Low-permeability reservoir carbon dioxide flooding numerical simulation method based on Maxwell-Stefan cross diffusion

Abstract

The invention discloses a low-permeability reservoir carbon dioxide flooding numerical simulation method based on Maxwell-Stefan cross diffusion, and belongs to the technical field of reservoir numerical simulation and recovery efficiency improvement. Comprising the following steps: establishing a mass conservation equation of each component based on an oil reservoir thermodynamic field, a rock physical property field, an effective diffusion coefficient matrix and reaction kinetic parameters; a diffusion flux item is expressed by a generalized Fick law based on a Maxwell-Stefan theory so as to describe a multi-component cross diffusion effect; a reaction source convergence item is determined by a COand crude oil reversible miscible phase kinetic model so as to describe a miscible phase and split phase dynamic process; sequentially executing a diffusion transmission step and a chemical reaction step for solving by adopting an operator splitting method to obtain an updated concentration field, and synchronously updating the thermodynamic field, the rock physical property field, the reaction kinetic parameters and the effective diffusion coefficient matrix; repeatedly iterating until each field and parameter change meet a convergence condition, and completing simulation of each chemical component concentration field; according to the method, the physical simplification defect in low-permeability simulation is overcome, and high-precision prediction of the displacement process is achieved.

Description

Technical Field

[0001] This invention relates to the field of reservoir numerical simulation and enhanced oil recovery technology, and particularly to a numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion. Background Technology

[0002] Carbon dioxide (CO2) flooding is an important method for enhancing oil recovery, offering the dual benefits of increasing crude oil production and achieving CO2 geological sequestration. In conventional medium-to-high permeability reservoirs, this technology and corresponding numerical simulation methods are relatively mature. However, as global oil and gas development focuses on unconventional reservoirs such as low-permeability, ultra-low-permeability, and tight reservoirs, traditional numerical simulation methods have revealed significant physical limitations in describing the complex CO2 miscible flooding processes in these reservoirs. This leads to distorted predictions and seriously affects the scientific validity and effectiveness of development schemes. Therefore, developing a novel numerical simulation method capable of accurately characterizing the complex physical processes of CO2 miscible flooding in low-permeability reservoirs has become a critical technological bottleneck that urgently needs to be overcome in this field.

[0003] Currently, industry and academia primarily rely on traditional black oil models or component models based on Darcy's flow theory when simulating CO2 flooding in low-permeability oil reservoirs. These methods typically simplify fluid flow in porous media as macroscopic flow dominated by pressure gradients and treat the miscibility of CO2 and crude oil as an instantaneous equilibrium based on phase equilibrium constants. Furthermore, although some models attempt to introduce diffusion terms in the form of Fick's law to describe microscopic mass transfer, they are generally treated as additional linear correction terms to the flow equation. However, these methods suffer from significant problems when applied to low-permeability reservoirs: First, the models oversimplify physical processes, neglecting the delayed effects of miscibility kinetics and local reversible phase separation caused by slow mass transfer rates. Second, key physical mechanisms are missing; their diffusion models assume independent diffusion of each component, failing to describe cross-diffusion effects in multi-component systems and failing to reflect the strong nonlinear characteristics of the diffusion coefficient's dynamic changes with fluid composition, phase state, temperature, and pressure. Finally, traditional methods suffer from inherent defects in low-permeability reservoirs, including oversimplification, missing key mechanisms, and distortion due to scale effects. Summary of the Invention

