A DEM-based microscopic simulation method for nanofluid flooding in shale oil reservoirs

Abstract

The present invention discloses a microscopic simulation method for nanofluid oil displacement in shale oil reservoirs based on DEM, which belongs to the field of oil and gas exploitation, and includes the following steps: step 1, extracting typical pore throat structures, initializing flow fields and particle properties, and defining various parameters; step 2, establishing an oil / water two-phase flow simulation model; step 3, establishing a CFD module; step 4, establishing a DEM module; step 5, establishing a coupling module to couple the data obtained by the CFD module and the DEM module; step 6, nanoparticle parameter optimization and performance evaluation. The present invention constructs a nanoparticle / oil / water multiphase seepage microscopic simulation method based on the coupling of computational fluid dynamics and discrete element method, characterizes the fluid-solid coupling behavior characteristics of nanofluid composite system oil displacement, and further guides the structural performance design and field use of nano-stereoscopic oil displacement materials.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas exploitation, and in particular relates to a DEM-based shale oil reservoir nanofluid oil displacement microscopic simulation method. Background Art

[0002] Nanofluids, with their superior surface activity and unique volume effect, are of great significance in improving the recovery rate of low and ultra-low permeability reservoirs in my country, and have significant advantages in solving the problems of poor water injection capacity and high flow resistance in low and ultra-low permeability reservoirs. However, the microscopic mechanism of how nanoparticles reduce flow resistance and improve water injection capacity is still unclear, and factors such as the type, particle size, and concentration of nanoparticles may affect the oil recovery effect. Traditional microscopic experimental design is difficult and costly, and there are problems such as unclear mechanism characterization. Therefore, it is urgent to establish a nanoparticle / oil / water multiphase seepage coupling model through numerical simulation to balance accuracy and efficiency.

[0003] Discrete Element Method (DEM) has unique advantages in the field of microscopic research on particle material behavior, and can fully consider factors such as particle shape, material properties, and particle size distribution. By using CFD-DEM (Computational Fluid Dynamics-Discrete Element) method and combining it with particle dynamics theory, precise numerical simulation research is conducted on the flow of nanoparticles in the matrix pores with the liquid phase fluid and the two-way coupling between the liquid and solid phases, which can more accurately describe the movement of nanoparticles and their microscopic oil displacement behavior under the conditions of fluid-solid two-way coupling.

[0004] However, the microscopic mechanism of nanofluid oil displacement in shale reservoirs is still unclear, and there is a lack of theoretical guidance for field production. It is crucial to explore the microscopic oil displacement mechanism of the composite system and establish an accurate and efficient nanoparticle / oil / water multiphase seepage coupling model. To this end, the present invention is based on computational fluid dynamics and discrete element method, by establishing a nanoparticle / oil / water multiphase seepage coupling model, constructing a method for optimizing nanofluid composite system oil displacement materials, further guiding the structural performance design and field use of nanofluid oil displacement materials, and promoting the efficient exploitation and sustainable development of low permeability / ultra-low permeability shale oil reservoirs in my country. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a DEM-based nanofluid oil recovery microscopic simulation method for shale oil reservoirs, and constructs a nanoparticle / oil / water multiphase seepage microscopic simulation method based on the coupling of computational fluid dynamics and discrete element method to characterize the fluid-solid coupling behavior characteristics of nanofluid composite system oil recovery, and further guide the structural performance design and field use of nano-stereoscopic oil recovery materials.

[0006] The technical solution of the present invention is as follows:

[0007] A DEM-based microscopic simulation method for nanofluid oil displacement in shale oil reservoirs comprises the following steps:

[0008] Step 1: Extract typical pore throat structure, initialize flow field and particle properties, and define various parameters;

[0009] Step 2, establishing an oil / water two-phase flow simulation model, the oil / water two-phase flow simulation model includes an oil / water two-phase flow Euler model and an oil / water two-phase turbulence model;

[0010] Step 3, establishing a CFD module, in which computational fluid dynamics is used to simulate the oil-water two-phase flow, and the particle phase data is calculated by the CFD module, and the particle phase data includes the fluid force exerted on the nanoparticles, the resistance of the liquid phase fluid, the pressure gradient force, the increased virtual mass force, and the lift;

[0011] Step 4, establishing a DEM module, in which the discrete element method is used to establish the solid-phase control equation of nanoparticle motion, the normal contact force model between particles, and the tangential contact force model between particles;

[0012] Step 5: Establish a coupling module to couple the data obtained by the CFD module and the DEM module;

[0013] Step 6: Nanoparticle parameter optimization and performance evaluation.

