Cellular automaton-based oil-water distribution simulation method, device, equipment, medium and product in oil reservoir polymer flooding process

Abstract

The invention discloses an oil reservoir polymer flooding process oil-water distribution simulation method and device based on a cellular automaton, equipment, a medium and a product, and relates to the field of oil reservoir engineering and seepage mechanics. The method comprises the steps that rectangular grid division is conducted on an oil reservoir space, three-dimensional coordinates of cellular nodes are determined, and a cellular automaton model is constructed; according to the polymer oil displacement scheme parameters, oil reservoir state parameters at the end moment of water displacement are initialized, and boundary conditions of the mole-type cellular automaton model are established; making an evolution rule for each neighborhood cellular node in the Moire cellular automaton model; according to a numerical value stability rule, adjusting parameters of each neighborhood cellular node in the Mohr-type cellular automaton model; according to the method, a relative permeability nonlinear regulation and control rule is formulated for the Mohr-type cellular automaton model to simulate oil-water distribution in the oil reservoir polymer flooding process of the Mohr-type cellular automaton model, the starting pressure gradient is accurately simulated, and the low-permeability oil reservoir evolution process is accurately described.

Description

Technical Field

[0001] The present application relates to the fields of reservoir engineering and seepage mechanics, and in particular to a method, device, equipment, medium and product for simulating oil-water distribution in a polymer flooding process of an oil reservoir based on cellular automata. Background Art

[0002] Low permeability reservoirs face many challenges during development due to their unique seepage characteristics, especially during polymer flooding, where the oil-water distribution is complex and difficult to accurately characterize. Low permeability reservoirs generally have a high starting pressure gradient, and the seepage process exhibits non-Darcy flow characteristics, that is, the fluid cannot flow uniformly according to Darcy's law under low-speed conditions, but instead has obvious threshold effects and nonlinear changes. Current mainstream reservoir numerical simulation software (Computer Modeling Group, CMG) and Eclipse are mostly based on the Darcy flow assumption and solve the seepage equation through finite difference or finite volume methods. However, under low permeability conditions, traditional numerical simulation methods face the following limitations: (1) It is difficult to accurately simulate the starting pressure gradient, resulting in large calculation errors in the low-speed seepage zone; (2) It is impossible to accurately characterize the microscopic residual oil distribution and fully describe the formation and evolution of bound oil; (3) The calculation complexity is high and the convergence is poor, especially under nonlinear seepage conditions, which easily leads to calculation divergence; (4) During polymer flooding, polymers mainly play a role in regulating plugging, but existing simulation software does not clearly describe the specific oil-increasing process and mechanism, making it inconvenient to characterize. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium and product for simulating oil-water distribution in the polymer flooding process of an oil reservoir based on cellular automata, so as to solve the problem that it is difficult to accurately simulate the starting pressure gradient and cannot accurately describe the evolution process of low permeability oil reservoirs.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata, comprising:

[0006] Divide the reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes;

[0007] According to the three-dimensional coordinates of the cell nodes, the neighborhood cell nodes are determined, and a cellular automaton model is constructed; the cellular automaton model is a Moore-type cellular automaton model; the Moore-type cellular automaton model is used to characterize the oil reservoir storage space;

[0008] Initializing the reservoir state at the end of water flooding according to the parameters of the polymer flooding scheme, and establishing the boundary conditions of the Moore-type cellular automaton model;

[0009] Formulate an evolution rule for each neighborhood cellular node in the Moore-type cellular automaton model based on the boundary conditions;

[0010] Based on the evolution rule and in accordance with numerical stability rules, adjusting the parameters of each neighborhood cellular node in the Moore-type cellular automaton model;

[0011] According to the parameters, a nonlinear control rule of relative permeability is formulated for the Moore-type cellular automaton model to simulate the oil-water distribution in the polymer flooding process of the Moore-type cellular automaton model.

[0012] In one embodiment, the reservoir space is divided into rectangular grids according to a spatial rectangular coordinate system, with each rectangular grid serving as a cell node; the spatial rectangular coordinate system is established with any corner point of the reservoir as the origin, the horizontal direction of the reservoir as the x-axis, the vertical direction of the reservoir as the y-axis, and the depth direction of the reservoir as the z-axis; the corner point is the fixed angle of any rectangular grid;

[0013] Determining cellular nodes with different attributes according to different positions of the cellular nodes in the divided rectangular grid; the attributes include permeability, oil saturation, porosity, sand layer thickness, oil production, water injection rate, natural gamma and mud content;

[0014] The three-dimensional coordinates of the cell nodes are determined according to the coordinates of the cell nodes with different attributes in the spatial rectangular coordinate system.

[0015] In one embodiment, reservoir state parameters at the end of water flooding are initialized based on polymer flooding scheme parameters; the reservoir state parameters at the end of water flooding include initial pressure, oil saturation distribution, initial polymer concentration at the injection well location, polymer concentration in the remaining area excluding the injection well location, a starting pressure gradient threshold, and a residual oil saturation threshold;

[0016] Based on the reservoir state parameters at the end of the initial water flooding, the boundary conditions of the Moore-type cellular automaton model are established.

