Low-permeability reservoir nanofluid oil displacement simulation method based on immersed boundary method

Abstract

The invention discloses a low-permeability reservoir nanofluid oil displacement simulation method based on an immersed boundary method, belongs to the technical field of petroleum and natural gas extraction, is used for fluid oil displacement simulation, and comprises the steps of constructing a two-dimensional pore throat image, constructing an oil-water two-phase seepage simulation method and updating macroscopic fluid parameters. Analyzing the stress condition of the active nano-particles, constructing an active nano-particle kinematical equation, and tracking the motion trail of the active nano-particles; an interaction coupling format between active nanoparticles and fluid is constructed based on an immersed boundary method (IBM), a nanofluid oil displacement process is represented, behavior dynamic parameters of the nanoparticles under different injection conditions are analyzed, and main control factor analysis is carried out. According to the method, the inter-phase acting force is simulated through the pseudo-potential function, accurate simulation of oil displacement by water is achieved, an active nano-particle kinematics control equation is constructed, updating of the flow speed and the position of the active nano-particles is achieved, and design and use of nano-fluid in a low-permeability reservoir site are further guided.

Description

Technical Field

[0001] The invention discloses a low-permeability oil reservoir nanofluid oil displacement simulation method based on an immersed boundary method, and belongs to the technical field of oil and natural gas exploitation. Background Art

[0002] In the field of oil and gas extraction, nanofluids have shown great potential in improving the recovery rate of low-permeability reservoirs, mainly reflected in the fact that nanoparticles can change the wettability of the rock surface, reduce the oil-water interfacial tension, and improve the mobility ratio. The oil recovery process of nanofluids in low-permeability reservoirs involves multiphase interactions between nanoparticles / rock walls / oil / water. Traditional experimental methods are difficult to accurately observe the movement of nanoparticles at the microscopic scale, the interaction between particles and pore walls, etc. Numerical simulation methods are an important means to study the oil recovery of nanofluids in low-permeability reservoirs. The lattice Boltzmann method (LBM) has advantages in dealing with complex boundaries and multiphase flow problems, the discrete element method (DEM) can effectively describe the movement and interaction of particles, and the immersed boundary method (IBM) can effectively deal with boundary problems with complex geometric shapes and accurately simulate the interaction between fluids and dynamic boundaries. Regarding the nanofluid oil recovery process in low permeability reservoirs, effective microscopic mechanism research methods are still urgently needed at the oil field site. Therefore, constructing a microscopic numerical simulation method for nanofluid oil recovery in low permeability reservoirs combining LBM-DEM-IBM is of great significance for in-depth research on the microscopic behavior characteristics of nanoparticles in low permeability reservoirs, nanofluid oil recovery process, and improving the recovery rate of low permeability reservoirs. Summary of the invention

[0003] The object of the present invention is to provide a low permeability reservoir nanofluid oil displacement simulation method based on the immersed boundary method, so as to solve the problem of inaccurate nanofluid oil displacement simulation results in the prior art.

[0004] A nanofluid flooding simulation method for low permeability reservoirs based on an immersed boundary method, comprising: Step 1, extracting the pore throat characteristics of low permeability reservoirs and constructing a two-dimensional pore throat image; Step 2: Based on the lattice Boltzmann method and the Shan-Chen model, an oil-water two-phase seepage simulation method is constructed to update the macroscopic fluid parameters; Step 3: Analyze the stress of active nanoparticles based on discrete element method, construct kinematic equations of active nanoparticles, and track the motion trajectory of active nanoparticles; Step 4: construct the interaction coupling format between active nanoparticles and fluid based on the immersed boundary method IBM; Step 5: Characterize the nanofluid flooding process, analyze the dynamic parameters of the behavior of nanoparticles under different injection conditions, conduct main control factor analysis, and realize the screening of nanofluid injection parameters.

[0005] Step 1 includes making the core sample into a thin slice suitable for observation under a scanning electron microscope, scanning and imaging the core surface, controlling the scanning area through computer software, obtaining high-resolution images at different positions, processing the image data using image processing software, reconstructing a three-dimensional model, and selecting a cross section to obtain a two-dimensional pore throat image for subsequent simulation.

