A method for simulating micro-pore flow of CO2 flooding enhanced by porous medium nanofluid

Abstract

The application discloses a kind of porous medium nanofluid enhanced CO2 flooding micro flow simulation methods, it is related to the technical field of oil and gas exploitation.The application constructs oil phase-rock wall wetting system model based on molecular dynamics simulation method, obtains the dynamic change process of rock wall wetting angle under the influence of oil phase and their interaction parameters after simulation, the micro flow model of multiphase fluid constructed based on lattice Boltzmann method is coupled with the micro dynamics model of nanoparticle constructed based on Langevin kinematics method, the random force, viscous resistance, electrostatic repulsion and van der waals force of nanoparticle are comprehensively considered, the nanoparticle-fluid two-way coupling model is obtained, the influence of nanofluid enhanced CO2 flooding on heterogeneous porous medium recovery efficiency is simulated and verified, the micro mechanism of nanofluid and CO2 synergistic enhanced oil displacement is revealed, and technical support is provided 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:

[0005] (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;

[0006] (2) There is no description of the micro-mechanical interaction between nanoparticles and multi-phase fluid;

[0007] (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.

[0008] 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

[0009] 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.

[0010] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0011] A porous medium nanofluid enhanced CO2 flooding micro percolation simulation method, comprising the following steps:

[0012] Step 1, based on the molecular dynamics simulation method, an oil phase-rock wall wetting system model is constructed, molecular dynamics simulation is carried out by using the oil phase-rock wall wetting system model, the wetting angle between the oil phase and the rock wall under the condition of no CO2 and under the condition of CO2 is obtained, and the interaction parameters between the oil phase and the rock wall are determined;

[0013] 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;

[0014] Step 3, based on the Langevin kinematics method, a nanometer particle micro dynamics model is constructed, the random Brownian motion of nanometer particles in fluid is simulated by using the nanometer particle micro dynamics model, and the interaction between nanometer particles and fluid, nanometer particles and nanometer particles, and nanometer particles and rock wall is obtained;

[0015] 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;

[0016] 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.

[0017] Preferably, in the step 1, the following sub-steps are included:

[0018] Step 101, generating an oil phase molecular model and a rock wall surface model based on a 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;

[0019] 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;

[0020] 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 an equilibrium state, to obtain the interaction parameters between the oil phase and the rock wall surface;

[0021] 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;

[0022] Adding CO2 to the oil phase-rock wall surface wetting system model that has reached a stable state in step 102 for molecular dynamics simulation, obtaining the wetting angle evolution graph of the oil phase under 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.

[0023] Preferably, the oil phase molecular model is constructed by using the Forcite module to perform geometric optimization and energy minimization on the oil phase molecular structure according to the structural characteristics of the oil phase molecules, combining the component content and the relative molecular mass of each component in the oil phase molecules, calculating the ratio of the amount of substance 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, using the steepest descent method to preliminarily optimize the oil phase molecular model, and then using the conjugate gradient method to perform geometric optimization on the oil phase molecular model to make the oil phase molecular model reach a stable state, which is used to represent the molecular structure of the crude oil droplet;

[0024] 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.

[0025] 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.

[0026] 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:

[0027] ;

[0028] in,

[0029] ;

[0030] 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;

[0031] 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:

[0032] ;

[0033] wherein, is the serial number of the fluid type; is the external force on the th fluid; is the interaction force between fluids; is the interaction force between the fluid and the rock wall; is the body force;

[0034] The interaction force between the fluids is:

[0035] ;

[0036] wherein, is the total number of fluid types; is the serial number of the fluid that interacts with the th fluid; is the pseudo-potential function of the th fluid, which is used to represent the effective density of the fluid, dimensionless, wherein, is the exponential function, is the fluid density; is the interaction parameter between fluids, when , the interaction force between fluids is attractive, and when , the interaction force between fluids is repulsive; , are the interaction parameters between the th fluid and the th fluid, is the interaction parameter between the th fluid and the th fluid, when , and is not equal to 0, when , , at this time, the single-phase fluid is in equilibrium state, and the attractive force and the repulsive force in the fluid are balanced; is the weight coefficient in the th direction;

[0037] The interaction force between the fluid and the rock wall is:

[0038] ;

[0039] wherein, is the interaction force between the th 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, ;

[0040] 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.

[0041] 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:

[0042] ;

[0043] 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 .

