Carbon dioxide injection parameter optimization method, device, equipment and storage medium

Abstract

The embodiment of the present application provides a method, device, equipment and storage medium for optimizing carbon dioxide injection parameters, which relates to the field of oil and gas extraction technology. The method includes: calculating the comprehensive heat transfer coefficient of carbon dioxide injection into the target injection well; obtaining the temperature and pressure profile data of the target injection well according to the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide; constructing an injection parameter optimization function, and the parameters to be optimized include the wellbore structure parameters and the fluid injection parameters; determining the value range of each parameter to be optimized, and initializing the population to obtain an initialized population, and each individual in the initialized population contains a set of parameters to be optimized; using a non-dominated sorting genetic algorithm to generate the optimal solution of the injection parameter optimization function, and the optimal solution contains an optimized set of parameters to be optimized. The technical solution provided by the embodiment of the present application can improve the carbon storage effect and oil recovery effect of the injection well.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of oil and gas extraction, and in particular to a method, device, equipment and storage medium for optimizing carbon dioxide injection parameters. Background Art

[0002] At present, Carbon Capture Utilization and Storage (CCUS) technology has received widespread attention as an important means to reduce greenhouse gas emissions. Among them, the carbon dioxide flooding and storage technology can not only improve the oil recovery rate, but also safely store a large amount of carbon dioxide, and has broad application prospects. In the CO2 flooding and storage operations, the fluid temperature and pressure at the bottom of the well directly determine the fluidity and oil solubility of CO2. In order to achieve the best crude oil displacement and storage effects, it is necessary to design a reasonable CCUS wellhead injection temperature, injection pressure and wellbore structure so that the CO2 fluid entering the bottom of the well reaches the most suitable temperature and pressure.

[0003] In the research of injection well models, some early models revealed the changing trend of fluid temperature in the target input well over time, but failed to achieve true steady-state simulation; although some domestic scholars have gradually incorporated factors such as physical property parameter changes, heat capacity effect, and phase transition to improve the applicability and accuracy of the model, CO2 is a special working fluid, and its physical property parameters fluctuate greatly with temperature and pressure during the injection process, making it difficult for traditional models to accurately describe all key dynamic processes. In the research of gas injection strategy optimization methods, the CO2 injection well parameter optimization methods proposed by some scholars have limitations. They can only be optimized under the condition of fixing a certain part of the parameters (such as production parameters or wellbore structure), and it is difficult to simultaneously obtain the optimal injection parameters to combine with the wellbore structure, which affects the difficulty of formulating carbon dioxide injection strategies, thereby affecting the carbon storage effect and oil recovery effect of the injection well. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment and storage medium for optimizing carbon dioxide injection parameters, which can improve the carbon sequestration effect and oil recovery effect of the injection well. The technical solutions provided by the embodiments of the present application are as follows:

[0005] According to one aspect of an embodiment of the present application, a method for optimizing carbon dioxide injection parameters is provided, the method comprising:

[0006] Construct temperature gradient calculation model and pressure gradient calculation model of target injection well;

[0007] Calculate the comprehensive heat transfer coefficient of carbon dioxide injection into the target injection well according to the size parameters of the target injection well, the size parameters and temperature of the oil pipe, and the size parameters and temperature of the casing;

[0008] According to the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide, a flow simulation of the carbon dioxide fluid injection process is performed on the target injection well to obtain the temperature and pressure profile data of the target injection well; wherein the temperature and pressure profile data include the temperature data and pressure data of carbon dioxide corresponding to multiple transverse sections of the target injection well;

[0009] Constructing an injection parameter optimization function, wherein the injection parameter optimization function includes parameters to be optimized, the parameters to be optimized include wellbore structure parameters and fluid injection parameters, the wellbore structure parameters include the inner diameter and outer diameter of the oil pipe and the inner diameter and outer diameter of the casing, and the fluid injection parameters include the injection pressure, injection temperature and injection flow rate of carbon dioxide;

[0010] Based on the target temperature range and target pressure range of the bottom of the target injection well, the value range of each parameter to be optimized is determined, and the population is initialized to obtain an initialized population, where each individual in the initialized population contains a set of parameters to be optimized;

[0011] Based on the initialized population and temperature and pressure profile data, a non-dominated sorting genetic algorithm is used to generate the optimal solution of the injection parameter optimization function. The optimal solution contains a set of optimized parameters to be optimized.

[0012] In some embodiments, based on the initialization population and the temperature and pressure profile data, a non-dominated sorting genetic algorithm is used to generate an optimal solution for the injection parameter optimization function, including:

[0013] Based on the temperature and pressure profile data and the injection parameter optimization function, the fitness corresponding to each individual in the population is obtained. The fitness is used to indicate the prediction accuracy of the parameters to be optimized.

[0014] According to the fitness of each individual in the population, the population is sorted in a non-dominated manner;

[0015] According to the sorting results of non-dominated sorting, the individuals in the population are divided into multiple levels;

[0016] For each level, the corresponding crowding degree is calculated, which is used to indicate the degree of concentration of individual distribution in the level;

[0017] Update the population according to the sorting results of non-dominated sorting and the congestion corresponding to each level;

[0018] The method starts from the step of obtaining the fitness corresponding to each individual in the population based on the temperature and pressure profile data and the injection parameter optimization function, and executes until the stopping condition is reached, and the optimal solution of the injection parameter optimization function and the values ​​of a set of parameters to be optimized corresponding to the optimal solution of the injection parameter optimization function are obtained.

[0019] In some embodiments, the injection parameter optimization function is expressed according to the following formula:

[0020] ,

[0021] are the parameters to be optimized, and the ranges of the parameters to be optimized are as follows:

[0022] ,

[0023] Among them, F is the injection parameter optimization function, Q is the injection flow rate of carbon dioxide, T0 is the injection temperature of carbon dioxide, P0 is the injection pressure of carbon dioxide, , , , are the inner diameter of the tubing, the outer diameter of the tubing, the inner diameter of the casing, and the outer diameter of the casing, respectively. m is the bottom temperature of the target injection well, P m The bottom pressure of the target injection well, is the target temperature at the bottom of the target injection well, is the target pressure at the bottom of the target injection well, , are the minimum flow rate and maximum flow rate when carbon dioxide is injected into the target injection well, , are the minimum and maximum temperatures when carbon dioxide is injected into the target injection well, , are the minimum pressure and maximum pressure when carbon dioxide is injected into the target injection well.

[0024] In some embodiments, the method further comprises:

[0025] Obtain the Reynolds number Re corresponding to each area of ​​the target injection well for carbon dioxide;

[0026] According to the Reynolds number Re corresponding to each area of ​​the target injection well, the friction coefficient between each area of ​​the target injection well and the oil pipe is calculated;

[0027] Based on the friction coefficient between the carbon dioxide in each area of ​​the target injection well and the oil pipe, a pressure gradient calculation model is obtained.

[0028] In some embodiments, when Re is less than 2300, the friction coefficient is calculated as:

[0029] ;

[0030] When Re is greater than or equal to 2300 and Re is less than or equal to 3400, the friction coefficient is calculated as follows:

[0031] ;

[0032] When Re is greater than 3400 and Re is less than 2×10 6 In the case of, the friction coefficient is calculated as:

[0033] ;

[0034] When Re is greater than 2×10 6 In the case of, the friction coefficient is calculated as:

[0035] ;

[0036] Among them, f is the friction coefficient, ε is the average roughness of the oil pipe, Re is the Reynolds number of the carbon dioxide fluid, and r1 is the inner diameter of the oil pipe.

[0037] In some embodiments, the target injection well includes m well sections, where m is an integer greater than 1, and the method further includes:

[0038] Obtain the pressure and temperature at the front end of the first well section among the m well sections, where the initialized first well section is the first well section counted from the front to the back among the m well sections;

[0039] According to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, the pressure change and the temperature change of the first well section are calculated;

[0040] Calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section and the pressure change and temperature change of the first well section;

[0041] Calculate the physical property parameters and phase type of carbon dioxide in the first well section according to the average pressure and average temperature of the first well section, wherein the physical property parameters include pressure and temperature;

[0042] Based on the physical property parameters and phase type of carbon dioxide in the first well section, a temperature gradient calculation model is used to generate a first temperature of carbon dioxide in the first well section;

[0043] Based on the physical property parameters and phase type of carbon dioxide in the first well section, a pressure gradient calculation model is used to generate a first pressure of carbon dioxide in the first well section;

[0044] When the absolute value of the difference between the temperature change of the first well section and the first temperature is less than or equal to the first threshold value, and the absolute value of the difference between the pressure change of the first well section and the first pressure is less than or equal to the second threshold value, the next well section of the first well section is determined as a new first well section, and the step of calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data is executed until the first well section is the last well section of the m well sections, and the temperature and pressure profile data is generated;

[0045] When the absolute value of the difference between the temperature change of the first well section and the first temperature is greater than the first threshold, or the absolute value of the difference between the pressure change of the first well section and the first pressure is greater than the second threshold, the first temperature is taken as the temperature change of the first well section, and the first pressure is taken as the pressure change of the first well section, and the step of calculating the average pressure and average temperature of the first well section based on the pressure and temperature at the front end of the first well section and the pressure change and temperature change of the first well section is executed.

[0046] In some embodiments, the average pressure and average temperature of the first well section are calculated based on the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, including:

[0047] Obtaining the depth of the first well section, where the depth of the first well section refers to the distance between the front end point of the first well section and the rear end point of the first well section;

[0048] According to the depth of the first well section, the pressure and temperature of the front end point of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, the pressure increment and the temperature increment between the front end point of the first well section and the rear end point of the first well section are calculated;

[0049] The average pressure and average temperature of the first well section are calculated based on the pressure and temperature at the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section.

[0050] In some embodiments, the method further comprises:

[0051] Calculate the pressure and temperature of the rear end point of the first well section according to the pressure and temperature of the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section;

[0052] The pressure and temperature at the rear end point of the first well section are determined as the pressure and temperature at the front end point of the new first well section.

