Productivity evaluation model determination method, productivity evaluation method, medium and electronic equipment
By establishing a production capacity evaluation model based on reservoir physical parameters and production dynamic information of old wells, the problem of predicting the production capacity of new deep coalbed methane wells has been solved, and efficient and accurate production capacity evaluation has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2024-11-04
- Publication Date
- 2026-05-08
AI Technical Summary
Existing technologies cannot effectively evaluate the production capacity of deep coalbed methane wells, especially in new wells where existing production capacity evaluation data cannot be used for prediction. Furthermore, traditional methods require repeated establishment of simulation geometric models, which is a heavy workload.
By acquiring reservoir physical properties and production dynamics information from old wells, a production capacity evaluation model is established. The production dynamics prediction model is then used for historical fitting to determine sensitive parameters and target relationships, which are then used for production capacity evaluation of new wells.
This enabled accurate evaluation of production capacity in new wells, reduced repetitive work, and improved the efficiency and accuracy of production capacity forecasting.
Smart Images
Figure CN121998221A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of coalbed methane technology, and in particular, to a method, apparatus, medium, and electronic equipment for determining a deep coalbed methane production capacity evaluation model. Background Technology
[0002] Deep coalbed methane reservoirs are characterized by low porosity, low permeability, high stress, and high free gas. During production, adsorbed gas gradually becomes desorbed gas. The original free gas, desorbed gas, water in the coal seam, and coal dust will undergo three-phase flow of gas, water, and coal dust. The permeability of the coal reservoir exhibits dynamic changes. Conventional oil and gas well productivity equations cannot encompass the above mechanisms and are not suitable for evaluating and predicting the productivity of deep coalbed methane.
[0003] Currently, when evaluating the production capacity of coalbed methane wells, one approach is to assess the production potential based on static geological parameters, but this method has poor accuracy. Another approach is to establish a horizontal well simulation geometric model for the coalbed methane well. This involves first calculating the production capacity evaluation data of the horizontal well simulation geometric model, and then fitting it with the actual production capacity evaluation data to obtain the final production capacity evaluation data for evaluating the coalbed methane well's production capacity. However, this method requires rebuilding the horizontal well simulation geometric model each time the production capacity is evaluated, which is quite labor-intensive. Since there is no actual production data for new wells, the above methods are not applicable to new wells. Summary of the Invention
[0004] The embodiments of this application provide a method, apparatus, medium, and electronic equipment for determining a deep coalbed methane production capacity evaluation model, which solves the technical problem that existing production capacity evaluation data cannot be applied to the prediction of new well production capacity.
[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.
[0006] According to a first aspect of this application, a method for determining a production capacity evaluation model is provided, applicable to deep coalbed methane wells, comprising:
[0007] Obtain at least two reservoir physical property parameters before the commissioning of at least two old wells, and obtain production dynamic information, wherein the production dynamic information includes recoverable reserves and at least two production capacity data;
[0008] Based on the recoverable reserves, obtain capacity evaluation data for evaluating capacity from the at least two types of capacity data;
[0009] Based on the production capacity evaluation data, obtain the sensitive parameters that affect the production capacity evaluation data from the at least two reservoir physical property parameters;
[0010] A first target relationship is determined between the production capacity evaluation data and the sensitive parameters, and a second target relationship is determined between the production capacity evaluation data and the recoverable reserves. The first target relationship is used to determine the production capacity evaluation data of a new well located in the same block as the at least two old wells, and the second target relationship is used to determine the recoverable reserves of the new well located in the same block as the at least two old wells.
[0011] In some embodiments of this application, based on the foregoing scheme, the reservoir physical property parameters include a first physical property parameter and a second physical property parameter, and obtaining at least two reservoir physical property parameters before the commissioning of at least two old wells includes:
[0012] The first physical property parameter was obtained by testing at least two old wells.
[0013] Obtain historical bottom hole flowing pressure data, historical casing pressure data, and historical production capacity data of the at least two old wells, and obtain the initial value of the second physical property parameter;
[0014] The historical bottom hole flowing pressure data, historical casing pressure data, historical production capacity data, and the initial value of the second physical property parameter are input into a pre-established production dynamic prediction model. The second physical property parameter is obtained by historical fitting through the production dynamic prediction model.
[0015] In some embodiments of this application, based on the foregoing scheme, after obtaining the second physical property parameter by performing historical fitting according to a pre-established production dynamic prediction model, the method further includes:
[0016] Based on the changing trends of the historical bottom hole flowing pressure data and the historical casing pressure data, the historical bottom hole flowing pressure data and the historical casing pressure data are extended to obtain bottom hole flowing pressure data and casing pressure data;
[0017] The bottom hole flowing pressure data, the casing pressure data, the first physical property parameter, and the second physical property parameter are input into the production dynamic prediction model. The production dynamic prediction model predicts the at least two types of production capacity data, wherein the first physical property parameter and the second physical property parameter change according to a preset rule.