[0044] 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:

[0045] ;

[0046] In the formula, The serial number of the nanoparticle; For the first The mass of each nanoparticle; For the first The speed of each 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;

[0047] The random force exerted by the fluid on the nanoparticles is:

[0048] ;

[0049] 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;

[0050] The viscous resistance exerted by the fluid on the nanoparticles is:

[0051] ;

[0052] In the formula, For time At the location of the nanoparticle The fluid velocity at that location;

[0053] The van der Waals forces between the nanoparticles and other nanoparticles are:

[0054] ;

[0055] in,

[0056] ;

[0057] 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.

[0058] The van der Waals force exerted on the nanoparticles by the rock wall is:

[0059] ;

[0060] 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.

[0061] The electrostatic repulsion force generated by the surface charge of the nanoparticles is:

[0062] ;

[0063] In the formula, For the first The electrostatic repulsion between individual nanoparticles due to their surface charge; The reciprocal of the length of Debye; is the interaction constant between nanoparticles;

[0064] The electrostatic force between the nanoparticles and the rock wall is:

[0065] ;

[0066] In the formula, For the first The electrostatic force between the nanoparticle rock walls.

[0067] Preferably, 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:

[0068] ;

[0069] in,

[0070] ;

[0071] In the formula, The distance weight between the nanoparticles and the surrounding fluid grid points; is the transverse coordinate of the surrounding fluid; is the longitudinal coordinate of the surrounding fluid; is a two-dimensional calculation domain, which is divided into and wherein, is a directional region, , is a directional region, , , all are nanoparticle position coordinates; is an adjustable parameter; is a weight function of the integer points around the nanoparticle;

[0072] is normalized to obtain:

[0073] ;

[0074] wherein, is a two-dimensional calculation domain is the total number of transverse grid divisions; is a two-dimensional calculation domain is the total number of longitudinal grid divisions;

[0075] 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:

[0076] ;

[0077] wherein,

[0078] ;

[0079] wherein, is the fluid flow rate at the nanoparticle;

[0080] The viscous resistance at the nanoparticle is determined by converting the force form of the Langevin kinetic system into the lattice Boltzmann form to express the viscous resistance pulse density function and the random force pulse density function of the nanoparticle;

[0081] The viscous resistance pulse density function caused by the viscous resistance is:

[0082] ;

[0083] wherein, is the viscous resistance pulse density function caused by the viscous resistance;

[0084] The random force pulse density function is: ​

[0085] ;

[0086] In the formula, is a random force impulse density function caused by a random force;

[0087] 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, 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.

[0088] The nanoparticle-fluid bidirectional coupling model is:

[0089] ;

[0090] In the formula, 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.

[0091] 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.

[0092] 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 efficiency curves of the first production scheme and the second production scheme, draw a comparison chart of the recovery efficiency curves, and verify the strengthening effect of nanofluid on the CO2 oil displacement efficiency in a heterogeneous porous medium.

[0093] 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 in the second production plan 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.

[0094] The beneficial technical effects brought by the present application are:

[0095] (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 crude oil and the dynamic change of the wall 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.

[0096] (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.

[0097] (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.

[0098] (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, and being beneficial to improving the oil displacement efficiency, reducing the waste of nanomaterials and the ineffective circulation of CO2, and improving the oil extraction efficiency and reducing the CO2 channeling, and being beneficial to the oil extraction 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

[0099] Figure 1 The flow chart of the porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method is shown.

[0100] Figure 2 The schematic diagram of the oil droplet construction process in the embodiment is shown.

[0101] Figure 3 The temperature change curve of the oil phase-rock wall wetting system model in the molecular dynamics simulation process is shown.

[0102] Figure 4 The rock wall wetting angle evolution diagram corresponding to different types of crude oil conditions under the condition of CO2 is shown.

[0103] Figure 5 The rock wall wetting angle evolution diagram corresponding to different types of crude oil conditions under the condition of CO2 is shown.

[0104] Figure 6 The schematic diagram of the lattice Boltzmann D2Q9 model is shown.

[0105] Figure 7 The schematic diagram of the two production schemes in the embodiment is shown.

[0106] Figure 8 The recovery efficiency curve comparison diagram of the two production schemes in the embodiment is shown.

[0107] Figure 9 The recovery efficiency curve comparison diagram of the two production schemes in the embodiment is shown. DETAILED DESCRIPTION

[0108] The application will be further described in detail below with reference to the drawings and the embodiments.

