Seepage prediction method based on foam oil evolution and local time step encryption

Abstract

The invention relates to the technical field of foam oil evolution, in particular to a seepage prediction method based on foam oil evolution and local time step encryption, which comprises the following steps: constructing a reservoir geological conceptual model by using rock physical parameters, fluid characteristic parameters and reservoir basic data; defining CO2 foam oil reaction and reaction parameters of the reservoir geological conceptual model in the reservoir geological conceptual model; setting phase permeability parameters of the three-phase fluid; well group parameters are imported, and a production system is determined; setting initial parameters of the reservoir geological conceptual model; solving a pressure field of each step in an allowable material balance error range based on material balance, a continuity equation and a phase state balance equation of the seepage field; and adjusting local encryption time steps, and optimizing model numerical calculation parameters. The method solves the problem that the traditional method is difficult to consider the generation, breaking and phase change of each substance, so that the substance balance error is large.

Description

Technical Field

[0001] This invention relates to the field of foamed oil evolution technology, and in particular to a seepage prediction method based on foamed oil evolution and local time step encryption. Background Technology

[0002] CO2 enhanced oil recovery (EOR) technology has been widely used as an important means. When CO2 is injected into an oil reservoir, it undergoes a physicochemical reaction with the crude oil, forming foam oil. Physically, CO2 dissolves in crude oil, reducing its viscosity and enhancing its fluidity. Simultaneously, CO2 expands the volume of crude oil, increasing the internal pressure of the reservoir. Chemically, CO2 may react with certain components in the crude oil, altering its chemical composition and properties, further promoting crude oil extraction. Furthermore, CO2 can form foam oil in the reservoir, and the unique rheological properties of foam oil significantly influence the seepage patterns of fluids within the reservoir. This complex multiphase flow system and physicochemical processes greatly increase the difficulty of prediction.

[0003] Traditional reservoir numerical simulation methods are mostly based on simulators of black oil models. They are effective in dealing with conventional reservoir development problems, but they have limitations when facing the complex process of CO2 flooding. In terms of material balance calculation, due to the complex mass transfer and chemical reactions between multiple components and phases, traditional methods are difficult to consider the generation, destruction and phase changes of each substance, resulting in large material balance errors. Summary of the Invention

[0004] To address the shortcomings of existing methods, this invention solves the problem that traditional methods are unable to account for the formation, destruction, and phase changes of various substances, resulting in large errors in material balance.

[0005] The technical solution adopted in this invention is: a seepage prediction method based on foam oil evolution and local time step refinement, comprising the following steps: Step 1: Construct a reservoir geological conceptual model using rock physical parameters, fluid characteristic parameters, and basic reservoir data; Step 2: Define the CO2 foam oil reaction and the reaction parameters of the reservoir geological conceptual model in the reservoir geological conceptual model; In a preferred embodiment of the present invention, the CO2 foam oil reaction includes: generation, aggregation and collapse.

[0006] Step 3: Set the phase permeation parameters for the three-phase fluid; Step 4: Import well group parameters and determine the production system; Step 5: Set the initial parameters for the reservoir geological conceptual model; Step 6: Based on the material balance, continuity equation, and phase equilibrium equation of the seepage field, solve for the pressure field at each step within the allowable error range of the material balance. In a preferred embodiment of the present invention, step six includes: Step 61: During the phase state calculation at the current time step, perform a mass balance check; Step 62: Calculate the mass balance error using the following formula: ; in, i Indicates components; j Indicates phase state; k Indicates a reaction; Indicates porosity; S Indicates saturation; Indicates density; X Indicates mole fraction; v Indicates stoichiometry; r Indicates the reaction rate; Indicates Darcy velocity; q j w This indicates the flow rate at the bottom of the well.

[0007] Step 63: Calculate the residual terms in the energy conservation equation; In a preferred embodiment of the present invention, the formula for the residual term is: ; in, U Indicates internal energy; Indicates porosity; Represents the gradient; T Indicates temperature; H Indicates enthalpy; q H' This indicates the energy injection rate.