[0018] In some embodiments of this application, based on the foregoing scheme, obtaining capacity evaluation data for evaluating capacity from the at least two types of capacity data according to the recoverable reserves includes:
[0019] For the at least two types of production capacity data, a first factor is determined that affects the recoverable reserves, wherein the first factor characterizes the magnitude of the impact of the production capacity data on the recoverable reserves;
[0020] From the at least two types of production capacity data, the production capacity data with the largest first factor is selected as the production capacity evaluation data.
[0021] In some embodiments of this application, based on the foregoing scheme, obtaining sensitive parameters affecting the production capacity evaluation data from the at least two reservoir physical property parameters according to the production capacity evaluation data includes:
[0022] For the at least two reservoir physical property parameters, a second factor is determined that affects the production capacity evaluation data, wherein the second factor characterizes the magnitude of the influence of the reservoir physical property parameter on the production capacity evaluation value;
[0023] From the at least two reservoir physical property parameters, the reservoir physical property parameter in which the second factor is greater than the first threshold is selected as the sensitive parameter.
[0024] In some embodiments of this application, based on the foregoing scheme, when the sensitive parameter is at least two reservoir physical property parameters, determining the first target relationship between the productivity evaluation data and the sensitive parameter includes:
[0025] Construct the first combination function of the sensitive parameters;
[0026] A third factor affecting the capacity evaluation data is determined. If the third factor is greater than a second threshold, the first combination function and the capacity evaluation data are linearly fitted to obtain the first target relationship. If the third factor is less than or equal to the second threshold, the sensitive parameter with the smallest second factor is removed, and the step of constructing the first combination function of the sensitive parameter is returned. The third factor represents the magnitude of the influence of the first combination function on the capacity evaluation value.
[0027] In some embodiments of this application, based on the foregoing scheme, when the sensitive parameter is a reservoir physical property parameter, determining the first target relationship between the productivity evaluation data and the sensitive parameter includes:
[0028] The first target relationship is obtained by linearly fitting the capacity evaluation data and the sensitive parameters.
[0029] According to a second aspect of this application, a production capacity evaluation method is provided, applied to deep coalbed methane wells, comprising:
[0030] According to any embodiment of the first aspect of this application, a method for determining a capacity evaluation model is used to obtain the first target relationship and the second target relationship;
[0031] Obtain the sensitive parameters of the new well;
[0032] Based on the first target relationship and the sensitive parameters of the new well, the production capacity evaluation data of the new well is determined, wherein the new well and the at least two old wells are located in the same block;
[0033] Based on the production capacity evaluation data of the new well and the second target relationship, the recoverable reserves of the new well are determined.
[0034] According to a third aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method described in any embodiment of the first aspect of this application.
[0035] According to a fourth aspect of this application, an electronic device is provided, comprising: one or more processors; and a memory for storing executable instructions of the processors, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any embodiment of the first aspect of this application.
[0036] The beneficial effects of this application are as follows:
[0037] Based on recoverable reserves, production capacity evaluation data is obtained from the at least two types of production capacity data. Based on the production capacity evaluation data, sensitive parameters affecting the production capacity evaluation data are obtained from the at least two types of reservoir physical property parameters. A first target relationship between the production capacity evaluation data and the sensitive parameters is determined. The first target relationship can be used to determine the production capacity evaluation data of new wells located in the same block as the at least two old wells, thereby evaluating the production capacity of the new wells based on the production capacity evaluation data of the new wells.
[0038] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:
[0040] Figure 1 A flowchart of a method for determining a deep coalbed methane production capacity evaluation model is shown in an embodiment of this application;
[0041] Figure 2 This application illustrates a schematic diagram of historical bottom hole flowing pressure data, historical casing pressure data, and historical production capacity data in an embodiment of the present application.
[0042] Figure 3 A first scatter plot showing the highest daily gas production and recoverable reserves in an embodiment of this application is shown;
[0043] Figure 4 This paper presents a first scatter plot showing the first annual average daily gas production and recoverable reserves in an embodiment of this application.
[0044] Figure 5 This application shows a first scatter plot of the second-year average daily gas production and recoverable reserves in an embodiment of the present application;
[0045] Figure 6 A second scatter plot of the second annual average daily gas production and absolute permeability is shown in an embodiment of this application;
[0046] Figure 7 This application illustrates a second scatter plot showing the second annual average daily gas production and supply radius in an embodiment of the present application.
[0047] Figure 8 A second scatter plot showing the second annual average daily gas production and the thickness of the producing layer in an embodiment of this application is shown;
[0048] Figure 9 This paper shows a third scatter plot of the first combination function and the capacity evaluation data in an embodiment of this application;
[0049] Figure 10 A schematic diagram of a computer-readable storage medium in an embodiment of this application is shown;
[0050] Figure 11 A schematic diagram of the system structure of an electronic device in an embodiment of this application is shown. Detailed Implementation
[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0052] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.
[0053] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0054] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0055] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.