[0017] In one embodiment, the evolution rules include fluid transport rules between adjacent cells, saturation update rules, polymer propagation and viscosity influence rules, and immune rules;

[0018] The fluid transmission rule between adjacent cells is: in, is the volume of water phase flowing from neighboring cell node i to neighboring cell node j per unit time; k ij is the equivalent permeability, k i is the permeability of the neighborhood cell node i, k j is the permeability of domain cell node j; Aij is the contact area of ​​the cell nodes in two domains; L ij is the distance between the centers of two neighboring cell nodes; is the average water phase viscosity between two neighboring cell nodes; ΔP ij is the pressure difference, ΔP ij =P i -P j , P i is the pressure of the neighborhood cell node i, P j is the pressure of the neighborhood cell node j; ΔP th is the starting pressure difference threshold; To start the pressure gradient;

[0019] The saturation update rules include water saturation update rules and oil saturation update rules;

[0020] The water saturation update rule is: in, is the water phase saturation of the neighborhood cell node i at time Δt; is the water phase saturation of the neighborhood cell node i at time t; i is the porosity of the neighborhood cell node i; V i is the volume of the neighborhood cell node i; N i is the set of adjacent cells of the neighborhood cell node i; is the flow of water from the neighboring cell node j to the neighboring cell node i; is the flow of water from the neighboring cell node i to the neighboring cell node j;

[0021] The oil saturation update rule is: using in, is the oil phase saturation of the neighborhood node i at time t+Δt;

[0022] use Determine the rules affecting polymer propagation and viscosity; among them, is the polymer concentration of the neighborhood cell node i at time t; is the polymer concentration of the neighborhood cell node i at time t+Δt; is the polymer concentration of the neighborhood cell node i; is the polymer concentration of the neighborhood cell node j; D ij and D ji is the diffusion-convection transport coefficient; λ is the adsorption loss factor;

[0023] The immune rules include the oil saturation constraint of the neighborhood cell nodes and the pressure difference threshold barrier of the neighborhood cell nodes;

[0024] The oil saturation constraint condition of the neighborhood cell node is: and in, is the current oil phase saturation of the neighborhood cell i; S or is the residual oil threshold; is the flow rate of oil phase from neighboring cell i to neighboring cell j;

[0025] The pressure difference threshold barrier of the neighborhood cell node is: in, is the pressure difference threshold barrier function of the neighborhood cell node; is the flow of oil phase from neighboring cell node i to neighboring cell node j.

[0026] In one embodiment, based on the evolution rule, it is determined whether the parameters of each neighborhood cell node in the Moore-type cellular automaton model meet the numerical stability rule; the parameters include time step, saturation change and polymer concentration gradient; the numerical stability rule includes time step condition, saturation change restriction condition and polymer concentration gradient condition; the time step condition is ΔT≤min The saturation change restriction condition is: The polymer concentration gradient condition is Among them, φi is the porosity of the neighborhood cell node; Vi is the volume of the neighborhood cell node; is the estimated value of the maximum instantaneous flow rate; S safe is the safety factor; N i is the set of adjacent cells of the neighborhood cell node i; ΔT is the time step; is the current water phase saturation of the neighborhood cell node i; is the average water saturation of the neighborhood cell node i; ε s is the saturation change threshold; is the maximum concentration gradient of the neighborhood cell node i; is the polymer concentration of the neighborhood cell node i; δ c is the polymer concentration change limit; N i is the set of adjacent cells of the neighborhood cell node i;

[0027] If yes, then formulating a nonlinear control rule of relative permeability for the Moore-type cellular automaton model according to the parameters to simulate the oil-water distribution in the polymer flooding process of the Moore-type cellular automaton model;

[0028] If not, adjust the time step and return to "based on the evolution rule, determine whether the parameters of each neighborhood cellular node in the Moore-type cellular automaton model meet the numerical stability rule."

[0029] In one embodiment, using and The dynamic evolution of oil-water relative permeability with saturation is carried out; the dynamic evolution process is the nonlinear control rule of relative permeability; where k ro is the oil relative permeability; is the oil phase endpoint permeability; n o and n w is the nonlinear exponential parameter that controls the shape of the permeability curve; k rw is the water relative permeability; is the endpoint permeability of the water phase; is the normalized water saturation;

[0030] Based on the nonlinear control rule of relative permeability, the corresponding oil-water relative permeability is dynamically updated according to the current saturation of each cell node;

[0031] The oil-water distribution in the reservoir polymer flooding process of the Moore-type cellular automaton model is simulated according to the updated oil-water relative permeability.

[0032] In a second aspect, the present application provides an oil-water distribution simulation device for an oil reservoir polymer flooding process based on cellular automata, comprising:

[0033] A three-dimensional coordinate determination module is used to divide the oil reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes;

[0034] A cellular automaton model construction module is used to determine neighboring cellular nodes based on the three-dimensional coordinates of the cellular nodes and to construct a cellular automaton model; the cellular automaton model is a Moore-type cellular automaton model; the Moore-type cellular automaton model is used to characterize the oil reservoir storage space;

[0035] A boundary condition establishment module is used to initialize the reservoir state parameters at the end of water flooding according to the polymer flooding scheme parameters and establish the boundary conditions of the Moore-type cellular automaton model;

[0036] An evolution rule determination module, configured to formulate an evolution rule for each neighborhood cellular node in the Moore-type cellular automaton model based on the boundary conditions;

[0037] A parameter adjustment module, configured to adjust the parameters of each neighborhood cellular node in the Moore-type cellular automaton model based on the evolution rule and in accordance with a numerical stability rule;

[0038] The control rule formulation module is used to formulate a nonlinear control rule for relative permeability for the Moore-type cellular automaton model according to the parameters, so as to simulate the oil-water distribution in the oil reservoir polymer flooding process of the Moore-type cellular automaton model.

[0039] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata.

[0041] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata.

