Microcosmic seepage simulation method for porous medium nanofluid reinforced CO2 oil displacement

Abstract

The invention discloses a porous medium nanofluid reinforced CO2 oil displacement microscopic seepage simulation method, and relates to the technical field of oil and gas extraction. The method comprises the following steps: constructing an oil phase-rock wall surface wetting system model based on a molecular dynamics simulation method, and simulating to obtain a dynamic change process of a rock wall surface wetting angle under the influence of an oil phase and interaction parameters of the oil phase and the rock wall surface wetting angle; the method comprises the following steps: coupling a multi-phase fluid microscopic percolation model constructed based on a lattice Boltzmann method with a nano-particle microscopic dynamic model constructed based on a Langevan kinematics method, and comprehensively considering random force, viscous resistance, electrostatic repulsion and Van der Waals force of nano-particles to obtain a nano-particle-fluid bidirectional coupling model; the method simulates and verifies the influence of nanofluid enhanced CO2 oil displacement on the recovery ratio of the heterogeneous porous medium, reveals the micromechanism of nanofluid and CO2 synergistic enhanced oil displacement, and provides technical support for nanofluid material preparation and CO2-nanofluid displacement production parameter optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploitation, in particular to a method for simulating micro seepage of CO2 flooding enhanced by porous medium nanofluid. BACKGROUND

[0002] CO2 flooding technology can effectively improve the mobility of crude oil and supplement the formation energy, and has become a promising enhanced oil recovery method for low permeability reservoirs. However, CO2 is prone to extract light components in crude oil during the flooding process, resulting in the deposition of heavy components such as asphaltene on the rock wall surface, forming an oil film that is difficult to flow, thereby reducing the flooding efficiency. In recent years, nanofluid technology has attracted widespread attention due to its unique interface regulation ability. It can effectively peel off the adsorbed oil film by changing the wettability of the rock surface, providing a new technical idea for enhancing CO2 flooding. However, existing researches mainly focus on single displacement system, and there is no established method for micro-coupling simulation of CO2, oil, water and nanoparticles. The synergistic mechanism of CO2 and nanofluid is not clear. The lack of this key issue makes the optimization of nanofluid and the optimization of CO2 injection parameters lack theoretical support, which seriously restricts the large-scale application of nanofluid enhanced CO2 flooding technology.

[0003] As a pseudo-molecular method, lattice Boltzmann method can solve macroscopic variables based on the distribution function integral of particle distribution function of molecular micro-cluster. Therefore, this method can directly capture the multi-phase interface (such as the miscible interface of CO2 and oil, and the immiscible interface of oil and water) by setting the phase diffusion of limited width and solving the particle distribution function. The Langevin dynamics method can comprehensively consider the Brownian motion of particles, energy dissipation and particle-fluid dynamic interaction at microscale, providing theoretical guidance for micro-simulation of nanoparticles.

[0004] However, at present, the micro-simulation of CO2 and nanofluid flooding in low permeability reservoirs mainly has the following problems: (1) The current micro seepage model of multi-phase fluid cannot accurately consider the dynamic change process of CO2 extraction of wall crude oil and the wettability of the wall after extraction; (2) There is no description of the micro-mechanical interaction between nanoparticles and multi-phase fluid; (3) It is difficult to effectively realize the multi-phase fluid-solid coupling of CO2, oil, water, nanoparticles and rock wall, which cannot meet the urgent needs of accurate and efficient micro-simulation of nanoparticles and multi-phase fluid and characterization of the micro-process of nanofluid enhanced CO2 flooding.

[0005] Therefore, it is urgent to propose a porous medium nanofluid enhanced CO2 flooding micro percolation simulation method, which couples molecular simulation, lattice Boltzmann method and Langevin dynamics, constructs a multiphase fluid-structure coupling micro simulation model for porous media, and reveals the synergistic enhanced oil displacement mechanism of nanofluid and CO2, so as to provide rule understanding and model basis for green, efficient and sustainable development of low permeability reservoirs. SUMMARY

[0006] The present application aims to solve the above problems, and provides a porous medium nanofluid enhanced CO2 flooding micro percolation simulation method, which is based on molecular dynamics simulation method, constructs an oil phase-rock wall wetting system model, accurately restores the dynamic change process of rock wall wettability, combines the lattice Boltzmann method to construct a multiphase fluid micro percolation model, and cooperates with the Langevin kinematics method to construct a nanometer particle micro dynamics model, fully considers the interface regulation characteristics of nanometer particles, couples the multiphase fluid micro percolation model with the nanometer particle micro dynamics model, constructs a nanometer particle-fluid two-way coupling model and applies it to simulate the nanofluid enhanced CO2 flooding process, thereby providing a technical means for guiding fluid enhanced oil displacement.

[0007] To achieve the above object, the present application adopts the following technical scheme: A porous medium nanofluid enhanced CO2 flooding micro percolation simulation method, comprising the following steps: Step 1, based on the molecular dynamics simulation method, an oil phase-rock wall wetting system model is constructed, and molecular dynamics simulation is carried out by using the oil phase-rock wall wetting system model to obtain the wetting angle between the oil phase and the rock wall under the conditions of no CO2 and with CO2, and determine the interaction parameters between the oil phase and the rock wall; Step 2, based on the lattice Boltzmann method, a multiphase fluid micro percolation model is constructed in combination with the interaction parameters between the oil phase and the rock wall; Step 3, based on the Langevin kinematics method, a nanometer particle micro dynamics model is constructed, the random Brownian motion of nanometer particles in the fluid is simulated by using the nanometer particle micro dynamics model, and the interactions between the nanometer particles and the fluid, the nanometer particles and the nanometer particles, and the nanometer particles and the rock wall are obtained; Step 4, the multiphase fluid micro percolation model is coupled with the nanometer particle micro dynamics model to construct a nanometer particle-fluid two-way coupling model; Step 5, the nanometer particle-fluid two-way coupling model is used for simulation to verify the influence of nanofluid enhanced CO2 flooding on the recovery rate of heterogeneous porous media.

[0008] Preferably, in step 1, the following sub-steps are included: Step 101, generating an oil phase molecular model and a rock wall surface model based on the Materials Studio software, constructing an oil phase-rock wall surface wetting system model, and setting the molecular simulation conditions of the oil phase-rock wall surface wetting system model; Step 102, using the oil phase-rock wall surface wetting system model to analyze the oil phase-wall surface wettability under the condition of no CO2; Setting the total simulation time and the simulation temperature, using the oil phase-rock wall surface wetting system model to perform molecular dynamics simulation, obtaining the temperature change curve of the oil phase-rock wall surface wetting system model during the molecular dynamics simulation, and determining the wetting angle between the oil phase and the rock wall surface in the oil phase-rock wall surface wetting system model after the oil phase-rock wall surface wetting system model reaches the equilibrium state, obtaining the interaction parameters between the oil phase and the rock wall surface; Step 103, using the oil phase-rock wall surface wetting system model to analyze the oil phase-wall surface wettability under the condition of CO2; Adding CO2 to the oil phase-rock wall surface wetting system model that has reached a stable state in step 102 to perform molecular dynamics simulation, obtaining the wetting angle evolution graph of the oil phase in the CO2 environment during the molecular dynamics simulation, fitting to obtain the fitting relationship curve of the oil phase-rock wall surface wetting angle, and obtaining the interaction parameters between the oil phase and the rock wall surface under the CO2 environment.