[0109] The embodiment discloses a porous medium nanofluid enhanced CO2 oil displacement micro-pore flow simulation method, as shown in the figure, which comprises the following steps: Figure 1

[0110] ​Step 1, based on the method of molecular dynamics simulation, an oil phase-rock wall wetting system model is constructed, and the oil phase-rock wall wetting system model is used for molecular dynamics simulation to obtain the wetting angle between the oil phase and the rock wall under the condition of no CO2 and under the condition of CO2, and the interaction parameters between the oil phase and the rock wall are determined to provide parameter basis for the lattice Boltzmann method, which specifically includes the following sub-steps:

[0111] Step 101, based on the Materials Studio software, an oil phase molecular model and a rock wall model are generated, an oil phase-rock wall wetting system model is constructed, and the molecular simulation conditions of the oil phase-rock wall wetting system model are set.

[0112] The oil droplet is generated by using the Materials Studio software, as shown in Figure 2 The oil phase molecular model is constructed according to the structural characteristics of the oil phase molecules. In order to ensure that the oil phase molecular structure is the optimized structure of the corresponding molecules, the Forcite module in the Materials Studio software is used to perform geometric optimization and energy minimization on the oil phase molecular structure. The amount-of-substance ratio of each component is calculated based on the component content and relative molecular mass of each component in the oil phase molecules. 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 oil phase molecular model is optimized by using the steepest descent method, and then the conjugate gradient method is used for geometric optimization to achieve a stable state, thereby obtaining the molecular structure of the crude oil droplet.

[0113] In this embodiment, five commonly used oil phase molecular models of crude oil are constructed, which are light crude oil, relatively light crude oil, medium crude oil, relatively heavy crude oil and heavy crude oil, which are used to restore the molecular structure of the crude oil droplet.

[0114] The rock wall model is constructed according to the lithological characteristics of the reservoir. In this embodiment, the rock is located in a sandstone reservoir, and the composition of the sandstone reservoir is mainly quartz, feldspar and rock debris, and the composition monomer is mainly silicon oxide compound, mainly silicon dioxide monomer. The rock model is constructed by using the Amorphous Cell module. The rock model is provided with a silicon dioxide wall surface, which is treated to be oil-wet for simulating the long-term immersion environment of crude oil. The silicon dioxide wall surface in the rock model pore is cut by using the Cleave Surface module, and the silicon atoms not constituting a tetrahedron in the base silicon dioxide wall surface are removed. Then, the silicon dioxide wall surface is saturated with broken bonds by using the Saturate Surface module to obtain the base silicon dioxide wall surface.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the 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.

[0119] 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.

[0120] 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.

[0121] Step 103, using the oil phase-rock wall wetting system model to analyze the oil phase-wall wettability under CO2 condition.

[0122] 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.

[0123] 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.

[0124] 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:

[0125] ;

[0126] Among them,

[0127] ;

[0128] 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 ; For the first External force in each direction, unit: ; 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.

[0129] 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:

[0130] ;

[0131] 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... .

[0132] 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.

[0133] Specifically, the interaction force between the fluids for:

[0134] ;

[0135] 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... , , .

[0136] The interaction force between the fluid and the rock wall for:

[0137] ;

[0138] 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, .

[0139] 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:

[0140] ;

[0141] 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 .

[0142] Step 3, based on the Langevin kinematics method, a nanoparticle micro-kinetics model is constructed, the nanoparticle micro-kinetics model is used to simulate the random Brownian motion of the nanoparticle in the fluid, and the interaction between the nanoparticle and the fluid, the nanoparticle and the nanoparticle, and the nanoparticle and the rock wall surface is obtained.

[0143] In this embodiment, since the Langevin kinetics method can effectively depict the random Brownian motion of the solid particles suspended in the aqueous phase or other fluids, considering that the nanoparticle undergoes random Brownian motion in the fluid, the force acting on the nanoparticle includes random force, viscous resistance, electrostatic repulsion and van der Waals force, and the nanoparticle micro-kinetics model is determined as:

[0144] ;

[0145] In the formula, is the serial number of the nanoparticle; is the mass of the first nanoparticle, and the unit is ; is the velocity of the first nanoparticle, and the unit is ; is the random force applied by the fluid to the first nanoparticle, and the unit is ; is the viscous resistance applied by the fluid to the first nanoparticle, and the unit is ; is the van der Waals force between the first nanoparticle and other nanoparticles, and the unit is ; is the van der Waals force acting on the first nanoparticle from the rock wall surface, and the unit is ; is the electrostatic repulsion generated by the surface charge of the first nanoparticle, and the unit is ; is the electrostatic force between the first nanoparticle and the rock wall surface, and the unit is . The random force has Gaussian white noise characteristics and a mean value of 0, and is related to the damping coefficient and temperature, specifically, the random force applied by the fluid to the nanoparticle is: ; In the formula, is the calculation domain

[0146] The random force has Gaussian white noise characteristics and a mean value of 0, and is related to the damping coefficient and temperature, specifically, the random force applied by the fluid to the nanoparticle is:

[0147] ;

[0148] In the formula, is the calculation 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; This is the damping coefficient, in units of... ; Here is the Boltzmann constant, which takes the value of ; Temperature, in Kelvin (K). For the serial number , The Kronecker function of nanoparticles; For the computational domain , The Kronecker function; This is the Dirac function.

