Simulation method, device and equipment for predicting porous medium component transmission and medium

Abstract

The invention discloses a numerical simulation method and device for predicting a porous medium component transmission behavior, computer equipment and a medium, and belongs to the field of porous medium numerical simulation, the method adopts a porous medium fluid-solid unified kinetic equation, and the dependence of a traditional method on permeability parameters is avoided. The control equation adopted by the method is based on the microcosmic multiphase interface conservation law, so that the method is suitable for pore size simulation and macroscopic simulation. The invention provides a numerical simulation method capable of predicting the transmission behavior of the porous medium without depending on permeability parameters, and the provided method can consider complex porous medium transmission phenomena such as non-Darcy flow, heat and mass transfer and the like. The method is expected to provide reliable theoretical basis and prediction means for the technical development related to the porous medium transmission process.

Description

Technical Field

[0001] The present application relates to the technical field of numerical simulation of porous media, and specifically to a new method, device, computer equipment and medium for numerical simulation of porous media transport process based on unified fluid-solid kinetic equation and independent of permeability parameters, which can be used for predicting porous media transport behavior. Background Art

[0002] The porous media transport process refers to the flow, heat transfer and mass transfer process with fluid as carrier in porous media. Numerical simulation is an important means to study porous media transport problems. Darcy's law is often used in engineering to express the relationship between fluid flow and pressure difference through porous media. The process prediction depends on the permeability parameter and is only applicable to low flow rate seepage conditions. When the average flow rate is high, or there are dominant channels in the complex pore structure that lead to local high flow rates, the use of Darcy's law often produces large errors.

[0003] Compared with Darcy's law, the Navier-Stokes equations can be used to solve the pore-scale flow in porous media to obtain accurate results. However, due to the complexity of the pores, the discretization of the computational domain often requires tens of millions of grids, and the solution of the corresponding Navier-Stokes equations consumes a lot of computing resources, which is difficult to use for the simulation of the pore-scale transport process in complex porous media. In addition, the analysis process relies on the wall integral to obtain the fluid resistance, so the results are difficult to analyze.

[0004] Actual numerical simulations usually use the macroscopic dynamics control equations of porous media at the representative unit scale. This type of method is based on the volume-averaged Navier-Stokes equations and uses the Darcy resistance term to consider the effect of solids on fluid flow. This method also relies on the permeability information of porous media and cannot predict porous media transport problems with unknown permeability information.

[0005] In many porous media-related studies, permeability information is often unknown, and the permeability test process is often complex, long, and dependent on professional physical test equipment. Therefore, it is urgent to develop new methods for predicting porous media transport processes that do not rely on permeability parameters to simplify related R&D test processes, which is of great significance for technical applications related to porous media transport processes. Summary of the invention

[0006] The embodiments of the present application provide a numerical simulation method, device, computer equipment and medium for predicting porous media transport processes based on the unified fluid-solid kinetic equation, so as to facilitate the research and development and application of technologies related to porous media.

[0007] In a first aspect, the present application provides a numerical simulation method for predicting the transport behavior of components in porous media, comprising: Select the computational domain and simulation method, generate the corresponding computational grid according to the adopted simulation method, and initialize the porosity of the computational domain according to the existing porous media porosity distribution information; Determine the solution method to be adopted, determine the solution time step, set the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group, where the control equation group includes the dynamic equation and the scalar transport equation; Analyze and display the solution results.

[0008] In some examples, the control equations are obtained as follows: From the continuity equation According to the conservation law of microscopic continuous medium and implicitly considering the effect of solid existence, the kinetic equation and scalar transport equation are derived, where the kinetic equation is: , the scalar transport equation is: , is the porosity of porous media, is the effective momentum coefficient, is the pore flow velocity at the next time step to be solved, For time, is the dynamic pressure, is the viscosity of the fluid, is the known pore flow velocity at the current time, is the time step, is the effective transfer coefficient, It is a universal symbol and can represent any scalar quantity such as temperature and component concentration. is a scalar The diffusivity of diffusion transport, is the known current time scalar value, The standard amount in solid The value of .

[0009] In some examples, the number of scalar transport equations required is selected according to the actual research needs of the problem; if only the flow problem is to be solved, there is no need to solve the scalar transport equations.

[0010] In some examples, the simulation means includes a pore-scale simulation means or a macro-scale simulation means; The determining of the solution method to be adopted, determining the solution time step, and setting the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group include: For pore-scale simulations, the effective momentum coefficient and the effective transfer coefficient Take 1 for both; For macroscale simulations, the effective momentum coefficient and the effective transfer coefficient Obtained from the resistance of fluid passing through porous media.