[0009] Preferably, the oil phase molecular model is constructed by using the Forcite module to perform geometric optimization and energy minimization on the structure of the oil phase molecule, combining the component content and relative molecular mass of each component in the oil phase molecule, calculating the molar ratio of each component, determining the number of molecules of each component in the oil phase, and then using the Amorphous Cell module to construct the oil phase molecular model. The oil phase molecular model is initially optimized using the steepest descent method, and then geometrically optimized using the conjugate gradient method to make the oil phase molecular model reach a stable state, which is used to represent the molecular structure of the crude oil droplet. The rock wall surface model is constructed by using the Amorphous Cell module according to the lithology characteristics of the reservoir, and the rock model is provided with a silicon dioxide wall surface. The silicon dioxide wall surface is treated to be lipophilic to simulate the long-term immersion environment of crude oil. The Cleave Surface module is used to cut the silicon dioxide wall surface in the rock model pores, remove the silicon atoms that do not form tetrahedrons in the silicon dioxide wall surface, and then use the Saturate Surface module to saturate the broken bonds of the silicon dioxide wall surface to obtain the silicon dioxide wall surface. A model of the oil phase molecular model and the rock wall model was constructed by combining the oil phase molecular model and the rock wall wetting system model. The basic conditions for the molecular simulation of the oil phase-rock wall wetting system model were set, including the boundary conditions of the molecular simulation, the temperature control method, the force field system, the simulation ensemble, and the geometric optimization method. The boundary conditions were set as periodic boundaries, the temperature control method adopted the Andersen method, the force field system was set as a second type of force field including the CFF force field and the COMPASS II force field, the simulation ensemble was set as the NVT canonical ensemble, and the geometric optimization method adopted the Smart algorithm.

[0010] Preferably, in step 2, a multiphase fluid micro-flow model is constructed using the lattice Boltzmann method, and the governing equations of the multiphase fluid micro-flow model are set as follows: ; in, ; In the formula, Direction number; It is the density distribution function; This is the density distribution function in equilibrium. It is a spatial vector; For time; The unit time step; The collision time; For the first External forces in each direction; For the first The unit direction vector of particle migration in each direction; It is a cosine function; It is a sine function; In the multiphase fluid micro-seepage model, the external forces acting on the fluid are used to describe the interactions between adjacent fluid particles and between fluid particles and rock. The calculation formula is as follows: ; In the formula, The sequence number is the fluid type. For the first External forces acting on a fluid; The interaction force between fluids; It is the interaction force between the fluid and the rock wall; For physical strength; The interaction force between fluids for: ; In the formula, The total number of fluid types; In order to be with the first The fluid numbers that interact with each other; For the first A pseudopotential function for a fluid, used to represent the effective density of the fluid, dimensionless. ,in, It is an exponential function. For fluid density; For the interaction parameters between fluids, when At that time, the force between fluids is an attractive force. At this time, the force between fluids is a repulsive force; , All are the first The fluid and the first Interaction parameters between fluids For the first The fluid and the first Interaction parameters between fluids, when hour, And not equal to 0, when hour, At this point, the single-phase fluid is in equilibrium, and the attractive and repulsive forces inside the fluid are in balance. For the first Weighting coefficients in each direction; The interaction force between the fluid and the rock wall for: ; In the formula, For the first The interaction force between the fluid and the rock wall; For solid-liquid indicator functions, dimensionless, when When at a solid lattice point, ,when When at a fluid lattice point, ; The physical forces are introduced from the outside, and the physical forces acting on the fluid in the multiphase fluid micro-permeation model are ignored.

[0011] Preferably, an exponential form is introduced to correct the interaction force between the fluid and the rock wall, resulting in the following interaction force between the oil phase and the rock wall: ; In the formula, This refers to the interaction force between the oil phase and the rock wall. These are the interaction parameters between the oil phase and the rock wall. This is the pseudopotential function for the oil phase; For the first The distance between the fluid and the rock wall in each direction, when the... When there are no rock walls in any direction ; The parameters affecting the thickness of the rock wall region are denoted by .

[0012] Preferably, in step 3, considering that the forces acting on nanoparticles during random Brownian motion in a fluid include random forces, viscous drag, electrostatic repulsion, and van der Waals forces, the microscopic dynamic model of the nanoparticles is determined as follows: ; In the formula, The serial number of the nanoparticle; For the first The mass of each nanoparticle; For the first The speed of a nanoparticle; For the first The random force exerted by the fluid on each nanoparticle; For the first The viscous resistance exerted by the fluid on each nanoparticle; For the first Van der Waals forces between individual nanoparticles and other nanoparticles; For the first The van der Waals forces experienced by each nanoparticle from the rock wall; For the first The electrostatic repulsion between individual nanoparticles due to their surface charge; For the first The electrostatic force between the nanoparticles and the rock wall; The random force exerted by the fluid on the nanoparticles is: ; In the formula, For computational domain The Middle The random force exerted by the fluid on each nanoparticle; For computational domain The Middle The random force exerted by the fluid on each nanoparticle; , All are computational domains. ,in, The x-axis represents the nanoparticles. The vertical axis represents the nanoparticles; The damping coefficient; Boltzmann's constant; For temperature; For the serial number , Cronkhite function of the nanoparticles; is the Cronkhite function of the computational domain , ; is the Dirac function; is the viscous drag force exerted by the fluid on the nanoparticle: ; where is the fluid velocity at the location of the nanoparticle at time ; is the van der Waals force between the nanoparticle and other nanoparticles: ; where ; ; where is the van der Waals force between the th nanoparticle and other nanoparticles; is the van der Waals potential; is the radius of the th nanoparticle; is the radius of the th nanoparticle; is the distance between the center of the th nanoparticle and the center of the th nanoparticle; is the Hamaker constant; is the total number of nanoparticles; is the van der Waals force exerted by the rock wall on the nanoparticle: ; where is the van der Waals force exerted by the rock wall on the th nanoparticle; is the perpendicular distance between the nanoparticle and the rock wall; is the electrostatic repulsion force due to surface charge on the nanoparticle: ; where is the electrostatic repulsion force due to surface charge on the th nanoparticle; is the inverse of the Debye length; is the interaction constant between the nanoparticles; is the electrostatic force between the nanoparticle and the rock wall: ; wherein, is the electrostatic force between the th nanoparticle and the rock wall surface.

