[0097] Binomial regression analysis was used to determine the relationship between pseudo-pressure and pressure: ψ(p) = ap 2+bp+c's a, b, c coefficient values.

[0098] Step (c) uses the modified formula of the gas well pressure square binomial productivity equation obtained by evolving the gas well pseudo-pressure binomial productivity equation of the present application to analyze and predict the gas well production and calculate the absolute open-flow rate.

[0099] In schematic form, the terminal can use formula (1-5) to calculate the production of gas wells under different formation pressures and bottom hole flow pressure conditions, thereby maintaining an accuracy similar to that of the pseudo-pressure form binomial production capacity equation analysis method and significantly simplifying the calculation process.

[0100] Optionally, the terminal can also use formula (1-6) to calculate the absolute unobstructed flow rate of the gas well under different formation pressure conditions, so as to maintain an accuracy similar to that of the pseudo-pressure form binomial production capacity equation analysis method while significantly simplifying the calculation process.

[0101] In summary, the application method of the binomial production capacity equation provided in this embodiment is that the terminal can process the historical well test data according to the linear regression analysis method to obtain a binomial production capacity method, and at the same time obtain the binomial relationship that meets the physical environment parameters of the target gas well. Combining the above binomial production capacity method and the binomial relationship, the production capacity parameters of the target gas well can be obtained, wherein the parameters finally obtained can indicate the natural gas generation capacity of the target gas well. Since the above process can avoid using complex professional engineering software to calculate the production capacity parameters, the embodiment of the present application has the ability to reduce the calculation complexity of the production capacity parameters, and at the same time can ensure the closeness of the production capacity parameters to the actual data.

[0102] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0103] Please refer to Figure 4 , Figure 4 This is a structural block diagram of an application device for a binomial capacity equation provided by an exemplary embodiment of the present application. The application device for the binomial capacity equation can be implemented as all or part of a terminal through software, hardware, or a combination of both. The device includes:

[0104] The first acquisition module 410 is used to process the historical well test data according to the linear regression analysis method to obtain the binomial production capacity equation;

[0105] A second acquisition module 420 is used to acquire a binomial relationship that meets the physical environment parameters of the target gas well;

[0106] The production capacity calculation module 430 is used to combine the binomial production capacity equation and the binomial relationship to obtain the production capacity parameter of the target gas well, and the production capacity parameter is used to indicate the natural gas production capacity of the target gas well.

[0107] In an optional embodiment, the first acquisition module 410 is used to draw a scatter plot based on the historical well test data, and the scatter plot is used to indicate the correlation between the production of the target gas well and the intermediate parameter, and the intermediate parameter is calculated by the formation pressure, the bottom hole flow pressure and the production of the target gas well; for the scatter plot, the linear term coefficient of the binomial relationship and the quadratic term coefficient of the binomial relationship are determined by the linear regression analysis method.

[0108] In an optional embodiment, the historical well test data involved in the device includes gas well stable well test data, or modified isochronous well test data.

[0109] In an optional embodiment, the production capacity parameter involved in the device includes the gas well production, or the absolute unobstructed flow of the gas well.

[0110] In an optional embodiment, the production capacity calculation module 430 is used to substitute the binomial production capacity equation into the binomial relationship to obtain a gas well production calculation formula, and the gas well production calculation formula is used to indicate the relationship between the gas well production and the formation pressure and the bottom hole flow pressure, and the production capacity parameter is the gas well production.

[0111] In an optional embodiment, the production capacity calculation module 430 is used to obtain the absolute open-flow rate calculation formula for the gas well under a second condition by combining the binomial production capacity equation and the binomial relationship; wherein the second condition is that the bottom hole flow pressure is equal to the standard atmospheric pressure, the absolute open-flow rate calculation formula for the gas well is used to calculate the absolute open-flow rate of the gas well, and the production capacity parameter is the absolute open-flow rate of the gas well.

[0112] In summary, the application device of the binomial production capacity equation provided in this embodiment, the terminal can process the historical well test data according to the linear regression analysis method to obtain a binomial production capacity method, and at the same time obtain the binomial relationship that meets the physical environment parameters of the target gas well. Combining the above binomial production capacity method and the binomial relationship, the production capacity parameters of the target gas well can be obtained, wherein the parameters finally obtained can indicate the natural gas generation capacity of the target gas well. Since the above process can avoid using complex professional engineering software to calculate the production capacity parameters, the embodiment of the present application has the ability to reduce the calculation complexity of the production capacity parameters, and at the same time can ensure the closeness of the production capacity parameters to the actual data.

[0113] An embodiment of the present application also provides a computer-readable medium storing at least one instruction, wherein the at least one instruction is loaded and executed by the processor to implement the application method of the binomial capacity equation as described in the above embodiments.