[0053] In some embodiments, the size parameters of the oil pipe include the outer diameter, inner diameter and outer wall thickness of the oil pipe, the size parameters of the casing include the outer diameter, inner diameter and inner wall thickness of the casing, the temperature of the oil pipe includes the outer wall temperature of the oil pipe, the temperature of the casing includes the inner wall temperature of the casing, and the size parameters of the target injection well include the radius of the target injection well; the method further includes:

[0054] According to the outer diameter and inner diameter of the oil pipe and the equivalent thermal conductivity of the fluid in the annulus, the heat transfer coefficient of the annulus fluid is calculated;

[0055] The heat transfer coefficient of annular space thermal radiation is calculated according to the outer wall temperature of the tubing, the inner wall temperature of the casing, the outer wall thickness of the tubing, the inner wall thickness of the casing, the radiation angle coefficient and the Stefan-Boltzmann constant;

[0056] The annular convection heat transfer coefficient is calculated based on the outer diameter of the casing, the radius of the target injection well and the thermal conductivity of the cement annulus;

[0057] The comprehensive heat transfer coefficient is calculated based on the annular space fluid heat exchange coefficient, the annular space heat radiation heat transfer coefficient and the annular space convection heat transfer coefficient.

[0058] According to one aspect of an embodiment of the present application, a device for optimizing carbon dioxide injection parameters is provided, the device comprising:

[0059] A model building module, used to build a temperature gradient calculation model and a pressure gradient calculation model for a target injection well;

[0060] The parameter acquisition module is also used to calculate the comprehensive heat transfer coefficient of carbon dioxide injection into the target injection well according to the size parameters of the target injection well, the size parameters and temperature of the oil pipe, and the size parameters and temperature of the casing;

[0061] The parameter acquisition module is also used to perform flow simulation of the carbon dioxide fluid injection process on the target injection well according to the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide, and obtain the temperature and pressure profile data of the target injection well; wherein the temperature and pressure profile data include the temperature data and pressure data of carbon dioxide corresponding to multiple transverse sections of the target injection well;

[0062] A function construction module is used to construct an injection parameter optimization function, wherein the injection parameter optimization function includes parameters to be optimized, the parameters to be optimized include wellbore structure parameters and fluid injection parameters, the wellbore structure parameters include the inner diameter and outer diameter of the oil pipe and the inner diameter and outer diameter of the casing, and the fluid injection parameters include the injection pressure, injection temperature and injection flow rate of carbon dioxide;

[0063] A range determination module is used to determine the value range of each parameter to be optimized based on the target temperature range and target pressure range of the bottom of the target injection well, and initialize the population to obtain an initialized population, each individual in the initialized population contains a set of parameters to be optimized;

[0064] The parameter optimization module is used to generate the optimal solution of the injection parameter optimization function based on the initialization population and temperature and pressure profile data by using a non-dominated sorting genetic algorithm. The optimal solution includes a set of optimized parameters to be optimized.

[0065] In some embodiments, the parameter optimization module is used to:

[0066] Based on the temperature and pressure profile data and the injection parameter optimization function, the fitness corresponding to each individual in the population is obtained. The fitness is used to indicate the prediction accuracy of the parameters to be optimized.

[0067] According to the fitness of each individual in the population, the population is sorted in a non-dominated manner;

[0068] According to the sorting results of non-dominated sorting, the individuals in the population are divided into multiple levels;

[0069] For each level, the corresponding crowding degree is calculated, which is used to indicate the degree of concentration of individual distribution in the level;

[0070] Update the population according to the sorting results of non-dominated sorting and the congestion corresponding to each level;

[0071] The method starts from the step of obtaining the fitness corresponding to each individual in the population based on the temperature and pressure profile data and the injection parameter optimization function, and executes until the stopping condition is reached, and the optimal solution of the injection parameter optimization function and the values ​​of a set of parameters to be optimized corresponding to the optimal solution of the injection parameter optimization function are obtained.

[0072] In some embodiments, the injection parameter optimization function is expressed according to the following formula:

[0073] ,

[0074] are the parameters to be optimized, and the ranges of the parameters to be optimized are as follows:

[0075] ,

[0076] Among them, F is the injection parameter optimization function, Q is the injection flow rate of carbon dioxide, T0 is the injection temperature of carbon dioxide, P0 is the injection pressure of carbon dioxide, , , , are the inner diameter of the tubing, the outer diameter of the tubing, the inner diameter of the casing, and the outer diameter of the casing, respectively. m is the bottom temperature of the target injection well, P m The bottom pressure of the target injection well, is the target temperature at the bottom of the target injection well, is the target pressure at the bottom of the target injection well, , are the minimum flow rate and maximum flow rate when carbon dioxide is injected into the target injection well, , are the minimum and maximum temperatures when carbon dioxide is injected into the target injection well, , are the minimum pressure and maximum pressure when carbon dioxide is injected into the target injection well.

[0077] In some embodiments, the apparatus further comprises:

[0078] A Reynolds number acquisition module is used to obtain the Reynolds number Re corresponding to each area of ​​the target injection well;

[0079] A friction calculation module is used to calculate the friction coefficient between each area of ​​the target injection well and the oil pipe according to the Reynolds number Re corresponding to each area of ​​the target injection well;

[0080] The model generation module is used to obtain a pressure gradient calculation model based on the friction coefficient between the carbon dioxide in various areas in the target injection well and the oil pipe.

[0081] In some embodiments, when Re is less than 2300, the friction coefficient is calculated as:

[0082] ;

[0083] When Re is greater than or equal to 2300 and Re is less than or equal to 3400, the friction coefficient is calculated as follows:

[0084] ;

[0085] When Re is greater than 3400 and Re is less than 2×10 6 In the case of, the friction coefficient is calculated as:

[0086] ;

[0087] When Re is greater than 2×10 6 In the case of, the friction coefficient is calculated as:

[0088] ;

[0089] Among them, f is the friction coefficient, ε is the average roughness of the oil pipe, Re is the Reynolds number of the carbon dioxide fluid, and r1 is the inner diameter of the oil pipe.

[0090] In some embodiments, the target injection well includes m well sections, where m is an integer greater than 1; the device also includes a data generation module for:

[0091] Obtain the pressure and temperature at the front end of the first well section among the m well sections, where the initialized first well section is the first well section counted from the front to the back among the m well sections;

[0092] According to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, the pressure change and the temperature change of the first well section are calculated;

[0093] Calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section and the pressure change and temperature change of the first well section;

[0094] Calculate the physical property parameters and phase type of carbon dioxide in the first well section according to the average pressure and average temperature of the first well section, wherein the physical property parameters include pressure and temperature;

[0095] Based on the physical property parameters and phase type of carbon dioxide in the first well section, a temperature gradient calculation model is used to generate a first temperature of carbon dioxide in the first well section;

[0096] Based on the physical property parameters and phase type of carbon dioxide in the first well section, a pressure gradient calculation model is used to generate a first pressure of carbon dioxide in the first well section;

[0097] When the absolute value of the difference between the temperature change of the first well section and the first temperature is less than or equal to the first threshold value, and the absolute value of the difference between the pressure change of the first well section and the first pressure is less than or equal to the second threshold value, the next well section of the first well section is determined as a new first well section, and the step of calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data is executed until the first well section is the last well section of the m well sections, and the temperature and pressure profile data is generated;

[0098] When the absolute value of the difference between the temperature change of the first well section and the first temperature is greater than the first threshold, or the absolute value of the difference between the pressure change of the first well section and the first pressure is greater than the second threshold, the first temperature is taken as the temperature change of the first well section, and the first pressure is taken as the pressure change of the first well section, and the step of calculating the average pressure and average temperature of the first well section based on the pressure and temperature at the front end of the first well section and the pressure change and temperature change of the first well section is executed.

[0099] In some embodiments, the data generation module is used to:

[0100] Obtaining the depth of the first well section, where the depth of the first well section refers to the distance between the front end point of the first well section and the rear end point of the first well section;

[0101] According to the depth of the first well section, the pressure and temperature of the front end point of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, the pressure increment and the temperature increment between the front end point of the first well section and the rear end point of the first well section are calculated;

[0102] The average pressure and average temperature of the first well section are calculated based on the pressure and temperature at the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section.

[0103] In some embodiments, the data generation module is used to:

[0104] Calculate the pressure and temperature of the rear end point of the first well section according to the pressure and temperature of the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section;

[0105] The pressure and temperature at the rear end point of the first well section are determined as the pressure and temperature at the front end point of the new first well section.

[0106] In some embodiments, the size parameters of the oil pipe include the outer diameter, inner diameter and outer wall thickness of the oil pipe, the size parameters of the casing include the outer diameter, inner diameter and inner wall thickness of the casing, the temperature of the oil pipe includes the outer wall temperature of the oil pipe, the temperature of the casing includes the inner wall temperature of the casing, and the size parameters of the target injection well include the radius of the target injection well; the device also includes a coefficient calculation module for:

[0107] According to the outer diameter and inner diameter of the oil pipe and the equivalent thermal conductivity of the fluid in the annulus, the heat transfer coefficient of the annulus fluid is calculated;

[0108] The heat transfer coefficient of annular space thermal radiation is calculated according to the outer wall temperature of the tubing, the inner wall temperature of the casing, the outer wall thickness of the tubing, the inner wall thickness of the casing, the radiation angle coefficient and the Stefan-Boltzmann constant;

[0109] The annular convection heat transfer coefficient is calculated based on the outer diameter of the casing, the radius of the target injection well and the thermal conductivity of the cement annulus;

[0110] The comprehensive heat transfer coefficient is calculated based on the annular space fluid heat exchange coefficient, the annular space heat radiation heat transfer coefficient and the annular space convection heat transfer coefficient.

[0111] According to one aspect of an embodiment of the present application, a computer device is provided, the computer device comprising a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the above-mentioned carbon dioxide injection parameter optimization method.

[0112] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the above-mentioned carbon dioxide injection parameter optimization method.

[0113] According to one aspect of an embodiment of the present application, a computer program product is provided. The computer program product includes a computer program. The computer program is loaded and executed by a processor to implement the above-mentioned carbon dioxide injection parameter optimization method.