[0013] Preferably, in step 4, based on the convolution kernel function, the distance weight of the nanoparticle and the surrounding fluid grid points in the micro-flow model of the multiphase fluid is obtained, and the following formula is obtained: ; wherein, ; wherein, is the distance weight of the nanoparticle and the surrounding fluid grid points; is the horizontal coordinate of the surrounding fluid; is the vertical coordinate of the surrounding fluid; is a two-dimensional calculation domain, and the two-dimensional calculation domain is divided into and , wherein, is a directional area, , is a directional area, , , all are the coordinates of the nanoparticle position; is an adjustable parameter; is a weight function of the integer points around the nanoparticle; is normalized to obtain: ; wherein, is the total number of horizontal division grids of the two-dimensional calculation domain ; is the total number of vertical division grids of the two-dimensional calculation domain ; According to the weight function of the integer points around the nanoparticle, the viscous resistance at the nanoparticle is expressed in the form of the Langevin kinetic system force as follows: ; wherein, ; wherein, is the fluid flow rate at the nanoparticle; The viscous resistance pulse density function and the random force pulse density function of the nanoparticle are determined by converting the force form of the Langevin kinetic system into the lattice Boltzmann form: The viscous resistance pulse density function is: ; wherein, is a viscous resistance impulse density function caused by viscous resistance; The random force impulse density function is: ; wherein, is a random force impulse density function caused by random force; Based on the viscous resistance impulse density function and the random force impulse density function, the impulse density function is converted into a force source distribution function, the force source distribution function is introduced into a lattice Boltzmann migration collision equation, and a nanoparticle-fluid bidirectional coupling model is constructed to obtain the influence of the nanoparticle on the fluid by using the nanoparticle-fluid bidirectional coupling model. The nanoparticle-fluid bidirectional coupling model is: ; wherein, is a force source distribution function; is a weight coefficient of a fluid particle, is a fluid velocity vector direction; is a unit direction vector of fluid particle migration; is a lattice sound speed.

[0014] Preferably, in the step S5, a production scheme of the target well is preset, including a first production scheme and a second production scheme, wherein the first production scheme is to continuously inject supercritical CO2 into the target well, and the second production scheme includes three stages, a first stage of injecting supercritical CO2 into the target well, a second stage of injecting nanofluid into the target well, and a third stage of stopping the injection of nanofluid into the target well and continuing to inject supercritical CO2; the total displacement fluid injection amount in the first production scheme is the same as that in the second production scheme, the critical point of the first stage and the second stage in the second production scheme is a gas channeling moment, nanofluid is injected after the gas channeling occurs, the cumulative injection amount of the injected nanofluid reaches a preset value to enter the third stage, supercritical CO2 is continuously injected until the preset total displacement fluid injection amount is reached, and then the injection is stopped; The nanoparticle-fluid bidirectional coupling model is used to simulate the first production scheme and the second production scheme of the target well respectively, to obtain the recovery curves of the first production scheme and the second production scheme, draw a comparison chart of the recovery curves, and verify the strengthening effect of nanofluid on the CO2 oil displacement efficiency in a heterogeneous porous medium. The nanoparticle-fluid two-way coupling model is used to simulate multiple times according to the second production plan of the target well, the concentration of the injected nanofluid is changed each time, the recovery curve of the target well under different nanofluid injection concentrations is simulated by using the nanoparticle-fluid two-way coupling model, and the strengthening effect of the nanofluid concentration on the CO2 oil displacement efficiency in the heterogeneous porous medium is verified.

[0015] The beneficial technical effects brought by the present application are: (1) The porous medium nanofluid enhanced CO2 oil displacement microflow simulation method provided by the present application, based on the molecular dynamics simulation method, constructs an oil phase-rock wall wetting system model, simulates the dynamic evolution of the rock wall wetting, and is coupled with the lattice Boltzmann method, effectively describes the CO2 extraction of crude oil and the dynamic change process of the wall wetting, realizes the micro quantitative characterization of the CO2 extraction of the wall crude oil and the dynamic change of the wetting, and significantly improves the simulation accuracy of the multiphase fluid microflow model, which provides a reliable basis for studying the enhancement of nanofluid on CO2 oil displacement.

[0016] (2) The porous medium nanofluid enhanced CO2 oil displacement microflow simulation method provided by the present application, based on the Langevin dynamics method, constructs a nanoparticle micro-dynamics model, uses the nanoparticle micro-dynamics model to simulate the random Brownian motion of the nanoparticle in the fluid, obtains the interaction force between the nanoparticle and the fluid, the nanoparticle and the nanoparticle, and the nanoparticle and the rock wall, solves the problem that the nanoparticle micro-motion process is difficult to describe and the micro-force is difficult to characterize, and can accurately obtain the kinetic action and interface regulation performance of the nanoparticle, which provides a micro technical means for accurately characterizing the oil displacement enhancement of nanofluid.

[0017] (3) The porous medium nanofluid enhanced CO2 oil displacement microflow simulation method provided by the present application, for the complex physical scene of nanofluid and CO2 synergistic oil displacement, the multiphase fluid microflow model is coupled with the nanoparticle micro-dynamics model, a nanoparticle-fluid two-way coupling model is constructed, and a multiphase multi-component fluid-solid coupling model composed of rock internal wall, water phase, oil phase, nanoparticle and CO2 is realized. Efficient simulation, the model takes into account the molecular level precision and macroscopic calculation efficiency, and provides an efficient tool for studying the strengthening mechanism of nanofluid on CO2 oil displacement.

[0018] (4) The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method, which is based on a micro-pore simulation method, and systematically studies the influence of the concentration of nanoparticles, the injection mode of CO2 oil displacement and the properties of the nanofluid on the oil displacement efficiency, thereby providing a method for accurately optimizing the injection mode and the injection concentration of the displacement fluid, improving the oil displacement efficiency, reducing the waste of nanomaterials and the ineffective circulation of CO2, reducing the CO2 gas channeling and the oil extraction efficiency, and balancing the oil production effect and the carbon sequestration potential, thereby providing technical support for the nanofluid and CO2 oil displacement technology, and laying a foundation for the sustainable development of low-permeability oil reservoirs. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0020] Figure 2 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0021] Figure 3 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0022] Figure 4 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0023] Figure 5 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0024] Figure 6 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0025] Figure 7 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0026] Figure 8 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method.

[0027] Figure 9 The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method. DETAILED DESCRIPTION

[0028] The application will be further described in detail below with reference to the drawings and examples.

[0029] The application provides a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method, as shown in the accompanying drawings, which comprises the following steps: Figure 1 ​Step 1: Based on molecular dynamics simulation, construct an oil phase-rock wall wetting system model. Use this model to perform molecular dynamics simulations, obtaining the wetting angles between the oil phase and the rock wall under CO2-free and CO2-containing conditions. Determine the interaction parameters between the oil phase and the rock wall, providing a parameter basis for the lattice Boltzmann method. This step includes the following sub-steps: Step 101: Generate an oil phase molecular model and a rock wall model using Materials Studio software, construct an oil phase-rock wall wetting system model, and set the molecular simulation conditions for the oil phase-rock wall wetting system model.

