Miscible-phase oil displacement model construction method and system based on critical path seepage and medium

Abstract

The invention discloses a miscible-phase oil displacement model construction method and system based on critical path seepage and a medium. Relates to the technical field of oil-gas seepage. According to the scheme, a critical radius model is established based on a nuclear magnetic logging technology and a critical theory; a miscible-phase oil displacement numerical model suitable for the oil reservoir single well scale is established; the miscible-phase oil displacement numerical model comprises a miscible-phase flow model and a pressure field; performing simulation solution on the miscible-phase oil displacement numerical model to obtain a miscible-phase oil displacement model; according to the scheme, a miscible-phase oil displacement numerical model is finely represented based on microscopic parameters (critical radius and critical path length) of a critical path seepage theory, so that details of complex physical phenomena such as capture dissolution and diffusion of carbon dioxide can be realized, and the stress state and the flow change process of each part of an oil reservoir can be more directly reflected; the pressure wave and range and the CO2-oil two-phase fluid interface space position and moving state are directly reflected, a miscible-phase oil displacement model can be conveniently adjusted in time, and the precision problem of the miscible-phase oil displacement model is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas seepage, and in particular to a method, system and medium for constructing a miscible oil displacement model based on critical path seepage. Background Art

[0002] Oil and gas resources are one of the main energy sources in modern society. However, as high-quality conventional oil and gas resources are gradually depleted, the research and application of enhanced oil recovery technology has become increasingly urgent. Technological innovation in the field of oil and gas seepage aims to more fully develop and utilize existing oil and gas resources to cope with the growing energy demand and environmental protection pressure. In this context, CO2 flooding technology has attracted widespread attention as an effective enhanced oil recovery (EOR) method. This technology reduces crude oil viscosity, increases reservoir pressure and crude oil fluidity by injecting carbon dioxide (CO2) into the reservoir, thereby enhancing the crude oil recovery effect. Achieving a miscible state between CO2 and crude oil is one of the most critical steps in the CO2 flooding process, because the miscible state can significantly improve the contact efficiency between CO2 and crude oil and further improve the recovery rate. However, in the complex and changeable reservoir environment, the miscible driving process of CO2 and oil is affected by many factors, including reservoir temperature and pressure, the complexity of rock pore structure, and the complexity of multiphase interaction between fluids. These challenges have resulted in the failure of traditional miscible flooding simulations based on empirical formulas and simple phase models to effectively describe and predict the dynamic behavior and efficiency of the miscible flooding process. At the same time, the simulation accuracy is not ideal due to the lack of consideration of the microscopic factors of rock pore throat characteristics and pore-scale dynamic seepage characteristics.

[0003] The CO2 flooding model is mainly used in the tertiary oil recovery stage of oil fields (also known as enhanced oil and gas recovery, EOR). However, in the complex and changeable reservoir environment, CO2 and oil will merge with each other during the miscible driving process of CO2 and oil. The conventional miscible flooding model is based on a general grid system and uses material balance equations to describe the flow of oil, water and gas three-phase fluids in porous media. It is generally a large-scale grid. It assumes that the fluid is incompressible or only considers volume changes. It is not suitable for complex compressible fluid systems. It does not consider molecular diffusion and multiphase mass transfer behavior between fluids. At the same time, in large grids or non-uniform grids, conventional models may cause the calculated saturation front to be too smooth and cannot accurately capture the front of the real displacement front. Using a coarser grid may miss the details of complex physical phenomena such as the capture, dissolution and diffusion of CO2, while too fine a grid will greatly increase the amount of calculation and calculation time. Therefore, more advanced numerical simulation techniques are needed to improve the simulation accuracy of the model and miscible flooding. Summary of the invention

[0004] The technical problem to be solved by the present invention is that the conventional miscible oil recovery model cannot accurately reflect the stress state and flow rate change process of each detail of the reservoir, and the oil production according to the conventional miscible oil recovery model will have the problem of low CO2 oil recovery simulation efficiency; the purpose of the present invention is to provide a miscible oil recovery model construction method, system and medium based on critical path seepage, and improve the modeling method on the basis of the existing miscible oil recovery model. Based on the critical path seepage theory, a miscible oil recovery numerical model suitable for a single well scale of the reservoir is established for the pore fluid pressure diffusion and concentration diffusion equations of each grid (spatial position) in the CO2-oil two-phase flow process, thereby greatly refining the grid and increasing the model accuracy. The miscible oil recovery numerical model is characterized by microscopic parameters (critical radius and critical path length) based on the critical path seepage theory, which can more directly reflect the stress state and flow rate change process of each detail of the reservoir, and can directly reflect the pressure sweep range, the spatial position and movement state of the CO2-oil two-phase fluid interface, so as to facilitate timely adjustment of the miscible oil recovery model and solve the accuracy and efficiency problems of the miscible oil recovery model.