[0008] Step 64: Perform mass balance calculations based on the phase equilibrium equation; In a preferred embodiment of the present invention, the formula for the phase equilibrium equation is: ; in, f Indicates fugacity, o Represents the oil phase. g It represents the gas phase.

[0009] Step 65, when the mass balance error residuals The simulation is accurate when the material balance does not exceed the preset threshold.

[0010] Step 7: Adjust the local encryption time step and optimize the model's numerical calculation parameters; As a preferred embodiment of the present invention, optimizing the numerical calculation parameters of the model specifically includes: Step 71: Set different production pressure ranges and production gas-oil ratios (GOR) for different production pressures; Step 72: Construct a system based on the gas-oil ratio g r The univariate quadratic nonlinear equation for the balance error; In a preferred embodiment of the present invention, the formula for the quadratic nonlinear equation is as follows: ; Where a, b, and c are correction coefficients.

[0011] Step 73: Predict the equilibrium error using a quadratic nonlinear equation.

[0012] As a preferred embodiment of the present invention, the simulation is further characterized by terminating when the maximum prediction time, the maximum number of iterations, or the target recovery rate is reached.

[0013] As a preferred embodiment of the present invention, a seepage prediction system based on foam oil evolution and local time step encryption includes: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the seepage prediction method based on foam oil evolution and local time step encryption.

[0014] As a preferred embodiment of the present invention, a computer-readable medium storing computer program code implements a seepage prediction method based on foam oil evolution and local time step encryption when executed by a processor.

[0015] The beneficial effects of this invention are: 1. This invention defines the formation and collapse reaction of foam oil, which can effectively capture the changes of CO2 in different phases, making reservoir numerical simulation more accurate and providing a strong basis for development decisions; 2. The material balance detection and local densification time step adjustment mechanism can promptly detect and resolve simulation problems, ensuring reliable results and improving computational efficiency; 3. Optimizing the phase permeability relationship of multiphase fluids, rationally introducing well group parameters, and determining the production system enhanced the oil displacement effect of foam oil and improved crude oil recovery rate. 4. Output key data such as CO2 foam distribution, gas-oil ratio, and recovery rate to help evaluate and optimize oil displacement schemes and promote efficient reservoir development. Attached Figure Description

[0016] Figure 1 This is a flowchart of the seepage prediction method based on foam oil evolution and local time step encryption of the present invention; Figure 2 This is a diagram illustrating the mechanism of CO2 foam formation and collapse as defined in this invention. Figure 3 This is a chemical reaction diagram illustrating the dynamic changes in the formation and collapse of CO2 foam oil according to the present invention. Figure 4 This is the gasoline ratio curve of the present invention; Figure 5 This invention uses CMG to construct a reservoir geological conceptual model diagram; Figure 6 This is a distribution diagram of CO2 foam intensity from the numerical model of this invention; Figure 7 This is a map showing the oil saturation distribution in the numerical model of this invention; Figure 8 This is a three-phase saturation distribution diagram of the numerical model of this invention; Figure 9 This is a graph showing the quadratic function relationship between GOR and mass balance error in this invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments. The drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0018] like Figure 1 As shown, a seepage prediction method based on foam oil evolution and local time step refinement includes the following steps: Traditional numerical simulation methods, when simulating the dissolution and other processes of CO2 in crude oil and water, fail to reflect the dynamic changes in actual oil reservoirs, leading to discrepancies between simulation results and reality. Furthermore, when encountering complex chemical reactions, such as the reaction of multiple components under the influence of CO2, traditional numerical simulation methods lack effective adjustment mechanisms. This is because traditional numerical simulation methods typically use pre-set reaction rates and pathways, making it difficult to adjust the reaction process in a timely manner according to the constantly changing temperature, pressure, and other factors within the oil reservoir. Consequently, they cannot realistically reproduce the impact of complex reactions on fluids, resulting in significant deviations between predictions of oil reservoir development outcomes and actual conditions.