[0030] Using Materials Studio software to generate oil droplets, such as Figure 2 As shown, the oil phase molecular model is based on the structural characteristics of oil phase molecules. To ensure that the oil phase molecular structure is the optimal structure of the corresponding molecules, the Forcite module in Materials Studio software is used to perform geometric optimization and energy minimization on the oil phase molecular structure. Combining the component content and relative molecular mass of each component in the oil phase molecule, the ratio of the amount of substance of each component is calculated. After determining the number of molecules of each component in the oil phase, the Amorphous Cell module is used to construct the oil phase molecular model. In order to ensure that the molecular composition of the oil phase system matches the actual composition of the target crude oil, the steepest descent method is used to perform preliminary optimization of the oil phase molecular model. Then, the conjugate gradient method is used to perform geometric optimization of the oil phase molecular model to make it reach a stable state, thereby obtaining the molecular structure of crude oil droplets.

[0031] In this embodiment, five common crude oil phase molecular models were constructed: light crude oil, lighter crude oil, medium crude oil, heavier crude oil, and heavy crude oil, to reduce the molecular structure of crude oil droplets.

[0032] The rock wall model is based on the lithological characteristics of the reservoir. In this embodiment, the rock is located in a sandstone reservoir, which is mainly composed of quartz, feldspar, and rock fragments. The constituent monomers are mostly silicon oxide compounds, mainly silica monomers. The rock model is constructed using the Amorphous Cell module, and a silica wall is set in the rock model. The silica wall is oleophilized to simulate the long-term crude oil wetting environment. The Cleave Surface module is used to cut the silica wall in the pores of the rock model, remove the silicon atoms that do not form tetrahedra in the bastized silica wall, and then the Saturate Surface module is used to saturate and break the bonds of the silica wall to obtain the bastized silica wall.

[0033] The oil phase molecular model and the rock wall surface model are combined to construct an oil phase-rock wall surface wetting system model, which is used to simulate the change process of the wettability of the rock wall surface under the influence of crude oil.

[0034] Basic conditions for setting the molecular simulation of the oil phase-rock wall surface wetting system model are set, including boundary conditions of the molecular simulation, a temperature control method, a force field system, a simulation ensemble, and a geometry optimization method. The boundary conditions are set as periodic boundary conditions, the temperature control method is the Andersen method, the force field system is set as a second type of force field including a CFF force field and a COMPASS II force field, the simulation ensemble is set as an NVT canonical ensemble, that is, the number of atoms, the volume, and the temperature of the oil phase-rock wall surface wetting system model are constant during the entire simulation process, the oil phase-rock wall surface wetting system model is kept in a stable thermodynamic state, and the total momentum thereof is zero. The geometry optimization method is the Smart algorithm, that is, the steepest descent method is used in the initial stage to rapidly reduce the system energy of the oil phase-rock wall surface wetting system model, and then the conjugate gradient method or the Newton-Raphson iteration method is switched to improve the optimization accuracy and accelerate the convergence speed, so that the oil phase-rock wall surface wetting system model reaches a stable energy minimization state.

[0035] In step 102, the oil phase-wall surface wettability under the condition of no CO2 is analyzed by using the oil phase-rock wall surface wetting system model, which is used to simulate the influence of different types of crude oil on the wettability of the rock wall surface.

[0036] In this embodiment, the oil phase-rock wall surface wetting system model includes an oil phase and a rock wall surface, the total simulation time is set to 2000 ps, the simulation temperature is set to 310 K, the temperature control method is the Andersen temperature control method, in order to improve the calculation accuracy of long-range Coulomb interaction, the electrostatic interaction adopts the Ewald summation method, and in order to improve the calculation efficiency, the van der Waals interaction adopts the Atom-based truncation summation method based on atom pairs.

[0037] At the same time, in order to determine whether the oil phase-rock wall surface wetting system model has reached the equilibrium stage in the molecular dynamics simulation process, the temperature change curve of the oil phase-rock wall surface wetting system model is obtained, as shown in Figure 3 It is found that the first 250 ps~300 ps is the system equilibrium process, and when the time is greater than 300 ps, the temperature of the oil phase-rock wall surface wetting system model reaches equilibrium.

[0038] For five types of crude oil, the oil phase-rock wall wetting system model is used for molecular dynamics simulation, and the temperature change curve of the oil phase-rock wall wetting system model in the molecular dynamics simulation process is obtained. After the oil phase-rock wall wetting system model reaches the equilibrium state, the wetting angle between the oil phase and the rock wall in the oil phase-rock wall wetting system model is determined, and the rock wall wettability change curve as shown in Figure 4 is obtained, and the interaction parameters between different types of oil phase and rock wall are determined.

[0039] Step 103, using the oil phase-rock wall wetting system model to analyze the oil phase-wall wettability under CO2 condition.

[0040] In order to further clarify the oil displacement characteristics of CO2 in the reservoir, especially the wettability of the rock wall to the crude oil after the extraction of light components by CO2, CO2 is added to the oil phase-rock wall wetting system model which has reached a stable state in step 102 for molecular dynamics simulation, and the wetting angle evolution diagram of the oil phase in the CO2 environment during the molecular dynamics simulation process is obtained as shown in Figure 5 The fitting relationship curve of the oil phase-rock wall wetting angle is obtained by fitting, and the interaction parameters between the oil phase and the rock wall under the CO2 environment are obtained.

[0041] Step 2, based on the lattice Boltzmann method, combining the interaction parameters between the oil phase and the rock wall, a multiphase fluid micro-flow model is constructed.

[0042] In this embodiment, the lattice Boltzmann method is used to construct a multiphase fluid micro-flow model, and the control equation of the multiphase fluid micro-flow model is set as: ; Wherein, ; In the formula, is the direction number, is a positive integer in the interval [1, 9]; is the density distribution function, and the unit is ; is the density distribution function in the equilibrium state, and the unit is ; is a space vector, and the unit is ; is time, and the unit is ; is the unit time step, and the unit is ; is the collision time, and the unit is ; is the external force in the th direction, and the unit is ; For the first The unit direction vector of particle migration in each direction, with units of . ,like Figure 6 As shown; It is a cosine function; It is a sine function.

[0043] In the multiphase fluid micro-seepage model, the external forces acting on the fluid are used to describe the interactions between adjacent fluid particles and between fluid particles and rock. The calculation formula is as follows: ; In the formula, This refers to the sequence number of the fluid type. In this embodiment, the fluid types include CO2, oil phase, and water phase. For the first The external force acting on a fluid, in units of ; The force between fluids, measured in units of . ; The force between the fluid and the rock wall, expressed in units of . ; For physical strength, the unit is... .