[0114] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0115] By constructing the temperature gradient calculation model and pressure gradient calculation model of the target injection well, and then combining the comprehensive heat transfer coefficient of the target injection well and the thermodynamic properties of carbon dioxide, the flow simulation of the carbon dioxide injection process of the target injection well is performed, so that the flow simulation is more consistent with the real carbon dioxide injection process; and based on the flow simulation, the pressure stabilization profile data of the target injection well is obtained, and then the injection parameter optimization function is constructed. The target temperature range and target pressure range of the bottom of the target injection well are used, and the injection parameter optimization function is optimized by combining the non-dominated sorting genetic algorithm to obtain the optimal solution containing a set of optimized functions. The optimal solution can be used to guide the carbon dioxide injection of the target injection well. This method simulates carbon dioxide injection and optimizes the injection parameter optimization function, which is convenient for determining a reasonable carbon dioxide injection strategy, reduces the difficulty of formulating the carbon dioxide injection strategy, and is conducive to improving the carbon storage effect and oil recovery effect of the injection well.

[0116] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0118] Figure 1 is a flow chart of a method for optimizing carbon dioxide injection parameters provided by an embodiment of the present application;

[0119] Figure 2 is a schematic structural diagram of a target injection well provided by an embodiment of the present application;

[0120] Figure 3 This is a schematic diagram of temperature change provided by an embodiment of the present application;

[0121] Figure 4 This is a schematic diagram of pressure change provided by an embodiment of the present application;

[0122] Figure 5 It is a schematic diagram of an algorithm provided by an embodiment of the present application;

[0123] Figure 6 is a flow chart of the NSGA-II algorithm provided by an embodiment of the present application;

[0124] Figure 7 is a schematic block diagram of an NSGA-II algorithm provided by an embodiment of the present application;

[0125] Figure 8 is a block diagram of a device for optimizing carbon dioxide injection parameters provided by an embodiment of the present application;

[0126] Fig. 9 It is a block diagram of a computer device provided by one embodiment of the present application. DETAILED DESCRIPTION

[0127] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of methods consistent with some aspects of the present application as detailed in the appended claims.

[0128] In the method provided in the embodiment of the present application, the execution subject of each step may be a computer device, which refers to an electronic device with data calculation, processing and storage capabilities. The computer device may be a terminal such as a PC (Personal Computer), a tablet computer, a smart phone, a wearable device, an intelligent robot, etc.; or it may be a server. Among them, the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0129] The technical solution of the present application is introduced and explained below through several embodiments.

[0130] Please refer to Figure 1, which shows a flow chart of a method for optimizing carbon dioxide injection parameters provided by an embodiment of the present application. In this embodiment, the method is mainly illustrated by applying it to the computer device introduced above. The method may include at least one of the following steps (110-160).

[0131] Step 110, constructing a temperature gradient calculation model and a pressure gradient calculation model for the target injection well.

[0132] As the most important greenhouse gas, the sharp increase in carbon dioxide emissions has had a profound impact on climate change. At present, the development of CCUS technology has received widespread attention as an important means to reduce greenhouse gas emissions. Among them, carbon dioxide oil recovery technology can not only improve crude oil recovery by utilizing it, but also safely store a large amount of carbon dioxide. In the carbon dioxide oil recovery and storage operation, it is necessary to ensure that the carbon dioxide at the bottom of the well is in a reasonable temperature and pressure state to ensure better oil recovery and storage effects. To achieve this goal, it is necessary to accurately control the temperature and pressure of the carbon dioxide fluid injected into the bottom of the well.

[0133] In some embodiments, the temperature gradient calculation model and the pressure gradient calculation model are collectively referred to as a temperature-pressure coupling model, which is used to calculate and analyze the temperature data and pressure data at various locations in the target injection well, that is, to obtain the temperature and pressure profile data of the target injection well.

[0134] In some embodiments, a temperature gradient calculation model and a pressure gradient calculation model of the target injection well are pre-built, and the temperature and pressure profile data are calculated based on them. In some embodiments, during the CO2 injection process, the ground is used as the coordinate zero point, and the target injection well is divided by the depth iteration method. A depth increment is taken at any depth z in the target injection well. , calculate the infinitesimal segment The heat absorption is the temperature gradient calculation model of the target injection well. In some embodiments, the target injection well is divided by the depth iteration method, and the same depth increment is taken at any depth z of the target injection well. , calculate the infinitesimal segment The pressure gradient of the target injection well is calculated using the pressure gradient calculation model.

[0135] In some embodiments, the entire target injection well is divided into sections, and according to the heat transfer and pressure drop model, the injection wellhead conditions are taken as the starting point, and the temperature and pressure at the end point of each section are obtained through a cycle until the temperature and pressure profile of the entire target injection well is obtained, thereby obtaining a CCUS injection well temperature-pressure coupling model.

[0136] Step 120, calculating the comprehensive heat transfer coefficient of carbon dioxide injection into the target injection well according to the size parameters of the target injection well, the size parameters and temperature of the oil pipe, and the size parameters and temperature of the casing.

[0137] In some embodiments, Figure 2 The structure of the target injection well shown in the figure extends into the formation. The target injection well includes a casing and an oil pipe inside the casing. The gap between the oil pipe and the casing can be called an annulus. The outer wall of the casing is in contact with the formation. Fluids such as carbon dioxide, oil and gas flow in the oil pipe.

[0138] In some embodiments, the target temperature range and the target pressure range may be pre-set, such as by relevant technicians based on actual conditions.

[0139] In some embodiments, the comprehensive heat transfer coefficient of carbon dioxide during the injection process is calculated. During the carbon dioxide injection process, the fluid (i.e., carbon dioxide fluid), tubing, annulus, casing, cement ring and formation in the target injection well will transfer heat respectively. Therefore, it is necessary to calculate the thermal resistance of the tubing, the thermal resistance of the annulus, the thermal resistance of the casing and the thermal resistance of the cement ring respectively, and then calculate the comprehensive heat transfer coefficient of the carbon dioxide injection process.

[0140] In some embodiments, the dimensional parameters of the target injection well may include the diameter and length of the target injection well, the dimensional parameters of the oil pipe may include the diameter of the oil pipe (such as the inner diameter and / or outer diameter of the oil pipe), and the dimensional parameters of the casing may include the diameter of the casing (such as the inner diameter and / or outer diameter of the casing).

[0141] Step 130, based on the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide, a flow simulation of the carbon dioxide fluid injection process is performed on the target injection well to obtain the temperature and pressure profile data of the target injection well; wherein the temperature and pressure profile data include the temperature data and pressure data of the carbon dioxide corresponding to multiple transverse profiles of the target injection well.

[0142] In some embodiments, a fluid physical property calculation module of carbon dioxide can be used to calculate the thermodynamic property data of carbon dioxide. Figure 3 , 4 As shown, you can use the professional thermodynamic property calculation library CoolProp to directly derive the thermodynamic properties of carbon dioxide at different injection temperatures and injection pressures. The injection temperature refers to the temperature of the carbon dioxide fluid when it is just injected from the wellhead into the target injection well. The injection pressure refers to the pressure of the carbon dioxide fluid when it is just injected from the wellhead into the target injection well. As the carbon dioxide fluid flows along the pipeline in the target injection well, the temperature and pressure of carbon dioxide will also change. When the injection temperatures are -40°C, -20°C, 0°C, 20°C, and 40°C, respectively, the temperature changes of carbon dioxide along the target injection well (i.e., changes with depth) are shown below. Figure 3When the injection temperatures are -40℃, -20℃, 0℃, 20℃, and 40℃, the pressure of carbon dioxide along the target injection well (i.e., with depth) changes as shown in Figure 4 shown.

[0143] In some embodiments, the temperature and pressure profile data refers to the temperature data and pressure data of the fluid in the target injection well at various locations in the target injection well. In some embodiments, the various parameters of the fluid on the transverse section of the target injection well (such as temperature, pressure, phase state, etc.) are the same everywhere, so the target injection well can be ideally simplified into a line, and each transverse section of the target injection well becomes a point on this line. That is, each point on this line represents a transverse section of the target injection well.

[0144] In some embodiments, the target temperature range and the target pressure range of the bottom of the target injection well are obtained to obtain the optimization target of the injection parameter optimization function. In some embodiments, the bottom of the target injection well refers to the tail end or end of the target injection well.

[0145] Step 140, constructing an injection parameter optimization function, wherein the injection parameter optimization function includes parameters to be optimized, the parameters to be optimized include wellbore structure parameters and fluid injection parameters, the wellbore structure parameters include the inner diameter and outer diameter of the oil pipe and the inner diameter and outer diameter of the casing, and the fluid injection parameters include the injection pressure, injection temperature and injection flow rate of carbon dioxide.

[0146] In some embodiments, based on the target temperature range and the target pressure range at the bottom of the well, a corresponding injection parameter optimization function can be constructed by various parameters to be optimized. The parameters to be optimized refer to parameters that can be manually controlled by relevant technicians. In some embodiments, the wellbore structure parameters may include the size parameters of the oil pipe and the size parameters of the casing. In some embodiments, the fluid injection parameters are related parameters used to describe the injected carbon dioxide fluid.

[0147] In some embodiments, when the carbon dioxide fluid is just injected into the target injection well, its phase state can be liquid or supercritical. The injection pressure refers to the pressure of the carbon dioxide fluid just injected into the target injection well, the injection temperature refers to the temperature of the carbon dioxide fluid just injected into the target injection well, and the injection flow rate refers to the flow rate of the carbon dioxide fluid injected into the target injection well.

[0148] Step 150, based on the target temperature range and target pressure range of the bottom of the target injection well, determine the value range of each parameter to be optimized, and initialize the population to obtain an initialized population, each individual in the initialized population contains a set of parameters to be optimized.

[0149] In some embodiments, the value range of each parameter to be optimized is generally not an arbitrary number, and there is a value range restriction. In some embodiments, the value range of each parameter to be optimized can be specifically set by relevant technical personnel according to actual conditions, and the embodiments of this application do not specifically limit this.

[0150] In some embodiments, based on the target temperature range and the target pressure range, the value range of the fluid injection parameter can be determined, for example, the injection temperature is higher than the minimum value of the target temperature range; or for another example, the injection pressure is lower than the maximum value of the target pressure range.

