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

Abstract

The present invention discloses a method, system and medium for constructing a miscible oil recovery model based on critical path seepage; relates to the technical field of oil and gas seepage; the scheme establishes a critical radius model based on nuclear magnetic resonance logging technology and critical theory; establishes a miscible oil recovery numerical model applicable to a single well scale of an oil reservoir; the miscible oil recovery numerical model includes a miscible flow model and a pressure field; simulates and solves the miscible oil recovery numerical model to obtain the miscible oil recovery model; the scheme uses microscopic parameters (critical radius and critical path length) of the critical path seepage theory to finely characterize the miscible oil recovery numerical model, which can realize the capture, dissolution, diffusion and other complex physical phenomena of carbon dioxide, more directly reflect the stress state and flow change process of various parts of the oil reservoir, directly reflect the pressure sweep range, the spatial position and movement state of the CO2-oil two-phase fluid interface, facilitate timely adjustment of the miscible oil recovery model, and solve the accuracy problem of the miscible oil recovery model.

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 primary energy sources in modern society. However, with the gradual depletion of high-quality conventional oil and gas resources, the research and application of enhanced oil recovery (ERR) technologies has become increasingly urgent. Technological innovations in oil and gas percolation aim to more fully develop and utilize existing oil and gas resources to address growing energy demands and environmental pressures. Against this backdrop, CO2 flooding has garnered widespread attention as an effective enhanced oil recovery (EOR) method. This technology injects carbon dioxide (CO2) into the reservoir to reduce crude oil viscosity, increase reservoir pressure, and enhance oil mobility, thereby enhancing oil recovery. Achieving miscibility between CO2 and crude oil is one of the most critical steps in the CO2 flooding process, as this miscible state significantly improves the contact efficiency between CO2 and crude oil, further enhancing oil recovery. However, in complex and variable reservoir environments, the CO2-oil miscible flooding process is influenced by numerous factors, including reservoir temperature and pressure, the complexity of rock pore structure, and the complexity of multiphase interactions between fluids. These challenges have led to 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] CO2 flooding models are primarily used in the tertiary oil recovery (tertiary oil recovery) phase of oilfields (also known as enhanced oil and gas recovery, EOR). However, in complex and variable reservoir environments, CO2 and oil can intermix during miscible flooding. Conventional miscible flooding models are based on a general grid system and utilize material balance equations to describe the flow of oil, water, and gas in porous media. These models typically use large-scale grids, assume incompressible fluids, or consider only volume changes. These models are not suitable for complex compressible fluid systems and do not account for molecular diffusion and multiphase mass transfer between fluids. Furthermore, on large or non-uniform grids, conventional models can result in overly smooth calculated saturation fronts, failing to accurately capture the true displacement front. Using a coarser grid can miss details of complex physical phenomena such as CO2 capture, dissolution, and diffusion, while using an excessively fine grid significantly increases computational complexity and time. Therefore, more advanced numerical simulation techniques are needed to improve the accuracy of these models and miscible flooding simulations. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that conventional miscible flooding models cannot accurately reflect the stress state and flow rate changes at detailed locations in an oil reservoir. Consequently, oil production using conventional miscible flooding models suffers from low CO2 flooding simulation efficiency. The present invention aims to provide a method, system, and medium for constructing a miscible flooding model based on critical path flow. This improves upon existing miscible flooding models by establishing a miscible flooding numerical model applicable to a single well in an oil reservoir based on the critical path flow theory and the pore fluid pressure diffusion and concentration diffusion equations for each grid (spatial location) during the CO2-oil two-phase flow process. This significantly refines the grid and increases model accuracy. The miscible flooding numerical model is characterized using microscopic parameters (critical radius and critical path length) derived from the critical path flow theory. This model can more directly reflect the stress state and flow rate changes at detailed locations in the oil reservoir, directly reflecting the pressure sweep range, the spatial location, and the movement state of the CO2-oil two-phase fluid interface, and facilitating timely adjustment of the miscible flooding model, thereby addressing the accuracy and efficiency issues of the miscible flooding 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 flooding numerical model applicable to a single well in an oil reservoir is established based on the critical radius model; the miscible flooding 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 Solution: Conventional miscible flooding models cannot reflect the stress state and flow rate changes at various locations in an oil reservoir. Oil recovery using conventional miscible flooding models results in low CO2 flooding efficiency. The present invention aims to provide a method, system, and medium for constructing a miscible flooding model based on critical path flow, primarily for use in the tertiary recovery phase of an oil field. The miscible fluid consists of CO2 and crude oil, with CO2 serving as the displacement phase. This solution improves upon existing miscible flooding models by developing a numerical model applicable to a single well in an oil reservoir based on critical path flow theory, using the pore fluid pressure diffusion and concentration diffusion equations at each grid (spatial location) during the CO2-oil two-phase flow process. Microscopic parameters (critical radius and critical path length) derived from critical path flow theory are used to refine the miscible flooding numerical model. This model can more directly reflect the stress state and flow rate changes at various locations in the reservoir, directly reflecting the pressure sweep range, the spatial location, and the movement of the CO2-oil two-phase fluid interface, facilitating timely adjustment of the miscible flooding model and addressing model accuracy issues.