[0044] Since the body force is introduced from the outside, the body force on the fluid in the multiphase fluid micro-permeation model is ignored in this embodiment.

[0045] Specifically, the interaction force between the fluids for: ; In the formula, The total number of fluid types; In order to be with the first The fluid numbers that interact with each other; For the first A pseudopotential function for a fluid, used to represent the effective density of the fluid, dimensionless. ,in, It is an exponential function. For fluid density; The interaction parameter between fluids, in units of ,when At that time, the force between fluids is an attractive force. At this time, the force between fluids is a repulsive force; , All are the first The fluid and the first Interaction parameters between fluids For the first The fluid and the first Interaction parameters between fluids, when hour, And not equal to 0, when hour, At this point, the single-phase fluid is in equilibrium, and the attractive and repulsive forces inside the fluid are in balance. For the first The weighting coefficients in each direction, where... , , .

[0046] The interaction force between the fluid and the rock wall for: ; In the formula, For the first The interaction force between a fluid and a rock wall, measured in units of ; For solid-liquid indicator functions, dimensionless, when When at a solid lattice point, ,when When at a fluid lattice point, .

[0047] Furthermore, in this embodiment, an exponential form is introduced to correct the interaction force between the fluid and the rock wall, resulting in the following interaction force between the oil phase and the rock wall: ; In the formula, This refers to the interaction force between the oil phase and the rock wall. These are the interaction parameters between the oil phase and the rock wall. This is the pseudopotential function for the oil phase; For the first The distance between the fluid and the rock wall in each direction, when the... When there are no rock walls in any direction ; The parameters affecting the thickness of the rock wall region are denoted by .

[0048] Step 3: Based on Langevin's kinematics method, construct a microscopic dynamic model of nanoparticles. Use the microscopic dynamic model of nanoparticles to simulate the random Brownian motion of nanoparticles in fluid, and obtain the interactions between nanoparticles and fluid, between nanoparticles, and between nanoparticles and rock walls.

[0049] In this embodiment, since the Langevin dynamics method can effectively characterize the random Brownian motion of solid particles suspended in an aqueous phase or other fluids, and considering that the forces experienced by nanoparticles during random Brownian motion in a fluid include random forces, viscous drag, electrostatic repulsion, and van der Waals forces, the microscopic dynamic model of the nanoparticles is determined as follows: ; In the formula, The serial number of the nanoparticle; For the first The mass of a nanoparticle, in units of ; For the first The velocity of a nanoparticle, in units of ; For the first The random force exerted by the fluid on a nanoparticle, in units of ; For the first The viscous resistance exerted by the fluid on a nanoparticle, in units of ; For the first The van der Waals forces between individual nanoparticles and other nanoparticles, in units of _ ; For the first The van der Waals force exerted on each nanoparticle by the rock wall, in units of ; For the first The electrostatic repulsion force generated by the surface charge of a nanoparticle, in units of _ ; For the first The electrostatic force between a nanoparticle and the rock wall, in units of .

[0050] The random force exhibits Gaussian white noise characteristics with a mean of 0 and is related to the damping coefficient and temperature. Specifically, the random force exerted by the fluid on the nanoparticles is: ; In the formula, For computational domain The Middle The random force exerted by the fluid on each nanoparticle; For computational domain The Middle The random force exerted by the fluid on each nanoparticle; , All are computational domains. ,in, The x-axis represents the nanoparticles. is the ordinate of the nanoparticle; is the damping coefficient, with the unit of ; is the Boltzmann constant, with the value of ; is the temperature, with the unit of K; is the Kronecker function about the nanoparticle with the serial number of , ; is the Kronecker function about the calculation domain , ; is the Dirac function.

[0051] The viscous resistance between the fluid and the nanoparticle is caused by the fluid viscosity, and is related to the particle size of the nanoparticle, the fluid flow rate, and the nanoparticle-fluid flow rate difference. The viscous resistance received by the nanoparticle is proportional to the velocity difference between the nanoparticle and the fluid, and the direction is opposite to that of the velocity difference. Based on the Stokes law, the viscous resistance received by the nanoparticle is determined as follows: ; In the formula, is the fluid velocity at the position of the nanoparticle at the time of .

[0052] The van der Waals force between the nanoparticle and the nanoparticle is caused by the instantaneous dipole moment of the atoms inside the nanoparticle. The Hamaker theory can be used to describe the van der Waals potential energy between two spherical nanoparticles. Specifically, the van der Waals force between the nanoparticle and other nanoparticles in the embodiment is as follows: ; In the formula, ; In the formula, is the van der Waals force between the th nanoparticle and other nanoparticles, with the unit of ; is the van der Waals potential energy, with the unit of J; is the radius of the th nanoparticle, with the unit of nm; is the radius of the th nanoparticle, with the unit of nm; is the distance between the center of the th nanoparticle and the center of the th nanoparticle, with the unit of nm; is the Hamaker constant, with the unit of J, and the value is ; is the total number of nanoparticles.

[0053] Based on the van der Waals potential of the nanoparticle, the van der Waals force on the nanoparticle from the rock wall surface is determined as: ; In the formula, is the van der Waals force on the nanoparticle from the rock wall surface, and the unit is ; is the vertical distance between the nanoparticle and the rock wall surface, and the unit is nm.

[0054] Further, the electrostatic repulsion of the nanoparticle due to surface charge is: ; In the formula, is the electrostatic repulsion of the nanoparticle due to surface charge, and the unit is ; is the inverse of the Debye length, and the unit is ; is the interaction constant between the nanoparticles, and the unit is .

[0055] The electrostatic force between the nanoparticle and the rock wall surface is: ; In the formula, is the electrostatic force between the nanoparticle and the rock wall surface, and the unit is .

[0056] In summary, the method of the present application further determines the interaction between the nanoparticles, the fluid and the rock wall surface in the rock, including the random force and the viscous resistance between the nanoparticles and the fluid, the van der Waals force and the electrostatic force between the nanoparticles and the nanoparticles, and the van der Waals force and the electrostatic force between the nanoparticles and the rock wall surface.

[0057] Step 4: Coupling the multiphase fluid microseepage model with the nanoparticle micro-dynamics model to construct a nanoparticle-fluid two-way coupling model.

[0058] Since the seepage process random force and viscous resistance act on the nanoparticles and the fluid at the same time, based on Newton's third law "the action of force is mutual", the influence of random force and viscous resistance on fluid motion is the key to the coupling of nanoparticles and fluid, which can ensure the momentum and energy conservation of the nanoparticle and fluid system in the model.