[0151] In some embodiments, the value range of the wellbore structure data may be determined by existing equipment parameters. For example, the value range of the size parameter of the oil pipe may be the size parameter of at least one optional model of the oil pipe. For example, if the inner diameters of at least one optional model of the oil pipe are a1, a2, and a3, respectively, then the value range of the inner diameter of the oil pipe is {a1, a2, a3}.

[0152] Step 160, based on the target temperature range and target pressure range of the bottom of the target injection well, determine the value range of each parameter to be optimized, and initialize the population to obtain an initialized population, each individual in the initialized population contains a set of parameters to be optimized.

[0153] In some embodiments, the present application uses a non-dominated sorting genetic algorithm with an elite strategy to solve the injection parameter optimization function. The basic idea of ​​the genetic algorithm is to start from the initial population, select individuals using the natural law of survival of the fittest, and produce a new generation of populations through hybridization and mutation, and evolve from generation to generation until the goal is met. The genetic algorithm gradually evolves from the initial population by simulating the selection, crossover and mutation operations in the biological evolution process to find the optimal solution. In an embodiment of the present application, the population includes multiple individuals, each of which represents a set of parameters to be optimized, that is, a set of possible CCUS injection parameters.

[0154] In some embodiments, a population refers to a set of multiple solutions initially given when a genetic algorithm is used to solve a problem. The genetic algorithm solution process starts from this set. An individual refers to a single element in a population, which is usually represented by a data structure used to describe its basic genetic structure. For example, an array string consisting of 0 and 1 can be used to represent an individual.

[0155] In some embodiments, the genetic algorithm includes genetic operations, which refers to simulating the evolutionary process of things and genetic operations (selection, crossover, mutation) on a computer. Genetic operations include selection, hybridization and mutation.

[0156] By adopting the technical solution provided in the embodiment of the present application, the values ​​of the corresponding parameters to be optimized when the injection parameter optimization function takes the optimal solution can be obtained, so that the actual temperature at the bottom of the well is within the target temperature range, the actual pressure is within the target pressure range, and the phase state of carbon dioxide reaching the bottom of the well meets the requirements.

[0157] In some embodiments, starting from the wellbore structure data and fluid injection parameters of the target injection well, the parameters to be optimized in the CCUS injection process are determined; according to the set optimal injection well bottom temperature (i.e., the optimal temperature for the fluid to reach the well bottom) and the optimal injection well bottom pressure (the optimal pressure for the fluid to reach the well bottom), the value range of each parameter to be optimized is determined, and based on the CCUS injection well temperature-pressure coupling model constructed in the above steps, an injection parameter optimization function for the CCUS gas injection strategy is designed. Based on the NSGA-II (Non-dominated Sorting Genetic Algorithms 2, the second generation non-dominated sorting genetic algorithm) algorithm, an intelligent optimization model for oil well injection parameters is constructed to optimize the parameters to be optimized and obtain the most suitable structural parameters and injection parameters.

[0158] In summary, the technical solution provided by the embodiment of the present application, by constructing a temperature gradient calculation model and a pressure gradient calculation model of the target injection well, and then combining the comprehensive heat transfer coefficient of the target injection well and the thermodynamic properties data of carbon dioxide, the flow simulation of the carbon dioxide injection process is performed on the target injection well, so that the flow simulation is more consistent with the real carbon dioxide injection process; and based on the flow simulation, the pressure stabilization profile data of the target injection well is obtained, and then the injection parameter optimization function is constructed, and the target temperature range and target pressure range of the bottom of the target injection well are used to optimize the injection parameter optimization function in combination with the non-dominated sorting genetic algorithm to obtain the optimal solution containing a set of optimized functions to be optimized, and the optimal solution can be used to guide the carbon dioxide injection of the target injection well. This method is convenient for determining a reasonable injection strategy of carbon dioxide by simulating carbon dioxide injection and optimizing the injection parameter optimization function, which reduces the difficulty of formulating the carbon dioxide injection strategy, and is conducive to improving the carbon storage effect and oil recovery effect of the injection well.

[0159] The embodiment of the present application constructs a CCUS injection well temperature-pressure coupling model, which can accurately simulate the temperature, pressure and phase changes of the fluid along the target injection well during the CO2 injection process. This makes the optimization process more adapted to the actual situation and can better guide the actual operation, thereby improving the optimization effect of the parameters to be optimized.

[0160] In the embodiment of the present application, the fluid temperature and pressure at various locations of the target injection well after the carbon dioxide is injected into the target injection well are simulated and analyzed through various parameters and coefficients, thereby achieving automatic optimization of the structural parameters of the target injection well and the injection parameters of the carbon dioxide injected into the target injection well, thereby improving the optimization effect and optimization efficiency of the relevant parameters of the carbon dioxide injection into the target injection well.

[0161] In the embodiment of the present application, during the carbon dioxide displacement and oil storage process, the fluid temperature and pressure at the bottom of the well directly determine the fluidity and oil solubility of carbon dioxide. Therefore, it is necessary to design the injection temperature, injection pressure and wellbore structure of the CCUS wellhead so that the carbon dioxide fluid entering the bottom of the well is at the most suitable temperature and pressure to ensure the optimal crude oil displacement and storage effects.

[0162] In the embodiment of the present application, the target injection well is a single well, and the technical solution provided in the embodiment of the present application is used to optimize the parameters of a single well. That is, for wells in different situations, the corresponding injection parameter optimization functions are also different. Through the study of a single well, the embodiment of the present application can optimize the wellbore structure parameters and fluid injection parameters of a specific well in a targeted manner, thereby improving the optimization effect of the carbon dioxide injection parameters.

[0163] In some possible implementations, optimizing the structural parameters of the target injection well and the injection parameters of carbon dioxide into the target injection well according to the temperature and pressure profile data may also include at least one of the following steps (1.1-1.4).

[0164] 1.1. Obtain a target bottom temperature range and a target bottom pressure range of a target injection well.

[0165] 1.2. Construct an injection parameter optimization function. The parameters to be optimized in the injection parameter optimization function include wellbore structure parameters and fluid injection parameters.

[0166] 1.3. According to the target temperature range and target pressure range, determine the value range of each parameter to be optimized.

[0167] 1.4. Generate the optimal solution of the injection parameter optimization function according to the value range of each parameter to be optimized. The values ​​of each parameter to be optimized corresponding to the optimal solution of the injection parameter optimization function include the optimized wellbore structure parameters and the optimized fluid injection parameters.

[0168] In some embodiments, generating an optimal solution of the injection parameter optimization function according to the value range of each parameter to be optimized includes at least one of the following steps (2.1-2.6).

[0169] 2.1. Based on the temperature and pressure profile data and the injection parameter optimization function, the fitness corresponding to each individual in the population is obtained, and the fitness is used to represent the prediction accuracy of the parameter to be optimized.

[0170] In some embodiments, based on the injection parameter optimization function, the fitness corresponding to each individual in the population is calculated by constructing a fitness function. In some embodiments, the fitness function measures individual adaptability, and the fitness function can be constructed by comparing the predicted value and the actual value of the parameter to be optimized to calculate the fitness value corresponding to each group of parameters to be optimized. In some embodiments, temperature and pressure profile data are used to participate in the calculation of fitness.

[0171] 2.2. According to the fitness of each individual in the population, the population is sorted in a non-dominated manner.

[0172] 2.3. According to the sorting results of non-dominated sorting, the individuals in the population are divided into multiple levels.

[0173] In some embodiments, non-dominated sorting may be fast non-dominated sorting (FNS), which can divide a solution set into multiple non-dominated levels by introducing the concept of Pareto dominance. The sorting process of non-dominated sorting is as follows: first find all non-dominated individuals in the population and assign them a shared fitness to obtain the first non-dominated optimal layer; then, ignore this group of stratified individuals, continue to stratify other unstratified individuals in the population according to the dominance and non-domination relationship, and assign them a new fitness, the fitness value of this level is less than the fitness value of the previous level; continue the above operation for the remaining individuals until all individuals in the population are stratified. Among them, the first level is the optimal solution set, the second level is the suboptimal solution set, and so on. The fast non-dominated sorting algorithm can find solution sets of different levels, and can also calculate the ranking position of each solution in these levels, so as to better reflect the pros and cons of different solutions.

[0174] In some embodiments, assume that each individual i has the following two parameters n(i) and S(i), where n(i) represents the number of solution individuals that dominate individual i in the population (i.e., the number of other solutions that dominate individual i), and S(i) represents the set of solution individuals dominated by individual i (i.e., the set of other solutions dominated by individual i).

[0175] In some embodiments, Figure 5 As shown, the process of fast non-dominated sorting can include the following steps:

[0176] (1) First, find all individuals in the population that are not dominated by other solutions: find all individuals in the population with n(i) = 0 (that is, all individuals i in the population that are not dominated by other individuals) and store them in the current set Z(1);

[0177] (2) Then, for other solutions, remove the number of solutions dominated by the first layer, that is, subtract one: for each individual j in the current set Z (1), examine the set of individuals S (j) it dominates, and subtract 1 from n (k) of each individual k in the set S (j), that is, the number of solution individuals that dominate individual k is reduced by 1 (because the individual j that dominates individual k has been stored in the current set Z (1)). If n (k) - 1 = 0, then store individual k in another set H;

[0178] (3) Finally, all hierarchical operations are completed according to the methods of (1) and (2): set Z (1) is taken as the first-level non-dominated individual set, and the individuals in this set are assigned the same non-dominated order i (rank). After that, the above hierarchical operations are continued on set H and the corresponding non-dominated order is assigned until all individuals are graded. Its computational complexity can be O(aN 2 ), a is the number of objective functions, and N is the population size.

[0179] In this embodiment, the hierarchical method of non-dominated sorting is adopted, so that good individuals have a greater chance of being inherited to the next generation; the fitness allows the individuals to be distributed more evenly, and the diversity of the group is maintained as much as possible, overcoming the over-reproduction of super individuals and preventing premature convergence; in addition, compared with the ordinary non-dominated sorting, the fast non-dominated sorting reduces the computational complexity of the algorithm, thereby helping to improve the optimization efficiency of the injection parameter optimization function.

[0180] 2.4. For each level, calculate the corresponding crowding degree, which is used to indicate the degree of concentration of individual distribution in the level.