[0019] Step 1: Constructing a reservoir geological conceptual model for the target block; In constructing a reservoir geological conceptual model, CMG inputs rock physical parameters, which affect the seepage characteristics of fluids in the reservoir; it also inputs fluid characteristic parameters to accurately describe the physical properties of crude oil and CO2; and it sets boundary conditions and initial conditions to provide basic data for simulation calculations. Collect basic reservoir data. Figure 5To construct a reservoir geological conceptual model using CMG, the model includes rock physical parameters such as porosity and permeability, as well as fluid characteristic parameters such as crude oil viscosity and CO2 solubility. A reservoir geological conceptual model is established using reservoir geological data to accurately depict the spatial structure and physical property distribution of the reservoir; a five-point well network is laid out, with injection wells located in the center and production wells located in the four corners; Reservoir geological data, such as core parameters and well pattern parameters.

[0020] Step 2: Define the CO2 foam oil reaction and model reaction parameters in CMG; Through experimental research and theoretical analysis, the process of foam oil formation and collapse is clarified and broken down into multiple chemical reactions; the kinetic parameters of each reaction are determined, which can accurately reflect the reaction under different reservoir conditions. Among them, CO2 foam oil reaction, such as Figure 2 , Figure 3 To define the mechanism diagram and chemical reaction diagram of CO2 foam oil formation and collapse; to study the formation, aggregation and collapse process of CO2 foam oil, and to determine its main reactions, including foam oil formation and collapse reactions; The model response parameters include: 1. Frequency coefficients of foam oil generation and collapse reaction are used to predict the generation rate and amount of foam oil during the injection process, as well as the stability and collapse behavior of foam oil in the reservoir. 2. Setting the pressure threshold for the reservoir: Considering the pressure distribution range of the reservoir, set a reasonable pressure limit to ensure that the model can accurately reflect the generation and collapse of foam oil under different pressure conditions. 3. Set relative permeability and viscosity parameters, analyze the variation of these parameters with factors such as temperature, pressure, and foam quality, and establish corresponding mathematical models to accurately describe the influence of foam oil on the flow of oil, water, and gas phases.

[0021] Step 3: Set the phase permeation parameters for the three-phase fluids of oil, water, and gas; The phase permeability parameters for three-phase fluids (oil, water, and gas) include: 1. Optimization of relative permeability curves for three-phase fluids: The relative permeability curves of oil, water, and gas enable the model to accurately reflect the competitive flow behavior of different fluids in porous media. Figure 8 A diagram showing the three-phase saturation distribution under foamed oil seepage conditions; 2. Adjustment of relative permeability under the influence of CO2 foam oil: By adjusting the relative permeability parameter, the reservoir geological concept model can accurately reflect the phenomenon of gas phase flow obstruction after foam oil is generated, thereby more realistically simulating the seepage process of multiphase fluid in the reservoir. 3. Optimization of the influence of wettability and contact angle: The wettability parameters in the model are optimized to consider the changes in reservoir wettability in different regions and different production stages, so as to more accurately simulate the flow characteristics of foam oil fluid in multiphase systems and improve the accuracy of the model. 4. Adjustment of parameters affecting interfacial tension and capillary pressure: Considering the influence of the presence of foamed oil on interfacial properties, optimize the parameters affecting capillary pressure on the phase permeability relationship so that the model can accurately reflect the interfacial interaction between foamed oil fluid and oil and water, and further improve the phase permeability relationship model of multiphase fluid. 5. Dynamic adjustment of relative permeability relationship based on foam oil stability and collapse behavior: Based on the evolution of foam oil formation and collapse, combined with material balance detection (Equation 1-4), when the error exceeds the threshold, the model is encrypted using a quadratic function of GOR and material balance error (Equation 5-16), thereby dynamically updating parameters such as relative permeability and capillary pressure. This allows the model to more accurately simulate the flow changes of foam oil formation and collapse, improving the accuracy of the simulation. It also enables the reservoir geological conceptual model to more accurately simulate the changes in flow characteristics of foam oil during dynamic collapse, improving the accuracy and reliability of the simulation.