[0014] Furthermore, the specific process of step 1 is: extracting typical pore throat structures, establishing a microscopic three-dimensional model of multiphase seepage in porous media, and using the microscopic three-dimensional model of multiphase seepage in porous media as the geometric boundary of the simulation, defining no-slip wall boundary conditions, and using standard wall functions for near-wall treatment; initializing the flow field and particle properties, defining the initial volume fraction of the flow field, the liquid phase injection velocity, and the velocity, number, and diameter of the nanoparticles, and defining the inlet and outlet of the fluid as the pressure outlet boundary condition and the velocity inlet boundary condition, respectively.

[0015] Furthermore, the specific process of step 2 is as follows:

[0016] Step 2.1, establish the Euler model of oil / water two-phase flow; the Euler model of oil / water two-phase flow includes the average mass conservation equation and the average momentum conservation equation, which are as follows:

[0017] Average mass conservation equation:

[0018] (1);

[0019] The mean momentum conservation equation is:

[0020] (2);

[0021] in, Indicates time; represents the fluid volume fraction; represents the density of the fluid; represents the fluid velocity; represents the gradient operator; represents the mass exchange term between phases; Indicates stress; It represents the effective viscosity; represents transpose; represents the gravitational acceleration vector; represents the momentum exchange term between phases;

[0022] When calculating with the particle phase, the interphase mass exchange term The value of is zero, and the momentum exchange term The value of is the force exerted by the particles on the fluid phase , the specific equation form is as follows:

[0023] (3);

[0024] in, represents the volume of the computational unit, represents the number of particles in the calculation unit, represents the fluid force exerted on the nanoparticles;

[0025] Step 2.2, define the oil / water two-phase turbulence model; use the RNG k-ɛ model to simulate the turbulence phenomenon in the oil / water two-phase flow and update the fluid velocity field; the RNG k-ɛ model includes the turbulent kinetic energy equation and the dissipation rate equation, as follows:

[0026] Turbulent kinetic energy equation:

[0027] (4);

[0028] Dissipation rate equation:

[0029] (5);

[0030] in, is the turbulent kinetic energy; and are the horizontal and vertical coordinates in the coordinate direction respectively; The fluid is The velocity component in the direction; is the turbulent Prandtl number of the turbulent kinetic energy equation; is the turbulent kinetic energy generated by the laminar velocity gradient; is the turbulent kinetic energy generated by buoyancy; It represents the arithmetic mean of the two-phase density; is the turbulent kinetic energy dissipation rate; is the turbulent Prandtl number for the dissipation rate equation; , , for different constants.

[0031] Furthermore, the specific process of step 3 is as follows:

[0032] The fluid forces exerted on nanoparticles can be divided into two categories: resistance and non-resistance, namely:

[0033] (6);

[0034] Under fluid-solid coupling conditions, the movement of the particle phase is subject to resistance from the liquid phase. , the expression is as follows:

[0035] (7);

[0036] (8);

[0037] in, is the drag coefficient; is the projected area of ​​the particle in the flow direction; is the particle velocity; is the relative Reynolds number between the fluid and solid phases;

[0038] In non-resistance, there is a pressure gradient force , increased virtual mass force , lift , the expressions are as follows:

[0039] (9);

[0040] (10);

[0041] (11);

[0042] in, is the volume of the particle; is the local pressure gradient; is the virtual quality coefficient; is the relative acceleration of the particle; is the lift coefficient; is the relative velocity between the fluid and the particles.