[0006] Step 2 includes: Step 2.1, establishing a computational domain, and the fluid control equation of the fluid grid is the lattice Boltzmann equation: ; ; ; ; ; ; ; In the formula, is the particle swarm distribution function, is the fluid position, is the discrete velocity direction, is the unit direction vector of the particle group velocity component, is the time step, For time, is the collision operator approximated by BGK, is the equilibrium distribution function, is the single relaxation time, is the fluid injection speed, is the weight coefficient, is the fluid density, is the lattice sound speed, is the change in fluid position, is the grid speed.

[0007] Step 2 includes, step 2.2, initialization and , the Shan-Chen model pseudo-potential function is used to simulate the interaction force, and the pseudo-potential force is calculated to represent the interaction between the phases: ; ; ; In the formula, is the pseudo potential function, and are the local densities of oil and water, respectively, and are the reference densities of oil and water, respectively, is the interphase force, is the interaction strength parameter, is a natural constant, is the fluid density in the discrete velocity direction; Step 2.3, calculate and update the macroscopic parameters of the fluid, which include fluid injection velocity, fluid density, fluid pressure and fluid dynamic viscosity: ; ; ; ; In the formula, is the fluid pressure, is the fluid dynamic viscosity.

[0008] Step 3 includes: step 3.1, establishing a contact force model between active nanoparticles and between active nanoparticles and a wall surface, wherein the contact force model includes a normal contact force model and a tangential contact force model; The normal contact force model between active nanoparticles and between active nanoparticles and the wall is established based on the Hertz spring-damper model: ; ; ; ; ; ; ; In the formula, is the normal contact force, is the normal contact coefficient, is the contact normal overlap, is the normal damping coefficient, is the time derivative of the contact normal overlap, is the effective Young's modulus, is the Young's modulus of the active nanoparticles in contact with each other, is the Young's modulus of the active nanoparticles in contact with the wall, and is Poisson's ratio, is the equivalent radius, , are the sizes of the two contacting active nanoparticles, is the size of the active nanoparticles in contact with the wall, is the damping ratio, is the loss factor, is the effective contact quality, and are the masses of the two active nanoparticles in contact, respectively.

[0009] Step 3.1 includes establishing the tangential contact force model between active nanoparticles and between active nanoparticles and the wall based on the Mindlin-Deresiewicz model: ; ; ; ; In the formula, is the tangential contact force, is the friction coefficient, is the tangential relative displacement at the contact point, is the tangential component of the relative velocity at the contact point, is the maximum relative tangential displacement when the particle starts to slide, is the tangential damping ratio, is the interaction restitution coefficient, is the intermediate parameter.

[0010] Step 3 includes: step 3.2, establishing a non-contact force model between active nanoparticles and between active nanoparticles and the wall, the non-contact force includes van der Waals force and electrostatic force: ; ; In the formula, is the van der Waals force on the active nanoparticles, is the van der Waals potential energy, is the distance between the two interacting active nanoparticles, is the Hamaker constant, is the radius of the active nanoparticle, is the distance between the interacting active nanoparticles and the wall, is the electrostatic force on the active nanoparticles, is the reciprocal of the Debye length, is the interaction constant.

[0011] Step 3 includes: step 3.3, performing force analysis on the active nanoparticles based on the discrete element method, tracking the trajectory of the active nanoparticles in the Lagrangian coordinate system, and constructing the kinematic control equation of the active nanoparticles: ; ; In the formula, is the mass of active nanoparticles, is the moving speed of active nanoparticles, is the contact force on the active nanoparticles, is the non-contact force on the active nanoparticles, is the fluid-solid interaction force, is the rotational inertia of the mass of the active nanoparticles, is the angular velocity of the active nanoparticle, is the total torque acting on the active nanoparticles, is the acceleration due to gravity; The discretized format of the kinematic control equations of active nanoparticles is: ; ; The motion parameter information of active nanoparticles updated at each time step is: ; ; In the formula, yes The displacement of active nanoparticles at each moment, yes The movement angle of active nanoparticles at each moment.

[0012] Step 4 includes introducing additional collision terms Modified lattice Boltzmann equation: ; ; ; ; In the formula, is the weight coefficient, is the lattice node solid content, which is the ratio of the node particle control area on the boundary to the total area of ​​the lattice control volume. for The opposite direction of is the number of boundary nodes, is the number of discrete speeds for each node.