[0042] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0043] The present application provides a method, apparatus, equipment, medium and product for simulating oil-water distribution in a polymer flooding process of an oil reservoir based on cellular automata. According to the parameters of the polymer flooding scheme, the reservoir state parameters at the end of water flooding are initialized, and the boundary conditions of the Moore-type cellular automaton model are established. Based on the boundary conditions, evolution rules are formulated for each neighborhood cell in the Moore-type cellular automaton model, which can accurately characterize the microscopic residual oil distribution and fully describe the formation of bound oil and its evolution process in low-permeability reservoirs. Based on the evolution rules, numerical stability rules and nonlinear control rules for relative permeability are formulated for the Moore-type cellular automaton model to simulate the water distribution after flooding of the low-permeability reservoir of the Moore-type cellular automaton model, which can accurately simulate the start-up pressure gradient and improve the calculation accuracy of the low-speed seepage zone. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flow chart of a method for simulating oil-water distribution in a polymer flooding process of an oil reservoir based on cellular automata in one embodiment of the present application;

[0046] Figure 2 A schematic diagram of formation parameters for constructing a cellular node provided in one embodiment of the present application;

[0047] Figure 3A schematic diagram of an initial parameter field constructed based on residual oil sensitive parameters provided in one embodiment of the present application;

[0048] Figure 4 A schematic diagram of the system hardware and software environment requirements provided for another embodiment of the present application;

[0049] Figure 5 (a) is a color code diagram showing oil saturation provided by another embodiment of the present application; Figure 5 (b) is a schematic diagram of the initial state parameters of the cellular reservoir after water flooding; Figure 5 (c) is a schematic diagram of reservoir changes in the first stage of polymer flooding development; Figure 5 (d) is a schematic diagram of the oil saturation field during polymer flooding (water cut 98%); Figure 5 (e) is a partial enlarged schematic diagram of the oil saturation field during polymer flooding (water cut 98%);

[0050] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] Cellular Automata (CA) is a simulation method based on discrete grid points and local rule evolution, which can simulate complex seepage behavior at a low computational cost. Compared with traditional numerical methods, the CA method has the following innovations: (1) It is naturally suitable for non-Darcy seepage simulation, can construct local seepage rules, and flexibly introduce starting pressure gradients and nonlinear flow characteristics; (2) It has microscale simulation capabilities, can intuitively depict the evolution of oil-water distribution after polymer displacement, and study the formation mechanism of bound oil; (3) It has strong computational parallelism, is suitable for large-scale reservoir simulation, can improve computational efficiency and reduce the risk of computational divergence. Therefore, this application proposes a method and system for simulating oil-water distribution in the polymer flooding process of oil reservoirs based on cellular automata, constructs oil-water migration rules suitable for non-Darcy seepage, and designs an efficient parallel computing framework to break through the limitations of traditional methods, provide a scientific basis for the refined development of low permeability reservoirs, and develop a new method for simulating oil-water distribution in the polymer flooding process of low permeability reservoirs, which is of great significance for improving the accuracy of polymer flooding effect evaluation and optimizing injection-production strategies.

[0054] like Figure 1 As shown, an embodiment of the present application provides an oil-water distribution simulation method in an oil reservoir polymer flooding process based on cellular automata, which specifically includes the following contents.

[0055] S1: Divide the reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes.

[0056] S2: According to the three-dimensional coordinates of the cell nodes, determine the neighborhood cell nodes and construct a cellular automaton model; the cellular automaton model is a Moore-type cellular automaton model; the Moore-type cellular automaton model is used to characterize the oil reservoir storage space.

[0057] S3: Initializing the reservoir state parameters at the end of the water flooding according to the polymer flooding scheme parameters, and establishing the boundary conditions of the Moore-type cellular automaton model.

[0058] S4: Based on the boundary conditions, an evolution rule is formulated for each neighborhood cellular node in the Moore-type cellular automaton model.

[0059] S5: Based on the evolution rule and according to the numerical stability rule, the parameters of each neighborhood cellular node in the Moore-type cellular automaton model are adjusted.

[0060] S6: formulating a nonlinear control rule of relative permeability for the Moore-type cellular automaton model according to the parameters, so as to simulate the oil-water distribution in the polymer flooding process of the oil reservoir of the Moore-type cellular automaton model.

[0061] In order to solve technical problems such as low accuracy of oil-water distribution simulation in the polymer flooding process of low permeability oil reservoirs and difficulty in describing non-Darcy seepage, the present application provides a method for simulating oil-water distribution in the polymer flooding process of oil reservoirs based on cellular automata. There are four basic types of neighbor determination in cellular automata models (von Neumann type, Moore type, extended Moore type and Magolus type). In this application, the Moore type cellular three-dimensional neighborhood space that can identify the closest cells around the central cell is selected, that is, the Moore type cellular automaton model.

[0062] Further, in an exemplary embodiment, S1 may be replaced by the following steps.

[0063] S101: Divide the reservoir space into rectangular grids according to a spatial rectangular coordinate system, with each rectangular grid serving as a cell node; the spatial rectangular coordinate system is established with any corner point of the reservoir as the origin, the horizontal direction of the reservoir as the x-axis, the vertical direction of the reservoir as the y-axis, and the depth direction of the reservoir as the z-axis; the corner point is the corner of any rectangular grid.

[0064] S102: determining cell nodes with different attributes according to the different positions of the cell nodes in the divided rectangular grid; the attributes include permeability, oil saturation, porosity, sand layer thickness, oil production, water injection rate, natural gamma and mud content.

[0065] S103: Determine the three-dimensional coordinates of the cellular nodes according to the coordinates of the cellular nodes with different attributes in the spatial rectangular coordinate system.

[0066] According to the three-dimensional distribution of oil reservoirs in space, well network system and oil-water seepage pattern, an independent coordinate system is established to divide the oil reservoir space into high-density rectangular grids. According to the common spacing between production wells and injection wells and the requirements of county-level engineering, the grid size of cellular node mapping is set. Each grid is regarded as a cellular node. Different types of nodes are set according to the different positions of the nodes in the well network, and their relative coordinates in physical space are used as the three-dimensional coordinates of the cellular node.