[0022] Step 4: Import well group parameters and determine the production system; Based on the geological conditions and development plan of the reservoir, parameters for injection wells and production wells are introduced, and the well network is rationally arranged; the production system is determined, including various control parameters during injection and production processes, to optimize the CO2 oil displacement effect; control parameters include: pressure conditions, flow conditions, and bottom hole pressure; Well group parameters and production system include: 1. Well location and well spacing: Based on the geological structure, reservoir distribution and fluid flow characteristics of the oil reservoir, the location and well spacing of injection wells and production wells are scientifically and rationally determined by combining geological analysis and numerical simulation. 2. Setting operating parameters for injection wells and production wells: Based on the reservoir's pressure, permeability, and foam oil generation requirements, set the injection pressure, injection rate, and CO2 injection volume for injection wells; and set reasonable production pressure differential, production volume, and production rate upper limits for production wells. 3. Well Group Type and Well Network Pattern Selection: By comparing the development effects of different well group and well network patterns, the optimal injection and production system is selected, the efficiency of the injection and production system is optimized, and the oil recovery rate is improved. 4. Injection system setting: Study the impact of CO2 and foam oil injection timing and cycle on oil displacement effect, determine the optimal injection method, and rationally set the injection cycle and injection time during periodic injection to enhance the plugging and oil displacement effect of foam oil and improve crude oil recovery rate. 5. Formulate production system and control strategy, set maximum output limit, pressure control and oil-gas ratio monitoring indicators for produced fluid; optimize production system through real-time monitoring and data analysis to ensure safe, stable and efficient fluid production during foam oil flooding.

[0023] Step 5: Set the initial parameters for the reservoir geological conceptual model; This includes: setting the initial time step of the simulation to ensure both computational accuracy and efficiency; setting the maximum number of iterations to prevent the calculation from getting stuck in an infinite loop; and inputting CO2 injection parameters and initial crude oil property parameters, which are key factors in simulating the CO2 oil displacement process. Initiate the reservoir geological concept model in CMG; during the simulation of foam oil generation and collapse, calculate the changes of various physical quantities (such as pressure, saturation, seepage velocity, etc.) according to the set time step to simulate the complex physicochemical process after CO2 injection into the reservoir; Step 6: Based on the material balance, continuity equation, and phase equilibrium equation of the seepage field, solve for the pressure field at each step within the allowable material balance error range. If the material balance error is too large, it will affect the convergence of the calculation. At each time step, during the phase state calculation for the current time step, a mass balance check is performed. Calculations are performed on each component and phase involved in each stage of the reaction process, taking into account the conservation of mass, energy, and phase equilibrium relationships. During the simulation, the mass conservation equation is used to calculate the mass balance at each step based on formula (1) in order to detect whether there is significant material loss or error in the fluid during the calculation process. The Darcy velocity was calculated using formula (2).

[0024] The energy conservation equation is used to calculate the residual term based on formula (3):

[0025] The phase equilibrium equation is based on formula (4) to calculate the mass equilibrium:

[0026] in, i Indicates components; j Indicates phase state; k Represents a reaction; e represents energy; R represents rock; Indicates the i The residual terms of the mass conservation equation for a given component in a porous medium; X Indicates mole fraction; D Indicates the burial depth, in meters (m). Density is expressed in kg / m³. 3 ; S Saturation is expressed as % (%) r Represents reaction rate, in units of mol / (m 3 ·s); u represents the seepage velocity, in m / s; K This represents the penetration rate, expressed in % %. Indicates the j Viscosity of the phase fluid, expressed in Pa·s; Porosity is expressed as a percentage (%). γ Indicates the gravity coefficient; Indicates the j Gravity coefficient of the phase fluid; v Indicates stoichiometry; This represents the residual term in the energy conservation equation; Represents the vector differential operator; T Temperature is expressed in Kelvin (K). Represents the temperature gradient; U This represents internal energy, measured in J. This represents the internal energy of the rock; H Enthalpy is expressed in J / kg; f This represents fugacity, expressed in Pa. q j w This indicates the bottom hole flow rate, in cubic meters per second (m³). 3 ; q H' Indicates the energy injection rate, in units of J / m² / s; superscript Used for state differentiation, distinguishing the same physical quantity before and after a reaction / phase change; o Represents the oil phase. g Represents the gas phase; By calculating the mass balance error , , ; Determine the accuracy of the simulation results; If the error exceeds the preset threshold (ideally 0), it indicates that there may be a problem with the simulation process and adjustments are needed.

[0027] The operation and material balance monitoring of the reservoir numerical simulation program include: 1. Simulation calculations are performed according to the set well group parameters, injection-production regime, and initial conditions. The program proceeds sequentially according to time steps. Within each time step, numerical calculation methods are used to solve the multiphase fluid flow equation, chemical reaction equation, and phase permeability relationship equation, completing the calculation of parameters such as fluid pressure field, saturation, and phase permeability during the foam oil driving process. Through iterative calculations, these parameters are gradually updated to simulate the generation, flow, coalescence, and collapse processes of CO2 foam oil, as well as the seepage behavior of multiphase fluids in the reservoir; for example... Figure 4 As shown, this reflects the dynamic balance between gas escape and collapse during the seepage of foamed oil, and can be used to determine the stage of reservoir productivity change. 2. Material Balance Detection: Within each time step, based on the principle of material balance, precise calculation methods (such as comprehensive conservation equations, higher-order finite difference methods, finite element methods, and material reaction processes) are used to calculate the total amount of substances such as CO2, crude oil, swelling oil, and foam oil. Through detailed calculation of material inputs, outputs, and accumulations, the material balance error of the current step is calculated. Then, the calculated material balance error is rigorously compared with a preset material balance error threshold. If the material balance error is less than the threshold, the calculation result is considered reliable, and the calculation can continue to the next time step. If the material balance error exceeds the threshold, it indicates potential numerical errors, unreasonable parameters, or instability in the simulation process, requiring adjustment of the time step size. Figure 7 As shown, the red area represents the part of the well where the oil saturation is far from the well area. The area with color changes in the middle corresponds to the area around the injection well where the oil saturation is below 30% around the injection well and above 60% far from the well area, forming a clear displacement front and providing a target area for subsequent infill well deployment.

[0028] 3. Adjust the time step. When the mass balance error is detected to exceed the threshold, the time step adjustment algorithm is automatically used to reduce the current time step and recalculate. When the mass balance error is within the allowable range, the time step can be appropriately increased based on the analysis of computational stability and convergence to improve computational efficiency.

[0029] Step 7: Adjust the local encryption time step and optimize the model's numerical calculation parameters; Adaptive encryption of local time steps is achieved by constructing a critical error identification mechanism. The critical error is the key threshold that triggers local time step encryption, and its value is determined by... Figure 9 The curve shown is determined; Specifically, the calculation is performed using formulas (5)-(16). For different production pressure ranges and production gas-oil ratio (GOR) ranges, a quadratic function relationship between material balance error and GOR is established. When the material balance error calculated in real time exceeds the corresponding critical value, the local time step densification is triggered. The specific adjustment range can be determined by combining dynamic material balance and empirical formulas. 1. When 0 < GOR < 500: When the production pressure is 200 kPa:

[0030] When the production pressure is 2000 kPa:

[0031] When the production pressure is 5000 kPa:

[0032] 2. When 500 < GOR < 1000: When the production pressure is 200 kPa:

[0033] When the production pressure is 2000 kPa:

[0034] When the production pressure is 5000 kPa:

[0035] 3. When 1000 < GOR < 1500: When the production pressure is 200 kPa:

[0036] When the production pressure is 2000 kPa:

[0037] When the production pressure is 5000 kPa:

[0038] 4. When 1500 < GOR: When the production pressure is 200 kPa:

[0039] When the production pressure is 2000 kPa:

[0040] When the production pressure is 5000 kPa:

[0041] in, The equilibrium error after the difference was completed in the previous time step, g r This indicates the gas-oil ratio.