[0004] The purpose of this invention is to provide a numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion, which overcomes the physical simplification defects in low-permeability simulation and achieves high-precision prediction of the displacement process.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion, comprising the following steps: Based on the thermodynamic state field, reservoir rock physical property field, effective diffusion coefficient matrix, and reaction kinetic parameters of low-permeability reservoirs, a mass conservation equation describing the chemical components within the reservoir is established. The diffusion flux term in the mass conservation equation is expressed in the form of a generalized Fick law based on Maxwell-Stefan theory. The reaction source and sink terms in the mass conservation equation are determined by a reversible reaction kinetic model describing the miscibility and separation mechanism of carbon dioxide and crude oil. The effective diffusion coefficient matrix characterizes the cross-diffusion coupling effect among multiple components. Based on the initial thermodynamic state field, reservoir rock physical property field, effective diffusion coefficient matrix, and reaction kinetic parameters, the mass conservation equation is solved using the operator splitting method. The diffusion transport step and the chemical reaction step are executed sequentially to obtain the updated concentration fields of each chemical component. Based on the updated concentration fields of each chemical component, the thermodynamic state field and the reservoir rock physical property field are updated simultaneously, and the reaction kinetic parameters and the effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and the reservoir rock physical property field. Repeat the above steps, performing time-progression and internal coupling iterations, until the changes in the concentration field, effective diffusion coefficient matrix, and reaction kinetic parameters are all less than their respective preset convergence error thresholds, thus completing the numerical simulation of the concentration fields of each chemical component during the carbon dioxide flooding process in low-permeability reservoirs.

[0006] In some optional embodiments, the steps for establishing the mass conservation equations describing the various chemical components within the reservoir are as follows: The mass conservation equations describing the various chemical components within the reservoir are as follows: ; In the formula, Components i molar concentration; t For time; Components i The diffusion flux term; i Components i The reaction source and sink; All chemical components in the reservoir fluid model; Among them, diffusion flux term The expression is: ; In the formula, Total molar concentration; The effective diffusion coefficient matrix; Components j mole fraction; For diffusion flux; Reaction source sink iThe expression is based on reversible reactions. aA + bB ⇌ gM Determine where A represents CO2, B represents key components of crude oil, M represents miscible fluid, and net reaction rate. ; In the formula, , These are the rate constants for the forward and reverse reactions, respectively. , and This represents the molar concentration of the corresponding component; , b and c The reaction order is [number]. The source and sink terms of the reactions of components A, B, and M are as follows: ; In the formula, , and For component A (CO2), component B (key pseudo-component of crude oil), and component M (miscible fluid), there are reaction source and sink terms; , and These are stoichiometric coefficients; Reaction source and sink terms of inert components that do not participate in reversible reactions .

[0007] In some optional embodiments, the diffusion flux term in the mass conservation equation is expressed in the form of a generalized Fick's law based on Maxwell-Stefan theory, as follows: The Maxwell-Stefan equation is expressed as follows: ; In the formula, Components i and j The basic interdiffusion coefficient between them Components i The average speed; The Maxwell-Stefan equation is transformed mathematically into a equation expressed in terms of molar flux. J i =C i v i The generalized Fick's law in explicit form yields the diffusion flux term. The expression is: ; In the formula, Total molar concentration; The effective diffusion coefficient matrix; Components j mole fraction; For diffusion flux; Effective diffusion coefficient matrix Based on the basic interdiffusion coefficient After correction for the transport characteristics of porous media, the expression is: ; In the formula, The effective diffusion coefficient matrix; Components i and j The basic interdiffusion coefficient between them; , and These are correction factors characterizing the effects of pore geometry, stress sensitivity, and multiphase distribution.

[0008] In some optional embodiments, the specific steps for obtaining the initial thermodynamic state field, reservoir rock physical property field, initial effective diffusion coefficient matrix, and reaction kinetic parameters are as follows: The initial thermodynamic state field, including temperature field, pressure field, fluid composition distribution and phase state, is obtained through reservoir engineering testing and production data interpretation; The reservoir rock physical properties, including porosity field, permeability field and rock stress state, are obtained through geological modeling, well logging interpretation and rock mechanics experiments; The basic interdiffusion coefficient on which the initial effective diffusion coefficient matrix depends is obtained by measuring the bulk fluid diffusion experiment under high pressure and high temperature using the pressure attenuation method or a microchip interferometer. The reaction kinetic parameters, including the forward and reverse reaction rate constants and reaction order of the reversible reaction, are obtained by recording the dynamics of the miscible interface through a high-pressure visible window miscibility experiment and combining it with the model history fitting and inversion. The geometric structure factor, stress sensitivity factor, and phase distribution factor required to determine the effective diffusion coefficient matrix based on the basic interdiffusion coefficient are obtained by combining core-scale displacement experiments, microscopic CT scanning pore structure characterization, and cross-scale simulation calibration using a pore network model or lattice Boltzmann method.