[0013] Step 5 includes characterizing the nanofluid oil recovery process, changing different injection conditions, analyzing the characteristics of the interaction behavior between nanoparticles, fluids, and walls, and evaluating the performance of the nanoparticles. The injection conditions include the injection volume fraction of the nanoparticles, the particle size of the nanoparticles, and the flow rate of the injected fluid. The characteristics of the interaction behavior include adsorption behavior characteristics.

[0014] Compared with the prior art, the present invention has the following beneficial effects: the present invention simulates the interphase forces through the pseudopotential function to achieve accurate simulation of water-to-oil displacement, constructs the kinematic control equations of active nanoparticles to achieve the update of the flow velocity and position of the active nanoparticles, and further guides the design and use of nanofluids in low-permeability reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for simulating oil displacement by nanofluids in low permeability reservoirs based on the immersed boundary method in an embodiment of the present invention; Figure 2 This is a flow chart of a coupled algorithm based on the lattice Boltzmann-discrete element method-immersed boundary method in an embodiment of the present invention; Figure 3 It is a partial schematic diagram of the simulation of nanofluid oil flooding in low permeability reservoirs based on the immersed boundary method in an embodiment of the present invention; Figure 4 This is a curve diagram of the change in adsorption concentration of active nanoparticles under different injection volume fraction conditions in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] A nanofluid flooding simulation method for low permeability reservoirs based on an immersed boundary method, comprising: Step 1, extracting the pore throat characteristics of low permeability reservoirs and constructing a two-dimensional pore throat image; Step 2: Based on the lattice Boltzmann method and the Shan-Chen model, an oil-water two-phase seepage simulation method is constructed to update the macroscopic fluid parameters; Step 3: Analyze the stress of active nanoparticles based on discrete element method, construct kinematic equations of active nanoparticles, and track the motion trajectory of active nanoparticles; Step 4: construct the interaction coupling format between active nanoparticles and fluid based on the immersed boundary method IBM; Step 5: Characterize the nanofluid flooding process, analyze the dynamic parameters of the behavior of nanoparticles under different injection conditions, conduct main control factor analysis, and realize the screening of nanofluid injection parameters.

[0018] Step 1 includes making the core sample into a thin slice suitable for observation under a scanning electron microscope, scanning and imaging the core surface, controlling the scanning area through computer software, obtaining high-resolution images at different positions, processing the image data using image processing software, reconstructing a three-dimensional model, and selecting a cross section to obtain a two-dimensional pore throat image for subsequent simulation.

[0019] Step 2 includes: Step 2.1, establishing a computational domain, and the fluid control equation of the fluid grid is the lattice Boltzmann equation: ; ; ; ; ; ; ; In the formula, is the particle swarm distribution function, is the fluid position, is the discrete velocity direction, is the unit direction vector of the particle group velocity component, is the time step, For time, is the collision operator approximated by BGK, is the equilibrium distribution function, is the single relaxation time, is the fluid injection speed, is the weight coefficient, is the fluid density, is the lattice sound speed, is the change in fluid position, is the grid speed.

[0020] Step 2 includes, step 2.2, initialization and , the Shan-Chen model pseudo-potential function is used to simulate the interaction force, and the pseudo-potential force is calculated to represent the interaction between the phases: ; ; ; In the formula, is the pseudo potential function, and are the local densities of oil and water, respectively, and are the reference densities of oil and water, respectively, is the interphase force, is the interaction strength parameter, is a natural constant, is the fluid density in the discrete velocity direction; Step 2.3, calculate and update the macroscopic parameters of the fluid, which include fluid injection velocity, fluid density, fluid pressure and fluid dynamic viscosity: ; ; ; ; In the formula, is the fluid pressure, is the fluid dynamic viscosity.