[0067] According to the relative independence of the reservoirs, each reservoir is set as a grid slice. Each grid slice forms a two-dimensional grid in the horizontal direction. According to the inter-layer interference between the reservoirs, the neighbor relationship between the cell nodes of the upper and lower slices is set. The 26 cell nodes that have an adjacent relationship with a cell node in the physical space are taken as its neighbor nodes to construct a Moore-type cellular three-dimensional neighborhood space. The adjacent cell nodes of the same reservoir in the neighborhood space have a complex and close percolation relationship, and the adjacent cell nodes belonging to the upper and lower slices have a simple and loose inter-layer interference relationship, such as Figure 2 The following are the formation parameters for constructing cells. Figure 3 The initial parameter field constructed based on the residual oil sensitivity parameters is shown.

[0068] The model consists of multiple cells, each with attributes such as porosity, permeability, oil saturation, and water saturation. Oil saturation provides a direct reflection of the remaining oil distribution, and the oil saturation value for each grid cell is set as the state of the corresponding cell node. The oil saturation range is divided into intervals, with each interval mapped to a state. Statistical analysis of all reservoir data for the target block revealed the following 10 intervals based on the density distribution of oil saturation: (0-20%), (20%-25%), (25%-30%), (30%-35%), (35%-40%), (40%-45%), (45%-50%), (50%-55%), (55%-60%), and (60%-100%).

[0069] To accurately reflect the starting conditions of the polymer flooding phase after waterflooding, the initial state parameters of the cellular automaton model must be set to the reservoir state at the end of waterflooding, taking into account the actual reservoir development history. Specifically, after the spatial grid is constructed, each cell must be assigned basic attributes corresponding to the current moment, including residual oil saturation after waterflooding, water saturation, formation pressure, and initial polymer concentration distribution. These initial state parameters form the starting point for polymer flooding simulations, ensuring physical consistency and engineering realism during the model's evolution. After constructing the basic cell state and attributes, the cell state needs to be initialized to reflect actual production conditions. Based on the polymer flooding scheme parameters, the state parameters of the cellular automaton model are initialized, including the initial pressure of each cell, the oil-water saturation distribution, and the initial polymer concentration at the injection well location, with the polymer concentration set to zero in all other areas. A threshold for the starting pressure gradient and residual oil saturation under low permeability conditions is also set.

[0070] For homogeneous reservoirs, this application mainly assumes that the injection well pressure is Pj and the production well pressure is Pord, then the initial pressure of the neighborhood cell node i is:

[0071]

[0072] in, is the distance from the neighborhood cell node i to the injection well (m); is the distance from the neighborhood cell node i to the production well (m); P inj , P prod For a given injection-production pressure boundary (such as 10MPa and 4MPa).

[0073] For heterogeneous reservoirs, consider the reservoir permeability distribution k(x,y,z) and solve the steady-state Darcy equation Approximate the solution on the CA grid using finite difference / finite element or SOR methods to obtain (If you use a commercial simulator such as CMG / Eclipse to export the pressure field, you can also assign values ​​directly).

[0074] Establish a realistic and reliable polymer concentration diffusion front to provide the model with initial impact zone information. Set the injection well cell, and set the concentration of the injection well corresponding cell to the initial maximum concentration:

[0075] If i∈Ω inj ;

[0076] Among them, C0 is generally 1000-2000ppm (provided by experimental / design data).

[0077] The rest of the cell settings can be set as: Pure water front model: Diffusion initialization model (more physical):

[0078] Where λ is the concentration decay coefficient (controls the diffusion slope) and the distance from the cell to the injection well; This function form can more realistically reproduce the diffusion phenomenon in the early stage of polymer flooding.

[0079] Polymer-water phase viscosity initialization:

[0080] According to the initial concentration, calculate the viscosity of the water phase of each cell: And assigned to the cell for use in the calculation of water phase flow in subsequent evolution.

[0081] Verification of numerical and physical consistency:

[0082] The pressure field must satisfy P inj >P prod , otherwise the displacement direction is not valid.

[0083] The polymer concentration field must decrease monotonically. From the injection well outward, each cell should have P0, C poly 、 Three parameters.

[0084] Further, in an exemplary embodiment, S3 may be replaced by the following steps.

[0085] S301: Initializing reservoir state parameters at the end of water flooding based on polymer flooding scheme parameters; the reservoir state parameters at the end of water flooding include initial pressure, oil saturation distribution, initial polymer concentration at the injection well location, polymer concentration in the remaining area excluding the injection well location, starting pressure gradient threshold, and residual oil saturation threshold.

[0086] S302: Based on the reservoir state parameters at the end of the initialized water flooding, boundary conditions of the Moore-type cellular automaton model are established.

[0087] Further, in an exemplary embodiment, S4 may be replaced by the following steps.

[0088] S401: The evolution rules include fluid transmission rules between adjacent cells, saturation update rules, polymer propagation and viscosity influence rules, and immune rules.

[0089] S402: Exploitation Formulate fluid transport rules between adjacent cells; among them, is the volume of water phase flowing from neighboring cell node i to neighboring cell node j per unit time; k ij is the equivalent permeability, k i is the permeability of the neighborhood cell node i, k j is the permeability of the neighborhood cell node j; A ij is the contact area between two neighboring cell nodes; L ij is the distance between the centers of two neighboring cell nodes; is the average water phase viscosity between two neighboring cell nodes; ΔP ij is the pressure difference, ΔP ij =Pi-Pj, Pi is the pressure of the neighborhood cell node i, Pj is the pressure of the neighborhood cell node j; ΔP th is the starting pressure difference threshold; To start the pressure gradient.

[0090] The local evolution rules of cellular automata are defined to simulate oil-water two-phase flow and polymer interaction, including the formulation of fluid transport rules between adjacent cells (the displacement direction is determined by the pressure gradient, and the transport amount is related to the pressure difference and permeability coefficient).

[0091] S403: Exploit Formulate rules for determining water saturation updates; among them, is the water phase saturation of the neighborhood cell node i at time Δt; is the water phase saturation of the neighborhood cell node i at time t; i is the porosity of the neighborhood cell node i; V i is the volume of the neighborhood cell node i; N i is the set of adjacent cells of the neighborhood cell node i; is the flow of water from the neighboring cell node j to the neighboring cell node i; is the flow of water from neighboring cell node i to neighboring cell node j.