[0009] In some optional embodiments, the mass conservation equation is solved using the operator splitting method, and the diffusion transport step and chemical reaction step are executed sequentially to obtain an updated concentration field. Based on the updated concentration field, the thermodynamic state field and reservoir rock property field are updated simultaneously, and the reaction kinetic parameters and effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and reservoir rock property field. The specific steps are as follows: time step ΔtInternally, with a fixed fluid composition, the effective diffusion coefficient matrix is ​​based on the current time step. Solve the pure diffusion equation containing only the diffusion flux term: ; In the formula, This represents the intermediate concentration field caused by diffusion. This is the current time step; With intermediate concentration field Using these as initial conditions, within the same time step, solve the reaction kinetic equations that contain only the aforementioned reaction source and sink terms: ; In the formula, The concentration field is corrected by the chemical reaction. For temperature; For pressure; This represents the current concentration of all components; according to Update fluid composition Phase equilibrium calculations are performed to determine the system phase state and update the phase distribution factor. ; Based on the updated thermodynamic state field and reservoir rock physical field, the reaction kinetic parameters and the effective diffusion coefficient matrix required for the next time step are recalculated.

[0010] Embodiments of the present invention also provide a numerical simulation method system for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion, comprising: The mathematical model construction module is used to establish mass conservation equations describing the chemical components within a low-permeability reservoir based on the thermodynamic state field, reservoir rock physical properties, effective diffusion coefficient matrix, and reaction kinetic parameters. The diffusion flux term in these mass conservation equations is expressed using the generalized Fick law based on Maxwell-Stefan theory. The reaction source and sink terms in these equations are determined by a reversible reaction kinetic model describing the miscibility and separation mechanism of carbon dioxide and crude oil. The effective diffusion coefficient matrix characterizes the cross-diffusion coupling effect between multiple components. The diffusion-reaction coupled solution module is used to solve the mass conservation equation using the operator splitting method based on the initial thermodynamic state field, reservoir rock property field, effective diffusion coefficient matrix, and reaction kinetic parameters. It sequentially executes the diffusion transport step and the chemical reaction step to obtain the updated concentration fields of each chemical component. Based on the updated concentration fields of each chemical component, the thermodynamic state field and the reservoir rock property field are updated simultaneously, and the reaction kinetic parameters and the effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and the reservoir rock property field. The simulation execution and convergence control module is used to repeatedly execute the above steps, perform time advancement and internal coupling iteration until the changes in the concentration field, effective diffusion coefficient matrix and reaction kinetic parameters are all less than their respective preset convergence error thresholds, thus completing the numerical simulation of the concentration field of each chemical component during the carbon dioxide flooding process in low-permeability reservoirs.

[0011] Embodiments of the present invention also provide a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion.

[0012] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when run by a processor, is capable of executing the above-described numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion.