[0043] Furthermore, the specific process of step 4 is as follows:

[0044] Step 4.1, establish the solid phase control equation of nanoparticle motion as follows:

[0045] (12);

[0046] (13);

[0047] in, is the mass of nanoparticles; is the contact force between nanoparticles and between particles and the wall; is the fluid force exerted on the nanoparticles; is the moment of inertia and inertia tensor; is the angular velocity vector; is the net torque produced by the tangential force causing the particle to rotate; is the additional torque caused by the liquid phase velocity gradient;

[0048] Step 4.2, establish a normal contact force model between particles;

[0049] The normal contact force between particles adopts a linear hysteresis spring model as follows:

[0050] (14);

[0051] (15);

[0052] in, and Current time and the previous time The normal elastic-plastic contact force is is the time step; and are the values ​​of contact stiffness during collision compression and collision recovery, respectively; is the change in contact normal overlap during the current time; and The current time and the previous time Normal overlap value of ; is a small dimensionless constant;

[0053] Step 4.3, establishing a tangential contact force model between particles; the tangential contact force model between particles adopts a linear spring Coulomb limit model;

[0054] Current time Pure elastic tangential force for:

[0055] (16);

[0056] in, It was the time before The tangential force; is the tangential relative displacement of the particle in the time period; is the tangential stiffness;

[0057] Current time Full tangential force for:

[0058] (17);

[0059] in, is the coefficient of friction; is the normal force.

[0060] Furthermore, in step 5, the coupling module performs coupling data interaction based on computational fluid dynamics-discrete element method, and the specific process is as follows:

[0061] Step a, establishing a nanoparticle motion simulation model and an oil / water two-phase flow Euler model, initializing the flow field and particle properties; the nanoparticle motion simulation model includes a particle-to-particle normal contact force model and a particle-to-particle tangential contact force model;

[0062] Step b, entering the iteration process, during which it is determined whether the current simulation time has been reached. When the simulation time has been reached, the entire data interaction process is terminated; otherwise, the liquid-solid phase interaction force, the particle motion control equation, and the particle-particle normal / tangential contact force are calculated in sequence; the liquid-solid phase interaction force includes the force generated by the particle relative to the fluid phase corresponding to formula (3) and the fluid force applied to the nanoparticles corresponding to formula (6);

[0063] Step c: Judgment Is it equal to ; is a multiple; is the time step of the DEM module; is the time step of the CFD module;

[0064] when When the particle position, velocity and pressure are updated, the particle phase data and the phase interaction force data are calculated and input into the oil / water two-phase flow simulation model, the oil / water two-phase turbulence model is calculated, the fluid velocity field is updated and it is judged whether the convergence condition is met at present. When the convergence condition is met, the fluid phase data is calculated and the DEM flow field force is updated, the time step is updated, and then the step b is returned to continue to judge whether the simulation time is reached; when the convergence condition is not met, the oil / water two-phase flow simulation model is returned to calculate again;

[0065] when When , it returns to recalculate the liquid-solid interaction force, the particle motion control equations, and the normal / tangential contact forces between particles.

[0066] Furthermore, in step 6, a porous medium multiphase seepage microscopic three-dimensional model is set according to a preset nanoparticle injection scheme, and the flow field and particle properties are initialized to simulate the operation state of nanoparticles and fluids under different porous media, oil phase parameters, and nanofluid parameter conditions. The nanoparticle migration distance, model residual oil distribution, and saturation at different time steps are simulated, and a comparative analysis and performance evaluation of different nanofluid composite system oil displacement schemes are performed.

[0067] The beneficial technical effects brought about by the present invention are as follows.

[0068] The present invention can truly depict the physical scenario of oil displacement by a nanofluid composite system; it conducts simulation and analysis on nanoparticle / oil / water multiphase seepage, and constructs a nanoparticle / oil / water multiphase seepage coupling model. The model can comprehensively consider the fluid-solid bidirectional coupling effect, the influence of the physical property parameters of the nanoparticles, and the multiphase fluid dynamics effect. It has high simulation accuracy and stability, and can truly depict the physical scenario of oil displacement by a nanofluid composite system.

[0069] The present invention can accurately characterize the microscopic displacement behavior of nanoparticle / oil / water multiphase seepage; analyze and study the microscopic displacement behavior of nanoparticle / oil / water multiphase seepage, regard solid phase particles as discrete phases, solve the particle motion control equation based on Newton's second law to simulate the movement of particles, regard liquid phase fluid as continuous phase, solve and simulate the flow of pore fluid based on Navier-Stokes (NS) equation. The data calculated by CFD module and DEM module are exchanged and transmitted by coupling module, iteratively updated to calculate the interaction between nanoparticles and fluid solid phase; at the same time, the contact and collision between solid phase particles are fully considered, and the flow state and law of particle phase between solid and liquid phases are accurately characterized and analyzed.