[0059] ​​​In the Langevin dynamics method, the nanoparticles adopt Lagrangian grid, i.e. the nanoparticles are arranged at non-integer grid points; while in the lattice Boltzmann method, the fluid adopts Eulerian grid, i.e. the two-dimensional calculation domain of the fluid is divided into grid and the fluid migrates and collides at integer grid points. Therefore, in the coupling process, the problem that the nanoparticles at non-integer grid points cannot obtain the fluid information at the position (i.e. the fluid information at the position is null) will occur, so it is necessary to obtain the fluid information of the surrounding integer grid points and solve the fluid information at the position of the nanoparticles by interpolation. In order to obtain the fluid information more accurately, a bicubic convolution interpolation method is used to obtain the distance weight of the nanoparticles and the surrounding fluid grid points, and the following formula is obtained: ; wherein, ; In the formula, is the distance weight of the nanoparticles and the surrounding fluid grid points; is the horizontal coordinate of the surrounding fluid; is the vertical coordinate of the surrounding fluid; is a two-dimensional calculation domain, and the two-dimensional calculation domain is divided into and wherein, is a directional area, , is a directional area, , , all are the position coordinates of the nanoparticles; is an adjustable parameter; is a weight function of the integer points around the nanoparticles.

[0060] After normalization processing, the following formula is obtained: ; wherein, is the total number of horizontal grid division of the two-dimensional calculation domain ; is the total number of vertical grid division of the two-dimensional calculation domain .

[0061] According to the weight function of the integer points around the nanoparticles, the viscous resistance at the position of the nanoparticles is expressed by using the force form of the Langevin dynamics system as follows: ; wherein, ; In the formula, Fluid flow rate at the nanoparticle.

[0062] The viscous drag impulse density function and the random force impulse density function of the nanoparticle are determined by converting the force form of the Langevin kinetic system into a lattice Boltzmann form.

[0063] The viscous drag impulse density function is: ; In the formula, is the viscous drag impulse density function caused by the viscous drag, used to represent the influence of the nanoparticle on the fluid.

[0064] The random force impulse density function is: ; In the formula, is the random force impulse density function caused by the random force;

[0065] Based on the viscous drag impulse density function and the random force impulse density function, the impulse density function is converted into a force source distribution function, the force source distribution function is introduced into a lattice Boltzmann migration collision equation, a nanoparticle-fluid bidirectional coupling model is constructed, and the influence of the nanoparticle on the fluid is obtained by using the nanoparticle-fluid bidirectional coupling model.

[0066] Specifically, the nanoparticle-fluid bidirectional coupling model is: ; In the formula, is the force source distribution function, and the unit is ; is a weight coefficient of the fluid particle, is a fluid velocity vector direction, and takes a positive integer of [1, 9]; is a unit direction vector of the fluid particle migration; is a lattice sound speed, and the unit is , .

[0067] In the embodiment, the nanoparticle-fluid bidirectional coupling model is used to realize the bidirectional coupling of the nanoparticle and the fluid, and the interfacial tension and the wettability regulation performance of the nanoparticle are comprehensively considered.

[0068] Step 5: Simulate by using the nanoparticle-fluid bidirectional coupling model, and verify the influence of the nanoparticle fluid enhanced CO2 oil displacement on the recovery rate of the heterogeneous porous medium.

[0069] In the embodiment, a production scheme of a target well is preset, including a first production scheme and a second production scheme, such as Figure 7The first production scheme is shown in the figure, wherein the first production scheme is uninterrupted injection of supercritical CO2 into the target well, and the second production scheme includes three stages, the first stage is injection of 3 PV of supercritical CO2 into the target well, the second stage is injection of nanofluid into the target well, and the third stage is to stop injection of nanofluid into the target well and continue injection of supercritical CO2; the crude oil type in the first production scheme and the second production scheme is heavy crude oil, the total displacement fluid injection amount of the first production scheme and the second production scheme is the same, the critical point of the first stage and the second stage in the second production scheme is the gas channeling moment, after the gas channeling occurs, the second stage of injection of nanofluid is entered, after the cumulative injection amount of injection of nanofluid reaches 1.2 PV, the third stage of continuing injection of supercritical CO2 is entered until 3 PV is reached, and then the injection of supercritical CO2 is stopped.

[0070] The nanoparticle-fluid bidirectional coupling model is used to simulate the first production scheme and the second production scheme of the target well respectively, the recovery curves of the first production scheme and the second production scheme are obtained, and a comparison chart of the recovery curves is drawn, as shown in Figure 8 It is found through comparison that the first production scheme is pure supercritical CO2 flooding, and the second production scheme is supercritical CO2, nanofluid and supercritical CO2 injection alternately, and the supercritical CO2 in the first stage of the first production scheme and the second production scheme drives oil at a relatively fast speed, and when 0.39 PV is injected, gas channeling occurs, and after the gas channeling, the pure CO2 flooding of the first production scheme drives oil at a relatively slow speed, because a large amount of supercritical CO2 flows along the main seepage channel, and compared with the pure CO2 flooding of the first production scheme, the second production scheme greatly improves the recovery of crude oil by injecting nanofluid in the second stage, which is mainly achieved by stripping oil film by nanofluid and strengthening oil displacement by promoting deep migration of supercritical CO2, and when supercritical CO2 continues to be injected in the third stage, the slope of the recovery curve of the second production scheme is still slightly higher than that of the first production scheme, because the nanofluid can act as a front plug to inhibit the fingering of CO2 in the third stage, and at the same time, the supercritical CO2 pushes the nanofluid to the deep part of the pore throat, and the nanofluid further strips the oil film in the deep part of the pore throat, and the two synergistically maximize the oil displacement efficiency.

[0071] The recovery of the first production scheme is 70.61%, and the recovery of the second production scheme is 83.52%, and compared with the first production scheme, the recovery of the second production scheme is improved by 12.91%, and the effect of increasing production is significant. Therefore, the second production scheme of supercritical CO2 and nanofluid injection alternately is preferred, which synergistically enhances oil displacement, thereby verifying the strengthening effect of nanofluid on CO2 oil displacement efficiency in heterogeneous porous media.

[0072] Considering that the nanofluid is a key material for stripping the oil film of the wall surface, its concentration has an important influence on the effect of CO2 flooding oil, therefore, further, according to the second production scheme of the target well, the two-way coupling model of nanoparticles-fluid is used to simulate multiple times, the concentration of the injected nanofluid is changed in each simulation, the influence mechanism is analyzed by taking the concentration of the nanofluid as a variable, the concentration of the nanofluid is changed to 0.01wt%, 0.03wt%, 0.06wt% and 0.10wt% in turn, the recovery curve of the target well under different concentrations of the injected nanofluid is simulated by using the two-way coupling model of nanoparticles-fluid, as shown in Figure 9 It can be seen from Figure 9 that with the increase of the mass concentration of the nanofluid, the final oil recovery corresponding to the mass concentration of the nanofluid of 0wt%, 0.01wt%, 0.03wt%, 0.06wt% and 0.10wt% is 70.38%, 74.52%, 82.42%, 87.31% and 89.59% respectively. Compared with the second stage of injecting pure water, the maximum recovery is increased by 19.21%, that is, the nanofluid can greatly improve the CO2 flooding efficiency, thereby verifying the strengthening effect of the concentration of the nanofluid on the CO2 flooding efficiency in the heterogeneous porous medium.