[0013] The numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion provided by this invention has at least the following beneficial effects: The numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion provided in this invention overcomes the inherent defects of traditional methods in low-permeability reservoirs, such as oversimplification, missing key mechanisms, and scale effect distortion, by constructing a unified model system with multi-component coupled diffusion as the core transport mechanism and dynamically describing the miscibility process as a reversible chemical reaction. This method not only accurately characterizes the dynamic evolution of the diffusion coefficient with respect to composition, phase state, temperature, pressure, and reservoir geological conditions, but also realistically reflects the delayed effects and reversible characteristics of miscibility dynamics. Combined with an efficient operator splitting solution strategy, it ensures the stability and efficiency of solving complex, highly nonlinear problems. Ultimately, this method provides a physically sound and computationally reliable high-precision numerical simulation tool for the displacement dynamics, phase evolution, and recovery prediction of carbon dioxide miscibility flooding in low-permeability reservoirs, and has significant engineering application value for optimizing injection-production design and assessing carbon sequestration potential. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0015] Figure 1This is a flowchart of a numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion, according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0017] One embodiment of the present invention relates to a numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion. The implementation details of the numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0018] The specific procedure of the numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 101: Based on the thermodynamic state field, reservoir rock physical property field, effective diffusion coefficient matrix, and reaction kinetic parameters of the low-permeability reservoir, establish a mass conservation equation describing each chemical component within the reservoir. The diffusion flux term in the mass conservation equation is expressed in the form of a generalized Fick's law based on Maxwell-Stefan theory. The reaction source and sink terms in the mass conservation equation are determined by a reversible reaction kinetic model describing the miscibility and separation mechanism of carbon dioxide and crude oil. The effective diffusion coefficient matrix characterizes the cross-diffusion coupling effect between multiple components. The mass conservation equations describing the various chemical components within the reservoir are as follows: ; In the formula, Components i molar concentration; t For time; Components i The diffusion flux term; i Components i The reaction source and sink; All chemical components in the reservoir fluid model; Among them, diffusion flux term The expression is: ; In the formula, Total molar concentration; The effective diffusion coefficient matrix; Components j mole fraction; For diffusion flux; Reaction source sink i The expression is based on reversible reactions. aA + bB ⇌ gM Determine, where A represents CO2, B represents key components of crude oil, M represents miscible fluid, and net reaction rate. ; In the formula, , These are the rate constants for the forward and reverse reactions, respectively. , and This represents the molar concentration of the corresponding component; , b and c The reaction order is [number]. The source and sink terms of the reactions of components A, B, and M are as follows: ; In the formula, , and For component A (CO2), component B (key pseudo-component of crude oil), and component M (miscible fluid), there are reaction source and sink terms; , and These are stoichiometric coefficients; Reaction source and sink terms of inert components that do not participate in reversible reactions .

[0019] The Maxwell-Stefan equation is expressed as follows: ; In the formula, Components i and j The basic interdiffusion coefficient between them Components i The average speed; The Maxwell-Stefan equation is transformed mathematically into a equation expressed in terms of molar flux. J i =C i v i The generalized Fick's law in explicit form yields the diffusion flux term. The expression is: ; In the formula, Total molar concentration; The effective diffusion coefficient matrix; Components j mole fraction; For diffusion flux; Effective diffusion coefficient matrix Based on the basic interdiffusion coefficient After correction for the transport characteristics of porous media, the expression is: ; In the formula, The effective diffusion coefficient matrix; Components i and j The basic interdiffusion coefficient between them; , and These are correction factors characterizing the effects of pore geometry, stress sensitivity, and multiphase distribution.

[0020] Geometric structure factor : To characterize the influence of the tortuosity and connectivity of pore space. in, Porosity The tortuosity factor (is the water saturation) The function, because the distribution of the aqueous phase will change the diffusion paths of CO2 and oil. is the pore size constraint factor (considering the Knudsen diffusion effect in nanopores).

[0021] Stress-sensitive factors This reflects the impact of changes in effective stress on pore compression and throat closure on the mass transfer channel.

[0022] in, The stress sensitivity coefficient, This represents the current effective stress.

[0023] Phase distribution factor Considering the influence of the saturation and relative permeability of each phase on the effective diffusion cross-sectional area when multiple phases coexist.

[0024]

[0025] in, This refers to relative penetration rate. For saturation, It is the mass transfer enhancement factor at the phase interface.