[0005] The present invention is achieved through the following technical solutions:

[0006] This solution provides a method for constructing a miscible flooding model based on critical path seepage, including:

[0007] Establish a critical radius model based on nuclear magnetic logging technology and critical theory;

[0008] A miscible oil recovery numerical model applicable to a single well scale of an oil reservoir is established based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field;

[0009] The miscible oil displacement numerical model is simulated and solved to obtain a miscible oil displacement model.

[0010] Working principle of this scheme: conventional miscible oil recovery model cannot reflect the stress state and flow rate change process of each part of the reservoir, and oil recovery according to the conventional miscible oil recovery model will have the problem of low CO2 oil recovery efficiency; the purpose of the present invention is to provide a method, system and medium for constructing a miscible oil recovery model based on critical path seepage, which is mainly used in the tertiary oil recovery stage of the oil field; the miscible fluid consists of CO2 and crude oil, and CO2 is used as the displacement phase; based on the existing miscible oil recovery model, this scheme improves the modeling method, based on the critical path seepage theory, for the pore fluid pressure diffusion and concentration diffusion equations of each grid (spatial position) in the CO2-oil two-phase flow process, a miscible oil recovery numerical model suitable for the single well scale of the reservoir is established, based on the microscopic parameters (critical radius and critical path length) of the critical path seepage theory, the miscible oil recovery numerical model is refined to characterize, which can more directly reflect the stress state and flow rate change process of each part of the reservoir, and can directly reflect the pressure sweep range, the spatial position and movement state of the CO2-oil two-phase fluid interface, so as to facilitate timely adjustment of the miscible oil recovery model and solve the accuracy problem of the miscible oil recovery model.

[0011] A further optimization scheme is to establish a critical radius model based on nuclear magnetic logging technology and critical theory, including the following method:

[0012] Construct a well location grid model and calculate the permeability and porosity of each grid;

[0013] Based on the critical path seepage theory, the actual rock pore throat characteristics and seepage channel characteristics are considered in the well location grid model to construct a critical radius model; the critical radius model includes:

[0014] The critical radius r between grids i and j cij for:

[0015]

[0016] Among them, φ ij represents the porosity between adjacent grids i and j; k ij represents the permeability between adjacent grids i and j; τ is the tortuosity; σ / <r>is the rock pore throat variation coefficient, where σ represents the standard deviation of the pore throat radius distribution curve, <r>represents the average value of the pore throat radius distribution curve; z represents the rock pore throat coordination number; s is a parameter related to the sandstone grain size; h is a parameter related to the sandstone pore throat shape factor.

[0017] A further optimization scheme is to establish a miscible oil recovery numerical model suitable for a single well scale in an oil reservoir based on the critical radius model, including the following method:

[0018] When mixed phase fluid coexists in the critical path, the pressure difference between adjacent grids i and j is calculated;

[0019] A mixed-phase flow model is established based on the pressure difference, critical radius model and mass conservation equation, and the pressure distribution of each grid is solved based on the conjugate gradient method to form a pressure field.

[0020] A further optimization scheme is that the miscible oil recovery model is used in the tertiary oil recovery stage of the oil field; the miscible fluid consists of CO2 and crude oil, and CO2 serves as the displacement phase.