[0056] Figure 1 A flowchart illustrating a method for determining a deep coalbed methane production capacity evaluation model in an embodiment of this application is shown. See [link to relevant documentation]. Figure 1 This paper presents a method for determining a production capacity evaluation model, applicable to deep coalbed methane wells, including at least S1 to S4, detailed below:
[0057] In step S1, at least two reservoir physical property parameters are obtained before the commissioning of at least two old wells, and production dynamic information is obtained. The production dynamic information includes recoverable reserves and at least two types of production capacity data. The old wells are deep coalbed methane wells that have already been put into production. The at least two reservoir physical property parameters may include two or more of the following: gas layer depth, porosity, production layer thickness, formation temperature, original formation pressure, reservoir compressibility, Langmuir volume, Langmuir pressure, absolute permeability, critical desorption pressure, supply radius, fracture half-length, fracture stress sensitivity coefficient, reservoir stress sensitivity coefficient, and original gas saturation. The recoverable reserves may be the cumulative gas production over 5000 days. The at least two types of production capacity data may include two or more of the following: daily gas production, daily water production, cumulative gas production, cumulative water production, highest daily gas production, first-year average daily gas production, and second-year average daily gas production.
[0058] In step S2, based on the recoverable reserves, capacity evaluation data for evaluating capacity is obtained from the at least two types of capacity data. The capacity evaluation data is one of the at least two types of capacity data.
[0059] In step S3, based on the production capacity evaluation data, sensitive parameters affecting the production capacity evaluation data are obtained from the at least two reservoir physical property parameters. The sensitive parameters are one or more of the at least two reservoir physical property parameters.
[0060] In step S4, a first target relationship between the production capacity evaluation data and the sensitive parameter is determined, and a second target relationship between the production capacity evaluation data and the recoverable reserves is determined. The first target relationship is used to determine the production capacity evaluation data of the new well located in the same block as the at least two old wells, and the second target relationship is used to determine the recoverable reserves of the new well located in the same block as the at least two old wells.
[0061] In some embodiments, the reservoir physical parameters include a first physical parameter and a second physical parameter. Obtaining at least two reservoir physical parameters before commissioning at least two old wells includes: testing the at least two old wells to obtain the first physical parameter; obtaining historical bottomhole flowing pressure data, historical casing pressure data, and historical production capacity data of the at least two old wells, and obtaining initial values for the second physical parameter; inputting the historical bottomhole flowing pressure data, historical casing pressure data, historical production capacity data, and the initial values for the second physical parameter into a pre-established production dynamic prediction model, and performing historical fitting through the production dynamic prediction model to obtain the second physical parameter. The first physical parameter is one or more of the following: gas layer depth, porosity, production layer thickness, formation temperature, original formation pressure, reservoir compressibility coefficient, Langmuir volume, and Langmuir pressure. The second physical parameter is one or more of the following: absolute permeability, critical desorption pressure, supply radius, fracture half-length, fracture stress sensitivity coefficient, reservoir stress sensitivity coefficient, and original gas saturation.
[0062] In some embodiments, the first physical property parameter is the gas layer depth, porosity, producing layer thickness, formation temperature, original formation pressure, reservoir compressibility factor, Langmuir volume, and Langmuir pressure. The testing of at least two old wells to obtain the first physical property parameter includes: obtaining the gas layer depth, porosity, producing layer thickness, and formation temperature from well logging; obtaining the original formation pressure from drilling or fracturing; obtaining the reservoir compressibility factor from core testing or calculation; and obtaining the Langmuir volume and Langmuir pressure from isothermal adsorption-desorption experiments or parameters from adjacent wells.
[0063] In some implementations, the step of inputting the historical bottom hole flowing pressure data, historical casing pressure data, historical production capacity data, and the initial value of the second physical property parameter into a pre-established production dynamic prediction model, and performing historical fitting through the production dynamic prediction model to obtain the second physical property parameter, includes: adjusting the value based on the initial value of the second physical property parameter to obtain an adjusted value; inputting the historical bottom hole flowing pressure data, historical casing pressure data, and the adjusted value into the production dynamic prediction model to obtain the production capacity data corresponding to the adjusted value; if the difference between the production capacity data corresponding to the adjusted value and the historical production capacity data is within a preset difference range, then the production capacity data corresponding to the adjusted value is used as the second physical property parameter; if the difference between the production capacity data corresponding to the adjusted value and the historical production capacity data is not within the preset difference range, then the step of adjusting the value based on the initial value of the second physical property parameter to obtain the adjusted value is returned to the previous step.
[0064] In some embodiments, the second physical property parameter is absolute permeability, critical desorption pressure, supply radius, fracture half-length, fracture stress sensitivity coefficient, reservoir stress sensitivity coefficient, and initial gas saturation. Obtaining the initial value of the second physical property parameter includes: obtaining the initial value of absolute permeability based on well testing or dynamic inversion; determining the initial value of critical desorption pressure based on gas content, desorption curve, and formation pressure; using half the well spacing as the initial value of the supply radius; obtaining the fracture half-length based on fracture monitoring or well testing; determining the initial value of reservoir stress sensitivity coefficient based on core testing; and setting the initial values of fracture stress sensitivity coefficient and initial value of initial gas saturation based on expert knowledge.