[0073] It can be seen from the above that the method of the present application verifies that the nanofluid can greatly improve the CO2 flooding efficiency, and the way of alternately injecting high-concentration nanofluid, CO2 and high-concentration nanofluid can maximize the oil recovery, thereby reducing the waste of nanomaterials and the ineffective circulation of CO2 in the oil production process, and taking into account the oil production effect and carbon sequestration potential, which is conducive to guiding the sustainable development of low-permeability reservoirs.

[0074] Of course, the above description is not a limitation of the present application, and the present application is not limited to the above examples, the changes, modifications, additions or replacements made by the person skilled in the art within the essential scope of the present application should also belong to the protection scope of the present application.

Claims

1. A method for simulating microscopic seepage in CO2 flooding enhanced by porous media nanofluids, characterized in that, Includes the following steps: Step 1: Based on molecular dynamics simulation, construct an oil phase-rock wall wetting system model, use the oil phase-rock wall wetting system model to perform molecular dynamics simulation, obtain the wetting angle between the oil phase and the rock wall under CO2-free and CO2-containing conditions, and determine the interaction parameters between the oil phase and the rock wall. Step 2: Based on the lattice Boltzmann method, and combined with the interaction parameters between the oil phase and the rock wall, a multiphase fluid micro-permeation model is constructed. Step 3: Based on Langevin's kinematics method, construct a microscopic dynamic model of nanoparticles. Use the microscopic dynamic model of nanoparticles to simulate the random Brownian motion of nanoparticles in fluid, and obtain the interaction between nanoparticles and fluid, between nanoparticles, and between nanoparticles and rock wall. Step 4: Couple the multiphase fluid micro-percolation model with the nanoparticle micro-dynamic model to construct a nanoparticle-fluid bidirectional coupling model; Step 5: The nanoparticle-fluid bidirectional coupling model is used to simulate and verify the effect of nanofluid-enhanced CO2 flooding on the recovery rate of heterogeneous porous media.

2. The porous media nanofluid-enhanced CO2 flooding micro-percolation simulation method according to claim 1, characterized in that, Step 1 includes the following sub-steps: Step 101: Generate an oil phase molecular model and a rock wall model using Materials Studio software, construct an oil phase-rock wall wetting system model, and set the molecular simulation conditions for the oil phase-rock wall wetting system model. Step 102: Analyze the oil phase-wall wettability under CO2-free conditions using an oil phase-rock wall wetting system model. Set the total simulation duration and simulation temperature, and use the oil phase-rock wall wetting system model to perform molecular dynamics simulation. Obtain the temperature change curve of the oil phase-rock wall wetting system model during the molecular dynamics simulation. After the oil phase-rock wall wetting system model reaches equilibrium, determine the wetting angle between the oil phase and the rock wall in the oil phase-rock wall wetting system model, and obtain the interaction parameters between the oil phase and the rock wall. Step 103: Analyze the oil-wall wettability under CO2 conditions using an oil-rock wall wettability system model. CO2 was added to the oil phase-rock wall wetting system model that had reached a stable state in step 102 and molecular dynamics simulation was performed. The evolution diagram of the wetting angle of the oil phase under CO2 environment was obtained during the molecular dynamics simulation. The fitting relationship curve of the wetting angle of the oil phase-rock wall was obtained, and the interaction parameters between the oil phase and the rock wall under CO2 environment were obtained.

3. The porous media nanofluid-enhanced CO2 flooding micro-percolation simulation method according to claim 2, characterized in that, The oil phase molecular model is based on the structural characteristics of oil phase molecules. The Forcite module is used to perform geometric optimization and energy minimization on the oil phase molecular structure. Combining the component content and relative molecular mass of each component in the oil phase molecule, the ratio of the amount of substance of each component is calculated. After determining the number of molecules of each component in the oil phase, the Amorphous Cell module is used to construct the oil phase molecular model. The steepest descent method is used to initially optimize the oil phase molecular model. Then, the conjugate gradient method is used to perform geometric optimization on the oil phase molecular model so that the oil phase molecular model reaches a stable state, which is used to characterize the molecular structure of crude oil droplets. The rock wall model is constructed using the Amorphous Cell module based on the lithological characteristics of the reservoir. The rock model contains silica walls, which are oleophilized to simulate the long-term crude oil immersion environment. The Cleave Surface module is used to cut the silica walls in the pores of the rock model, removing silicon atoms that do not form tetrahedra from the basicized silica walls. Then, the Saturate Surface module is used to saturate and break the bonds of the silica walls to obtain the basicized silica walls. A model of the oil phase molecular model and the rock wall model was constructed by combining the oil phase molecular model and the rock wall wetting system model. The basic conditions for the molecular simulation of the oil phase-rock wall wetting system model were set, including the boundary conditions of the molecular simulation, the temperature control method, the force field system, the simulation ensemble, and the geometric optimization method. The boundary conditions were set as periodic boundaries, the temperature control method adopted the Andersen method, the force field system was set as a second type of force field including the CFF force field and the COMPASS II force field, the simulation ensemble was set as the NVT canonical ensemble, and the geometric optimization method adopted the Smart algorithm.

4. The method for simulating microscopic seepage of CO2 flooding enhanced by porous media nanofluids according to claim 1, characterized in that, In step 2, a multiphase fluid micro-permeation model is constructed using the lattice Boltzmann method, and the governing equations of the multiphase fluid micro-permeation model are set as follows: ； in, ； In the formula, Direction number; It is the density distribution function; This is the density distribution function in equilibrium. It is a spatial vector; For time; The unit time step; The collision time; For the first External forces in each direction; For the first The unit direction vector of particle migration in each direction; It is a cosine function; It is a sine function; In the multiphase fluid micro-seepage model, the external forces acting on the fluid are used to describe the interactions between adjacent fluid particles and between fluid particles and rock. The calculation formula is as follows: ； In the formula, The sequence number is the fluid type. For the first External forces acting on a fluid; The interaction force between fluids; The interaction force between the fluid and the rock wall; For physical strength; The interaction force between fluids for: ； In the formula, The total number of fluid types; In order to be with the first The fluid numbers that interact with each other; For the first A pseudopotential function for a fluid, used to represent the effective density of the fluid, dimensionless. ,in, It is an exponential function. For fluid density; For the interaction parameters between fluids, when At that time, the force between fluids is an attractive force. At this time, the force between fluids is a repulsive force; , All are the first The fluid and the first Interaction parameters between fluids For the first The fluid and the first Interaction parameters between fluids, when hour, And not equal to 0, when hour, At this point, the single-phase fluid is in equilibrium, and the attractive and repulsive forces inside the fluid are in balance. For the first Weighting coefficients in each direction; The interaction force between the fluid and the rock wall for: ； In the formula, For the first The interaction force between the fluid and the rock wall; For solid-liquid indicator functions, dimensionless, when When at a solid lattice point, ,when When at a fluid lattice point, ; The physical forces are introduced from the outside, and the physical forces acting on the fluid in the multiphase fluid micro-permeation model are ignored.