[0021] A further optimization solution is that the pressure difference Δp between adjacent grids i and j is ij The calculation methods include:

[0022]

[0023] χ eff =X ij / M μ +1-X ij ;

[0024]

[0025] Among them, p i =p oi +ρgZ i , p oi represents the pore pressure of grid i, ρgZ i is the gravity of the fluid in grid i, ρ is the fluid density, g is the acceleration due to gravity, 9.8 m / s 2 ;p j =p oj +ρgZ j , p oj represents the pore pressure of grid j, ρgZ j represents the gravity of the fluid in grid j; Z i is the vertical height of grid i, Z j The vertical height of grid j; q ij represents the average flow rate of the mixed phase fluid in the critical path between grid i and grid j; τ is the tortuosity of the critical path, τ = l c / l ij , l c is the critical path length; l ij Represents the grid edge length; μ o Indicates the viscosity of the oil, Pa·s; φ cij represents the effective porosity; r cij represents the critical radius; χ eff M represents the effective viscosity of the mixed fluid when it coexists in the critical path; μ =μ o / μ e , ;μ o is the viscosity of the oil, Pa·s; μ e Indicates the viscosity of the displacement phase CO2 that has been mixed with the oil, Pa·s; μ co2 Indicates the viscosity of the displacement phase CO2, Pa·s; C s Represents the concentration of CO2 in the critical path; dimensionless.

[0026] A further optimization scheme is that the mixed phase flow model includes:

[0027]

[0028] A further optimization scheme is to simulate and solve the miscible oil displacement numerical model to obtain a miscible oil displacement model, including the following method:

[0029] Calculate the average CO2 concentration C between grid i and grid j based on the pressure field ij , and the average flow rate q of the mixed fluid in the critical path between grids i and j ij ;

[0030]

[0031] Among them, v mij represents the average flow rate of the mixed fluid in the critical path between grid i and grid j; t represents time; x represents the distance of CO2 diffusion; D L represents the Taylor-Aris diffusion coefficient;

[0032] The average flow rate q of the mixed fluid in the critical path between grid i and grid j ij The calculation method is:

[0033]

[0034] According to the law of mass conservation, the flow flux conservation is satisfied between any grid i and the six phase grids j, so for any grid i, we have:

[0035]

[0036] Where S ij is the cross-sectional area of ​​the critical path, m 2 ; Calculation method: S ij = l ij 2 φ cij ;

[0037] Based on the flow flux conservation solution, the average flow rate of the mixed phase fluid in the critical path between grid i and grid j at the next moment is calculated Thus updating the average flow rate at the next moment

[0038]

[0039] The numerical simulation process of the miscible oil displacement numerical model is completed by combining GPU distributed computing and algebraic multigrid algorithm for repeated iterative calculations.

[0040] A further optimization scheme is to repeatedly iterate the calculation by combining GPU distributed computing and algebraic multigrid algorithm, including the following method:

[0041] Each simulation calculation process is performed by multiple GPUs in distributed parallel computing;

[0042] The GPU is used as a distributed working node for simulation calculation, and the CPU is also used to collect GPU calculation results for global synchronization processing.

[0043] The present solution also provides a system for constructing a miscible oil recovery model based on critical path seepage, which is used to implement the above-mentioned method for constructing a miscible oil recovery model based on critical path seepage. The system includes:

[0044] The first model building module is used to establish a critical radius model based on nuclear magnetic logging technology and critical theory;

[0045] A second model building module is used to establish a miscible oil recovery numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field;

[0046] The solution module is used to simulate and solve the miscible oil displacement numerical model to obtain a miscible oil displacement model.

[0047] The present solution also provides a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the method for constructing a miscible oil recovery model based on critical path seepage as described above.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] 1. The present invention provides a method, system and medium for constructing a miscible oil recovery model based on critical path seepage. On the basis of the existing miscible oil recovery model, the modeling method is improved. Based on the critical path seepage theory, a miscible oil recovery numerical model suitable for a single well scale in an oil reservoir is established for the pore fluid pressure diffusion and concentration diffusion equations of each grid (spatial position) during the CO2-oil two-phase flow process. The microscopic parameters (critical radius and critical path length) based on the critical path seepage theory are used to refine the miscible oil recovery numerical model, which can realize the capture, dissolution, diffusion and other complex physical phenomena of carbon dioxide. These details can more directly reflect the stress state and flow change process of each part of the oil reservoir, and can directly reflect the pressure sweep range, the spatial position and movement state of the CO2-oil two-phase fluid interface, so as to facilitate timely adjustment of the miscible oil recovery model and solve the accuracy problem of the miscible oil recovery model.