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

[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 pore throat coordination number of the rock; 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 flooding numerical model applicable to a single well scale of an oil reservoir based on the critical radius model, including the following methods:

[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 flooding 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, 9.8m / 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 side 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 represents the effective viscosity of the mixed phase fluid when it coexists in the critical path; M 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 solution is that the mixed phase flow model includes:

[0027]

[0028] A further optimization scheme is to simulate and solve the miscible flooding numerical model to obtain a miscible flooding 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 phase fluid in the critical path between grid i and grid 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 conservation of mass, 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] Among them S ij is the cross-sectional area of the critical path, m 2 ; Calculation method: S ij =l ij 2 φ cij ;

[0037] Calculate the average flow rate of the mixed fluid in the critical path between grid i and grid j at the next moment based on the flow flux conservation solution Thus updating the average flow rate at the next moment

[0038]

[0039] Combining GPU distributed computing and algebraic multigrid algorithm to perform iterative calculations, the numerical simulation process of the miscible oil displacement numerical model is completed.

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

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

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

[0043] This solution also provides a system for constructing a miscible flooding model based on critical path seepage, which is used to implement the above-mentioned method for constructing a miscible flooding 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 displacement numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil displacement 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 having a computer program stored thereon, and the computer program is executed by a processor to implement the above-mentioned method for constructing a miscible oil displacement model based on critical path seepage.

[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 finely characterize 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 various parts 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 present invention provides a method, system, and medium for constructing a miscible flooding model based on critical path seepage. By combining reservoir models with critical path theory, the model scale can be greatly improved while ensuring modeling accuracy. This allows for the study of CO2-oil microscopic miscibility mechanisms at the reservoir scale while ensuring the accuracy of simulation results.

[0051] 3. The present invention provides a method, system, and medium for constructing a miscible oil displacement model based on critical path seepage. In the simulation program, each calculation process can fully utilize multiple GPUs for large-scale distributed parallel computing, with the GPUs serving as distributed working nodes for the simulation calculations, and the CPU being used to collect GPU calculation results for global synchronization processing, thereby significantly improving the efficiency of the 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 examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0053] Figure 1 A flow chart of the method for constructing a miscible flooding 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 examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0057] The CO2 flooding model is primarily used in the tertiary oil recovery (EOR) phase of oilfields (also known as enhanced oil and gas recovery, EOR). However, in complex and variable reservoir environments, during the miscible drive process of CO2 and oil, CO2 and oil will merge with each other. Conventional miscible flooding models (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 using large-scale grids) are unable to reflect the stress state and flow rate changes in various parts of the reservoir. Oil recovery using conventional miscible flooding models will suffer from low CO2 flooding simulation efficiency and accuracy. In view of this, this solution provides the following embodiments to address the above technical issues:

[0058] Example 1

[0059] This embodiment provides a method for constructing a miscible flooding 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, considering the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model, constructing 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 pore throat coordination number of the rock; s and h represent rock characteristic parameters. Taking 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 the sandstone pore throat shape factor, with a value range of [-3.97, -2.55].

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

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

[0069] S21, when mixed phase fluid coexists in the critical path, calculate the pressure difference between 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 Indicates 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 side 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 represents the effective viscosity of the mixed phase fluid when it coexists in the critical path; M 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 miscible flow model. The nonlinear equations can be generated by constructing matrices from 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 flooding numerical model to obtain a miscible flooding model. This step specifically includes the following methods:

[0079] When the initial pressure distribution is determined, it is necessary to update 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 phase fluid in the critical path between grid i and grid 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 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 conservation of mass, 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] Among them S ij is the cross-sectional area of the critical path, m 2 ; Calculation method: S ij =l ij 2 φ cij .

[0090] Calculate the average flow rate of the mixed fluid in the critical path between grid i and grid j at the next moment based on the flow flux conservation solution Thus updating the average flow rate at the next moment

[0091]

[0092] Combining GPU distributed computing and algebraic multigrid algorithm to perform iterative calculations, the numerical simulation process of the miscible oil displacement numerical model is completed.