[0065] In some embodiments, after obtaining the second physical property parameter by performing historical fitting based on a pre-established production dynamic prediction model, the method further includes: extending the historical bottom-hole flowing pressure data and the historical casing pressure data according to the changing trends of the historical bottom-hole flowing pressure data and the historical casing pressure data to obtain bottom-hole flowing pressure data and casing pressure data; inputting the bottom-hole flowing pressure data, the casing pressure data, the first physical property parameter, and the second physical property parameter into the production dynamic prediction model, and predicting the at least two types of production capacity data through the production dynamic prediction model, wherein the first physical property parameter and the second physical property parameter change according to a preset rule.
[0066] In some embodiments, the step of extending the historical bottom-hole flowing pressure data and the historical casing pressure data according to the changing trends of the historical bottom-hole flowing pressure data and the historical casing pressure data to obtain bottom-hole flowing pressure data and casing pressure data includes: plotting a first flowing pressure curve of the historical bottom-hole flowing pressure data relative to time and a first casing pressure curve of the historical casing pressure data relative to time; extending the first flowing pressure curve according to the changing trend of the first flowing pressure curve to obtain a second flowing pressure curve of the bottom-hole flowing pressure data relative to time; and extending the first casing pressure curve according to the changing trend of the first casing pressure curve to obtain a second casing pressure curve of the casing pressure data relative to time.
[0067] In some implementations, obtaining capacity evaluation data for evaluating capacity from the at least two types of capacity data based on the recoverable reserves includes: determining a first factor affecting the recoverable reserves for the at least two types of capacity data, wherein the first factor characterizes the magnitude of the impact of the capacity data on the recoverable reserves; and selecting the capacity data with the largest first factor from the at least two types of capacity data as the capacity evaluation data.
[0068] In some embodiments, determining the first factor affecting the recoverable reserves by the production capacity data includes: performing a linear fit on the production capacity data of the at least two old wells and the recoverable reserves of the at least two old wells to obtain a first fitting coefficient, wherein the first fitting coefficient is the first factor.
[0069] In some embodiments, the step of linearly fitting the production capacity data of the at least two old wells and the recoverable reserves of the at least two old wells to obtain a first fitting coefficient includes: constructing a first coordinate system with the recoverable reserves as the vertical axis and the production capacity data as the horizontal axis; constructing a first scatter plot based on the first coordinate system and the production capacity data and recoverable reserves of the at least two old wells; linearly fitting the first scatter plot to obtain a first straight line; obtaining a first linear model characterizing the first straight line based on the first straight line; and obtaining the first fitting coefficient based on the first straight line and the scatter points of the first scatter plot.
[0070] In some embodiments, obtaining sensitive parameters affecting the production capacity evaluation data from the at least two reservoir physical property parameters based on the production capacity evaluation data includes: determining a second factor affecting the production capacity evaluation data for the at least two reservoir physical property parameters, wherein the second factor characterizes the magnitude of the influence of the reservoir physical property parameter on the production capacity evaluation value; and selecting, from the at least two reservoir physical property parameters, the reservoir physical property parameter whose second factor is greater than a first threshold as the sensitive parameter.
[0071] In some embodiments, determining the second factor affecting the production capacity evaluation data of the reservoir physical property parameter includes: performing linear fitting on the reservoir physical property parameter of the at least two old wells and the production capacity evaluation data of the at least two old wells to obtain a second fitting coefficient, wherein the second fitting coefficient is the second factor.
[0072] In some embodiments, the step of linearly fitting the reservoir physical property parameters of the at least two old wells and the production capacity evaluation data of the at least two old wells to obtain a second fitting coefficient includes: constructing a second coordinate system with the production capacity evaluation data as the vertical axis and the reservoir physical property parameters as the horizontal axis; constructing a second scatter plot based on the second coordinate system according to the reservoir physical property parameters of the at least two old wells and the production capacity evaluation data of the at least two old wells; linearly fitting the second scatter plot to obtain a second straight line; obtaining a second linear model representing the second straight line based on the second straight line; and obtaining the second fitting coefficient based on the second straight line and the scatter points of the second scatter plot.
[0073] In some implementations, when the sensitive parameters are at least two reservoir physical properties, determining the first target relationship between the production capacity evaluation data and the sensitive parameters includes: constructing a first combination function of the sensitive parameters; determining a third factor that influences the production capacity evaluation data using the first combination function; if the third factor is greater than a second threshold, performing a linear fit between the first combination function and the production capacity evaluation data to obtain the first target relationship; if the third factor is less than or equal to the second threshold, eliminating the sensitive parameter with the smallest second factor, and returning to the step of constructing the first combination function of the sensitive parameters, wherein the third factor characterizes the magnitude of the influence of the first combination function on the production capacity evaluation value.