[0149] The viscous resistance between the fluid and nanoparticles is caused by the fluid viscosity and is related to the nanoparticle size, fluid velocity, and the velocity difference between the nanoparticles and the fluid. Furthermore, the viscous resistance experienced by the nanoparticles is directly proportional to the velocity difference between the nanoparticles and the fluid, but in the opposite direction. Based on Stokes' law, the viscous resistance exerted by the fluid on the nanoparticles is determined as follows:

[0150] ;

[0151] In the formula, For time At the location of the nanoparticle The fluid velocity at that location.

[0152] The van der Waals force between nanoparticles arises from the instantaneous dipole moments of the atoms within the nanoparticles. Hamaker theory can be used to describe the van der Waals potential energy between two spherical nanoparticles. Specifically, in this embodiment, the van der Waals force between the nanoparticles and other nanoparticles is:

[0153] ;

[0154] in,

[0155] ;

[0156] In the formula, For the first The van der Waals forces between individual nanoparticles and other nanoparticles, in units of _ ; The van der Waals potential energy is expressed in J. For the first The radius of a nanoparticle, in nm; For the first The radius of a nanoparticle, in nm; For the first The center of the nanoparticle and the first The distance between the centers of each nanoparticle is expressed in nm. Here is the Hamaker constant, in J, with a value of ; This represents the total number of nanoparticles.

[0157] Based on the van der Waals potential energy of nanoparticles, the van der Waals force exerted on the nanoparticles from the rock wall is determined as follows:

[0158] ;

[0159] In the formula, For the first The van der Waals force exerted on each nanoparticle by the rock wall, in units of ; The vertical distance between the nanoparticles and the rock wall is expressed in nm.

[0160] Furthermore, the electrostatic repulsion generated by the surface charge of the nanoparticles is as follows:

[0161] ;

[0162] In the formula, For the first The electrostatic repulsion force generated by the surface charge of a nanoparticle, in units of _ ; The reciprocal of the length of Debye, in units of ; The interaction constant between nanoparticles is given by . .

[0163] The electrostatic force between the nanoparticles and the rock wall is:

[0164] ;

[0165] In the formula, For the first The electrostatic force between individual nanoparticle rock surfaces, in units of .

[0166] In summary, the method further determines the interaction among nanoparticles, fluid and rock wall in the rock interior, including random force and viscous resistance between nanoparticles and fluid, van der Waals force and electrostatic force between nanoparticles and nanoparticles, and van der Waals force and electrostatic force between nanoparticles and rock wall.

[0167] Step 4: coupling the multiphase fluid microseepage model with the nanoparticle micro-dynamics model to construct a nanoparticle-fluid two-way coupling model.

[0168] Since the seepage process random force and viscous resistance act on the nanoparticles and 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.

[0169] In the Langevin dynamics method, the nanoparticle adopts a Lagrangian grid, that is, the nanoparticle is arranged at a non-integer grid point position; while in the lattice Boltzmann method, the fluid adopts an Eulerian grid, that is, the two-dimensional calculation domain of the fluid is divided into In the coupling process, the nanoparticle at the non-integer grid point cannot obtain the fluid information at this position (i.e. the fluid information at this position is null), so the fluid information of the surrounding integer grid points needs to be obtained first, and the fluid information at the nanoparticle position is solved by interpolation. In order to more accurately obtain the fluid information, a bicubic convolution interpolation method is used to obtain the distance weight of the nanoparticle and the surrounding fluid grid points, and the following formula is obtained:

[0170] ;

[0171] Wherein,

[0172] ;

[0173] In the formula, 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 direction area, , is a direction area, , , , , Both are nanoparticle position coordinates; is a tunable parameter; is the weight function of the integer points around the nanoparticle.

[0174] After normalization processing, we get:

[0175] ;

[0176] In the formula, is a two-dimensional calculation domain is the total number of transverse grid division; is a two-dimensional calculation domain is the total number of longitudinal grid division.

[0177] According to the weight function of the integer points around the nanoparticle, the viscous resistance at the nanoparticle is expressed in the form of Langevin dynamics system force as:

[0178] ;

[0179] In the formula,

[0180] ;

[0181] In the formula, is the fluid flow rate at the nanoparticle.