[0026] The initial thermodynamic state field, including temperature field, pressure field, fluid composition distribution and phase state, is obtained through reservoir engineering testing and production data interpretation; The reservoir rock physical properties, including porosity field, permeability field and rock stress state, are obtained through geological modeling, well logging interpretation and rock mechanics experiments; The basic interdiffusion coefficient on which the initial effective diffusion coefficient matrix depends is obtained by measuring the bulk fluid diffusion experiment under high pressure and high temperature using the pressure attenuation method or a microchip interferometer. The reaction kinetic parameters, including the forward and reverse reaction rate constants and reaction order of the reversible reaction, are obtained by recording the dynamics of the miscible interface through a high-pressure visible window miscibility experiment and combining it with the model history fitting and inversion. The geometric structure factor, stress sensitivity factor, and phase distribution factor required to determine the effective diffusion coefficient matrix based on the basic interdiffusion coefficient are obtained by combining core-scale displacement experiments, microscopic CT scanning pore structure characterization, and cross-scale simulation calibration using a pore network model or lattice Boltzmann method.

[0027] Step 102: Based on the initial thermodynamic state field, reservoir rock property field, effective diffusion coefficient matrix, and reaction kinetic parameters, the mass conservation equation is solved using the operator splitting method. The diffusion transport step and the chemical reaction step are executed sequentially to obtain the updated concentration fields of each chemical component. Based on the updated concentration fields of each chemical component, the thermodynamic state field and the reservoir rock property field are updated simultaneously, and the reaction kinetic parameters and the effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and the reservoir rock property field. time step Δt Internally, with a fixed fluid composition, the effective diffusion coefficient matrix is ​​based on the current time step. Solve the pure diffusion equation containing only the diffusion flux term: ; In the formula, This represents the intermediate concentration field caused by diffusion. This is the current time step; With intermediate concentration field Using these as initial conditions, within the same time step, solve the reaction kinetic equations that contain only the aforementioned reaction source and sink terms: ; In the formula, The concentration field is corrected by the chemical reaction. For temperature; For pressure; This represents the current concentration of all components; according to Update fluid composition Phase equilibrium calculations are performed to determine the system phase state and update the phase distribution factor. ; Based on the updated thermodynamic state field and reservoir rock physical field, the reaction kinetic parameters and the effective diffusion coefficient matrix required for the next time step are recalculated.

[0028] Step 103: Repeat the above steps to perform time-progression and internal coupling iteration until the changes in the concentration field, effective diffusion coefficient matrix and reaction kinetic parameters are all less than their respective preset convergence error thresholds, thus completing the numerical simulation of the concentration field of each chemical component during the carbon dioxide flooding process in low-permeability reservoirs.

[0029] The embodiments of the present invention also provide a numerical simulation system for carbon dioxide flooding in low-permeability reservoirs. The implementation details of the numerical simulation system for carbon dioxide flooding in low-permeability reservoirs in this embodiment are described in detail below. The following content is only for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0030] Specifically, the mathematical model construction module is used to establish mass conservation equations describing the various chemical components within a low-permeability reservoir based on the thermodynamic state field, reservoir rock physical properties, effective diffusion coefficient matrix, and reaction kinetic parameters. The diffusion flux term in the mass conservation equation is expressed in the form of a generalized Fick's law based on Maxwell-Stefan theory. The reaction source and sink terms in the mass conservation equation are determined by a reversible reaction kinetic model describing the miscibility and separation mechanism of carbon dioxide and crude oil. The effective diffusion coefficient matrix characterizes the cross-diffusion coupling effect between multiple components. The diffusion-reaction coupled solution module is used to solve the mass conservation equation using the operator splitting method based on the initial thermodynamic state field, reservoir rock property field, effective diffusion coefficient matrix, and reaction kinetic parameters. It sequentially executes the diffusion transport step and the chemical reaction step to obtain the updated concentration fields of each chemical component. Based on the updated concentration fields of each chemical component, the thermodynamic state field and the reservoir rock property field are updated simultaneously, and the reaction kinetic parameters and the effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and the reservoir rock property field. The simulation execution and convergence control module is used to repeatedly execute the above steps, perform time advancement and internal coupling iteration until the changes in the concentration field, effective diffusion coefficient matrix and reaction kinetic parameters are all less than their respective preset convergence error thresholds, thus completing the numerical simulation of the concentration field of each chemical component during the carbon dioxide flooding process in low-permeability reservoirs.