[0074] In some embodiments, determining the third factor affecting the production capacity evaluation data of the first combination function includes: performing linear fitting on the first combination function of the at least two old wells and the production capacity evaluation data of the at least two old wells to obtain a third fitting coefficient, wherein the third fitting coefficient is the third factor.
[0075] In some embodiments, the step of linearly fitting the first combination function of the at least two old wells and the production capacity evaluation data of the at least two old wells to obtain a third fitting coefficient includes: constructing a third coordinate system with the production capacity evaluation data as the vertical axis and the first combination function as the horizontal axis; constructing a third scatter plot based on the third coordinate system according to the first combination function of the at least two old wells and the production capacity evaluation data of the at least two old wells; performing linear fitting on the third scatter plot to obtain a third straight line; obtaining a third linear model characterizing the third straight line based on the third straight line; and obtaining the third fitting coefficient based on the third straight line and the scatter points of the third scatter plot, wherein the third linear model is the first target relationship.
[0076] In some implementations, when the sensitive parameter is a reservoir physical property parameter, determining the first target relationship between the production capacity evaluation data and the sensitive parameter includes: performing linear fitting on the production capacity evaluation data and the sensitive parameter to obtain the first target relationship.
[0077] In some implementations, the step of linearly fitting the production capacity evaluation data and the sensitive parameters to obtain the first target relationship includes: constructing a fourth linear model with the sensitive parameters as independent variables and the production capacity evaluation data as dependent variables; and determining the fourth slope and fourth intercept of the fourth linear model based on the sensitive parameters of the at least two old wells and the production capacity evaluation data of the at least two old wells.
[0078] In some embodiments, determining the fourth slope and fourth intercept of the fourth linear model based on the sensitive parameters of the at least two old wells and the production capacity evaluation data of the at least two old wells includes: constructing a fourth coordinate system with the production capacity evaluation data as the vertical axis and the sensitive parameters as the horizontal axis; constructing a fourth scatter plot based on the fourth coordinate system and the sensitive parameters of the at least two old wells and the production capacity evaluation data of the at least two old wells; performing linear fitting on the fourth scatter plot to obtain a fourth straight line characterizing the fourth linear model; and obtaining the fourth slope and the fourth intercept based on the fourth straight line.
[0079] In some implementations, determining the second target relationship between the production capacity assessment data and the recoverable reserves includes: performing a linear fit on the production capacity assessment data and the recoverable reserves to obtain the second target relationship.
[0080] In some implementations, the step of linearly fitting the production capacity evaluation data and the recoverable reserves to obtain the second target relationship includes: constructing a fifth linear model with the production capacity evaluation data as the independent variable and the recoverable reserves as the dependent variable; and determining the fifth slope and the fifth intercept of the fifth linear model based on the production capacity evaluation data of the at least two old wells and the recoverable reserves of the at least two old wells.
[0081] In some embodiments, determining the fifth slope and fifth intercept of the fifth linear model based on the production capacity evaluation data and the recoverable reserves of the at least two old wells includes: constructing a fifth coordinate system with the recoverable reserves as the vertical axis and the production capacity evaluation data as the horizontal axis; constructing a fifth scatter plot based on the fifth coordinate system and the production capacity evaluation data and the recoverable reserves of the at least two old wells; performing linear fitting on the fifth scatter plot to obtain a fifth straight line characterizing the fifth linear model; and obtaining the fifth slope and the fifth intercept based on the fifth straight line.
[0082] To better understand the embodiments of this application, an example is provided as follows:
[0083] The 31 old wells were tested to obtain the first physical property parameters, which are shown in Table 1. The first physical property parameters include gas reservoir depth, porosity, production layer thickness, formation temperature, original formation pressure, reservoir compressibility coefficient, Langmuir volume, and Langmuir pressure. Historical bottomhole flowing pressure data, historical casing pressure data, and historical production capacity data of the 31 old wells were obtained, as well as initial values for the second physical property parameters. The historical bottomhole flowing pressure data, historical casing pressure data, and historical production capacity data of the 31 old wells are shown in Table 1. Figure 2 , Figure 2In the diagram, from top to bottom, the first curve represents historical bottomhole flowing pressure data, the second curve represents historical casing pressure data, the third curve represents historical daily gas production, the fourth curve represents historical daily water production, the fifth curve represents historical cumulative gas production, and the sixth curve represents historical cumulative water production. Historical daily gas production, historical daily water production, historical cumulative gas production, and historical cumulative water production are historical production capacity data. Based on the initial value of the second physical property parameter, numerical adjustments are made to obtain adjusted values. The historical bottomhole flowing pressure data, historical casing pressure data, and the adjusted values are input into the production dynamic prediction model to obtain the production capacity data corresponding to the adjusted values. If the difference between the production capacity data corresponding to the adjusted values and the historical production capacity data is within a preset difference range, then the production capacity data corresponding to the adjusted values is used as the second physical property parameter. If the difference between the production capacity data corresponding to the adjusted value and the historical production capacity data is not within the preset difference range, then return to the step of adjusting the value based on the initial value of the second physical property parameter to obtain the adjusted value. The historical production capacity data is one of daily gas production, cumulative gas production, daily water production, and cumulative water production. If the second physical property parameter obtained based on daily gas production is different from the second physical property parameter obtained based on other historical production capacity data, then the second physical property parameter obtained based on daily gas production will be used as the second physical property parameter. The second physical property parameters of 31 old wells are shown in Table 2. The second physical property parameters are absolute permeability, critical desorption pressure, supply radius, fracture half-length, fracture stress sensitivity coefficient, reservoir stress sensitivity coefficient, and original gas saturation.