[0182] The force form in the Langevin dynamics system is converted into the lattice Boltzmann form to determine the viscous resistance pulse density function and the random force pulse density function of the nanoparticle.

[0183] The viscous resistance pulse density function is:

[0184] ;

[0185] In the formula, is the viscous resistance pulse density function caused by the viscous resistance, which is used to represent the influence of the nanoparticle on the fluid.

[0186] The random force pulse density function is:

[0187] ;

[0188] In the formula, is the random force pulse density function caused by the random force;

[0189] Based on the viscous resistance 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, 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.

[0190] Specifically, the nanoparticle-fluid bidirectional coupling model is:

[0191] ;

[0192] In the formula, is a 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 value of [1, 9]; is a unit direction vector of fluid particle migration; is a lattice sound speed, and the unit is , .

[0193] In this embodiment, the nanoparticle-fluid bidirectional coupling model is used to realize the bidirectional coupling of nanoparticles and fluid, and the interfacial tension and wettability regulation performance of nanoparticles are comprehensively considered.

[0194] Step 5, using the nanoparticle-fluid bidirectional coupling model for simulation, verifying the influence of nanofluid enhanced CO2 flooding on the recovery rate of heterogeneous porous media.

[0195] In this embodiment, a production scheme of a target well is preset, including a first production scheme and a second production scheme, as shown in Figure 7 , wherein the first production scheme is to continuously inject supercritical CO2 into the target well, and the second production scheme includes three stages, the first stage is to inject 3PV of supercritical CO2 into the target well, the second stage is to inject nanofluid into the target well, and the third stage is to stop injecting nanofluid into the target well and continue to inject 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, when gas channeling occurs, the second stage of injecting nanofluid is entered, when the cumulative injection amount of injecting nanofluid reaches 1.2PV, the third stage of continuing to inject supercritical CO2 is entered until 3PV is reached, and then the injection of supercritical CO2 is stopped.

[0196] Using the nanoparticle-fluid bidirectional coupling model, the first production scheme and the second production scheme of the target well are simulated respectively, and the recovery rate curves of the first production scheme and the second production scheme are obtained, and a comparison chart of the recovery rate curves is drawn, as shown in Figure 8As shown, a comparison reveals that the first production scheme uses pure supercritical CO2 flooding for displacement, while the second production scheme uses alternating injections of supercritical CO2, nanofluids, and supercritical CO2. In both schemes, supercritical CO2 displaces oil at a relatively rapid rate in the first stage. Gas channeling occurs when 0.39 PV is injected. The first production scheme, using pure CO2 flooding, displaces crude oil at a slower rate after gas channeling because a large amount of supercritical CO2 flows along the main permeation channel. In contrast, the second production scheme, using pure CO2 flooding, displaces crude oil at a slower rate after gas channeling. The second stage of injecting nanofluid significantly improved the oil recovery rate, mainly by using nanofluid to strip the oil film and promote the deep migration of supercritical CO2 to enhance oil displacement. When supercritical CO2 was injected in the third stage, the slope of the recovery rate curve of the second production scheme was still slightly higher than that of the first production scheme. This is because the nanofluid can act as a pre-slug to inhibit the fingering of CO2 in the third stage. At the same time, supercritical CO2 pushes the nanofluid to the depth of the pore throat, where the nanofluid further strips the oil film. The two work together to maximize the oil displacement efficiency.

[0197] The recovery rate of the first production scheme was 70.61%, while that of the second production scheme was 83.52%. The second production scheme showed a 12.91% improvement in recovery rate compared to the first, demonstrating a significant enhancement effect. Therefore, the second production scheme, which alternately injects supercritical CO2 and nanofluid, is preferred. The synergistic effect of these two methods strengthens oil displacement, thus verifying the enhancing effect of nanofluid on CO2 oil displacement efficiency in heterogeneous porous media.

[0198] Considering that nanofluids are key materials for stripping oil films from well walls, and their concentration has a significant impact on enhancing CO2 flooding, this study further utilized a nanoparticle-fluid bidirectional coupling model to conduct multiple simulations of the target well under the second production scheme. In each simulation, only the concentration of injected nanofluid in the second production scheme was changed, using nanofluid concentration as a variable to analyze the influence mechanism. The nanofluid concentration was successively changed to 0.01wt%, 0.03wt%, 0.06wt%, and 0.10wt%. The recovery curves of the target well under different nanofluid injection concentrations were obtained using the nanoparticle-fluid bidirectional coupling model, as shown below. Figure 9 As shown, by Figure 9It can be known that with the increase of the mass concentration of the nanofluid, the final oil recovery rates corresponding to the mass concentrations of 0wt%, 0.01wt%, 0.03wt%, 0.06wt% and 0.10wt% of the nanofluid are 70.38%, 74.52%, 82.42%, 87.31% and 89.59% respectively. Compared with the second stage of injecting pure water, the oil recovery rate is increased by 19.21% at most, that is, the nanofluid can greatly improve the CO2 oil displacement efficiency, thereby verifying the strengthening effect of the nanofluid concentration on the CO2 oil displacement efficiency in the heterogeneous porous medium.