[0070] The present invention can analyze the diffusion behavior of nanoparticles and the formation mechanism of residual oil, obtain the optimal physical parameters of nanofluid composite system oil displacement, analyze and study the nanoparticle / oil / water multiphase seepage behavior, and conduct a comprehensive evaluation of the oil displacement effect of the nanofluid composite system. The present invention can dynamically simulate the microscopic displacement behavior of nanoparticle / oil / water multiphase seepage through the analysis of the interaction between nanoparticles / fluids / walls, determine key parameters such as the limiting residual oil saturation and the optimal particle size, and provide specific guidance on the fluid injection flow rate and the physical parameters such as nanoparticle injection concentration, injection flow rate, and particle size in the actual production process, providing a key theoretical basis for nanofluid composite system oil displacement to improve the recovery rate of low-permeability reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The present invention is a flow chart of the DEM-based shale oil reservoir nanofluid oil recovery micro-simulation method.

[0072] Figure 2 This is a schematic diagram of the data interaction principle based on computational fluid dynamics-discrete element method coupling of the present invention.

[0073] Figure 3 It is a partial schematic diagram of the microscopic simulation of nanofluid oil displacement in shale oil reservoirs based on DEM in the experiment of the present invention.

[0074] Figure 4 It is a schematic diagram of the relationship curve between the dynamic oil displacement efficiency and time of the nanofluid composite system under the typical pore throat structure in the experiment of the present invention. DETAILED DESCRIPTION

[0075] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0076] like Figure 1 As shown, the present invention proposes a DEM-based shale oil reservoir nanofluid flooding microscopic simulation method, which specifically includes the following steps:

[0077] Step 1: Extract typical pore throat structure, initialize flow field and particle properties, and define various parameters; the specific process is as follows:

[0078] The typical pore throat structure is extracted, and a microscopic three-dimensional model of multiphase seepage in porous media is established. The microscopic three-dimensional model of multiphase seepage in porous media is used as the geometric boundary of the simulation, and the no-slip wall boundary condition is defined. The standard wall function is used for near-wall treatment. The flow field and particle properties are initialized, and the initial volume fraction of the flow field, the liquid injection velocity, and the velocity, number, diameter and other parameters of the nanoparticles are defined. The inlet and outlet of the fluid are defined as the pressure outlet boundary condition and the velocity inlet boundary condition, respectively.

[0079] Step 2: Establish an oil / water two-phase flow simulation model, which includes an oil / water two-phase flow Euler model and an oil / water two-phase turbulence model. The specific process is as follows:

[0080] Step 2.1, establish the Euler model of oil / water two-phase flow;

[0081] Taking into account the operation time and ensuring the calculation accuracy, the Euler model is used to simulate the oil / water two-phase flow. The model uses different momentum equations to describe the oil and water phases and the forces between the phases. The model equations mainly include the mass conservation equation and the momentum conservation equation. The derivation of the equations involved in the equations is to complete the local instantaneous balance of each stage through the overall average. The specific equation form is as follows:

[0082] Average mass conservation equation:

[0083] (1);

[0084] The mean momentum conservation equation is:

[0085] (2);

[0086] in, Indicates time; represents the fluid volume fraction; represents the density of the fluid; represents the fluid velocity; represents the gradient operator; represents the mass exchange term between phases; Indicates stress; It represents the effective viscosity; represents transpose; represents the gravitational acceleration vector; represents the momentum exchange term between phases.

[0087] When calculating with the particle phase, the interphase mass exchange term The value of is zero, and the momentum exchange term The value of is the force exerted by the particles on the fluid phase , the specific equation form is as follows:

[0088] (3);

[0089] in, represents the volume of the computational unit, represents the number of particles in the calculation unit, represents the fluid force applied to the nanoparticles, and its specific form is shown in formula (6).