[0114] It should be noted that: the application device of the binomial capacity equation provided in the above embodiment only uses the division of the above functional modules as an example when executing the application method of the binomial capacity equation. In actual application, 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 application device of the binomial capacity equation provided in the above embodiment and the embodiment of the application method of the binomial capacity equation belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0115] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0116] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0117] The above description is only an illustrative embodiment that can be implemented in the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. An application method of a binomial capacity equation, characterized in that: The method comprises: Process historical well test data using linear regression analysis to obtain a binomial production capacity equation; Obtaining a binomial relationship that satisfies the physical environment parameters of the target gas well; Combining the binomial production capacity equation and the binomial relationship, obtaining the production capacity parameter of the target gas well; the production capacity parameter includes the gas well production, or the absolute unimpeded flow of the gas well; the production capacity parameter is the gas well production, and combining the binomial production capacity equation and the binomial relationship to obtain the production capacity parameter of the target gas well includes: substituting the binomial relationship into the binomial production capacity equation to obtain a gas well production calculation formula, and the gas well production calculation formula is used to indicate the relationship between the gas well production and the formation pressure and the bottom hole flow pressure; The process of obtaining the gas well production calculation formula includes: Let ψ(p) = ap 2 +bp+c into ψ(p R )-ψ(p wf )=Aq+Bq 2 And simplify it, we get the relationship between gas well production and pressure: Based on the relationship between the gas well production and pressure, the gas well production calculation formula is obtained: Wherein, p is the pressure, ψ(p) is the pseudo-pressure corresponding to p, a is the coefficient of the quadratic term of the binomial relationship, b is the coefficient of the linear term of the binomial relationship, c is the constant term of the binomial relationship, and p R is the formation pressure, p wf is the bottom hole flowing pressure, A is the first-order coefficient of the gas well pseudo-pressure binomial production capacity equation, B is the second-order coefficient of the gas well pseudo-pressure binomial production capacity equation, and q is the gas well production.

2. The method according to claim 1, characterized in that The method of processing historical well test data according to the linear regression analysis method to obtain the binomial production capacity equation includes: Draw a scatter plot based on the historical well test data, the scatter plot is used to indicate the correlation between the production of the target gas well and an intermediate parameter, the intermediate parameter is calculated by the formation pressure, the bottom hole flowing pressure and the production of the target gas well; With respect to the scatter plot, the linear coefficient of the binomial capacity equation and the quadratic coefficient of the binomial capacity equation are determined by the linear regression analysis method.

3. The method according to claim 2, characterized in that The historical well test data includes stable well test data of gas wells, or modified isochronous well test data.

4. The method according to claim 1, characterized in that: The production capacity parameter is the absolute unimpeded flow rate of the gas well. The production capacity parameter of the target gas well is obtained by combining the binomial production capacity equation and the binomial relationship, including: Under the second condition, the binomial capacity equation and the binomial relationship are combined to obtain the absolute unobstructed flow calculation formula of the gas well; The second condition is that the bottom hole flow pressure is equal to the standard atmospheric pressure, and the absolute open-flow rate calculation formula for the gas well is used to calculate the absolute open-flow rate of the gas well.

5. An application device for the binomial capacity equation, characterized in that: The device comprises: The first acquisition module is used to process the historical well test data according to the linear regression analysis method to obtain the binomial production capacity equation; A second acquisition module is used to obtain a binomial relationship that meets the physical environment parameters of the target gas well; A production capacity calculation module is used to: combine the binomial production capacity equation and the binomial relationship to obtain the production capacity parameters of the target gas well; the production capacity parameters include the gas well production, or the absolute unobstructed flow of the gas well; The production capacity parameter is the gas well production. The production capacity calculation module is used to: substitute the binomial relationship into the binomial production capacity equation to obtain a gas well production calculation formula. The gas well production calculation formula is used to indicate the relationship between the gas well production and the formation pressure and the bottom hole flow pressure. The process of obtaining the gas well production calculation formula includes: Let ψ(p) = ap 2 +bp+c into ψ(p R )-ψ(p wf )=Aq+Bq 2 And simplify it, we get the relationship between gas well production and pressure: Based on the relationship between the gas well production and pressure, the gas well production calculation formula is obtained: Wherein, p is the pressure, ψ(p) is the pseudo-pressure corresponding to p, a is the coefficient of the quadratic term of the binomial relationship, b is the coefficient of the linear term of the binomial relationship, c is the constant term of the binomial relationship, and p R is the formation pressure, p wf is the bottom hole flowing pressure, A is the first-order coefficient of the gas well pseudo-pressure binomial production capacity equation, B is the second-order coefficient of the gas well pseudo-pressure binomial production capacity equation, and q is the gas well production.

6. The device according to claim 5, characterized in that The first acquisition module is used to: Draw a scatter plot based on the historical well test data, the scatter plot is used to indicate the correlation between the production of the target gas well and an intermediate parameter, the intermediate parameter is calculated by the formation pressure, the bottom hole flowing pressure and the production of the target gas well; With respect to the scatter plot, the linear coefficient of the binomial capacity equation and the quadratic coefficient of the binomial capacity equation are determined by the linear regression analysis method.

7. A terminal, characterized in that: The terminal includes a processor, a memory connected to the processor, and program instructions stored in the memory. When the processor executes the program instructions, the application method of the binomial capacity equation as described in any one of claims 1 to 4 is implemented.

8. A computer-readable storage medium, wherein program instructions are stored in the storage medium, characterized in that: When the program instructions are executed by a processor, the application method of the binomial capacity equation as described in any one of claims 1 to 4 is implemented.

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