[0199] It can be seen that the method of the present application verifies that the nanofluid can greatly improve the CO2 oil displacement efficiency, and the way of alternately injecting high-concentration nanofluid, CO2 and high-concentration nanofluid can maximize the oil recovery rate, 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 oil reservoirs.

[0200] Of course, the above description is not a limitation of the present application, and the present application is not limited to the above examples, and 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 percolation of CO2 flooding in porous media nanofluidic enhanced oil recovery, characterized in that, The method comprises the following steps: Step 1, based on a molecular dynamics simulation method, an oil phase-rock wall wetting system model is constructed, the oil phase-rock wall wetting system model is used for molecular dynamics simulation, the wetting angle between the oil phase and the rock wall under the condition of no CO2 and the condition of CO2 is obtained, and the interaction parameter between the oil phase and the rock wall is determined; Step 2, based on the lattice Boltzmann method, the interaction parameter between the oil phase and the rock wall is combined to construct a multiphase fluid micro-flow model; Step 3, based on the Langevin kinematics method, a nanoparticle micro-dynamics model is constructed, the nanoparticle micro-dynamics model is used to simulate the random Brownian motion of the nanoparticle in the fluid, and the interaction between the nanoparticle and the fluid, the nanoparticle and the nanoparticle and the nanoparticle and the rock wall is obtained; Step 4, the nanoparticle-fluid two-way coupling model is constructed by coupling the multiphase fluid micro-flow model and the nanoparticle micro-dynamics model; Step 5, the nanoparticle-fluid two-way coupling model is used for simulation, and the influence of the CO2 oil displacement of the nanofluid on the recovery rate of the heterogeneous porous medium is verified.

2. The method of claim 1, wherein the porous media nanofluidic enhanced CO2 flooding micro-pore flow simulation method is characterized by, In the step 1, the following sub-steps are included: Step 101, based on the Materials Studio software, an oil phase molecular model and a rock wall model are generated, the oil phase-rock wall wetting system model is constructed, and the molecular simulation conditions of the oil phase-rock wall wetting system model are set; Step 102, the oil phase-wall wettability under the condition of no CO2 is analyzed by using the oil phase-rock wall wetting system model; The total simulation time and the simulation temperature are set, the molecular dynamics simulation is carried out by using the oil phase-rock wall wetting system model, the temperature change curve of the oil phase-rock wall wetting system model in the molecular dynamics simulation process is obtained, and the wetting angle between the oil phase and the rock wall in the oil phase-rock wall wetting system model is determined after the oil phase-rock wall wetting system model reaches the equilibrium state, so that the interaction parameter between the oil phase and the rock wall is obtained; Step 103, the oil phase-wall wettability under the condition of CO2 is analyzed by using the oil phase-rock wall wetting system model; The CO2 is added to the oil phase-rock wall wetting system model which has reached the stable state in the step 102 for molecular dynamics simulation, the wetting angle evolution graph of the oil phase in the CO2 environment in the molecular dynamics simulation process is obtained, the fitting relationship curve of the oil phase-rock wall wetting angle is fitted, and the interaction parameter between the oil phase and the rock wall under the CO2 environment is obtained.

3. The method of claim 2, wherein the porous media nanofluidic enhanced CO2 flooding micro-pore flow simulation method is characterized by, The oil phase molecular model is constructed by using the Forcite module to perform geometric optimization and energy minimization on the oil phase molecular structure, combining the component content and the relative molecular mass of each component in the oil phase molecular, calculating the ratio of the amount of substance 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, adopting the steepest descent method to preliminarily optimize the oil phase molecular model, and then using the conjugate gradient method to perform geometric optimization on the oil phase molecular model, so that the oil phase molecular model reaches a stable state and is used to represent the molecular structure of the crude oil droplet. The rock wall model is constructed by using an Amorphous Cell module according to the lithological characteristics of the reservoir, and a silicon dioxide wall is arranged in the rock model, the silicon dioxide wall is subjected to oleophilic treatment for simulating a long-term oil immersion environment, the silicon dioxide wall in the rock model pore is cut by using a Cleave Surface module, and silicon atoms not constituting a tetrahedron in the base silicon dioxide wall are removed, and then the silicon dioxide wall is saturated with broken bonds by using a Saturate Surface module, so as to obtain the base silicon dioxide wall; The oil phase- rock wall wetting system model is constructed by combining the oil phase molecular model and the rock wall model, and basic conditions for molecular simulation of the oil phase- rock wall wetting system model are set, including boundary conditions, a temperature control method, a force field system, a simulation ensemble and a geometry optimization method, wherein the boundary conditions are set as periodic boundary conditions, the temperature control method is an 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 regular ensemble, and the geometry optimization method is a Smart algorithm.