[0050] 2. The method, system and medium for constructing a miscible flooding model based on critical path seepage provided by the present invention; combining the reservoir model with the critical path theory can greatly improve the model scale while ensuring the modeling accuracy, and study the CO2-oil microscopic miscibility mechanism from the reservoir scale while ensuring the accuracy of the simulation results;

[0051] 3. The method, system and medium for constructing a miscible oil recovery model based on critical path seepage provided by the present invention; in the simulation program, each calculation process can completely use multiple GPUs for large-scale distributed parallel calculations, using the GPU as a distributed working node for simulation calculations, and the CPU is used to collect GPU calculation results for global synchronization processing, thereby greatly improving the efficiency of simulation calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0053] Figure 1 A schematic diagram of the process of constructing a miscible oil recovery model based on critical path seepage;

[0054] Figure 2 Schematic diagram of the position of the two-phase interface when CO2-oil miscible phases coexist on the critical path;

[0055] Figure 3 Schematic diagram of CO2 flooding simulation. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary implementation modes of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0057] The carbon dioxide flooding model is mainly used in the tertiary oil recovery stage of oil fields (also known as enhanced oil and gas recovery, EOR). However, in the complex and changeable reservoir environment, during the miscible driving process of CO2 and oil, CO2 and oil will merge with each other. The conventional miscible flooding model (based on a general grid system, using material balance equations to describe the flow of oil, water, and gas three-phase fluids in porous media, generally a large-scale grid) cannot reflect the stress state and flow change process of various parts of the reservoir. According to the conventional miscible flooding model, oil recovery will have the problem of low CO2 flooding simulation efficiency and accuracy. In view of this, this solution provides the following embodiments to solve the above technical problems:

[0058] Example 1

[0059] This embodiment provides a method for constructing a miscible oil recovery model based on critical path seepage, such as Figure 1 As shown, including:

[0060] Step 1: Establish a critical radius model based on nuclear magnetic logging technology and critical theory;

[0061] This step specifically includes the following methods:

[0062] S11, construct a well location grid model and calculate the permeability and porosity of each grid;

[0063] S12, based on the critical path seepage theory, the actual rock pore throat characteristics and seepage channel characteristics are considered in the well location grid model to construct a critical radius model; the critical radius model includes:

[0064] The critical radius r between grids i and j cij for:

[0065]

[0066] Among them, φ ij represents the porosity between adjacent grids i and j; k ij represents the permeability between adjacent grids i and j; τ is the tortuosity; σ / <r>is the rock pore throat variation coefficient, where σ represents the standard deviation of the pore throat radius distribution curve, <r>represents the average value of the pore throat radius distribution curve; z represents the rock pore throat coordination number; s and h represent rock characteristic parameters. Taking the rock type of tight sandstone as an example, s is a parameter related to sandstone grain size, with a value range of [0.05, 1.87], and h is a parameter related to sandstone pore throat shape factor, with a value range of [-3.97, -2.55].

[0067] Step 2: establishing a miscible oil recovery numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field;

[0068] The miscible flooding model in this scheme is used in the tertiary recovery stage of the oil field; the miscible fluid consists of CO2 and crude oil, and CO2 is used as the displacement phase. This step specifically includes the following methods:

[0069] S21, when the mixed phase fluid coexists in the critical path, calculate the pressure difference between the adjacent grids i and j; in the CO2 flooding process, that is, when CO2 and oil coexist in the critical path, such as Figure 2 Therefore, according to Poiseuille's law, the pressure difference Δp between adjacent grids i and j can be obtained: ij The calculation methods include:

[0070]

[0071] χ eff =X ij / M μ +1-X ij ;

[0072]

[0073] Among them, p i =p oi +ρgZ i , p oi represents the pore pressure of grid i, ρgZ i represents the gravity of the fluid in grid i, ρ represents the fluid density, and g represents the acceleration due to gravity, 9.8 m / s 2 ;p j =p oj +ρgZ j , p oj represents the pore pressure of grid j, ρgZ j represents the gravity of the fluid in grid j; Z i represents the vertical height of grid i, Z j represents the vertical height of grid j; q ij represents the average flow rate of the mixed phase fluid in the critical path between grid i and grid j; τ represents the tortuosity of the critical path, τ = l c / l ij , l c represents the critical path length; l ij Represents the grid edge length; μ o Indicates the viscosity of the oil, Pa·s; φ cij represents the effective porosity; r cij represents the critical radius; χ eff M represents the effective viscosity of the mixed fluid when it coexists in the critical path; μ =μ o / μ e , ;μ o Indicates the viscosity of the oil, Pa·s; μ e Indicates the viscosity of the displacement phase CO2 that has been mixed with the oil, Pa·s; μ co2 Indicates the viscosity of the displacement phase CO2, Pa·s; C s Represents the concentration of CO2 in the critical path; dimensionless.