[0021] Step 3 includes: step 3.1, establishing a contact force model between active nanoparticles and between active nanoparticles and a wall surface, wherein the contact force model includes a normal contact force model and a tangential contact force model; The normal contact force model between active nanoparticles and between active nanoparticles and the wall is established based on the Hertz spring-damper model: ; ; ; ; ; ; ; In the formula, is the normal contact force, is the normal contact coefficient, is the contact normal overlap, is the normal damping coefficient, is the time derivative of the contact normal overlap, is the effective Young's modulus, is the Young's modulus of the active nanoparticles in contact with each other, is the Young's modulus of the active nanoparticles in contact with the wall, and is Poisson's ratio, is the equivalent radius, , are the sizes of the two contacting active nanoparticles, is the size of the active nanoparticles in contact with the wall, is the damping ratio, is the loss factor, is the effective contact quality, and are the masses of the two active nanoparticles in contact, respectively.

[0022] Step 3.1 includes establishing the tangential contact force model between active nanoparticles and between active nanoparticles and the wall based on the Mindlin-Deresiewicz model: ; ; ; ; In the formula, is the tangential contact force, is the friction coefficient, is the tangential relative displacement at the contact point, is the tangential component of the relative velocity at the contact point, is the maximum relative tangential displacement when the particle starts to slide, is the tangential damping ratio, is the interaction restitution coefficient, is the intermediate parameter.

[0023] Step 3 includes: step 3.2, establishing a non-contact force model between active nanoparticles and between active nanoparticles and the wall, the non-contact force includes van der Waals force and electrostatic force: ; ; In the formula, is the van der Waals force on the active nanoparticles, is the van der Waals potential energy, is the distance between the two interacting active nanoparticles, is the Hamaker constant, is the radius of the active nanoparticle, is the distance between the interacting active nanoparticles and the wall, is the electrostatic force on the active nanoparticles, is the reciprocal of the Debye length, is the interaction constant.

[0024] Step 3 includes: step 3.3, performing force analysis on the active nanoparticles based on the discrete element method, tracking the trajectory of the active nanoparticles in the Lagrangian coordinate system, and constructing the kinematic control equation of the active nanoparticles: ; ; In the formula, is the mass of active nanoparticles, is the moving speed of active nanoparticles, is the contact force on the active nanoparticles, is the non-contact force on the active nanoparticles, is the fluid-solid interaction force, is the rotational inertia of the mass of the active nanoparticles, is the angular velocity of the active nanoparticle, is the total torque acting on the active nanoparticles, is the acceleration due to gravity; The discretized format of the kinematic control equations of active nanoparticles is: ; ; The motion parameter information of active nanoparticles updated at each time step is: ; ; In the formula, yes The displacement of active nanoparticles at each moment, yes The movement angle of active nanoparticles at each moment.

[0025] Step 4 includes introducing additional collision terms Modified lattice Boltzmann equation: ; ; ; ; In the formula, is the weight coefficient, is the lattice node solid content, which is the ratio of the node particle control area on the boundary to the total area of ​​the lattice control volume. for The opposite direction of is the number of boundary nodes, is the number of discrete speeds for each node.

[0026] Step 5 includes characterizing the nanofluid oil recovery process, changing different injection conditions, analyzing the characteristics of the interaction behavior between nanoparticles, fluids, and walls, and evaluating the performance of the nanoparticles. The injection conditions include the injection volume fraction of the nanoparticles, the particle size of the nanoparticles, and the flow rate of the injected fluid. The characteristics of the interaction behavior include adsorption behavior characteristics.

[0027] The process of the low permeability reservoir nanofluid flooding simulation method based on the immersed boundary method of the present invention is as follows: Figure 1 As shown, it includes extracting the pore throat characteristics of low permeability reservoirs and constructing a two-dimensional pore throat image; constructing an oil-water two-phase seepage simulation method based on the lattice Boltzmann method combined with the Shan-Chen model to update the macroscopic fluid parameters; analyzing the stress conditions of active nanoparticles based on the discrete element method, constructing the kinematic equations of active nanoparticles, and tracking the motion trajectories of active nanoparticles; constructing the interaction coupling format between active nanoparticles and fluids based on the immersed boundary method IBM; characterizing the nanofluid flooding process, analyzing the dynamic parameters of the behavior of nanoparticles under different injection conditions, conducting main control factor analysis, and realizing the screening of nanofluid injection parameters.