[0042] In the experiment, different material balance errors and initial time steps were set to observe the accuracy and stability of the results; the obtained data were fitted to obtain the quadratic function relationship between GOR and material balance error in different production pressure ranges, thereby determining the empirical formulas (5)-(16).

[0043] Local encryption time step adjustments include: 1. Real-time monitoring of the frequency of foam generation, coalescence, and collapse reactions, as well as the rate of change in saturation in different regions of the reservoir; in identified key areas (such as... Figure 6 The key area refers to the region where the foamed oil is highly reactive and the fluid seepage characteristics are prone to abrupt changes (the seepage pattern is easily altered by the formation and collapse of the foamed oil). Figure 9 The balance error under the current working condition is calculated using formulas (5)-(16). If the actual material balance error exceeds the critical error, it is determined that the time step needs to be encrypted to capture the parameter mutation caused by the dynamics of foam oil. If the error does not exceed the critical value, encryption is not required for the time being.

[0044] 2. Dynamically adjust the time step. Within the critical identification area, based on the critical error, if the actual error > the critical error, use an adaptive time step encryption algorithm to encrypt (reduce) the time step; if the actual error ≤ the critical error, the time step can be appropriately increased. Figure 9 As shown, the curve shows that the material balance error and GOR have a typical quadratic function relationship under different production pressures, providing a quantitative basis for dynamically adjusting the time step. Material balance error refers to the deviation between the "input, output and cumulative quantities" of each component calculated based on the principles of mass conservation, energy conservation and phase equilibrium during reservoir numerical simulation. The critical error is a preset, quantified threshold used to determine whether time step refinement is needed for a local area. Its value is determined by a quadratic function relationship (Equation 5-16) within different production pressures and gas-to-oil ratios (GOR). The adaptive time step encryption algorithm refers to a dynamic optimization mechanism that adjusts the time step size in real time based on changes in material balance error, foam oil dynamic behavior (generation, aggregation, collapse) and seepage characteristics during reservoir simulation.

[0045] 3. Based on adaptive adjustment of material balance error, the material balance error in each time step is calculated in real time, especially in the local densification region; when the error exceeds the critical error, the time step size is further refined, especially in the local densification region; through adaptive adjustment, it is ensured that the high error region obtains a finer time step, thereby maintaining material balance, avoiding numerical deviations in the calculation, and improving the reliability and accuracy of the results. 4. Optimization of reaction frequency coefficients in encrypted time steps: Within locally encrypted time steps, the original reaction frequency coefficients may no longer be applicable due to the smaller time step size. Therefore, using encrypted time step data and more accurate kinetic analysis methods, the reaction frequency coefficients of foam oil formation and collapse reactions are recalculated and optimized to meet the numerical requirements after time step encryption. Based on the recalculated reaction frequency coefficients, optimization is performed, and relevant parameters and calculation methods are adjusted to more closely reflect the actual foam oil formation, coalescence, and collapse behavior, thereby improving accuracy and reliability. 5. Time step feedback and update: The time step strategy is updated in real time based on the calculation results and material balance in the encrypted region. The time step encryption or relaxation strategy is dynamically applied in different regions to ensure a balance between calculation accuracy and efficiency.

[0046] After the local encryption operation is completed, convergence detection is performed on the key nodes; by calculating the rate of change of each parameter at the key nodes, it is determined whether the calculation has reached the convergence state; if it has not converged, the calculation parameters are further encrypted or optimized; if it has converged, the normal time step is restored and the calculation continues.