[0084] Plot the first flow pressure curve of the historical bottom hole flowing pressure data relative to time and the first casing pressure curve of the historical casing pressure data relative to time. Based on the changing trend of the first flow pressure curve, extend the first flow pressure curve, that is... Figure 2 The tail (right end) of the first flow pressure curve is extended backward in the direction from left to right, thus extending the duration of the bottom hole flow pressure data from 800 days to 5000 days, resulting in a second flow pressure curve representing the bottom hole flow pressure data over time. This second flow pressure curve characterizes the bottom hole flow pressure data. Based on the changing trend of the first casing pressure curve, the first casing pressure curve is extended, that is... Figure 2The tail (right end) of the first set pressure curve is extended backward in the direction from left to right of the tail of the first set pressure curve. The duration of the casing pressure data is extended from 800 days to 5000 days to obtain the second casing pressure curve relative to time. The second casing pressure curve is used to characterize the casing pressure data. The bottom hole flowing pressure data, the casing pressure data, the first physical property parameter and the second physical property parameter are input into the production dynamic prediction model. The production dynamic prediction model predicts the at least two kinds of production capacity data. The first physical property parameter and the second physical property parameter change according to a preset law. The production capacity data of 31 old wells are shown in Table 3. The at least two kinds of production capacity data are the highest daily gas production, the average daily gas production in the first year and the average daily gas production in the second year. Well N in Tables 1, 2 and 3 represents the Nth old well. N is a positive integer from 1 to 31. The 5000-day cumulative gas production in Table 3 can be understood as recoverable reserves.
[0085] For the at least two types of production capacity data, a first coordinate system is constructed with the recoverable reserves as the vertical axis and the production capacity data as the horizontal axis. Based on the first coordinate system, a first scatter plot is constructed according to the production capacity data and recoverable reserves of 31 old wells. A linear fit is performed on the first scatter plot to obtain a first straight line. A first linear model characterizing the first straight line is obtained. The first fitting coefficient is obtained based on the first straight line and the scatter points of the first scatter plot. The production capacity data with the largest first fitting coefficient is selected as the production capacity evaluation data. Figure 3 This paper presents a first scatter plot showing the highest daily gas production and recoverable reserves in an embodiment of this application. Figure 4 This paper presents a first scatter plot showing the first annual average daily gas production and recoverable reserves in an embodiment of this application. Figure 5 This application illustrates a first scatter plot of the second-year average daily gas production and recoverable reserves in an embodiment of this application. See [link / reference] Figures 3 to 5 The first linear model corresponding to the highest daily gas production is y = 0.8076x + 0.6399, with a first fitting coefficient of 0.4593. The first linear model corresponding to the average daily gas production in the first year is y = 2.0291x + 0.1025, with a first fitting coefficient of 0.7633. The first linear model corresponding to the average daily gas production in the second year is y = 2.4206x + 0.8407, with a first fitting coefficient of 0.9014. The first fitting coefficient corresponding to the average daily gas production in the second year is the largest, so the average daily gas production in the second year is used as the production capacity evaluation data.
[0086] For the at least two reservoir physical properties, a second coordinate system is constructed with the production capacity evaluation data as the vertical axis and the reservoir physical property as the horizontal axis. Based on the second coordinate system, a second scatter plot is constructed using the reservoir physical properties and production capacity evaluation data of 31 old wells. A linear fit is performed on the second scatter plot to obtain a second straight line. A second linear model characterizing the second straight line is obtained based on the second straight line and the scatter points of the second scatter plot. The second fitting coefficient is obtained from the at least two reservoir physical properties, and the reservoir physical property with a second fitting coefficient greater than 0.1 is selected as the sensitive parameter, i.e., the first threshold is 0.1. Figure 6 This application presents a second scatter plot showing the second annual average daily gas production and absolute permeability in an embodiment of the present application. Figure 7 This application presents a second scatter plot showing the second annual average daily gas production and supply radius in an embodiment of the present application. Figure 8 A second scatter plot showing the second annual average daily gas production and producing layer thickness in an embodiment of this application is shown. See [link to relevant documentation]. Figures 6 to 8 The second linear model corresponding to absolute permeability is y = 4.0289x - 0.7799, with a second fitting coefficient of 0.6178. The second linear model corresponding to the supply radius is y = 0.0343x - 3.4285, with a second fitting coefficient of 0.3819. The second linear model corresponding to the producing layer thickness is y = 0.3056 + 0.5809, with a second fitting coefficient of 0.1126. The second fitting coefficients corresponding to other reservoir properties are less than or equal to 0.1 (not shown in the attached figure). Absolute permeability, supply radius, and producing layer thickness are used as sensitive parameters.