4. The method of claim 1, wherein the porous media nanofluidic enhanced CO2 flooding micro-pore flow simulation method is characterized by, In step 2, a lattice Boltzmann method is used to construct a multiphase fluid microseepage model, and control equations of the multiphase fluid microseepage model are set as: ； wherein, ； wherein is the direction index; is the density distribution function; is the density distribution function in equilibrium; is the spatial vector; is the time; is the unit time step; is the collision time; is the external force in the th direction; is the unit directional vector of the particle migration in the th direction; is the cosine function; is the sine function; The external force borne by the fluid in the multiphase fluid microseepage model is used to describe the interaction between adjacent fluid particles and between the fluid particles and the rock wall, and a calculation formula is: ； wherein, is the fluid type index; is the external force on the is the interaction force between fluids; is the interaction force between fluids; is the interaction force between fluids and rock wall; is the body force; The fluid-to-fluid interaction force Is: ； In the formula, is the total number of fluid types; is the fluid serial number of the fluid interacting with the th fluid; is the fluid serial number of the fluid interacting with the th fluid; is the pseudo-potential function of the th fluid, used to represent the effective density of the fluid, dimensionless, is the fluid density; is the fluid-fluid interaction parameter, when , the force between the fluids is attractive, and when , the force between the fluids is repulsive; , are the fluid-fluid interaction parameters between the th fluid and the th fluid, is the fluid-fluid interaction parameter between the th fluid and the th fluid, when , and is not equal to 0, when , , at this time the single-phase fluid is in equilibrium state, and the attractive force and the repulsive force inside the fluid are balanced; is the weight coefficient in the th direction; The interaction force between the fluid and the rock wall surface Is: ； wherein is the force of interaction between the fluid and the rock wall; is the force of interaction between the fluid and the rock wall; is a solid-liquid indicator function, dimensionless, when is a solid-liquid indicator function, dimensionless, when is a solid-liquid indicator function, dimensionless, when is a solid-liquid indicator function, dimensionless, when ; The body force is introduced from outside, and the body force borne by the fluid in the multiphase fluid microseepage model is ignored.

5. The method of claim 4, wherein, An exponential form is introduced to modify the interaction force between the fluid and the rock wall, and the interaction force between the oil phase and the rock wall is: ； wherein is the interaction force between the oil phase and the rock wall surface; is the interaction parameter between the oil phase and the rock wall surface; is the pseudo potential of the oil phase; is the distance between the fluid and the rock wall surface in the direction, when the rock wall surface is not present in the direction, ; is the rock wall surface area thickness influence parameter.