[0011] In some examples, for macroscale simulations, the effective momentum coefficient and the effective transfer coefficient Obtained from the resistance of fluid passing through porous media, including: Perform pore-scale simulations and set the effective momentum coefficient and the effective transfer coefficient All of them are 1, and the control equations are solved to obtain the resistance of the fluid passing through the porous medium. ; The macro-scale simulation method was used to simulate again, based on the coarsened computational grid, assuming the effective coefficient and All of them are 1, and the control equations are solved to obtain the resistance of the fluid passing through the porous medium. ; Depend on and Get the effective momentum coefficient , and then by the effective momentum coefficient Get the effective transfer coefficient .

[0012] In some instances, Get the effective momentum coefficient .

[0013] In some instances, Get the effective transfer coefficient .

[0014] In a second aspect, the present application provides a numerical simulation device for predicting the transport behavior of components in porous media, comprising: The pre-processing module is used to select the computational domain and simulation method, generate the corresponding computational grid according to the adopted simulation method, and initialize the porosity of the computational domain according to the existing porous media porosity distribution information; A solution module is used to determine the solution method to be adopted, determine the solution time step, and set the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group, wherein the control equation group includes the dynamic equation and the scalar transport equation; The result processing module is used to analyze and display the solution results.

[0015] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the computer program.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0017] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art: The present invention provides a numerical simulation method that can predict the transport behavior of porous media without relying on permeability parameters. The method provided can take into account complex porous media transport phenomena such as non-Darcy flow and heat and mass transfer. This method is expected to provide a reliable theoretical basis and prediction means for the development of technologies related to porous media transport processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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 those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a schematic diagram of a numerical simulation method for predicting the transport behavior of components in porous media provided in an embodiment of the present application; Figure 2 It is a schematic diagram of a numerical simulation device for predicting the transport behavior of components in porous media provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an embodiment of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0021] In the following description, the specific embodiments of the present application will be described with reference to the steps and symbols performed by one or more computers, unless otherwise specified. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and the computer execution referred to herein includes the operation of a computer processing unit by an electronic signal representing data in a structured form. This operation converts the data or maintains it at a location in the memory system of the computer, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure maintained by the data is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the above text, which does not represent a limitation, and testers in the field will understand that the following various steps and operations can also be implemented in hardware.

[0022] The term "module" or "unit" used in this article can be regarded as a software object executed on the computing system. The different components, modules, engines and services in this article can be regarded as implementation objects on the computing system. The apparatus and method in this article are preferably implemented in software, but can also be implemented in hardware, all within the scope of protection of this application.

[0023] Those skilled in the art will appreciate that, unless expressly stated otherwise, the singular forms "a", "an", "" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other elements, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" as used herein includes all or any unit and all combinations of one or more associated listed items.

[0024] The principle of a numerical simulation method for predicting the transport process of porous media in the present application is as follows: a unified porous media fluid-solid dynamic equation based on the conservation law of microscopic multiphase interfaces is adopted to avoid the dependence of traditional methods on permeability parameters. Therefore, this method is suitable for both pore-scale simulation and macroscopic simulation.

[0025] See also Figure 1 , Figure 1 A numerical simulation method for predicting the transport behavior of components in porous media provided in an embodiment of the present application includes: S1: Pre-processing. Select a suitable computational domain, choose to use pore-scale simulation methods or macro-scale simulation methods, generate the corresponding computational grid according to the simulation methods used, and initialize the porosity of the computational domain according to the existing porous media porosity distribution information; S2: Calculate and solve. Determine whether the solution method to be adopted is the pore scale method or the macro scale method, and determine the solution time step , set the corresponding effective momentum coefficient and the effective transfer coefficient Solve the control equations; In another specific example, the following mass conservation equation is used: (1) According to the Navier-Stokes equation that describes the flow law at the microscopic pore scale, the porosity is introduced. In order to express the volume ratio of solid and fluid, combined with the penalty function considering the existence effect of solid structure, the following momentum conservation equation is derived and adopted: (2) For problems that need to consider heat transfer and mass transfer or other component transport, the general symbol c is used to represent the scalar to be solved. Based on the microscopic pore scale scalar transport equation, combined with the penalty function that implicitly considers the microscopic phase interface boundary conditions, the following component transport equation is derived and adopted: (3) In the formula, is the porosity of porous media, is the effective momentum coefficient, is the pore flow velocity at the next time step to be solved, For time, is the dynamic pressure, is the viscosity of the fluid, is the known pore flow velocity at the current time, is the time step, is the effective transfer coefficient, It is a universal symbol and can represent any scalar quantity such as temperature and component concentration. is a scalar The diffusivity of diffusion transport, is the known current time scalar value, The standard amount in solid The value of .

[0026] The number of scalar transport equations (i.e., equation (3)) required is selected according to the actual research needs. If only the flow problem is to be solved, there is no need to solve equation (3).