[0074] S22, a mixed phase flow model is established based on the pressure difference, critical radius model and mass conservation equation, and the pressure distribution of each grid is solved based on the conjugate gradient method to form a pressure field.

[0075] The mixed phase flow model includes:

[0076]

[0077] Each grid in the well location grid model satisfies the mixed phase flow model. The nonlinear equations can be generated by constructing matrices through all grids, and the pressure field formed by the pressure distribution of all grids can be solved by the conjugate gradient method.

[0078] Step 3: simulate and solve the miscible oil displacement numerical model to obtain a miscible oil displacement model. This step specifically includes the following methods:

[0079] When the initial pressure distribution is determined, it is necessary to update and calculate the CO2 concentration distribution in the entire network, and calculate the average CO2 concentration C between grid i and grid j based on the pressure field. ij , and the average flow rate q of the mixed fluid in the critical path between grids i and j ij ;

[0080]

[0081] Among them, v mij represents the average flow rate of the mixed fluid in the critical path between grid i and grid j, m / s; t represents time, s; x represents the distance of CO2 diffusion, m; D L (m 2 / s) represents the Taylor-Aris diffusion coefficient, D L It is expressed as:

[0082]

[0083] Among them, D m is the molecular diffusion rate, m 2 / s.

[0084] Average concentration C ij The changes satisfy:

[0085] The average flow rate q of the mixed fluid in the critical path between grid i and grid j ij The calculation method is:

[0086]

[0087] According to the law of mass conservation, the flow flux conservation is satisfied between any grid i and the six phase grids j, so for any grid i, we have:

[0088]

[0089] Where S ij is the cross-sectional area of ​​the critical path, m 2 ; Calculation method: S ij = l ij 2 φ cij .

[0090] Based on the flow flux conservation solution, the average flow rate of the mixed phase fluid in the critical path between grid i and grid j at the next moment is calculated Thus updating the average flow rate at the next moment

[0091]

[0092] The numerical simulation process of the miscible oil displacement numerical model is completed by combining GPU distributed computing and algebraic multigrid algorithm for repeated iterative calculations.

[0093] The method of combining GPU distributed computing and algebraic multigrid algorithm for iterative computing includes:

[0094] Each simulation calculation process is performed by multiple GPUs in distributed parallel computing;

[0095] The GPU is used as a distributed working node for simulation calculation, and the CPU is also used to collect GPU calculation results for global synchronization processing.

[0096] In particular, during the numerical simulation of the miscible flooding numerical model, the internal part of the miscible flooding numerical model is set to be completely saturated with oil at the initial moment, and CO2 is heated at a constant speed q in Injection from the central inlet (CO2 flooding simulation diagram as shown in Figure 3 As shown in the figure, the number of grids is 1000×1000×20, and the model size is 1000m×1000m×20m). Under the combined action of displacement pressure difference, CO2-oil two-phase interface force and viscous force, the oil flows out from the inside of the model to the surrounding outlets.

[0097] In the simulation program, the inlet of the model is set to the injection phase, the outlet pressure is set to a constant value, and the CO2-oil two phases are simultaneously injected from the outlet at q out Except for the inlet and outlet, the outer boundary pressure of the core is set to a certain value, and the outside of the core is set as a solid boundary that the fluid cannot pass through.

[0098] According to the miscible oil recovery numerical model, initial fluid properties and boundary conditions, a set of nonlinear equations about the CO2-oil two-phase can be obtained by traversing all grids. The nonlinear equations are organized into matrix equation form, and the fluid volume fraction, grid pressure, flow rate and saturation of the CO2-oil two-phase are calculated. As the CO2-oil two-phase front (fluid interface) advances, it is necessary to repeatedly solve the nonlinear matrix equations according to the parameters at the current moment to update the parameters at the next moment until the termination condition set by the program is reached (this scheme sets the CO2-oil flow ratio in the outlet flow to be less than 0.01%).