[0181] 2.5. Update the population according to the sorting results of non-dominated sorting and the congestion degree corresponding to each level.

[0182] In some embodiments, crowding can refer to the density of individuals around a specified point in the population. In order to maintain the diversity of the population, a crowding comparison operator is required to ensure that the algorithm can converge to a uniformly distributed Pareto surface. After sorting and crowding calculation, each individual i in the population has at least two attributes: non-dominated order i (rank) and crowding i d . Then we can define a partial order relation: when the condition i (rank) is met d , or satisfy i(rank) = i d And i d >j d ​When \(i < j\) is defined. That is to say: if the non - dominated sorting of two individuals is different, select the individual with a smaller sorting number (i.e., the individual separated first during hierarchical sorting); if two individuals are at the same level, select the less crowded individual around.

[0183] 2.6. Start executing from the step of optimizing the function with injection parameters to obtain the fitness values corresponding to each individual in the population until the stop condition is reached, to obtain the optimal solution of the injection parameter optimization function, and to obtain a set of values of the parameters to be optimized corresponding to when the injection parameter optimization function takes the optimal solution.

[0184] In some embodiments, based on the NSGA - Ⅱ algorithm, a comprehensive optimization analysis of the temperature, pressure, and phase state of the fluid along the target injection well during the CO2 injection process is realized. It is possible to find the best combination of wellbore structure parameters (i.e., target injection well structure parameters) and fluid injection parameters while considering the target temperature and pressure at the bottom of the well, thereby realizing the comprehensive optimization of the injection process.

[0185] In some embodiments, the stop condition includes but is not limited to at least one of the following: the injection parameter optimization function reaches a specified number of iterations, and the objective function reaches a specified convergence condition.

[0186] In some embodiments, based on the standard oil casing size specification, the range of oil casing result parameters is set. Based on the actual on - site working environment, the ranges of CO2 injection pressure, temperature, and flow rate are set. The multi - objective optimization function of the CCUS gas injection strategy (i.e., the injection parameter optimization function above) is expressed according to the following formula:

[0187] ,

[0188] are the parameters to be optimized (i.e., optimization variables), and the ranges of each parameter to be optimized are as follows:

[0189] ,

[0190] Among them, \(F\) is the injection parameter optimization function; \(Q\) is the injection flow rate of CO2, with the unit of t / d; \(T_0\) is the injection temperature of CO2, with the unit of \(^{\circ}C\); \(P_0\) is the injection pressure of CO2, with the unit of Mpa (Million Pascal, megapascal); \(P\) m is the temperature at the bottom of the target injection well, that is, the pressure at the rear - end point of the \(m\) - th well section, with the unit of Mpa; is the target pressure at the bottom of the target injection well, that is, the target pressure at the rear - end point of the \(m\) - th well section, with the unit of Mpa; \(T\) m is the temperature at the bottom of the target injection well, that is, the temperature at the rear - end point of the \(m\) - th well section, with the unit of \(^{\circ}C\); is the target temperature at the bottom of the target injection well, i.e., the target temperature at the rear end of the mth well section, in °C. In some embodiments, Figure 2 As shown, , , , , are respectively the inner diameter of the oil pipe, the outer diameter of the oil pipe, the inner diameter of the casing, the outer diameter of the casing, and the radius of the target injection well, in meters. In some embodiments, , are the minimum flow rate and maximum flow rate when carbon dioxide is injected into the target injection well, , are the minimum and maximum temperatures when carbon dioxide is injected into the target injection well, , are the minimum pressure and maximum pressure when carbon dioxide is injected into the target injection well.

[0191] In some embodiments, since the sizes of the tubing and casing have engineering standard values, { } is the value range of the inner diameter of the oil pipe. The value range of the oil casing size can be found in the oil pipe.

[0192] In some embodiments, Figure 6 As shown, generating the optimal solution of the injection parameter optimization function according to the value range of each parameter to be optimized may include at least one of the following steps (3.1-3.6).

[0193] 3.1. Initialize the population, where each individual represents a set of CCUS injection well parameters.

[0194] 3.2. Using the constructed CCUS gas injection strategy multi-injection parameter optimization function (i.e. the above-mentioned injection parameter optimization function), calculate the fitness value of each individual in the population.

[0195] 3.3. According to the fitness value of the individuals, the population is non-dominated and sorted, and the individuals in the population are divided into different frontier levels. The non-dominated solutions are retained and the number of individuals they dominate is marked.

[0196] 3.4. For each frontier level, calculate the crowding degree between individuals.

[0197] 3.5. According to Figure 6 The non-dominated sorting and crowding calculation process shown in FIG. 1 selects a new generation of population, performs crossover and mutation operations on the selected individuals, and generates new individuals to increase the diversity of the population.

[0198] 3.6. Set the maximum evolutionary number Gen and the termination threshold, and repeat steps 3.2 to 3.5 until the specified number of iterations or convergence conditions are reached, completing the CCUS injection well parameter optimization design based on the NSGA-II algorithm.

[0199] The embodiment of the present application is based on the NSGA-Ⅱ algorithm, which realizes the comprehensive optimization analysis of the temperature, pressure and phase state of the fluid along the target injection well during the CO2 injection process. The best combination of wellbore structure parameters and fluid injection parameters can be found while considering the target bottom hole temperature and bottom hole pressure, so that the carbon dioxide fluid can maintain a mixed state or a reasonable temperature and pressure state until the bottom of the well, thereby realizing the comprehensive optimization of the injection process. In the above implementation method, the present application establishes an intelligent optimization method for CCUS injection strategy, and realizes the flow simulation of the injection process of the target injection well by establishing a temperature-pressure coupling model of the injection process; and intelligently optimizes the injection parameters and wellbore structure of the oil well based on the NSGA-Ⅱ algorithm. The optimal combination of wellbore structure parameters and fluid injection parameters is found by given the target bottom hole temperature and bottom hole pressure. On-site operators can also adjust the injection parameters according to the results of the model analysis, which greatly reduces the difficulty of injection strategy formulation and optimizes the carbon storage effect and oil recovery effect of the carbon dioxide system.

[0200] In some possible implementations, the target injection well includes m well sections, where m is an integer greater than 1; the process of generating temperature and pressure profile data may further include at least one of the following steps (5.1-5.9).

[0201] 5.1. Obtain the pressure and temperature at the front end of the first well section among the m well sections. The initialized first well section is the first well section counted from the front to the back among the m well sections.

[0202] 5.2. Based on the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, the pressure change and temperature change of the first well section are calculated.

[0203] 5.3. According to the pressure at the front end of the first well section, the temperature at the front end of the first well section, the pressure change of the first well section and the temperature change of the first well section, the average pressure and the average temperature of the first well section are calculated.

[0204] 5.4. According to the average pressure and the average temperature of the first well section, the physical property parameters and phase type of carbon dioxide in the first well section are calculated, and the physical property parameters include pressure and temperature.

[0205] 5.5. Based on the average pressure and average temperature of the first well section, calculate the physical property parameters and phase type of carbon dioxide in the first well section, where the physical property parameters include pressure and temperature.

[0206] 5.6. Based on the physical property parameters and phase type of carbon dioxide in the first well section, a temperature gradient calculation model is used to generate a first temperature of carbon dioxide in the first well section.

[0207] 5.7. Based on the physical parameters and phase type of carbon dioxide in the first well section, a pressure gradient calculation model is used to generate the first pressure of carbon dioxide in the first well section.

[0208] 5.8. When the absolute value of the difference between the average temperature of the first well section and the first temperature is less than or equal to the first threshold, and the absolute value of the difference between the average pressure of the first well section and the first pressure is less than or equal to the second threshold, the next well section of the first well section is determined as the new first well section, and the step of calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data is executed until the first well section is the last well section among the m well sections, and the temperature and pressure profile data is generated.

[0209] 5.9. When the absolute value of the difference between the temperature change in the first well section and the first temperature is greater than the first threshold, or the absolute value of the difference between the pressure change in the first well section and the first pressure is greater than the second threshold, the first temperature is taken as the temperature change in the first well section, and the first pressure is taken as the pressure change in the first well section, and the step of calculating the average pressure and the average temperature of the first well section based on the pressure at the front end of the first well section, the temperature at the front end of the first well section, the pressure change in the first well section, and the temperature change in the first well section is started.

[0210] In some embodiments, the first threshold refers to the maximum absolute value of the difference between the average temperature of the first well section and the first temperature, and the second threshold refers to the maximum absolute value of the difference between the average pressure of the first well section and the first pressure. The specific values ​​of the first threshold and the second threshold can be set by relevant technicians according to actual conditions, and the embodiments of the present application do not specifically limit this.

[0211] In some embodiments, the target injection well can be divided into m equal sections. The above number from front to back refers to the number from the wellhead of the target injection well to the bottom of the well. That is, the first well section among the m well sections refers to the first well section starting from the wellhead or the ground position, that is, the well section located at the wellhead or the well section closest to the ground; the last well section among the m well sections (that is, the mth well section) refers to the well section located at the bottom of the well or the well section closest to the bottom of the well, and is also the well section that the carbon dioxide fluid arrives at last after being injected from the wellhead. Each well section can be used as an iteration step. In some embodiments, the first well section initialized refers to the well section corresponding to the first iteration step.

[0212] In some embodiments, when the target injection well is a vertical well, the target injection well is placed in the oil well or gas well in a manner perpendicular to or approximately perpendicular to the ground, so that the m well sections are divided from top to bottom (also referred to as from front to back, from beginning to end) along the target injection well, and the first well section initialized refers to the first well section counted from top to bottom among the m well sections.

[0213] In some embodiments, when the target injection well is a horizontal well, the target injection well is placed in the oil well or gas well in a manner parallel to or approximately parallel to the ground, so that the m well sections can be divided from front to back of the target injection well, and the first well section initialized refers to the first well section counted from front to back (also referred to as from beginning to end) among the m well sections.

[0214] In some embodiments, the average pressure and average temperature of the first well section are calculated based on the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, and at least one of the following steps (6.1-6.3) may also be included.

[0215] 6.1. Obtain the depth of the first well section. The depth of the first well section refers to the distance between the front end point of the first well section and the rear end point of the first well section.