[0028] The algorithm flow based on the lattice Boltzmann-discrete element method-immersed boundary method coupling is as follows Figure 2 As shown, first initialize the fluid parameters and active nanoparticle properties, calculate the interaction force between oil and water phases, judge convergence, if converged, determine the fluid-solid boundary and solid content, calculate the fluid-solid interaction force, calculate the contact force / non-contact force of active nanoparticles, calculate and update the particle position and motion parameters, and finally judge that the simulation time has been reached to end. If it has not converged or the simulation time has not been reached, return to initialize the fluid parameters and active nanoparticle properties.

[0029] The established simulation method can accurately characterize the nanofluid flooding process. Figure 3 The following is a partial schematic diagram of the simulation of nanofluid flooding in oil reservoirs. On this basis, by changing different injection conditions, such as the injection volume fraction of nanoparticles, the particle size of nanoparticles, the flow rate of injected fluid, etc., the typical characteristics of the nanoparticle / fluid / wall interaction behavior (such as adsorption behavior characteristics) are analyzed to evaluate the performance of nanoparticles. For example, by setting the nanoparticle size to 20nm, the flow rate of injected fluid to 0.1m / s, and changing the injection volume fraction of nanoparticles to 0.06%, 0.09%, and 0.12%, different nanoparticle adsorption concentrations can be obtained, such as Figure 4 Therefore, based on this simulation method, the dynamic characterization of the behavior of nanoparticles in the nanofluid oil displacement process can be achieved, and the optimization of nanofluid injection parameters can be achieved according to the different needs of the oil field.

[0030] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features may be replaced by equivalents, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating oil displacement in low permeability reservoirs using nanofluids based on the immersed boundary method, characterized in that: include: Step 1, extracting the pore throat characteristics of low permeability reservoirs and constructing a two-dimensional pore throat image; Step 2: Based on the lattice Boltzmann method and the Shan-Chen model, an oil-water two-phase seepage simulation method is constructed to update the macroscopic fluid parameters; Step 3: Analyze the stress of active nanoparticles based on discrete element method, construct kinematic equations of active nanoparticles, and track the motion trajectory of active nanoparticles; Step 4: construct the interaction coupling format between active nanoparticles and fluid based on the immersed boundary method IBM; Step 5: Characterize the nanofluid flooding process, analyze the dynamic parameters of the behavior of nanoparticles under different injection conditions, conduct main control factor analysis, and realize the screening of nanofluid injection parameters.

2. The method for simulating oil displacement by nanofluids in low permeability reservoirs based on the immersed boundary method according to claim 1, characterized in that: Step 1 includes making the core sample into a thin slice suitable for observation under a scanning electron microscope, scanning and imaging the core surface, controlling the scanning area through computer software, obtaining high-resolution images at different positions, processing the image data using image processing software, reconstructing a three-dimensional model, and selecting a cross section to obtain a two-dimensional pore throat image for subsequent simulation.

3. The method for simulating oil displacement by nanofluid in low permeability reservoirs based on the immersed boundary method according to claim 1, characterized in that: Step 2 includes: Step 2.1, establishing a computational domain, and the fluid control equation of the fluid grid is the lattice Boltzmann equation: ； ； ； ； ； ； ； In the formula, is the particle swarm distribution function, is the fluid position, is the discrete velocity direction, is the unit direction vector of the particle group velocity component, is the time step, For time, is the collision operator approximated by BGK, is the equilibrium distribution function, is the single relaxation time, is the fluid injection speed, is the weight coefficient, is the fluid density, is the lattice sound speed, is the change in fluid position, is the grid speed.

4. The method for simulating oil displacement by nanofluid in low permeability reservoirs based on the immersed boundary method according to claim 3, characterized in that: Step 2 includes, step 2.2, initialization and , the Shan-Chen model pseudo-potential function is used to simulate the interaction force, and the pseudo-potential force is calculated to represent the interaction between the phases: ； ； ； In the formula, is the pseudo potential function, and are the local densities of oil and water, respectively, and are the reference densities of oil and water, respectively, is the interphase force, is the interaction strength parameter, is a natural constant, is the fluid density in the discrete velocity direction; Step 2.3, calculate and update the macroscopic parameters of the fluid, which include fluid injection velocity, fluid density, fluid pressure and fluid dynamic viscosity: ； ； ； ； In the formula, is the fluid pressure, is the fluid dynamic viscosity.