[0092] Formulate saturation update rules (update the respective oil and water saturations according to mass conservation when oil and water volume exchange occurs between cells).

[0093] S405: Exploitation Formulate oil saturation update rules; among them, is the oil phase saturation of neighborhood node i at time t+Δt.

[0094] S406: Exploitation Determine the rules affecting polymer propagation and viscosity; among them, is the polymer concentration of the neighborhood cell node i at time t; is the polymer concentration of the neighborhood cell node i at time t+Δt; is the polymer concentration of the neighborhood cell node i; is the polymer concentration of the neighborhood cell node j; D ij and D ji is the diffusion-convection transport coefficient; λ is the adsorption loss factor.

[0095] Formulate the rules for polymer propagation and viscosity influence, that is, the polymer moves with the water phase and increases the viscosity of the water phase in the cells it passes through, reducing its penetration ability.

[0096] S407: The immune rules include the oil saturation constraint of the neighborhood cell nodes and the pressure difference threshold barrier of the neighborhood cell nodes;

[0097] S408: The oil saturation constraint condition of the neighborhood cell node is: and in, is the current oil phase saturation of the neighborhood cell i; S or is the residual oil threshold; is the flow rate of oil phase from neighboring cell i to neighboring cell j.

[0098] S409: The pressure difference threshold barrier of the neighborhood cell node is: in, is the pressure difference threshold barrier function of the neighborhood cell node; is the flow of oil phase from neighboring cell node i to neighboring cell node j.

[0099] Further, in an exemplary embodiment, S5 may be replaced by the following steps.

[0100] S501: Based on the evolution rule, determine whether the parameters of each neighborhood cell node in the Moore-type cellular automaton model meet the numerical stability rule; the parameters include time step, saturation change and polymer concentration gradient; the numerical stability rule includes time step condition, saturation change restriction condition and polymer concentration gradient condition; the time step condition is The saturation change restriction condition is: The polymer concentration gradient condition is Among them, φi is the porosity of the neighborhood cell node; Vi is the volume of the neighborhood cell node; is the estimated value of the maximum instantaneous flow rate; S safe is the safety factor; N i is the set of adjacent cells of the neighborhood cell node i; ΔT is the time step; is the current water phase saturation of the neighborhood cell node i; is the average water saturation of the neighborhood cell node i; ε s is the saturation change threshold; is the maximum concentration gradient of the neighborhood cell node i; is the polymer concentration of the neighborhood cell node i; δ c is the polymer concentration change limit; N i is the set of adjacent cells of the neighborhood cell node i.

[0101] In order to ensure that the low permeability reservoir polymer flooding simulation based on cellular automata maintains physical consistency and numerical stability during the evolution process, this application constructs a set of numerical stability rules applicable to discrete local evolution mechanisms. Unlike traditional partial differential equation solving methods (such as the finite difference method or finite volume method used by Eclipse and CMG software), the method described in this application relies on local transmission rules between discrete cells to advance, so it is necessary to adopt a more adaptable local stability control strategy to avoid state oscillation, numerical divergence or simulation distortion. In order to limit the sudden change of cellular state in single-step evolution, the maximum available time step ΔT is set. This constraint mechanism replaces the explicit stability criterion based on CFL conditions in traditional methods, which is more in line with the characteristics of local evolution systems.

[0102] To prevent instability caused by drastic jumps in local spatial saturation, this application sets the following restrictions: When the threshold is exceeded, the neighborhood smoothing method is used for weighted correction.

[0103] To avoid nonlinear viscosity distortion caused by sudden changes in polymer concentration, set concentration change limits If it is not satisfied, its concentration update is delayed to simulate the real physical diffusion process. After each step of evolution, the volume conservation of each cell is checked: |ΔV in -ΔV out |<η·V i , where η≤1% is the maximum allowable quality error.

[0104] S502: If yes, formulate a nonlinear control rule of relative permeability for the Moore-type cellular automaton model according to the parameters to simulate the oil-water distribution in the polymer flooding process of the Moore-type cellular automaton model.

[0105] S503: If not, adjust the time step and return to “based on the evolution rule, determine whether the parameters of each neighborhood cell node in the Moore-type cellular automaton model meet the numerical stability rule”.

[0106] If a cell has any of the following numerical anomalies:

[0107] Saturation is out of range (<0 or >1).

[0108] Viscosity overflow

[0109] The local pressure difference is greater than the maximum physical threshold (such as 10 MPa).

[0110] Then automatically execute:

[0111] Roll back the current step evolution result.

[0112] The time step is halved: Δt < 0.5·Δt.

[0113] The local cell is marked as "monitoring state" and dynamic monitoring is added in subsequent steps.

[0114] Unlike the stability control based on the solution of linear equations used by existing numerical simulation software (such as Eclipse and CMG), this application is applicable to cellular automaton models, whose evolution does not rely on the numerical solution of partial differential equations, but is based on local rule-driven state updates. Therefore, the numerical stability rules are constructed based on the dynamic verification of local transmission volume, state change gradient and mass conservation. They can ensure the stability and convergence of the oil and water migration evolution process in discrete systems that lack a global Jacobian matrix or convergence criterion. They have good computational robustness and adaptability and are particularly suitable for low permeability reservoir simulation environments with complex nonlinear flow fields, starting pressure gradient constraints and significant polymer rheological effects.

[0115] like Figure 5 (d) and Figure 5 As shown in (e), further, in an exemplary embodiment, S6 can be replaced by the following steps.