[0090] Step 2.2, define the oil / water two-phase turbulence model;

[0091] In the oil / water two-phase flow, as the inlet fluid velocity increases, the oil / water two-phase produces different flow states, including: uniform flow, transitional flow, turbulent flow (heterogeneous flow), and turbulence will occur at high speed. The present invention uses the renormalization group RNG k-ɛ model to simulate the turbulent phenomenon in the oil / water two-phase flow and update the fluid velocity field. The RNG k-ɛ model includes the influence of swirl on turbulence, improves the accuracy of swirl, better simulates turbulence-related quantities, reasonably simulates the turbulent viscosity part, and takes into account the heterogeneity of turbulence. The RNG k-ɛ model equation is derived from the instantaneous Navier-stokes equation and obtained using the renormalization group (RNG) method. The RNG k-ɛ model includes the k equation and the ɛ equation, wherein the k equation is the turbulent kinetic energy equation, and the ɛ equation is the dissipation rate equation, as follows:

[0092] Turbulent kinetic energy equation:

[0093] (4);

[0094] Dissipation rate equation:

[0095] (5);

[0096] in, is the turbulent kinetic energy; and are the horizontal and vertical coordinates in the coordinate direction respectively; The fluid is The velocity component in the direction; is the turbulent Prandtl number of the turbulent kinetic energy equation; is the turbulent kinetic energy generated by the laminar velocity gradient; is the turbulent kinetic energy generated by buoyancy; It represents the arithmetic mean of the two-phase density; is the turbulent kinetic energy dissipation rate; is the turbulent Prandtl number for the dissipation rate equation; , , for different constants.

[0097] Step 3: Establish a CFD (computational fluid dynamics) module. In the CFD module, computational fluid dynamics is used to simulate the oil-water two-phase flow. The liquid phase is taken as the continuous phase. By considering the drag, lift, virtual mass force and other phase interaction mechanisms, the oil-water two-phase seepage model is accurately simulated. The particle phase data is calculated by the CFD module. The particle phase data includes the fluid force applied to the nanoparticles, the resistance of the liquid phase fluid, the pressure gradient force, the increased virtual mass force, and the lift. The specific process is as follows:

[0098] The fluid forces exerted on nanoparticles can be divided into two categories: resistance and non-resistance, namely:

[0099] (6);

[0100] Under fluid-solid coupling conditions, the movement of the particle phase is subject to resistance (also known as drag) from the liquid phase fluid. , the expression is as follows:

[0101] (7);

[0102] (8);

[0103] in, is the drag coefficient, defined according to the Schiller-Neumann drag model; is the projected area of ​​the particle in the flow direction; is the particle velocity; is the relative Reynolds number between the fluid and solid phases.

[0104] In non-resistance, there is a pressure gradient force , increased virtual mass force , lift , the expressions are as follows:

[0105] (9);

[0106] (10);

[0107] (11);

[0108] in, is the volume of the particle; is the local pressure gradient; represents the mass of liquid displaced by the particle; is the virtual mass coefficient, which represents the contribution of the particle volume of the replacement fluid to the effective virtual mass of the particle, and its theoretical value is 0.5; is the relative acceleration of the particle; is the lift coefficient; is the relative velocity between the fluid and the particles.

[0109] Step 4: Establish a DEM (discrete element method) module. The DEM module uses the discrete element method to establish a nanoparticle motion simulation model, establish the solid phase control equation for nanoparticle motion, and determine the force and basic motion laws of nanoparticles. The nanoparticle motion simulation model can simulate the motion of nanoparticles, and the interphase force data is calculated through the DEM module. The interphase force data is the force generated by the nanoparticles relative to the fluid phase. ; The nanoparticle motion simulation model includes the normal contact force model between particles and the tangential contact force model between particles; the specific process is as follows:

[0110] Step 4.1: The discrete element method tracks all particles in the model calculation domain in the Lagrangian coordinate system and explicitly solves Euler's first and second laws to define the translation and rotational motion of the nanoparticles. The specific form of the solid-phase control equation for the motion of the nanoparticles is as follows:

[0111] (12);

[0112] (13);

[0113] in, is the mass of nanoparticles; is the particle velocity; is the contact force between nanoparticles and between particles and the wall; is the fluid force exerted on the nanoparticles; is the moment of inertia and inertia tensor; is the angular velocity vector; is the net torque produced by the tangential force causing the particle to rotate; is the additional torque caused by the liquid phase velocity gradient.