5. The porous media nanofluid-enhanced CO2 flooding micro-percolation simulation method according to claim 4, characterized in that, By introducing an exponential form to correct the interaction force between the fluid and the rock wall, the interaction force between the oil phase and the rock wall is obtained as follows: ； In the formula, This refers to the interaction force between the oil phase and the rock wall. These are the interaction parameters between the oil phase and the rock wall. This is the pseudopotential function for the oil phase; For the first The distance between the fluid and the rock wall in each direction, when the... When there are no rock walls in any direction ; The parameters affecting the thickness of the rock wall region are denoted by .

6. The porous media nanofluid-enhanced CO2 flooding micro-percolation simulation method according to claim 4, characterized in that, In step 3, considering that the forces acting on nanoparticles during random Brownian motion in a fluid include random forces, viscous drag, electrostatic repulsion, and van der Waals forces, the microscopic dynamic model of the nanoparticles is determined as follows: ； In the formula, The serial number of the nanoparticle; For the first The mass of each nanoparticle; For the first The speed of a nanoparticle; For the first The random force exerted by the fluid on each nanoparticle; For the first The viscous resistance exerted by the fluid on each nanoparticle; For the first Van der Waals forces between individual nanoparticles and other nanoparticles; For the first The van der Waals forces experienced by each nanoparticle from the rock wall; For the first The electrostatic repulsion between individual nanoparticles due to their surface charge; For the first The electrostatic force between the nanoparticles and the rock wall; The random force exerted by the fluid on the nanoparticles is: ； In the formula, For computational domain The Middle The random force exerted by the fluid on each nanoparticle; For computational domain The Middle The random force exerted by the fluid on each nanoparticle; , All are computational domains. ,in, The x-axis represents the nanoparticles. The vertical axis represents the nanoparticles; The damping coefficient; Boltzmann's constant; For temperature; For the serial number , The Kronecker function of nanoparticles; For the computational domain , The Kronecker function; It is the Dirac function; The viscous resistance exerted by the fluid on the nanoparticles is: ； In the formula, For time At the location of the nanoparticle The fluid velocity at that location; The van der Waals forces between the nanoparticles and other nanoparticles are: ； in, ； In the formula, For the first Van der Waals forces between individual nanoparticles and other nanoparticles; For van der Waals potential energy; For the first The radius of a nanoparticle; For the first The radius of a nanoparticle; For the first The center of the nanoparticle and the first The distance between the centers of each nanoparticle; It is the Hamaker constant; This represents the total number of nanoparticles. The van der Waals force exerted on the nanoparticles by the rock wall is: ； In the formula, For the first The van der Waals forces experienced by each nanoparticle from the rock wall; This represents the vertical distance between the nanoparticles and the rock wall. The electrostatic repulsion force generated by the surface charge of the nanoparticles is: ； In the formula, For the first The electrostatic repulsion between individual nanoparticles due to their surface charge; The reciprocal of the length of Debye; The interaction constant between nanoparticles; The electrostatic force between the nanoparticles and the rock wall is: ； In the formula, For the first The electrostatic force between the nanoparticle rock walls.

7. The porous media nanofluid-enhanced CO2 flooding micro-percolation simulation method according to claim 6, characterized in that, In step 4, based on the convolution kernel function, the distance weights between nanoparticles and surrounding fluid grid points in the multiphase fluid micro-percolation model are obtained, resulting in: ； in, ； In the formula, The distance weight between the nanoparticles and the surrounding fluid grid points; The x-axis represents the surrounding fluid. The vertical axis represents the surrounding fluid. It is a two-dimensional computational domain, the two-dimensional computational domain Divided into and ,in, for Directional area, , for Directional area, , , All are coordinates of the nanoparticle positions; These are adjustable parameters; The weighting function for integer points around the nanoparticle; After normalization, we get: ； In the formula, Two-dimensional computational domain Total number of horizontally divided grids; Two-dimensional computational domain Total number of grid cells in the vertical division; Based on the weighting function of integer points around the nanoparticles, the viscous drag at the nanoparticles can be expressed in the form of Langevin dynamics: ； in, ； In the formula, The fluid velocity at the nanoparticles; The force form of the Langevin dynamics system is converted into the lattice Boltzmann form to determine the viscous drag pulse density function and the random force pulse density function of the nanoparticles. The viscous resistance pulse density function is: ； In the formula, The viscous resistance pulse density function is caused by viscous resistance. The random force pulse density function is: ； In the formula, Let be the random force impulse density function caused by random forces; Based on the viscous drag pulse density function and the random force pulse density function, the pulse density function is converted into a force source distribution function. The force source distribution function is introduced into the lattice Boltzmann migration collision equation to construct a nanoparticle-fluid bidirectional coupling model. The influence of nanoparticles on fluid is obtained by using the nanoparticle-fluid bidirectional coupling model. The nanoparticle-fluid bidirectional coupling model is as follows: ； In the formula, The force source distribution function; For the weighting coefficients of the fluid particles, The direction of the fluid velocity vector; This is the unit direction vector for fluid particle migration; For the speed of sound in a grid.

8. The method for simulating microscopic seepage of CO2 flooding enhanced by porous media nanofluids according to claim 1, characterized in that, In step 5, the production plan for the target well is preset, including a first production plan and a second production plan. The first production plan involves continuously injecting supercritical CO2 into the target well. The second production plan includes three stages: the first stage involves injecting supercritical CO2 into the target well; the second stage involves injecting nanofluid into the target well; and the third stage involves stopping the injection of nanofluid into the target well while continuing to inject supercritical CO2. The total displacement fluid injection volume is the same in both the first and second production plans. The critical point between the first and second stages in the second production plan is the gas channeling moment. When gas channeling occurs, the second stage of nanofluid injection begins. When the cumulative injection volume of nanofluid reaches a preset value, the third stage begins, where supercritical CO2 is injected until the preset total displacement fluid injection volume is reached, at which point the injection stops. The first and second production schemes of the target well were simulated using a nanoparticle-fluid bidirectional coupling model. The recovery rate curves of the first and second production schemes were obtained, and a comparison chart of the recovery rate curves was plotted to verify the enhancing effect of nanofluid on CO2 oil displacement efficiency in heterogeneous porous media. Multiple simulations were conducted on the target well using a nanoparticle-fluid bidirectional coupling model according to the second production scheme. The concentration of injected nanofluid was changed in each simulation. The recovery curves of the target well under different nanofluid injection concentrations were obtained by simulating the nanofluid concentration, verifying the enhancing effect of nanofluid concentration on CO2 oil displacement efficiency in heterogeneous porous media.

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