[0116] S601: Exploitation and The dynamic evolution of oil-water relative permeability with saturation is carried out; the dynamic evolution process is the nonlinear control rule of relative permeability; where k ro is the oil relative permeability; is the oil phase endpoint permeability; n o and n w is the nonlinear exponential parameter that controls the shape of the permeability curve; k rw is the water relative permeability; is the endpoint permeability of the water phase; is the normalized water saturation.

[0117] In this application's cellular automaton model, the competitive flow relationship between the oil and water phases at the microscopic pore scale has a decisive influence on the simulation results. To further improve the physical accuracy of the aforementioned saturation update and fluid transport rules, this rule explicitly incorporates the nonlinear response relationship between saturation and relative permeability into the evolution mechanism, dynamically adjusting the mobility of the oil and water phases between cells.

[0118] This rule is further refined on the basis of the saturation update rule and the polymer action rule. By introducing the Corey type empirical function into the cell state update, the dynamic evolution of oil-water relative permeability with saturation is realized. Each cell updates its saturation state S w After that, this rule is immediately called to dynamically update the corresponding oil-water relative permeabilities kro and krw and use them in the next fluid distribution calculation.

[0119] Based on the nonlinear control rule of relative permeability, the corresponding oil-water relative permeability is dynamically updated according to the current saturation of each cell node.

[0120] The oil-water ratio in the polymer flooding process of the Moore-type cellular automaton model is simulated according to the updated oil-water relative permeability.

[0121] In order to realize the full process numerical simulation of the evolution of oil-water distribution during polymer flooding, this application integrates the contents of each step into a unified computing framework on the basis of completing model construction, initial assignment, boundary condition setting and evolution rule construction, and realizes structured expression for machine execution.

[0122] First, the model encodes the three-dimensional cellular structure, attribute fields, boundary types, and initial state in tensor form, constructing an adjacency map and state matrix. At each time step, the system automatically traverses all cells, updates state variables (such as saturation, pressure, and concentration) according to local evolution rules, and performs numerical stability control and conservation checks.

[0123] Finally, the simulation system extracts intermediate states of evolution according to the set output cycle and outputs simulation results, including remaining oil distribution maps, oil-water profiles, polymer concentration fields, and data tables. These results can be used for subsequent visualization analysis, human-computer interaction, and well pattern optimization decisions, forming a complete automated closed-loop simulation process from rule-based logic to physical prediction.

[0124] In order to facilitate users to understand and analyze the oil-water migration state during polymer flooding simulation, this application designs a computer visualization and human-computer interaction system for CA simulation results, realizing the whole process presentation from data to images, such as Figure 4As shown, this application provides system hardware and software environment requirements, which can be adjusted.

[0125] Key points of visualization module design:

[0126] 1. State-image mapping mechanism:

[0127] like Figure 5 As shown in (a), all the cell states (S o , S w , C poly , P) is mapped to the PGB color scale layer.

[0128] 2. 3D Stereo Rendering:

[0129] A structural network-oriented visualization framework (such as PyVista, ParaView, and Unity) is used to render reservoir volume data and implement rotation, scaling, and transparency adjustment.

[0130] 3. Multi-time animation:

[0131] Supports outputting frame animation for each simulation step, forming a "dynamic oil displacement demonstration video" or interactive playback.

[0132] 4. User interaction function:

[0133] You can click to query the detailed properties of a cell (oil content, water content, pressure and polymer concentration) to assist in engineering decision-making.

[0134] Step 1: Construct the cellular space and neighborhood structure:

[0135] A low-permeability reservoir area was discretized into a 50x50x2 three-dimensional regular cubic grid, totaling 5000 cells. Each cell had a spatial scale of 30x30x10m'. A Moore-type 26-adjacency structure was used, and vertical interference between upper and lower layers was considered to form a complete three-dimensional connected cellular space.

[0136] Step 2: Cell state parameter setting:

[0137] Based on geological and test mining data, the following basic attribute fields are assigned to each cell. Average porosity: 11.2%

[0138] Permeability: 24.68md (heterogeneous distribution).

[0139] Stratigraphic depth range: 3800–3900m.

[0140] Crude oil saturation: initial average value is 0.65, locally ranging from 0.32 to 0.5.

[0141] Step 3: State initialization:

[0142] The simulation starting point is set at the end of water flooding and the beginning of polymer flooding. The injection and production wells are arranged as follows:

[0143] Injection well (INJ1): Located in row 5, column 5, set pressure P inj =41MPa.

[0144] Oil well (PROD1): Located in row 45, column 45, set pressure P prod =37MPa.

[0145] Initialization settings:

[0146] The pressure field of each cell is set using the distance interpolation method.

[0147] The polymer concentration field adopts an exponential decay form:

[0148]

[0149] All cells have completed loading of status fields: So, Sw, P, Cpoly.

[0150] Step 4: Boundary condition setting:

[0151] The injection well has a fixed concentration injection boundary: Sw=1.0, Cpoly=1500ppmS_w=1.0, C_{\text{poly}}=1500\ppmSw=1.0, Cpoly=1500ppm.

[0152] The oil well has a fixed pressure absorption boundary: P = 37 MPaP = 37\ MPaP = 37 MPa.

[0153] The rest of the model space boundaries are set as closed boundaries: no fluid passes through.

[0154] All boundary cells are marked as a special type and differential boundary processing logic is applied.

[0155] Step 5: Fluid Transfer Rules:

[0156] Calculate the fluid exchange volume based on the pressure difference and permeability between cells:

[0157]

[0158] Step 6: Saturation update and polymer action rules:

[0159] According to the principle of mass conservation, the oil-water saturation in the cell is updated, and the increase in the viscosity of the water phase due to changes in polymer concentration is taken into account.

[0160]

[0161] Step 7: Immune mechanism and relative permeability regulation:

[0162] If the starting pressure difference is less than 0.08 MPa / m, fluid exchange will be blocked.

[0163] If the oil saturation is lower than 0.25, it is considered as residual oil and further flooding should be stopped.