[0027] The following is the effective momentum coefficient and the effective transfer coefficient The determination of is described below.

[0028] For pore-scale simulations, the effective momentum coefficient and the effective transfer coefficient For macro-scale simulations, the effective momentum coefficient and the effective transfer coefficient Obtain it by following the steps below: 1) First, conduct pore-scale simulation and set the effective momentum coefficient and the effective transfer coefficient are all 1. According to the above solution steps, the resistance of fluid passing through porous media is obtained. For example, the mechanical energy loss at the fluid outlet and inlet can be calculated as: (4) In the formula, the right side of the equal sign is the fluid mechanical energy calculated at the system outlet, is the fluid mechanical energy at the system inlet. The kinetic energy term in the square brackets is actually calculated. Can be optionally ignored.

[0029] 2) Secondly, the macro-scale simulation method is used to simulate again, based on the coarsened computational grid, assuming that the effective coefficient and are all 1. According to the above solution steps, the resistance of fluid passing through porous media is obtained. For example, the mechanical energy loss at the fluid outlet and inlet can be calculated as: (5) In the formula, the right side of the equal sign is the fluid mechanical energy calculated at the system outlet, is the fluid mechanical energy at the system inlet. The kinetic energy term in the square brackets is actually calculated. Can be optionally ignored.

[0030] 3) Calculate the effective momentum coefficient for: ; Effective transfer coefficient It can be calculated as: .

[0031] The effective momentum coefficient and the effective transfer coefficient That is, it can be applied to the macro-scale simulation of porous media transport processes under other working conditions.

[0032] S3: Post-processing: Visual analysis, image display, secondary analysis, etc. of the calculation results.

[0033] The embodiment of the present invention provides a numerical simulation method that can predict the transport behavior of porous media without relying on permeability parameters through the above technical solution. The provided method can consider complex porous media transport phenomena such as non-Darcy flow and heat and mass transfer. The method is expected to provide a reliable theoretical basis and prediction means for the development of technologies related to porous media transport processes.

[0034] In the second aspect, in order to facilitate better implementation of the method provided in the embodiment of the present application, the embodiment of the present application also provides a numerical simulation device for predicting the transport behavior of components in porous media. The meanings of the terms are the same as those in the above method, and the specific implementation details can refer to the description in the method embodiment.

[0035] See also Figure 2 , Figure 2 A schematic diagram of the structure of a device provided in an embodiment of the present application, wherein the device 200 may include a pre-processing module 201, a solution module 202 and a result processing module 203, wherein: The pre-processing module 201 is used to select a computational domain and a simulation method, generate a corresponding computational grid according to the adopted simulation method, and initialize the porosity of the computational domain according to the existing porous medium porosity distribution information; A solution module 202 is used to determine the solution method to be adopted, determine the solution time step, set the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group, wherein the control equation group includes the dynamic equation and the scalar transport equation; The result processing module 203 is used to analyze and display the solution results.

[0036] In another specific example, the above control equations are obtained as follows: From the continuity equation According to the conservation law of microscopic continuous medium and implicitly considering the effect of solid existence, the kinetic equation and scalar transport equation are derived, where the kinetic equation is: , the scalar transport equation is: , is the porosity of porous media, is the effective momentum coefficient, is the pore flow velocity at the next time step to be solved, For time, is the dynamic pressure, is the viscosity of the fluid, is the known pore flow velocity at the current time, is the time step, is the effective transfer coefficient, It is a universal symbol and can represent any scalar quantity such as temperature and component concentration. is a scalar The diffusivity of diffusion transport, is the known current time scalar value, The standard amount in solid The value of .

[0037] In another specific example, the number of required scalar transport equations is selected according to the research needs of the actual problem; if only the flow problem is to be solved, there is no need to solve the scalar transport equations.

[0038] In another specific example, the simulation means includes a pore scale simulation means or a macro scale simulation means; The above judgment determines the solution method to be adopted, determines the solution time step, and sets the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equations, including: For pore-scale simulations, the effective momentum coefficient and the effective transfer coefficient Take 1 for both; For macroscale simulations, the effective momentum coefficient and the effective transfer coefficient Obtained from the resistance of fluid passing through porous media.

[0039] In another specific example, for the macroscale simulation above, the effective momentum coefficient and the effective transfer coefficient Obtained from the resistance of fluid passing through porous media, including: Perform pore-scale simulations and set the effective momentum coefficient and the effective transfer coefficient All of them are 1, and the control equations are solved to obtain the resistance of the fluid passing through the porous medium. ; The macro-scale simulation method was used to simulate again, based on the coarsened computational grid, assuming the effective coefficient and All of them are 1, and the control equations are solved to obtain the resistance of the fluid passing through the porous medium. ; Depend on and Get the effective momentum coefficient , and then by the effective momentum coefficient Get the effective transfer coefficient .