[0087] Construct a first combination function for the sensitive parameters, the first combination function being F = (e k -1)×r e ×h, where k is the absolute permeability, r eLet h be the production radius and h be the layer thickness. Using the production capacity evaluation data as the vertical axis and the first combination function as the horizontal axis, a third coordinate system is constructed. Based on this third coordinate system, a third scatter plot is constructed according to the first combination function of the at least two old wells and the production capacity evaluation data of the at least two old wells. A linear fit is performed on the third scatter plot to obtain a third straight line. A third linear model characterizing the third straight line is obtained. A third fitting coefficient is obtained based on the third straight line and the scatter points of the third scatter plot. If the third fitting coefficient is greater than 0.8, a linear fit is performed on the first combination function and the production capacity evaluation data to obtain the first target relationship, i.e., the third linear model is used as the first target relationship. If the third factor is less than or equal to 0.8, the sensitive parameter with the smallest second fitting coefficient is removed, and the process returns to the step of constructing the first combination function of the sensitive parameter, where the second threshold is 0.8. Figure 9 This application shows a third scatter plot of the first combination function and capacity evaluation data in an embodiment of the present application. See [link / reference] Figure 9 The third linear model is y = 0.0012242x + 0.65829, and the third fitting coefficient is 0.84395.
[0088] The production capacity evaluation data and the recoverable reserves are linearly fitted to obtain the second target relationship, that is, the first linear model y = 2.4206x + 0.8407 corresponding to the average daily gas production in the second year is used as the second target relationship.
[0089] Therefore, the first objective relation is PI = 0.0012242 × F + 0.65829 = 0.0012242 × (e k -1)×r e ×h+0.65829, the second objective relationship is EUR=2.4206×PI+0.8407, where PI is the average daily gas production in the second year, in units of 10. 3 m 3 / d, e is the natural constant, k is the absolute permeability, and the unit is mD, r e Supply radius (m), h (sediment thickness, m), EUR (recoverable reserves, 10⁻⁶). 6 m 3 .
[0090] Table 1
[0091]
[0092] Table 2
[0093]
[0094]
[0095] Table 3
[0096]
[0097]
[0098] In this application, the production dynamic prediction model includes a penetration rate model, a continuity equation, a boundary equation, and an initial equation.
[0099] According to a second aspect of this application, a production capacity evaluation method is provided, applied to deep coalbed methane wells, comprising:
[0100] According to any embodiment of the first aspect of this application, a method for determining a capacity evaluation model is used to obtain the first target relationship and the second target relationship;
[0101] Obtain the sensitive parameters of the new well;
[0102] Based on the first target relationship and the sensitive parameters of the new well, the production capacity evaluation data of the new well is determined, wherein the new well and the at least two old wells are located in the same block;
[0103] Based on the production capacity evaluation data of the new well and the second target relationship, the recoverable reserves of the new well are determined.
[0104] For example, if the absolute permeability of a new well is 1 mD, the supply radius is 200 m, and the producing layer thickness is 8 m, then the average daily gas production in the second year after the well is built is PI = 5.203(10 3 m 3 / d), recoverable reserves are EUR = 13.4351 (10 6 m 3 ).
[0105] In this application, based on recoverable reserves, production capacity evaluation data is obtained from at least two types of production capacity data. Based on the production capacity evaluation data, sensitive parameters affecting the production capacity evaluation data are obtained from at least two types of reservoir physical property parameters. A first target relationship between the production capacity evaluation data and the sensitive parameters is determined. The first target relationship can be used to determine the production capacity evaluation data of a new well located in the same block as the at least two old wells, thereby evaluating the production capacity of the new well based on the production capacity evaluation data of the new well.
[0106] Based on the same inventive concept, as a third aspect, this application also provides a computer-readable storage medium storing a program product capable of implementing the method for determining a deep coalbed methane production capacity evaluation model described above. In some possible embodiments, various aspects of this application can also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this application.
[0107] refer to Figure 10 As shown, a program product 200 for implementing the above-described method according to an embodiment of this application is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of this application is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0108] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0109] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0110] In another respect, this application also provides an electronic device capable of implementing the above-described method.
[0111] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."
[0112] The following reference Figure 11 To describe an electronic device 300 according to this embodiment of the present application. Figure 11 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0113] like Figure 11 As shown, the electronic device 300 is manifested in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, and a bus 330 connecting different system components (including storage unit 320 and processing unit 310).
[0114] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the "Embodiment Methods" section above according to various exemplary embodiments of this application.
[0115] Storage unit 320 may include readable media in the form of volatile storage units, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.
[0116] Storage unit 320 may also include a program / utility 324 having a set (at least one) of program modules 325, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.
[0117] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.