[0099] Since the well location grid model has a large number of grids and a large scale, the nonlinear equations and matrices are very large, which requires high computer computing power. At the same time, during the simulation solution process, the physical properties of some areas are quite different due to the heterogeneity of permeability, and the fluid parameters and model parameters need to be iteratively updated at each time step. Conventional CPU calculations can no longer meet the requirements. Therefore, this solution combines GPU distributed computing and algebraic multigrid algorithm solution methods; in the simulation program, each calculation process can fully use multiple GPUs for large-scale distributed parallel computing, using GPUs as distributed working nodes for simulation calculations, and CPUs are used to collect GPU calculation results for global synchronization processing, thereby greatly improving the efficiency of simulation calculations.

[0100] The multigrid method draws on the dual-grid recursive algorithm, which combines: (1) fine grid relaxation or smoothing iteration, using simple iterative methods such as Jacobi iteration or Gauss-Seidel iteration; (2) coarse grid correction, solving the residual equation on the coarse grid. In most cases, the algebraic multigrid algorithm is not used alone, but as a preprocessing for iterative Krylov subspace methods (such as conjugate gradient iteration method CG, generalized minimum residual method GMRES, etc.). In these cases, the cyclic simulation process is used as a preprocessing step.

[0101] Through the above steps, a complete CO2-oil miscible drive theoretical model is established. The numerical simulation solution process can directly reflect the sweep range, fluid front spatial position and movement state of the CO2-oil two-phase fluid.

[0102] Based on the critical path theory, this scheme considers the actual rock pore throat characteristics in the grid divided by the original black oil model, and establishes a reservoir scale equivalent model using the critical seepage path and critical radius, which can effectively consider the microscopic characteristics of fluid seepage in rocks, while ensuring the scale and accuracy of the model. This scheme combines the pore network seepage theory and considers the critical path, a dominant seepage channel feature, which can directly reflect the state changes of the two-phase flow during the simulation process.

[0103] Example 2

[0104] This embodiment provides a system for constructing a miscible oil recovery model based on critical path seepage, which is used to implement the method for constructing a miscible oil recovery model based on critical path seepage in Example 1. The system includes:

[0105] The first model building module is used to establish a critical radius model based on nuclear magnetic logging technology and critical theory;

[0106] A second model building module is used to establish a miscible oil recovery numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field;

[0107] The solution module is used to simulate and solve the miscible oil displacement numerical model to obtain a miscible oil displacement model.

[0108] Example 3

[0109] This embodiment provides a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the method for constructing a miscible oil recovery model based on critical path seepage as described in Example 1; specifically, the following steps are performed:

[0110] Step 1: Establish a critical radius model based on nuclear magnetic logging technology and critical theory;

[0111] Step 2: establishing a miscible oil recovery numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field;

[0112] Step three, simulating and solving the miscible oil recovery numerical model to obtain a miscible oil recovery model.

[0113] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.< / r> < / r> < / r> < / r>

Claims

1. A method for constructing a miscible oil recovery model based on critical path seepage, characterized in that: include: Establish a critical radius model based on nuclear magnetic logging technology and critical theory; A miscible oil recovery numerical model applicable to a single well scale of an oil reservoir is established based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field; The miscible oil displacement numerical model is simulated and solved to obtain a miscible oil displacement model.

2. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 1, characterized in that: The method of establishing a critical radius model based on nuclear magnetic logging technology and critical theory includes: Construct a well location grid model and calculate the permeability and porosity of each grid; Based on the critical path seepage theory, the actual rock pore throat characteristics and seepage channel characteristics are considered in the well location grid model to construct a critical radius model; the critical radius model includes: The critical radius r between grids i and j cij for: Among them, φ ij represents the porosity between adjacent grids i and j; k ij represents the permeability between adjacent grids i and j; τ is the tortuosity; σ / <r>is the rock pore throat variation coefficient, where σ represents the standard deviation of the pore throat radius distribution curve, <r> represents the average value of the pore throat radius distribution curve; z represents the rock pore throat coordination number; s represents the parameter related to the sandstone grain size; h represents the parameter related to the sandstone pore throat shape factor.< / r> < / r> 3. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 2, characterized in that: The method of establishing a miscible oil recovery numerical model suitable for a single well scale in an oil reservoir based on the critical radius model includes: When mixed phase fluid coexists in the critical path, the pressure difference between adjacent grids i and j is calculated; A mixed-phase flow model is established based on the pressure difference, critical radius model and mass conservation equation, and the pressure distribution of each grid is solved based on the conjugate gradient method to form a pressure field.

4. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 3, characterized in that: The miscible oil recovery model is used in the tertiary oil recovery stage of an oil field; the miscible fluid consists of CO2 and crude oil, with CO2 serving as a displacement phase.

5. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 4, characterized in that: The pressure difference Δp between adjacent grids i and j ij The calculation methods include: x eff =X ij / M μ +1-X ij ; Among them, p i =p oi +ρgZ i , p oi represents the pore pressure of grid i, ρgZ i represents the gravity of the fluid in grid i, ρ represents the fluid density, and g represents the gravitational acceleration; p j =p oj +ρgZ j , p oj represents the pore pressure of grid j, ρgZ j represents the gravity of the fluid in grid j; Z i represents the vertical height of grid i, Z j represents the vertical height of grid j; q ij represents the average flow rate of the mixed phase fluid in the critical path between grid i and grid j; τ represents the tortuosity of the critical path, τ = l c / l ij , l c represents the critical path length; l ij Represents the grid edge length; μ o Indicates the viscosity of the oil; φ cij represents the effective porosity; r cij represents the critical radius; χ eff M represents the effective viscosity of the mixed fluid when it coexists in the critical path; μ =μ o / μ e ;μ o Indicates the viscosity of the oil; μ e Indicates the viscosity of the displacement phase CO2 that has been mixed with the oil; μ CO2 Indicates the viscosity of the displacement phase CO2; C s represents the concentration of CO2 in the critical path.

6. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 5, characterized in that: The mixed phase flow model includes:

7. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 6, characterized in that: The miscible oil displacement numerical model is simulated and solved to obtain a miscible oil displacement model, including a method: Calculate the average CO2 concentration C between grid i and grid j based on the pressure field ij , and the average flow rate q of the mixed fluid in the critical path between grids i and j ij ; Among them, v mij represents the average flow rate of the mixed fluid in the critical path between grid i and grid j; t represents time; x represents the distance of CO2 diffusion; D L represents the Taylor-Aris diffusion coefficient; The average flow rate q of the mixed fluid in the critical path between grid i and grid j ij The calculation method is: According to the law of mass conservation, the flow flux conservation is satisfied between any grid i and the six phase grids j, so for any grid i, we have: S ij =l ij 2 f cij Among them, S ij represents the cross-sectional area of ​​the critical path; Based on the flow flux conservation solution, the average flow rate of the mixed phase fluid in the critical path between grid i and grid j at the next moment is calculated Thus updating the average flow rate at the next moment The numerical simulation process of the miscible oil displacement numerical model is completed by combining GPU distributed computing and algebraic multigrid algorithm for repeated iterative calculations.

8. The method for constructing a miscible oil displacement model based on critical path seepage according to claim 7, characterized in that: The method of combining GPU distributed computing and algebraic multigrid algorithm for iterative computing includes: Each simulation calculation process is performed by multiple GPUs in distributed parallel computing; The GPU is used as a distributed working node for simulation calculation, and the CPU is also used to collect GPU calculation results for global synchronization processing.

9. A system for constructing a miscible oil recovery model based on critical path seepage, characterized in that: A method for constructing a miscible oil recovery model based on critical path seepage according to any one of claims 1 to 8, the system comprising: The first model building module is used to establish a critical radius model based on nuclear magnetic logging technology and critical theory; A second model building module is used to establish a miscible oil recovery numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil recovery numerical model includes a miscible flow model and a pressure field; The solution module is used to simulate and solve the miscible oil displacement numerical model to obtain a miscible oil displacement model.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the method for constructing a miscible oil recovery model based on critical path seepage as described in any one of claims 1 to 8.

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