6. The method of claim 4, wherein the porous media nanofluidic enhanced CO2 flooding micro-pore flow simulation method is characterized by, In step 3, considering that when the nanoparticles perform random Brownian motion in the fluid, the force borne by the nanoparticles includes a random force, a viscous drag force, an electrostatic repulsive force and a van der Waals force, a nanoparticle micro-dynamics model is determined as: ； wherein, is the number of nanoparticles; is the mass of the nanoparticle; is the velocity of the nanoparticle; is the random force exerted on the nanoparticle by the fluid; is the viscous drag force exerted on the nanoparticle by the fluid; is the van der Waals force between the nanoparticle and other nanoparticles; is the van der Waals force exerted on the nanoparticle from the rock wall; is the electrostatic repulsion force due to surface charge on the nanoparticle; is the electrostatic force between the nanoparticle and the rock wall; The random force borne by the nanoparticle from the fluid is: ； wherein is the random force exerted by the fluid on the th nanoparticle in the domain ; is the random force exerted by the fluid on the th nanoparticle in the domain ; , are the domains of computation, wherein, is the horizontal coordinate of the nanoparticle, is the vertical coordinate of the nanoparticle; is the damping coefficient; is the Boltzmann constant; is the temperature; is the Kronecker function for the nanoparticle of serial number , ; is the Kronecker function for the domain of computation , ; is the Dirac function; The viscous drag force borne by the nanoparticle from the fluid is: ； wherein is time at the location of the nanoparticle the fluid velocity at the location of the nanoparticle; The van der Waals force between the nanoparticle and other nanoparticles is: ； wherein, ； wherein is the Hamaker constant; is the van der Waals potential between the first nanoparticle and the second nanoparticle; is the van der Waals potential; is the radius of the first nanoparticle; is the radius of the first nanoparticle; is the radius of the first nanoparticle; is the radius of the first nanoparticle; is the distance between the center of the first nanoparticle and the center of the second nanoparticle; is the distance between the center of the first nanoparticle and the center of the second nanoparticle; is the distance between the center of the first nanoparticle and the center of the second nanoparticle; is the Hamaker constant; is the total number of nanoparticles; The van der Waals force borne by the nanoparticle from the rock wall is: ； wherein is the van der Waals force experienced by the th nanoparticle from the rock wall surface; is the vertical distance between the nanoparticle and the rock wall surface; The electrostatic repulsive force generated by the surface charge of the nanoparticle is: ； wherein is the inverse of the Debye length; is the inverse of the Debye length; is the inverse of the Debye length; is the inverse of the Debye length; The electrostatic force between the nanoparticle and the rock wall is: ； wherein is the electrostatic force between the nanoparticle rock wall surfaces.

7. The method of claim 6, wherein the porous media nanofluidic enhanced CO2 flooding micro-pore flow simulation method is characterized by, In step 4, based on a convolution kernel function, the distance weight of the nanoparticle and the surrounding fluid lattice points in the multiphase fluid microseepage model is obtained, and: ； wherein, ； wherein is the distance weight of the nanoparticle to the surrounding fluid grid point; is the horizontal coordinate of the surrounding fluid; is the vertical coordinate of the surrounding fluid; is the two-dimensional computational domain, which is divided into and wherein is the directional area, , is the directional area, , , are the nanoparticle position coordinates; is an adjustable parameter; is the weight function of the integer points around the nanoparticle; After normalization, it is obtained that: ； wherein is a two-dimensional computational domain is the total number of grid cells in the lateral direction; is a two-dimensional computational domain is the total number of grid cells in the longitudinal direction; According to the weight function of the integer points around the nanoparticle, the viscous drag force at the nanoparticle is expressed in the form of the Langevin dynamics system force: ； wherein, ； wherein is the fluid flow rate at the nanoparticle; The viscous drag force pulse density function and the random force pulse density function of the nanoparticle are determined by converting the force form of the Langevin dynamics system into the lattice Boltzmann form; The viscous drag force pulse density function is: ； wherein is the viscous drag density function due to viscous drag forces; The random force pulse density function is: ； wherein is the random force density function due to the random force Based on the viscous resistance 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, and a nanoparticle-fluid two-way coupling model is constructed to obtain the influence of nanoparticles on fluid by using the nanoparticle-fluid two-way coupling model; The nanoparticle-fluid two-way coupling model is: ； wherein is the force source distribution function; is the weight coefficient of the fluid particle, is the fluid velocity vector direction; is the unit direction vector of the fluid particle migration; is the lattice sound speed.

8. The method of claim 1, wherein, In step 5, the production scheme of the target well is preset, including a first production scheme and a second production scheme, 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 injects supercritical CO2 into the target well, the second stage injects nanofluid into the target well, and the third stage stops injecting nanofluid into the target well and continues to inject supercritical CO2; The total displacement fluid injection amount in 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 time, after gas channeling, the second stage is entered to inject nanofluid, when the cumulative injection amount of nanofluid injection reaches a preset value, the third stage is entered, supercritical CO2 is continued to be injected until the preset total displacement fluid injection amount is reached, and then the injection is stopped; The nanoparticle-fluid two-way coupling model is used to simulate the first production scheme and the second production scheme of the target well respectively, and the recovery efficiency curves of the first production scheme and the second production scheme are obtained, and a recovery efficiency curve comparison chart is drawn to verify the strengthening effect of nanofluid on CO2 oil displacement efficiency in heterogeneous porous media; The nanoparticle-fluid two-way coupling model is used to simulate the second production scheme of the target well multiple times, and the concentration of nanofluid injection in the second production scheme is changed each time, and the recovery efficiency curve of the target well under different nanofluid injection concentrations is obtained by using the nanoparticle-fluid two-way coupling model, and the strengthening effect of nanofluid concentration on CO2 oil displacement efficiency in heterogeneous porous media is verified.

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