[0216] 6.2. According to the depth of the first well section, the pressure and temperature of the front end point of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data, the pressure increment and the temperature increment between the front end point of the first well section and the rear end point of the first well section are calculated.

[0217] 6.3. The average pressure and average temperature of the first well section are calculated based on the pressure and temperature at the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section.

[0218] In some embodiments, the pressure and temperature of the rear end point of the first well section are calculated based on the pressure and temperature of the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section; the pressure and temperature of the rear end point of the first well section are determined as the pressure and temperature of the front end point of the new first well section.

[0219] In some embodiments, Figure 7 As shown, the method may further include the following steps 151 to 158.

[0220] Step 151, divide the entire target injection well into m segments according to a fixed interval, the depth of the front end point of the first segment is z0, the depth of the back end point of the mth segment is z m .

[0221] Step 152, setting the pressure at the front end point of the first well section of the target injection well to P0 and the temperature to T0.

[0222] Step 153, setting the pressure increment corresponding to the front end point to the rear end point of the first well section to ΔP0 and the temperature increment to ΔT0.

[0223] Step 154, calculate P based on P0 and ΔP0 10 and T 10 .

[0224] In some embodiments, the pressure P1 at the rear end point of the first well section can be obtained based on P0 and ΔP0, and the average pressure P in the current iteration step can be calculated using P0 and P1. 10 ; Based on T0 and ΔT0, the temperature T1 of the rear end point of the first well section can be obtained, and then T0 and T1 can be used to calculate the average temperature T in the current iteration step 10 .

[0225] Step 155, according to P 10 and T 10 Calculate ΔP′0 and ΔT′0.

[0226] In some embodiments, according to P in the segment iteration 10 and T 10 , use the temperature gradient calculation model of the target injection well to calculate the temperature increment ΔT0 of the first well section; use the pressure gradient calculation model of the target injection well to calculate the temperature increment ΔT0 of the first well section; according to T0 and ΔT0, the rear end point temperature T1 of the first well section can be obtained, and then T0 and T1 can be used to calculate the average temperature T in the current iteration step 10 According to T 10 and P 10 , calculate the physical parameters and phase type of carbon dioxide in the first well section, and then substitute them into each model to obtain (i.e., the first pressure) and (ie the first temperature).

[0227] Step 156, calculate |ΔP0- | and |ΔT0- |, judge |ΔP0- | and |ΔT0- | is within the allowable range, if not, execute step 157; if so, execute step 158.

[0228] Step 157: and Substitute it as the new estimated value and continue executing steps 152 to 155.

[0229] Step 158, output P1 and T1 as the pressure and temperature of the rear end point of the first well section, and continue to execute steps 152 to 156 with the rear end point of the first well section as the front end point of the next well section until the pressure and temperature of the rear end point of the mth well section of the target injection well are obtained.

[0230] In some embodiments, sorting (z0, P0, T0)~(z m , P m , T m ) all values ​​are obtained to obtain the temperature and pressure profile of the entire target injection well, the physical properties and phase types of carbon dioxide along the way. The phase types of carbon dioxide in the entire target injection well include gas, liquid and supercritical states.

[0231] In some possible implementations, the dimensional parameters of the oil pipe include the outer diameter of the oil pipe, the inner diameter of the oil pipe and the thickness of the outer wall of the oil pipe, the dimensional parameters of the casing include the outer diameter of the casing, the inner diameter of the casing and the thickness of the inner wall of the casing, the temperature parameters of the oil pipe include the outer wall temperature of the oil pipe, the temperature parameters of the casing include the inner wall temperature of the casing, and the dimensional parameters of the target injection well include the radius of the target injection well. In some embodiments, the comprehensive heat transfer coefficient of carbon dioxide injection into the target injection well is calculated based on the size parameters of the target injection well, the size parameters of the oil pipe in the target injection well, the temperature parameters of the oil pipe, the size parameters of the casing in the target injection well, and the temperature parameters of the casing. The following steps may also be included: the annulus fluid heat exchange coefficient is calculated based on the outer diameter of the oil pipe, the inner diameter of the casing, and the equivalent thermal conductivity of the fluid in the annulus; the annulus thermal radiation heat transfer coefficient is calculated based on the outer wall temperature of the oil pipe, the inner wall temperature of the casing, the outer wall thickness of the oil pipe, the inner wall thickness of the casing, the radiation angle coefficient, and the Stefan-Boltzmann constant; the annulus convective heat transfer coefficient is calculated based on the outer diameter of the casing, the radius of the target injection well, and the thermal conductivity of the cement ring; the comprehensive heat transfer coefficient is calculated based on the annulus fluid heat exchange coefficient, the annulus thermal radiation heat transfer coefficient, and the annulus convective heat transfer coefficient.

[0232] In some embodiments, since the thermal conductivity of the tubing and casing is large and the wall thickness is thin, the temperature drop caused by the tubing and casing can be ignored. First, the annular fluid heat transfer coefficient h is calculated. c :

[0233] ,

[0234] Among them, λ c is the equivalent thermal conductivity of the fluid in the annulus, in J / (s·m·K); r2 and r3 are the outer diameter of the tubing and the inner diameter of the casing, in m. Then the annulus thermal radiation heat transfer coefficient h is calculated. r :

[0235] ,in, ,

[0236] Among them, T2 and T3 are the outer wall temperature of the oil pipe and the inner wall temperature of the casing, respectively, in units of ; F is the radiation angle coefficient, which is dimensionless; ε2 and ε3 are the thickness of the outer wall of the oil pipe and the inner wall of the casing, respectively, which are dimensionless; σ is the Stefan-Boltzmann constant. Then, the annular convection heat transfer coefficient h is calculated. cem :

[0237] ,

[0238] in, and are the outer diameter of the casing and the radius of the target injection well, respectively, in m; is the thermal conductivity of cement sheath, in J / (s·m·K). Finally, calculate the comprehensive heat transfer coefficient :

[0239] .

[0240] In some embodiments, the temperature gradient calculation model of the target injection well (ie, the temperature gradient calculation method of the target injection well) can be found in the following content.

[0241] Taking the ground as the coordinate zero point, the target injection well is divided by depth iteration method. Take a depth increment , the micro-segment energy equation is:

[0242] ,

[0243] in, is the comprehensive heat transfer coefficient of the formation, in units of ; is the fluid temperature of CO2 at this depth, in units of ; T h is the temperature at the interface between cement sheath and formation at this depth, in units of ; is the thermal conductivity of the formation, in units of ; T e is the formation temperature, in ; is the dimensionless heat transfer coefficient.

[0244] The temperature gradient equation of the fluid in the target injection well can be expressed as:

[0245] ,

[0246] Among them, the dimensionless heat transfer coefficient It can be calculated by the following formula:

[0247] ,

[0248] where t D is dimensionless time and is calculated as follows:

[0249] ,

[0250] Where, t is the production time, the unit is s (second); is the formation thermal diffusion coefficient, in m 2 / s; is the radius of the target injection well, in meters; M is the mass flow rate of CO2, in kg / s; is the heat capacity at constant pressure, with the unit of J / (kg·K).

[0251] In some embodiments, the method for generating the pressure gradient calculation model of the target injection well (ie, the pressure gradient calculation method of the target injection well) may include the following steps (7.1-7.3).

[0252] 7.1. Obtain the Reynolds number Re corresponding to each area of ​​carbon dioxide in the well section;

[0253] 7.2. Calculate the friction coefficient between the carbon dioxide in each area in the well section and the oil pipe according to the Reynolds number Re corresponding to each area in the well section;

[0254] 7.3. Based on the friction coefficient between carbon dioxide and the oil pipe in each area of ​​the well section, a pressure gradient calculation model is obtained.

[0255] In some embodiments, the friction coefficient It represents the friction loss between the CO2 and the pipe wall during the flow of CO2 in the oil pipe. The embodiment of the present application may adopt at least one of the following friction coefficient calculation methods.

[0256] In some embodiments, when Re is less than 2300, the friction coefficient is calculated as:

[0257] ;

[0258] In some embodiments, when Re is greater than or equal to 2300 and Re is less than or equal to 3400, the friction coefficient is calculated as follows:

[0259] ;

[0260] In some embodiments, when Re is greater than 3400 and Re is less than 2×10 6 In the case of, the friction coefficient is calculated as:

[0261] ;

[0262] In some embodiments, when Re is greater than 2×10 6 In the case of, the friction coefficient is calculated as:

[0263] ;

[0264] Wherein, f is the friction coefficient, ε is the average roughness of the oil pipe, in m; Re is the Reynolds number of the CO2 fluid, and r1 is the inner diameter of the oil pipe, in m. In some embodiments, the Reynolds number is a measure of the ratio of the inertial force to the viscous force of the fluid, and is a dimensionless number. When the Reynolds number is small, the effect of the viscous force on the flow field is greater than the inertial force, and the disturbance of the flow velocity in the flow field will be attenuated by the viscous force, and the fluid flow is stable and laminar; on the contrary, if the Reynolds number is large, the effect of the inertial force on the flow field is greater than the viscous force, the fluid flow is relatively unstable, and small changes in the flow velocity are easy to develop and enhance, forming a disordered and irregular turbulent flow field.

[0265] In some embodiments, the calculation model of the pressure gradient in the pump is:

[0266] ,

[0267] Where g is the acceleration due to gravity, in m / s 2 ; is the well inclination angle, in units of ; dp is the fluid pressure in the target injection well of the microelement segment, in Pa; ρ is the density of CO2, in kg / m 3 ; f is the friction coefficient in the oil pipe at the depth; v is the flow rate of CO2 fluid in the microelement segment, in m / s.

[0268] 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.

[0269] Please refer to Figure 8 , which shows a block diagram of a carbon dioxide injection parameter optimization device provided by an embodiment of the present application. The device has the function of implementing the above-mentioned carbon dioxide injection parameter optimization method example, and the function can be implemented by hardware, or by hardware executing corresponding software. The device can be the computer device introduced above, and can also be set on a computer device. The device 800 may include: a model construction module 810, a parameter acquisition module 820, a function construction module 830, a range determination module 840 and a parameter optimization module 850.

[0270] The model building module 810 is used to build a temperature gradient calculation model and a pressure gradient calculation model for the target injection well.