[0164] Oil-water relative permeability responds nonlinearly to saturation (Corey model).

[0165] Step 8: Visualization and result analysis:

[0166] Output during the simulation run:

[0167] Three-dimensional distribution of remaining oil saturation (every 10 steps).

[0168] Animation of a polymer front cross section.

[0169] Monthly production curve and water cut change chart of injection and production wells.

[0170] The simulation results are exported as VTK and Excel data files.

[0171] like Figure 5 (b) and Figure 5 As shown in (c), step 9: System operation mechanism description:

[0172] This simulation was implemented in Python, combining NumPy array processing and PyVista 3D visualization. The main control program loops through the various evolution rule modules, advancing the evolution at fixed time steps and periodically storing the results. All simulation logic has been codified and fully tested, ensuring the model's high stability and repeatability.

[0173] The embodiment of the present application provides an oil-water distribution simulation device for the polymer flooding process of an oil reservoir based on cellular automata, and the specific modules are described as follows.

[0174] The three-dimensional coordinate determination module is used to divide the oil reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes.

[0175] The cellular automaton model construction module is used to determine the neighborhood cellular nodes according to the three-dimensional coordinates of the cellular nodes and construct a cellular automaton model; the cellular automaton model is a Moore-type cellular automaton model; the Moore-type cellular automaton model is used to characterize the oil reservoir storage space.

[0176] The boundary condition establishment module is used to initialize the reservoir state parameters at the end of water flooding according to the polymer flooding scheme parameters and to establish the boundary conditions of the Moore-type cellular automaton model.

[0177] An evolution rule determination module is used to formulate an evolution rule for each neighborhood cellular node in the Moore-type cellular automaton model based on the boundary conditions.

[0178] A parameter adjustment module is used to adjust the parameters of each neighborhood cellular node in the Moore-type cellular automaton model based on the evolution rule and according to the numerical stability rule.

[0179] The control rule formulation module is used to formulate a nonlinear control rule for relative permeability for the Moore-type cellular automaton model according to the parameters, so as to simulate the oil-water distribution in the oil reservoir polymer flooding process of the Moore-type cellular automaton model.

[0180] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to simulate the oil-water distribution in the oil reservoir polymer flooding process of a cellular automaton. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it can realize the oil-water distribution in the oil reservoir polymer flooding process of a cellular automaton.

[0181] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0182] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0183] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0184] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0185] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0186] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0187] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0188] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0189] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for simulating oil-water distribution in a polymer flooding process of an oil reservoir based on cellular automata, characterized in that: include: Divide the reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes; According to the three-dimensional coordinates of the cell nodes, the neighborhood cell nodes are determined, and a cellular automaton model is constructed; the cellular automaton model is a Moore-type cellular automaton model; the Moore-type cellular automaton model is used to characterize the oil reservoir storage space; Initializing the reservoir state at the end of water flooding according to the parameters of the polymer flooding scheme, and establishing the boundary conditions of the Moore-type cellular automaton model; Formulate an evolution rule for each neighborhood cellular node in the Moore-type cellular automaton model based on the boundary conditions; Based on the evolution rule and in accordance with numerical stability rules, adjusting the parameters of each neighborhood cellular node in the Moore-type cellular automaton model; According to the parameters, a nonlinear control rule of relative permeability is formulated for the Moore-type cellular automaton model to simulate the oil-water distribution in the polymer flooding process of the oil reservoir of the Moore-type cellular automaton model.

2. The method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata according to claim 1, characterized in that: Divide the reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes, including: The reservoir space is divided into rectangular grids according to a spatial rectangular coordinate system, with each rectangular grid serving as a cell node; the spatial rectangular coordinate system is established with any corner point of the reservoir as the origin, the horizontal direction of the reservoir as the x-axis, the vertical direction of the reservoir as the y-axis, and the depth direction of the reservoir as the z-axis; the corner point is the vertex of any rectangular grid; Determining cellular nodes with different attributes according to different positions of the cellular nodes in the divided rectangular grid; the attributes include permeability, oil saturation, porosity, sand layer thickness, oil production, water injection rate, natural gamma and mud content; The three-dimensional coordinates of the cell nodes are determined according to the coordinates of the cell nodes with different attributes in the spatial rectangular coordinate system.

3. The method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata according to claim 1, characterized in that: Based on the parameters of the polymer flooding scheme, the reservoir state at the end of water flooding is initialized, and the boundary conditions of the Moore-type cellular automaton model are established, specifically including: Initializing reservoir state parameters at the end of water flooding according to polymer flooding scheme parameters; the reservoir state parameters at the end of water flooding include initial pressure, oil saturation distribution, initial polymer concentration at the injection well location, polymer concentration in the remaining area excluding the injection well location, a starting pressure gradient threshold, and a residual oil saturation threshold; Based on the reservoir state parameters at the end of the initial water flooding, the boundary conditions of the Moore-type cellular automaton model are established.