5. The method for simulating oil displacement by nanofluid in low permeability reservoirs based on the immersed boundary method according to claim 4, characterized in that: Step 3 includes: step 3.1, establishing a contact force model between active nanoparticles and between active nanoparticles and a wall surface, wherein the contact force model includes a normal contact force model and a tangential contact force model; The normal contact force model between active nanoparticles and between active nanoparticles and the wall is established based on the Hertz spring-damper model: ； ； ； ； ； ； ； In the formula, is the normal contact force, is the normal contact coefficient, is the contact normal overlap, is the normal damping coefficient, is the time derivative of the contact normal overlap, is the effective Young's modulus, is the Young's modulus of the active nanoparticles in contact with each other, is the Young's modulus of the active nanoparticles in contact with the wall, and is Poisson's ratio, is the equivalent radius, , are the sizes of the two contacting active nanoparticles, is the size of the active nanoparticles in contact with the wall, is the damping ratio, is the loss factor, is the effective contact quality, and are the masses of the two active nanoparticles in contact, respectively.

6. The method for simulating oil displacement in low permeability reservoirs using nanofluids based on the immersed boundary method according to claim 5, characterized in that: Step 3.1 includes establishing the tangential contact force model between active nanoparticles and between active nanoparticles and the wall based on the Mindlin-Deresiewicz model: ； ； ； ； In the formula, is the tangential contact force, is the friction coefficient, is the tangential relative displacement at the contact point, is the tangential component of the relative velocity at the contact point, is the maximum relative tangential displacement when the particle starts to slide, is the tangential damping ratio, is the interaction restitution coefficient, is the intermediate parameter.

7. The method for simulating oil displacement in low permeability reservoirs using nanofluids based on the immersed boundary method according to claim 6, characterized in that: Step 3 includes: step 3.2, establishing a non-contact force model between active nanoparticles and between active nanoparticles and the wall, the non-contact force includes van der Waals force and electrostatic force: ； ； In the formula, is the van der Waals force on the active nanoparticles, is the van der Waals potential energy, is the distance between the two interacting active nanoparticles, is the Hamaker constant, is the radius of the active nanoparticle, is the distance between the interacting active nanoparticles and the wall, is the electrostatic force on the active nanoparticles, is the reciprocal of the Debye length, is the interaction constant.

8. The method for simulating oil displacement in low permeability reservoirs using nanofluids based on the immersed boundary method according to claim 7, characterized in that: Step 3 includes: step 3.3, performing force analysis on the active nanoparticles based on the discrete element method, tracking the trajectory of the active nanoparticles in the Lagrangian coordinate system, and constructing the kinematic control equation of the active nanoparticles: ； ； In the formula, is the mass of active nanoparticles, is the moving speed of active nanoparticles, is the contact force on the active nanoparticles, is the non-contact force on the active nanoparticles, is the fluid-solid interaction force, is the rotational inertia of the mass of the active nanoparticles, is the angular velocity of the active nanoparticle, is the total torque acting on the active nanoparticles, is the acceleration due to gravity; The discretized format of the kinematic control equations of active nanoparticles is: ； ； The motion parameter information of active nanoparticles updated at each time step is: ； ； In the formula, yes The displacement of active nanoparticles at each moment, yes The movement angle of active nanoparticles at each moment.

9. The method for simulating oil displacement by nanofluid in low permeability reservoirs based on the immersed boundary method according to claim 8, characterized in that: Step 4 includes introducing additional collision terms Modified lattice Boltzmann equation: ； ； ； ； In the formula, is the weight coefficient, is the lattice node solid content, which is the ratio of the node particle control area on the boundary to the total area of ​​the lattice control volume. for The opposite direction of is the number of boundary nodes, is the number of discrete speeds for each node.

10. The method for simulating oil displacement in low permeability reservoirs using nanofluids based on the immersed boundary method according to claim 9, characterized in that: Step 5 includes characterizing the nanofluid oil recovery process, changing different injection conditions, analyzing the characteristics of the interaction behavior between nanoparticles, fluids, and walls, and evaluating the performance of the nanoparticles. The injection conditions include the injection volume fraction of the nanoparticles, the particle size of the nanoparticles, and the flow rate of the injected fluid. The characteristics of the interaction behavior include adsorption behavior characteristics.

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