[0271] The parameter acquisition module 820 is further used to calculate the comprehensive heat transfer coefficient of carbon dioxide injection into the target injection well according to the size parameters of the target injection well, the size parameters and temperature of the oil pipe, and the size parameters and temperature of the casing.

[0272] The parameter acquisition module 820 is also used to perform flow simulation of the carbon dioxide fluid injection process on the target injection well according to the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide, so as to obtain the temperature and pressure profile data of the target injection well; wherein the temperature and pressure profile data include the temperature data and pressure data of carbon dioxide corresponding to multiple transverse profiles of the target injection well.

[0273] The function construction module 830 is used to construct an injection parameter optimization function, wherein the injection parameter optimization function includes parameters to be optimized, the parameters to be optimized include wellbore structure parameters and fluid injection parameters, the wellbore structure parameters include the inner diameter and outer diameter of the oil pipe and the inner diameter and outer diameter of the casing, and the fluid injection parameters include the injection pressure, injection temperature and injection flow rate of carbon dioxide.

[0274] The range determination module 840 is used to determine the value range of each parameter to be optimized based on the target temperature range and target pressure range at the bottom of the target injection well, and initialize the population to obtain an initialized population, each individual in the initialized population contains a set of parameters to be optimized.

[0275] The parameter optimization module 850 is used to generate an optimal solution of the injection parameter optimization function based on the initialization population and the temperature and pressure profile data using a non-dominated sorting genetic algorithm, wherein the optimal solution includes a set of optimized parameters to be optimized.

[0276] In some embodiments, the parameter optimization module 850 is used to:

[0277] Based on the temperature and pressure profile data and the injection parameter optimization function, the fitness corresponding to each individual in the population is obtained, and the fitness is used to represent the prediction accuracy of the parameter to be optimized;

[0278] According to the fitness corresponding to each individual in the population, the population is non-dominatedly sorted;

[0279] According to the sorting result of the non-dominated sorting, the individuals in the population are divided into multiple levels;

[0280] For each of the levels, a corresponding crowding degree is calculated, where the crowding degree is used to indicate the degree of concentration of individual distribution in the level;

[0281] updating the population according to the sorting result of the non-dominated sorting and the congestion degree corresponding to each of the levels;

[0282] The process starts from the step of adopting the injection parameter optimization function to obtain the fitness corresponding to each individual in the population, and continues until the stopping condition is reached, thereby obtaining the optimal solution of the injection parameter optimization function and the values ​​of a set of parameters to be optimized corresponding to the optimal solution of the injection parameter optimization function.

[0283] In some embodiments, the injection parameter optimization function is expressed according to the following formula:

[0284] ,

[0285] are the parameters to be optimized, and the ranges of the parameters to be optimized are as follows:

[0286] ,

[0287] Wherein, F is the injection parameter optimization function, Q is the injection flow rate of carbon dioxide, T0 is the injection temperature of carbon dioxide, P0 is the injection pressure of carbon dioxide, , , , are the inner diameter of the oil pipe, the outer diameter of the oil pipe, the inner diameter of the casing, and the outer diameter of the casing, respectively; T m is the bottom temperature of the target injection well, P m is the bottom pressure of the target injection well, is the target temperature at the bottom of the target injection well, is the target pressure at the bottom of the target injection well, , are the minimum flow rate and the maximum flow rate when carbon dioxide is injected into the target injection well, respectively. , are the lowest temperature and the highest temperature when carbon dioxide is injected into the target injection well, respectively, , are respectively the minimum pressure and the maximum pressure when carbon dioxide is injected into the target injection well.

[0288] In some embodiments, the apparatus further comprises:

[0289] The Reynolds number acquisition module is used to obtain the Reynolds number Re corresponding to each area of ​​the carbon dioxide injection well.

[0290] A friction calculation module, for calculating the friction coefficient between each region of the target injection well and the oil pipe according to the Reynolds number Re corresponding to each region of the target injection well;

[0291] The model generation module is used to obtain the pressure gradient calculation model based on the friction coefficient between the oil pipe and various regions in the target injection well.

[0292] In some embodiments, when Re is less than 2300, the friction coefficient is calculated as:

[0293] ;

[0294] When Re is greater than or equal to 2300 and Re is less than or equal to 3400, the friction coefficient is calculated as follows:

[0295] ;

[0296] When Re is greater than 3400 and Re is less than 2×10 6 In the case of, the friction coefficient is calculated as:

[0297] ;

[0298] When Re is greater than 2×10 6 In the case of, the friction coefficient is calculated as:

[0299] ;

[0300] Among them, f is the friction coefficient, ε is the average roughness of the oil pipe; Re is the Reynolds number of the carbon dioxide fluid, and r1 is the inner diameter of the oil pipe.

[0301] In some embodiments, the target injection well includes m well sections, where m is an integer greater than 1; the parameter acquisition module 820 is used to:

[0302] Obtaining the pressure and temperature at the front end of the first well section among the m well sections, wherein the initialized first well section is the first well section counted from the front to the back among the m well sections;

[0303] Calculating the pressure change and temperature change of the first well section according to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data;

[0304] Calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section and the pressure change and temperature change of the first well section;

[0305] Calculating the physical property parameters and phase type of carbon dioxide in the first well section according to the average pressure and the average temperature of the first well section, wherein the physical property parameters include pressure and temperature;

[0306] Based on the physical property parameters and phase type of carbon dioxide in the first well section, the temperature gradient calculation model is used to generate a first temperature of carbon dioxide in the first well section;

[0307] Based on the physical property parameters and phase type of carbon dioxide in the first well section, the pressure gradient calculation model is used to generate a first pressure of carbon dioxide in the first well section;

[0308] In the case where the absolute value of the difference between the temperature change of the first well section and the first temperature is less than or equal to a first threshold value, and the absolute value of the difference between the pressure change of the first well section and the first pressure is less than or equal to a second threshold value, the well section next to the first well section is determined as a new first well section, and the step of calculating the average pressure and average temperature of the first well section according to the pressure and temperature at the front end of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data is executed until the first well section is the last well section of the m well sections, and the temperature-pressure profile data is generated;

[0309] When the absolute value of the difference between the temperature change of the first well section and the first temperature is greater than the first threshold, or the absolute value of the difference between the pressure change of the first well section and the first pressure is greater than the second threshold, the first temperature is taken as the temperature change of the first well section, and the first pressure is taken as the pressure change of the first well section, and the step of calculating the average pressure and average temperature of the first well section based on the pressure and temperature at the front end of the first well section and the pressure change and temperature change of the first well section is started.

[0310] In some embodiments, the parameter acquisition module 820 is used to:

[0311] Acquire the depth of the first well section, where the depth of the first well section refers to the distance between the front end point of the first well section and the rear end point of the first well section;

[0312] Calculate the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section according to the depth of the first well section, the pressure and temperature of the front end point of the first well section, the comprehensive heat transfer coefficient and the thermodynamic property data;

[0313] The average pressure and average temperature of the first well section are calculated based on the pressure and temperature at the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section.

[0314] In some embodiments, the parameter acquisition module 820 is used to:

[0315] Calculate the pressure and temperature of the rear end point of the first well section according to the pressure and temperature of the front end point of the first well section and the pressure increment and temperature increment between the front end point of the first well section and the rear end point of the first well section;

[0316] The pressure and temperature at the rear end point of the first well section are determined as the pressure and temperature at the front end point of the new first well section.

[0317] In some embodiments, the size parameters of the oil pipe include the outer diameter, inner diameter and outer wall thickness of the oil pipe, the size parameters of the casing include the outer diameter, inner diameter and inner wall thickness of the casing, the temperature of the oil pipe includes the outer wall temperature of the oil pipe, the temperature of the casing includes the inner wall temperature of the casing, and the size parameters of the target injection well include the radius of the target injection well; the parameter acquisition module 810 is used to:

[0318] The heat transfer coefficient of the annular space fluid is calculated according to the outer diameter and inner diameter of the oil pipe and the equivalent thermal conductivity of the fluid in the annular space;

[0319] The heat transfer coefficient of the annulus heat radiation is calculated according to the outer wall temperature of the oil pipe, the inner wall temperature of the casing, the outer wall thickness of the oil pipe, the inner wall thickness of the casing, the radiation angle coefficient and the Stefan-Boltzmann constant;

[0320] Calculating the annular convection heat transfer coefficient according to the outer diameter of the casing, the radius of the target injection well and the thermal conductivity of the cement annulus;

[0321] The comprehensive heat transfer coefficient is calculated based on the annular space fluid heat exchange coefficient, the annular space thermal radiation heat transfer coefficient and the annular space convection heat transfer coefficient.

[0322] In summary, the technical solution provided by the embodiment of the present application, by constructing a temperature gradient calculation model and a pressure gradient calculation model of the target injection well, and then combining the comprehensive heat transfer coefficient of the target injection well and the thermodynamic properties data of carbon dioxide, the flow simulation of the carbon dioxide injection process is performed on the target injection well, so that the flow simulation is more consistent with the real carbon dioxide injection process; and based on the flow simulation, the pressure stabilization profile data of the target injection well is obtained, and then the injection parameter optimization function is constructed, and the target temperature range and target pressure range of the bottom of the target injection well are used to optimize the injection parameter optimization function in combination with the non-dominated sorting genetic algorithm to obtain the optimal solution containing a set of optimized functions to be optimized, and the optimal solution can be used to guide the carbon dioxide injection of the target injection well. This method is convenient for determining a reasonable injection strategy of carbon dioxide by simulating carbon dioxide injection and optimizing the injection parameter optimization function, which reduces the difficulty of formulating the carbon dioxide injection strategy, and is conducive to improving the carbon storage effect and oil recovery effect of the injection well.

[0323] It should be noted that the device provided in the above embodiment, when implementing its functions, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0324] Please refer to Fig. 9 , which shows a block diagram of a computer device provided in one embodiment of the present application. The computer device is used to implement the carbon dioxide injection parameter optimization method provided in the above embodiment. Specifically:

[0325] The computer device 900 includes a CPU (Central Processing Unit) 901, a system memory 904 including a RAM (Random Access Memory) 902 and a ROM (Read-Only Memory) 903, and a system bus 905 connecting the system memory 904 and the central processing unit 901. The computer device 900 also includes a basic I / O (Input / Output) system 906 for facilitating information transmission between various components in the computer, and a large-capacity storage device 907 for storing an operating system 913, application programs 914, and other program modules 915.