4. The method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata according to claim 1, characterized in that: Based on the boundary conditions, an evolution rule is formulated for each neighborhood cell node in the Moore-type cellular automaton model, specifically including: The evolution rules include fluid transmission rules between adjacent cells, saturation update rules, polymer propagation and viscosity influence rules and immune rules; The fluid transmission rule between adjacent cells is: in, is the volume of water phase flowing from neighboring cell node i to neighboring cell node j per unit time; k ij is the equivalent permeability, k i is the permeability of the neighborhood cell node i, k j is the permeability of domain cell node j; A ij is the contact area of ​​the cell nodes in two domains; L ij is the distance between the centers of two neighboring cell nodes; is the average water phase viscosity between two neighboring cell nodes; ΔP ij is the pressure difference, ΔP ij =P i -P j , P i is the pressure of the neighborhood cell node i, P j is the pressure of the neighborhood cell node j; ΔP th is the starting pressure difference threshold; ΔP th =▽P start .L ij , ▽P start To start the pressure gradient; The saturation update rules include water saturation update rules and oil saturation update rules; The water saturation update rule is: in, is the water phase saturation of the neighborhood cell node i at time Δt; is the water phase saturation of the neighborhood cell node i at time t; i is the porosity of the neighborhood cell node i; V i is the volume of the neighborhood cell node i; N i is the set of adjacent cells of the neighborhood cell node i; is the flow of water from the neighboring cell node j to the neighboring cell node i; is the flow of water from the neighboring cell node i to the neighboring cell node j; The oil saturation update rule is: using in, is the oil phase saturation of the neighborhood node i at time t+Δt; use Determine the rules affecting polymer propagation and viscosity; among them, is the polymer concentration of the neighborhood cell node i at time t; is the polymer concentration of the neighborhood cell node i at time t+Δt; is the polymer concentration of the neighborhood cell node i; is the polymer concentration of the neighborhood cell node j; D ij and D ji is the diffusion-convection transport coefficient; λ is the adsorption loss factor; The immune rules include oil saturation constraints of neighboring cellular nodes and pressure difference threshold barriers of neighboring cellular nodes; The oil saturation constraint condition of the neighborhood cell node is: and in, is the current oil phase saturation of the neighborhood cell i; S or is the residual oil threshold; is the flow rate of oil phase from neighboring cell i to neighboring cell j; The pressure difference threshold barrier of the neighborhood cell node is: in, is the pressure difference threshold barrier function of the neighborhood cell node; is the flow of oil phase from neighboring cell node i to neighboring cell node j.

5. The method for simulating oil-water distribution in a polymer flooding process of an oil reservoir based on cellular automata according to claim 1, characterized in that: Based on the evolution rule and according to the numerical stability rule, the parameters of each neighborhood cell node in the Moore-type cellular automaton model are adjusted, specifically including: Based on the evolution rule, it is determined whether the parameters of each neighborhood cell node in the Moore-type cellular automaton model meet the numerical stability rule; the parameters include time step, saturation change and polymer concentration gradient; the numerical stability rule includes time step condition, saturation change restriction condition and polymer concentration gradient condition; the time step condition is The saturation change restriction condition is: The polymer concentration gradient condition is Among them, φi is the porosity of the neighborhood cell node; Vi is the volume of the neighborhood cell node; is the estimated value of the maximum instantaneous flow rate; S safe is the safety factor; N i is the set of adjacent cells of the neighborhood cell node i; ΔT is the time step; is the current water phase saturation of the neighborhood cell node i; is the average water saturation of the neighborhood cell node i; ε s is the saturation change threshold; is the maximum concentration gradient of the neighborhood cell node i; is the polymer concentration of the neighborhood cell node i; δ c is the polymer concentration change limit; N i is the set of adjacent cells of the neighborhood cell node i; If yes, then formulating a nonlinear control rule of relative permeability for the Moore-type cellular automaton model according to the parameters to simulate the oil-water distribution in the polymer flooding process of the Moore-type cellular automaton model; If not, adjust the time step and return to "based on the evolution rule, determine whether the parameters of each neighborhood cellular node in the Moore-type cellular automaton model meet the numerical stability rule".

6. The method for simulating oil-water distribution in a polymer flooding process of an oil reservoir based on cellular automata according to claim 1, characterized in that: According to the parameters, a relative permeability nonlinear control rule is formulated for the Moore-type cellular automaton model to simulate the oil-water distribution in the reservoir polymer flooding process of the Moore-type cellular automaton model, specifically including: use and The dynamic evolution of oil-water relative permeability with saturation is carried out; the dynamic evolution process is the nonlinear control rule of relative permeability; where k ro is the oil relative permeability; is the oil phase endpoint permeability; n o and n w is the nonlinear exponential parameter that controls the shape of the permeability curve; k rw is the water relative permeability; is the endpoint permeability of the water phase; is the normalized water saturation; Based on the nonlinear control rule of relative permeability, the corresponding oil-water relative permeability is dynamically updated according to the current saturation of each cell node; The oil-water distribution in the reservoir polymer flooding process of the Moore-type cellular automaton model is simulated according to the updated oil-water relative permeability.

7. A cellular automaton-based oil-water distribution simulation device for polymer flooding in oil reservoirs, characterized in that: The oil-water distribution simulation device for the polymer flooding process of an oil reservoir based on cellular automata comprises: A three-dimensional coordinate determination module is used to divide the oil reservoir space into rectangular grids and determine the three-dimensional coordinates of the cell nodes; A cellular automaton model construction module is used to determine neighboring cellular nodes based on the three-dimensional coordinates of the cellular nodes and to construct a cellular automaton model; the cellular automaton model is a Moore-type cellular automaton model; the Moore-type cellular automaton model is used to characterize the oil reservoir storage space; A boundary condition establishment module is used to initialize the reservoir state at the end of water flooding according to the parameters of the polymer flooding scheme and to establish the boundary conditions of the Moore-type cellular automaton model; An evolution rule determination module, configured to formulate an evolution rule for each neighborhood cellular node in the Moore-type cellular automaton model based on the boundary conditions; A parameter adjustment module, configured to adjust the parameters of each neighborhood cellular node in the Moore-type cellular automaton model based on the evolution rule and in accordance with a numerical stability rule; The control rule formulation module is used to formulate a nonlinear control rule for relative permeability for the Moore-type cellular automaton model according to the parameters, so as to simulate the oil-water distribution in the oil reservoir polymer flooding process of the Moore-type cellular automaton model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the oil-water distribution simulation method for the polymer flooding process in an oil reservoir based on cellular automation according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for simulating oil-water distribution in an oil reservoir polymer flooding process based on cellular automata according to any one of claims 1 to 6 is implemented.

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