[0031] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.

[0032] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0033] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0034] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion, characterized in that, The method includes: Based on the thermodynamic state field, reservoir rock physical property field, effective diffusion coefficient matrix, and reaction kinetic parameters of low-permeability reservoirs, a mass conservation equation describing the chemical components within the reservoir is established. The diffusion flux term in the mass conservation equation is expressed in the form of a generalized Fick law based on Maxwell-Stefan theory. The reaction source and sink terms in the mass conservation equation are determined by a reversible reaction kinetic model describing the miscibility and separation mechanism of carbon dioxide and crude oil. The effective diffusion coefficient matrix characterizes the cross-diffusion coupling effect among multiple components. Based on the initial thermodynamic state field, reservoir rock physical property field, effective diffusion coefficient matrix, and reaction kinetic parameters, the mass conservation equation is solved using the operator splitting method. The diffusion transport step and the chemical reaction step are executed sequentially to obtain the updated concentration fields of each chemical component. Based on the updated concentration fields of each chemical component, the thermodynamic state field and the reservoir rock physical property field are updated simultaneously, and the reaction kinetic parameters and the effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and the reservoir rock physical property field. Repeat the above steps, performing time-progression and internal coupling iterations, until the changes in the concentration field, effective diffusion coefficient matrix, and reaction kinetic parameters are all less than their respective preset convergence error thresholds, thus completing the numerical simulation of the concentration fields of each chemical component during the carbon dioxide flooding process in low-permeability reservoirs.

2. The numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion as described in claim 1, characterized in that, The specific steps for establishing the mass conservation equations describing the various chemical components within the reservoir are as follows: The mass conservation equations describing the various chemical components within the reservoir are as follows: ； In the formula, Components i molar concentration; t For time; Components i The diffusion flux term; i Components i The reaction source and sink; All chemical components in the reservoir fluid model; Among them, diffusion flux term The expression is: ； In the formula, Total molar concentration; The effective diffusion coefficient matrix; Components j mole fraction; For diffusion flux; Reaction source sink i The expression is based on reversible reactions. αA+βB⇌γM Determine where A represents CO2, B represents key components of crude oil, M represents miscible fluid, and net reaction rate. ； In the formula, , These are the rate constants for the forward and reverse reactions, respectively. , and This represents the molar concentration of the corresponding component; , b and c The reaction order is [number]. The source and sink terms of the reactions of components A, B, and M are as follows: ； In the formula, , and For component A (CO2), component B (key pseudo-component of crude oil), and component M (miscible fluid), there are reaction source and sink terms; , and These are stoichiometric coefficients; Reaction source and sink terms of inert components that do not participate in reversible reactions .

3. The numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion as described in claim 1, characterized in that, The diffusion flux term in the mass conservation equation is expressed in the form of a generalized Fick's law based on Maxwell-Stefan theory. The specific steps are as follows: The Maxwell-Stefan equation is expressed as follows: ； In the formula, Components i and j The basic interdiffusion coefficient between them Components i The average speed; The Maxwell-Stefan equation is transformed mathematically into a equation expressed in terms of molar flux. J i =C i v i The generalized Fick's law in explicit form yields the diffusion flux term. The expression is: ； In the formula, Total molar concentration; The effective diffusion coefficient matrix; Components j mole fraction; For diffusion flux; Effective diffusion coefficient matrix Based on the basic interdiffusion coefficient After correction for the transport characteristics of porous media, the expression is: ； In the formula, The effective diffusion coefficient matrix; Components i and j The basic interdiffusion coefficient between them; , and These are correction factors characterizing the effects of pore geometry, stress sensitivity, and multiphase distribution.