[0047] Step eight also includes: setting simulation termination conditions: reaching the maximum prediction time, the maximum number of iterations, or the target recovery rate; When the time condition meets the termination condition, the calculation ends and the result data is output, including the distribution of CO2 foam, gas-oil ratio, recovery rate, etc.

[0048] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A seepage prediction method based on foamed oil evolution and local time step refinement, characterized in that, Includes the following steps: Step 1: Construct a reservoir geological conceptual model using rock physical parameters, fluid characteristic parameters, and basic reservoir data; Step 2: Define the CO2 foam oil reaction and the reaction parameters of the reservoir geological conceptual model in the reservoir geological conceptual model; Step 3: Set the phase permeation parameters for the three-phase fluid; Step 4: Import well group parameters and determine the production system; Step 5: Set the initial parameters for the reservoir geological conceptual model; Step 6: Based on the material balance, continuity equation, and phase equilibrium equation of the seepage field, solve for the pressure field at each step within the allowable error range of the material balance. Step 7: Adjust the local encryption time step and optimize the model's numerical calculation parameters.

2. The seepage prediction method based on foamed oil evolution and local time step densification according to claim 1, characterized in that, Step six includes: Step 61: During the phase state calculation at the current time step, perform a mass balance check; Step 62: Calculate the mass balance error using the following formula: ； in, i Indicates components; j Indicates phase state; k Indicates a reaction; Indicates porosity; S Indicates saturation; Indicates density; X Indicates mole fraction; v Indicates stoichiometry; r Indicates the reaction rate; Indicates Darcy velocity; q j w Indicates the flow rate at the bottom of the well; Step 63: Analyze the residual terms in the energy conservation equation. Perform calculations; Step 64: Perform mass balance calculations based on the phase equilibrium equation; Step 65, when the mass balance error residuals The simulation is accurate when the material balance does not exceed the preset threshold.

3. The seepage prediction method based on foam oil evolution and local time step densification according to claim 2, characterized in that, The formula for the residual term is: ； in, U Indicates internal energy; Indicates porosity; Represents the gradient; T Indicates temperature; H Indicates enthalpy; q H' This indicates the energy injection rate.

4. The seepage prediction method based on foam oil evolution and local time step densification according to claim 2, characterized in that, The formula for the phase equilibrium equation is: ； in, f Indicates fugacity, o Represents the oil phase. g It represents the gas phase.

5. The seepage prediction method based on foam oil evolution and local time step refinement according to claim 1, characterized in that, The specific parameters for optimizing the numerical calculation of the model include: Step 71: Set different production pressure ranges and production gas-oil ratios (GOR) for different production pressures; Step 72: Construct a system based on the gas-oil ratio g r and balance error A quadratic nonlinear equation in one variable; Step 73: Predict the equilibrium error using a quadratic nonlinear equation.

6. The seepage prediction method based on foam oil evolution and local time step refinement according to claim 5, characterized in that, The formula for a quadratic nonlinear equation in one variable is: ； Where a, b, and c are correction coefficients.

7. The seepage prediction method based on foamed oil evolution and local time step refinement according to claim 1, characterized in that, The CO2 foam oil reaction includes: formation, aggregation, and collapse.

8. The seepage prediction method based on foam oil evolution and local time step refinement according to claim 1, characterized in that, Also includes: The simulation terminates when the maximum prediction time, maximum number of iterations, or target recovery rate is reached.

9. A seepage prediction system based on foam oil evolution and local time step refinement, characterized in that, include: Memory is used to store instructions that can be executed by the processor; A processor for executing instructions to implement the seepage prediction method based on foam oil evolution and local time step encryption as described in any one of claims 1-8.

10. A computer-readable medium storing computer program code, characterized in that, The computer program code, when executed by a processor, implements the seepage prediction method based on foam oil evolution and local time step encryption as described in any one of claims 1-8.

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