[0118] Electronic device 300 can also communicate with one or more external devices 400 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with the electronic device 300, and / or with any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Figure 11 As shown, network adapter 360 communicates with other modules of electronic device 300 via bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0119] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for determining a capacity evaluation model, characterized in that, Applications in deep coalbed methane wells, including: Obtain at least two reservoir physical property parameters before the commissioning of at least two old wells, and obtain production dynamic information, wherein the production dynamic information includes recoverable reserves and at least two production capacity data; Based on the recoverable reserves, obtain capacity evaluation data for evaluating capacity from the at least two types of capacity data; Based on the production capacity evaluation data, obtain the sensitive parameters that affect the production capacity evaluation data from the at least two reservoir physical property parameters; A first target relationship is determined between the production capacity evaluation data and the sensitive parameters, and a second target relationship is determined between the production capacity evaluation data and the recoverable reserves. The first target relationship is used to determine the production capacity evaluation data of a new well located in the same block as the at least two old wells, and the second target relationship is used to determine the recoverable reserves of the new well located in the same block as the at least two old wells.
2. The method for determining a production capacity evaluation model according to claim 1, characterized in that, The reservoir physical properties include a first physical property and a second physical property. Obtaining at least two types of reservoir physical properties before the commissioning of at least two old wells includes: The first physical property parameter was obtained by testing at least two old wells. Obtain historical bottom-hole flowing pressure data, historical casing pressure data, and historical production capacity data of the at least two old wells, and obtain the initial value of the second physical property parameter; The historical bottom hole flowing pressure data, historical casing pressure data, historical production capacity data, and the initial value of the second physical property parameter are input into a pre-established production dynamic prediction model. The second physical property parameter is obtained by historical fitting through the production dynamic prediction model.
3. The method for determining a production capacity evaluation model according to claim 2, characterized in that, After obtaining the second physical property parameter by performing historical fitting based on a pre-established production dynamic prediction model, the method further includes: Based on the changing trends of the historical bottom hole flowing pressure data and the historical casing pressure data, the historical bottom hole flowing pressure data and the historical casing pressure data are extended to obtain bottom hole flowing pressure data and casing pressure data; The bottom hole flowing pressure data, the casing pressure data, the first physical property parameter, and the second physical property parameter are input into the production dynamic prediction model. The production dynamic prediction model predicts the at least two types of production capacity data, wherein the first physical property parameter and the second physical property parameter change according to a preset rule.
4. The method for determining a capacity evaluation model according to claim 1, characterized in that, The step of obtaining capacity evaluation data for evaluating capacity from the at least two types of capacity data based on the recoverable reserves includes: For the at least two types of production capacity data, a first factor is determined that affects the recoverable reserves, wherein the first factor characterizes the magnitude of the impact of the production capacity data on the recoverable reserves; From the at least two types of production capacity data, the production capacity data with the largest first factor is selected as the production capacity evaluation data.
5. The method for determining a production capacity evaluation model according to claim 1, characterized in that, The step of obtaining sensitive parameters affecting the production capacity evaluation data from at least two reservoir physical property parameters based on the production capacity evaluation data includes: For the at least two reservoir physical property parameters, a second factor is determined that affects the production capacity evaluation data, wherein the second factor characterizes the magnitude of the influence of the reservoir physical property parameter on the production capacity evaluation value; From the at least two reservoir physical property parameters, the reservoir physical property parameter in which the second factor is greater than the first threshold is selected as the sensitive parameter.
6. The method for determining a capacity evaluation model according to claim 1, characterized in that, When the sensitive parameter is at least two reservoir physical property parameters, determining the first target relationship between the productivity evaluation data and the sensitive parameter includes: Construct the first combination function of the sensitive parameters; A third factor affecting the capacity evaluation data is determined. If the third factor is greater than a second threshold, the first combination function and the capacity evaluation data are linearly fitted to obtain the first target relationship. If the third factor is less than or equal to the second threshold, the sensitive parameter with the smallest second factor is removed, and the step of constructing the first combination function of the sensitive parameter is returned. The third factor represents the magnitude of the influence of the first combination function on the capacity evaluation value.
7. The method for determining a production capacity evaluation model according to claim 1, characterized in that, When the sensitive parameter is a reservoir physical property parameter, determining the first target relationship between the productivity evaluation data and the sensitive parameter includes: The first target relationship is obtained by linearly fitting the capacity evaluation data and the sensitive parameters.
8. A method for evaluating production capacity, characterized in that, Applications in deep coalbed methane wells, including: According to any one of claims 1-7, a method for determining a capacity evaluation model is used to obtain the first target relationship and the second target relationship; Obtain the sensitive parameters of the new well; Based on the first target relationship and the sensitive parameters of the new well, the production capacity evaluation data of the new well is determined, wherein the new well and the at least two old wells are located in the same block; Based on the production capacity evaluation data of the new well and the second target relationship, the recoverable reserves of the new well are determined.
9. A computer-readable storage medium, characterized in that, It stores a computer program thereon, the computer program including executable instructions that, when executed by a processor, implement the method of any one of claims 1-7.
10. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-7.