[0326] The basic input / output system 906 includes a display 908 for displaying information and an input device 909 such as a mouse and a keyboard for user inputting information. The display 908 and the input device 909 are connected to the central processing unit 901 through an input / output controller 910 connected to the system bus 905. The basic input / output system 906 may also include an input / output controller 910 for receiving and processing inputs from a plurality of other devices such as a keyboard, a mouse, or an electronic stylus. Similarly, the input / output controller 910 also provides output to a display screen, a printer, or other types of output devices.

[0327] The mass storage device 907 is connected to the central processing unit 901 via a mass storage controller (not shown) connected to the system bus 905. The mass storage device 907 and its associated computer readable medium provide non-volatile storage for the computer device 900. That is, the mass storage device 907 may include a computer readable medium (not shown) such as a hard disk or a CD-ROM (Compact Disc Read-Only Memory) drive.

[0328] Without loss of generality, the computer-readable medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash memory or other solid-state memory, CD-ROM, DVD (Digital Video Disc) or other optical storage, cassettes, tapes, disk storage or other magnetic storage devices. Of course, those skilled in the art will know that the computer storage medium is not limited to the above. The above-mentioned system memory 904 and mass storage device 907 can be collectively referred to as memory.

[0329] According to various embodiments of the present application, the computer device 900 can also be connected to a remote computer on the network through a network such as the Internet. That is, the computer device 900 can be connected to the network 912 through the network interface unit 911 connected to the system bus 905, or the network interface unit 911 can be used to connect to other types of networks or remote computer systems (not shown).

[0330] In an exemplary embodiment, a computer-readable storage medium is further provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the above-mentioned carbon dioxide injection parameter optimization method is implemented.

[0331] In an exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program. The computer program is loaded and executed by a processor to implement the above-mentioned carbon dioxide injection parameter optimization method.

[0332] It should be understood that the "plurality" mentioned in this article refers to two or more. The character " / " generally indicates that the objects before and after are in an "or" relationship.

[0333] The above description is only an exemplary embodiment of 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 shall be included in the protection scope of the present application.

Claims

1. A method for optimizing carbon dioxide injection parameters, characterized in that: The method comprises: Construct temperature gradient calculation model and pressure gradient calculation model of target injection well; Calculating the comprehensive heat transfer coefficient of carbon dioxide injected into the target injection well according to the size parameters of the target injection well, the size parameters and temperature of the oil pipe, and the size parameters and temperature of the casing; According to the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide, a flow simulation of the carbon dioxide fluid injection process is performed on the target injection well to obtain the temperature and pressure profile data of the target injection well; wherein the temperature and pressure profile data include the temperature data and pressure data of carbon dioxide corresponding to a plurality of transverse sections of the target injection well; Constructing an injection parameter optimization function, wherein the injection parameter optimization function includes parameters to be optimized, the parameters to be optimized include wellbore structure parameters and fluid injection parameters, the wellbore structure parameters include the inner diameter and outer diameter of the oil pipe and the inner diameter and outer diameter of the casing, and the fluid injection parameters include the injection pressure, injection temperature and injection flow rate of carbon dioxide; Based on the target temperature range and target pressure range of the bottom of the target injection well, determine the value range of each parameter to be optimized, and initialize the population to obtain an initialized population, each individual in the initialized population contains a set of parameters to be optimized; Based on the temperature and pressure profile data and the injection parameter optimization function, the fitness corresponding to each individual in the population is obtained, and the fitness is used to represent the prediction accuracy of the parameter to be optimized; According to the fitness corresponding to each individual in the population, the population is non-dominatedly sorted; According to the sorting result of the non-dominated sorting, the individuals in the population are divided into multiple levels; For each of the levels, a corresponding crowding degree is calculated, where the crowding degree is used to indicate the degree of concentration of individual distribution in the level; updating the population according to the sorting result of the non-dominated sorting and the congestion degree corresponding to each of the levels; The method starts from the step of obtaining the fitness corresponding to each individual in the population based on the temperature and pressure profile data and the injection parameter optimization function, and executes until the stopping condition is reached, thereby obtaining the optimal solution of the injection parameter optimization function and the values ​​of a set of parameters to be optimized corresponding to when the injection parameter optimization function takes the optimal solution, wherein the optimal solution includes the optimized set of parameters to be optimized.

2. The method according to claim 1, characterized in that The injection parameter optimization function is expressed according to the following formula: ， are the parameters to be optimized, and the ranges of the parameters to be optimized are as follows: ， Wherein, F is the injection parameter optimization function, Q is the injection flow rate of carbon dioxide, T0 is the injection temperature of carbon dioxide, P0 is the injection pressure of carbon dioxide, , , , are the inner diameter of the oil pipe, the outer diameter of the oil pipe, the inner diameter of the casing, and the outer diameter of the casing, respectively, T m is the bottom temperature of the target injection well, P m is the bottom pressure of the target injection well, is the target temperature at the bottom of the target injection well, is the target pressure at the bottom of the target injection well, , are the minimum flow rate and the maximum flow rate when carbon dioxide is injected into the target injection well, respectively. , are the lowest temperature and the highest temperature when carbon dioxide is injected into the target injection well, respectively, , are respectively the minimum pressure and the maximum pressure when carbon dioxide is injected into the target injection well.

3. The method according to claim 1, characterized in that The method further comprises: Obtain the Reynolds number Re corresponding to each area of ​​the target injection well for carbon dioxide; Calculating the friction coefficient between each region of the target injection well and the oil pipe according to the Reynolds number Re corresponding to each region of the target injection well; The pressure gradient calculation model is obtained based on the friction coefficient between the oil pipe and various regions in the target injection well.

4. The method according to claim 3, characterized in that When the Re is less than 2300, the friction coefficient is calculated as follows: ； When the Re is greater than or equal to 2300 and the Re is less than or equal to 3400, the friction coefficient is calculated as follows: ； When the Re is greater than 3400 and the Re is less than 2×10 6 In the case of, the friction coefficient is calculated as: ； When Re is greater than 2×10 6 In the case of, the friction coefficient is calculated as: ； Wherein, f is the friction coefficient, ε is the average roughness of the oil pipe, Re is the Reynolds number of the carbon dioxide fluid, and r1 is the inner diameter of the oil pipe.

5. A device for optimizing carbon dioxide injection parameters, characterized in that: The device comprises: A model building module, used to build a temperature gradient calculation model and a pressure gradient calculation model for a target injection well; A parameter acquisition module, used to calculate the comprehensive heat transfer coefficient of carbon dioxide injected into the target injection well according to the size parameters of the target injection well, the size parameters and temperature of the oil pipe, and the size parameters and temperature of the casing; The parameter acquisition module is further used to perform flow simulation of the carbon dioxide fluid injection process on the target injection well according to the comprehensive heat transfer coefficient, the temperature gradient calculation model, the pressure gradient calculation model and the thermodynamic property data of carbon dioxide, so as to obtain the temperature and pressure profile data of the target injection well; wherein the temperature and pressure profile data include the temperature data and pressure data of carbon dioxide corresponding to a plurality of transverse sections of the target injection well; A function construction module, used to construct an injection parameter optimization function, wherein the injection parameter optimization function includes parameters to be optimized, the parameters to be optimized include wellbore structure parameters and fluid injection parameters, the wellbore structure parameters include the inner diameter and outer diameter of the oil pipe and the inner diameter and outer diameter of the casing, and the fluid injection parameters include the injection pressure, injection temperature and injection flow rate of carbon dioxide; A range determination module, for determining the value range of each parameter to be optimized based on the target temperature range and target pressure range of the bottom of the target injection well, and initializing the population to obtain an initialized population, wherein each individual in the initialized population contains a set of parameters to be optimized; A parameter optimization module is used to obtain the fitness corresponding to each individual in the population based on the thermobaric profile data and the injection parameter optimization function, and the fitness is used to indicate the prediction accuracy of the parameter to be optimized; the population is non-dominated sorted according to the fitness corresponding to each individual in the population; the individuals in the population are divided into multiple levels according to the sorting result of the non-dominated sorting; for each level, the corresponding crowding degree is calculated, and the crowding degree is used to indicate the concentration degree of individual distribution in the level; the population is updated according to the sorting result of the non-dominated sorting and the crowding degree corresponding to each level; starting from the step of obtaining the fitness corresponding to each individual in the population based on the thermobaric profile data and the injection parameter optimization function, until the stop condition is reached, the optimal solution of the injection parameter optimization function is obtained, and the values ​​of a set of parameters to be optimized corresponding to the injection parameter optimization function when the optimal solution is obtained, and the optimal solution includes the optimized set of parameters to be optimized.

6. The device according to claim 5, characterized in that The injection parameter optimization function is expressed according to the following formula: ， are the parameters to be optimized, and the ranges of the parameters to be optimized are as follows: ， Wherein, F is the injection parameter optimization function, Q is the injection flow rate of carbon dioxide, T0 is the injection temperature of carbon dioxide, P0 is the injection pressure of carbon dioxide, , , , are the inner diameter of the oil pipe, the outer diameter of the oil pipe, the inner diameter of the casing, and the outer diameter of the casing, respectively, T m is the bottom temperature of the target injection well, P m is the bottom pressure of the target injection well, is the target temperature at the bottom of the target injection well, is the target pressure at the bottom of the target injection well, , are the minimum flow rate and the maximum flow rate when carbon dioxide is injected into the target injection well, respectively. , are the lowest temperature and the highest temperature when carbon dioxide is injected into the target injection well, respectively, , are respectively the minimum pressure and the maximum pressure when carbon dioxide is injected into the target injection well.

7. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the carbon dioxide injection parameter optimization method according to any one of claims 1 to 4 above.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is loaded and executed by a processor to implement the carbon dioxide injection parameter optimization method according to any one of claims 1 to 4.

9. A computer program product, characterized in that The computer program product comprises a computer program, which is loaded and executed by a processor to implement the method for optimizing carbon dioxide injection parameters according to any one of claims 1 to 4 above.

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