4. The numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion as described in claim 1, characterized in that, The specific steps for obtaining the initial thermodynamic state field, reservoir rock physical property field, effective diffusion coefficient matrix, and reaction kinetic parameters are as follows: The initial thermodynamic state field, including temperature field, pressure field, fluid composition distribution and phase state, is obtained through reservoir engineering testing and production data interpretation; The reservoir rock physical properties, including porosity field, permeability field and rock stress state, are obtained through geological modeling, well logging interpretation and rock mechanics experiments; The underlying interdiffusion coefficient upon which the effective diffusion coefficient matrix depends is obtained by measuring the bulk fluid diffusion experiment under high pressure and high temperature using the pressure attenuation method or a microchip interferometer. The reaction kinetic parameters, including the forward and reverse reaction rate constants and reaction order of the reversible reaction, are obtained by recording the dynamics of the miscible interface through a high-pressure visible window miscibility experiment and combining it with the model history fitting and inversion. The geometric structure factor, stress sensitivity factor, and phase distribution factor required to determine the effective diffusion coefficient matrix based on the basic interdiffusion coefficient are obtained by combining core-scale displacement experiments, microscopic CT scanning pore structure characterization, and cross-scale simulation calibration using a pore network model or lattice Boltzmann method.

5. The numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion as described in claim 1, characterized in that, The mass conservation equation is solved using the operator splitting method, sequentially executing diffusion transport and chemical reaction steps to obtain updated concentration fields for each chemical component. Based on these updated concentration fields, the thermodynamic state field and reservoir rock property field are simultaneously updated. The reaction kinetic parameters and effective diffusion coefficient matrix are then recalculated based on these updated thermodynamic state field and reservoir rock property field. The specific steps are as follows: time step Δt Internally, with a fixed fluid composition, the effective diffusion coefficient matrix is ​​based on the current time step. Solve the pure diffusion equation containing only the diffusion flux term: ; In the formula, This represents the intermediate concentration field caused by diffusion. This is the current time step; With intermediate concentration field Using these as initial conditions, within the same time step, solve the reaction kinetic equations that contain only the aforementioned reaction source and sink terms: ； In the formula, The concentration field is corrected by the chemical reaction. For temperature; For pressure; This represents the current concentration of all components; according to Update fluid composition Phase equilibrium calculations are performed to determine the system phase state and update the phase distribution factor. ; Based on the updated thermodynamic state field and reservoir rock physical field, the reaction kinetic parameters and the effective diffusion coefficient matrix required for the next time step are recalculated.

6. A numerical simulation system for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion, characterized in that, The system includes: The mathematical model construction module is used to establish mass conservation equations describing the chemical components within a low-permeability reservoir based on the thermodynamic state field, reservoir rock physical properties, effective diffusion coefficient matrix, and reaction kinetic parameters. The diffusion flux term in these mass conservation equations is expressed using the generalized Fick law based on Maxwell-Stefan theory. The reaction source and sink terms in these equations are determined by a reversible reaction kinetic model describing the miscibility and separation mechanism of carbon dioxide and crude oil. The effective diffusion coefficient matrix characterizes the cross-diffusion coupling effect between multiple components. The diffusion-reaction coupled solution module is used to solve the mass conservation equation using the operator splitting method based on the initial thermodynamic state field, reservoir rock property field, effective diffusion coefficient matrix, and reaction kinetic parameters. It sequentially executes the diffusion transport step and the chemical reaction step to obtain the updated concentration fields of each chemical component. Based on the updated concentration fields of each chemical component, the thermodynamic state field and the reservoir rock property field are updated simultaneously, and the reaction kinetic parameters and the effective diffusion coefficient matrix are recalculated based on the updated thermodynamic state field and the reservoir rock property field. The simulation execution and convergence control module is used to repeatedly execute the above steps, perform time advancement and internal coupling iteration until the changes in the concentration field, effective diffusion coefficient matrix and reaction kinetic parameters are all less than their respective preset convergence error thresholds, thus completing the numerical simulation of the concentration field of each chemical component during the carbon dioxide flooding process in low-permeability reservoirs.

7. A computer system, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, is capable of performing the numerical simulation method for carbon dioxide flooding in low-permeability reservoirs based on Maxwell-Stefan cross-diffusion as defined in any one of claims 1 to 5.

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