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

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

[0095] The GPU is used as a distributed working node for simulation calculations, 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 model is assumed to be completely saturated with oil at the initial moment, and CO2 is introduced at a constant rate q in Injection from the central inlet (CO2 flooding simulation diagram as shown in Figure 3 As shown in the figure, the grid number 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 along the interior 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] Based on the miscible flooding numerical model, initial fluid properties, and boundary conditions, a set of nonlinear equations for the CO2-oil two-phase system is obtained by traversing all grids. The nonlinear equations are organized into a matrix equation form, and parameters such as the fluid volume fraction, grid pressure, flow rate, and saturation of the CO2-oil two-phase system are calculated. As the CO2-oil two-phase front (fluid interface) advances, the nonlinear matrix equations need to be repeatedly solved based on the parameters at the current moment to update the parameters at the next moment. The simulation ends when the program-defined termination condition is reached (in this scheme, the CO2-oil flow rate ratio in the outlet flow is set to less than 0.01%).

[0099] Due to the large number of grid cells and large scale in the well location grid model, the nonlinear equations and matrices are extremely large, requiring high computer computing power. At the same time, during the simulation solution process, the physical properties of some regions vary greatly due to permeability heterogeneity, and each time step requires iterative updates of fluid parameters and model parameters. Conventional CPU calculations are no longer able to 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 utilize multiple GPUs for large-scale distributed parallel computing, using GPUs as distributed working nodes for simulation calculations, and CPUs for collecting GPU calculation results for global synchronization processing, thereby greatly improving the efficiency of simulation calculations.

[0100] Multigrid methods draw 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; and (2) coarse grid correction, solving the residual equation on the coarse grid. In most cases, algebraic multigrid algorithms are not used alone, but as a preprocessing for iterative Krylov subspace methods (such as conjugate gradient iteration (CG) and generalized minimum residual (GMRES). In these cases, a cyclic simulation process is used as a preprocessing step.

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

[0102] Based on critical path theory, this approach incorporates actual rock pore throat characteristics within the original black oil model's grid. Using the critical seepage path and critical radius, this approach establishes a reservoir-scale equivalent model. This effectively accounts for the microscopic characteristics of fluid flow in rock while ensuring both scale and accuracy. Incorporating pore network seepage theory, this approach considers the critical path, a key seepage channel, to directly reflect changes in the two-phase flow state during simulation.

[0103] Example 2

[0104] This embodiment provides a system for constructing a miscible flooding model based on critical path seepage, which is used to implement the method for constructing a miscible flooding 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 displacement numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil displacement 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 having a computer program stored thereon. The computer program is executed by a processor to implement the method for constructing a miscible flooding 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 flooding numerical model applicable to a single well in an oil reservoir based on the critical radius model; the miscible flooding numerical model includes a miscible flow model and a pressure field;

[0112] Step three: simulate and solve the miscible flooding numerical model to obtain a miscible flooding 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 flooding model based on critical path seepage, characterized in that: include: A critical radius model is established based on nuclear magnetic logging technology and critical theory; the specific methods include: 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 a parameter related to sandstone grain size; h represents a parameter related to the sandstone pore throat shape factor;< / r> < / r> A miscible flooding numerical model applicable to a single well in an oil reservoir is established based on the critical radius model; the miscible flooding numerical model includes a miscible flow model and a pressure field; the miscible flow model includes: 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, g represents the acceleration due to gravity; 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 Indicates 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 side length; μ o Indicates the viscosity of the oil; φ cij represents the effective porosity; r cij represents the critical radius; χ eff It represents the effective viscosity when the mixed phase fluid coexists in the critical path; The miscible oil displacement numerical model is simulated and solved to obtain a miscible oil displacement model.

2. The method for constructing a miscible flooding model based on critical path seepage according to claim 1, wherein The method of establishing a miscible oil displacement numerical model applicable to a single well scale of 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.

3. The miscible flooding model construction method based on critical path seepage according to claim 2, wherein The miscible flooding 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 the displacement phase.

4. The method for constructing a miscible flooding model based on critical path seepage according to claim 3, wherein 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, M 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.

5. The method for constructing a miscible flooding model based on critical path seepage according to claim 1, wherein The miscible flooding numerical model is simulated and solved to obtain a miscible flooding model, including the following 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 phase fluid in the critical path between grid i and grid 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 conservation of mass, 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; Calculate the average flow rate of the mixed fluid in the critical path between grid i and grid j at the next moment based on the flow flux conservation solution Thus updating the average flow rate at the next moment Combining GPU distributed computing and algebraic multigrid algorithms for repeated iterative calculations, the numerical simulation process of the miscible flooding numerical model is completed.

6. The method for constructing a miscible flooding model based on critical path seepage according to claim 5, wherein: The method of combining GPU distributed computing and algebraic multigrid algorithm for iterative calculation 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 calculations, and the CPU is also used to collect GPU calculation results for global synchronization processing.

7. A system for constructing a miscible flooding model based on critical path seepage, characterized in that: A method for constructing a miscible oil displacement model based on critical path seepage according to any one of claims 1 to 6, 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 displacement numerical model applicable to a single well scale of an oil reservoir based on the critical radius model; the miscible oil displacement 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.

8. 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 displacement model based on critical path seepage as described in any one of claims 1 to 6.

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