[0040] In another specific example, by Get the effective momentum coefficient .

[0041] In another specific example, by Get the effective transfer coefficient .

[0042] In a third aspect, the present application also provides a computer device, such as Figure 3 As shown, it shows a schematic diagram of the structure of the computer device involved in the embodiment of the present application, specifically: The computer device may include one or more processing core processors 301, one or more computer readable storage media memories 302, a power supply 303, an input unit 304 and other components. Those skilled in the art will appreciate that Figure 3 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them: The processor 301 is the control center of the computer device. It uses various interfaces and lines to connect various parts of the entire computer device. By running or executing software programs and / or modules stored in the memory 302 and calling data stored in the memory 302, it executes various functions of the computer device and processes data, thereby monitoring the computer device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operation storage medium, user interface and application program, etc., and the modem processor mainly processes wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0043] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating storage medium, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 can also include a controller to provide the processor 301 with access to the memory 302.

[0044] The computer device also includes a power supply 303 for supplying power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management storage medium, so as to manage charging, discharging, and power consumption management through the power management storage medium. The power supply 303 can also include any components such as one or more DC or AC power supplies, recharge storage media, power failure detection circuits, power converters or inverters, and power status indicators.

[0045] The computer device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0046] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the computer device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby implementing the steps in the above method embodiment.

[0047] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0048] To this end, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. The computer program is loaded by a processor to execute the steps in any method provided in the embodiment of the present application.

[0049] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0050] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0051] Since the computer program stored in the computer-readable storage medium can execute the steps in any method provided in the embodiments of the present application, the beneficial effects that can be achieved by any method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0052] The above is a detailed introduction to a numerical simulation method, device, computer equipment and medium for predicting the transport behavior of components in porous media provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A numerical simulation method for predicting the transport behavior of components in porous media, characterized in that: include: Select the computational domain and simulation method, generate the corresponding computational grid according to the adopted simulation method, and initialize the porosity of the computational domain according to the existing porous media porosity distribution information; Determine the solution method to be adopted, determine the solution time step, set the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group, where the control equation group includes the dynamic equation and the scalar transport equation; Analyze and display the solution results.

2. The method according to claim 1, characterized in that: The control equations are obtained as follows: From the continuity equation According to the conservation law of microscopic continuous medium and implicitly considering the effect of solid existence, the kinetic equation and scalar transport equation are derived, where the kinetic equation is: , the scalar transport equation is: , is the porosity of porous media, is the effective momentum coefficient, is the pore flow velocity at the next time step to be solved, For time, is the dynamic pressure, is the viscosity of the fluid, is the known pore flow velocity at the current time, is the time step, is the effective transfer coefficient, is a general symbol, is a scalar The diffusivity of diffusion transport, is the known current time scalar value, The standard amount in solid The value of .

3. The method according to claim 2, characterized in that The number of scalar transport equations required is selected according to the research needs of the actual problem; if only the flow problem is to be solved, there is no need to solve the scalar transport equations.

4. The method according to claim 3, characterized in that The simulation means include pore scale simulation means or macro scale simulation means; The determining of the solution method to be adopted, determining the solution time step, and setting the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group include: For pore-scale simulations, the effective momentum coefficient and the effective transfer coefficient Take 1 for both; For macroscale simulations, the effective momentum coefficient and the effective transfer coefficient Obtained from the resistance of fluid passing through porous media.

5. The method according to claim 4, characterized in that For macroscale simulations, the effective momentum coefficient and the effective transfer coefficient Obtained from the resistance of fluid passing through porous media, including: Perform pore-scale simulations and set the effective momentum coefficient and the effective transfer coefficient All are 1, and the control equations are solved to obtain the resistance of the fluid passing through the porous medium. ; The macro-scale simulation method was used to simulate again, based on the coarsened computational grid, assuming the effective coefficient and All are 1, and the control equations are solved to obtain the resistance of the fluid passing through the porous medium. ; Depend on and Get the effective momentum coefficient , and then by the effective momentum coefficient Get the effective transfer coefficient .

6. The method according to claim 5, characterized in that Depend on Get the effective momentum coefficient .

7. The method according to claim 6, characterized in that Depend on Get the effective transfer coefficient .

8. A numerical simulation device for predicting the transport behavior of components in porous media, characterized in that: include: The pre-processing module is used to select the computational domain and simulation method, generate the corresponding computational grid according to the adopted simulation method, and initialize the porosity of the computational domain according to the existing porous media porosity distribution information; A solution module is used to determine the solution method to be adopted, determine the solution time step, and set the corresponding effective momentum coefficient and effective transfer coefficient to solve the control equation group, wherein the control equation group includes the dynamic equation and the scalar transport equation; The result processing module is used to analyze and display the solution results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

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