[0114] Step 4.2, establish a normal contact force model between particles;

[0115] The normal contact force between particles uses a linear hysteretic spring model. This elastic-plastic normal contact model allows the simulation of plastic energy dissipation on contact without requiring a long simulation time. The linear hysteretic spring model is implemented in an incremental manner through the DEM method, as described by the following set of equations:

[0116] (14);

[0117] (15);

[0118] in, and Current time and the previous time The normal elastic-plastic contact force is is the time step; and are the values ​​of contact stiffness during collision compression and collision recovery, respectively; is the change in contact normal overlap at the current time. It is assumed to be positive when particles are approaching each other and negative when they are moving apart. and The current time and the previous time Normal overlap value of ; is a small dimensionless constant, taken as 0.001 in the DEM method, which ensures that during collision recovery, the normal force returns to zero when the overlap drops to zero.

[0119] Step 4.3, establishing a tangential contact force model between particles;

[0120] The tangential contact force model between particles adopts the linear spring Coulomb limit model. The tangential force in the model is elastic-friction force and is considered to be purely elastic. Therefore, the current time Pure elastic tangential force for:

[0121] (16);

[0122] in, It was the time before The tangential force; is the tangential relative displacement of the particle in the time period; is the tangential stiffness.

[0123] In the linear spring Coulomb limit model, the tangential force cannot exceed the Coulomb limit. Therefore, the current time Full tangential force for:

[0124] (17);

[0125] in, is the friction coefficient, which is divided into static and dynamic friction coefficients; is the normal force, which is the positive pressure at the contact point.

[0126] Step 5: Establish a coupling module to couple the data obtained by the CFD module and the DEM module, and realize the interaction between the nanoparticles and the fluid according to the principle of conservation of momentum, that is, to realize the interaction of the calculation data of the fluid phase and the solid phase, and calculate the interaction force between the liquid and solid phases. The interaction force between the liquid and solid phases includes the force generated by the particle phase on the fluid phase corresponding to formula (3) and the fluid force applied to the nanoparticles corresponding to formula (6); the coupling module transmits and exchanges the particle phase data and the phase force data, iterates the calculation and continuously updates; the microscopic simulation of nanofluid flooding in shale oil reservoirs is carried out through iterative updates; finally, it is applied to the study of the microscopic mechanism of nanofluid flooding in shale oil reservoirs.

[0127] Through the coupling module, the particle phase data and interphase force data calculated by the CFD module and the DEM module (i.e., the fluid mechanics module and the discrete element method module) are transmitted, exchanged, and calculated to each other, and the physical parameters such as particle position, velocity, and pressure are iteratively updated. The interaction force between the liquid and solid phases is calculated, and the DEM flow field force and fluid phase data are updated. At the same time, the precise coupling of nanoparticles and fluids is completed by updating the time step to determine whether the simulation time has been reached.

[0128] like Figure 2 As shown in the figure, the specific process of coupled data interaction based on computational fluid dynamics-discrete element method is as follows:

[0129] Step a, establishing a nanoparticle motion simulation model and an oil / water two-phase flow Euler model, and initializing the flow field and particle properties;

[0130] Step b, entering the iteration process, during which it is determined whether the current simulation time has been reached. When the simulation time has been reached, the entire data interaction process is terminated; otherwise, the liquid-solid phase interaction force, the particle motion control equation, and the normal / tangential contact force between particles are calculated in sequence;

[0131] Step c: Judgment Is it equal to ; is a multiple; is the time step of the DEM module; is the CFD module time step.

[0132] when When the particle position, velocity and pressure are updated, the particle phase data and the interphase force data are calculated and input into the oil / water two-phase flow simulation model, the oil / water two-phase turbulence model is calculated, the fluid velocity field is updated and it is judged whether the convergence condition is met at present. When the convergence condition is met, the fluid phase data is calculated and the DEM flow field force is updated, the time step is updated, and then the step b is returned to continue to judge whether the simulation time is reached; when the convergence condition is not met, the oil / water two-phase flow simulation model is returned to calculate again;

[0133] when When , it returns to recalculate the liquid-solid interaction force, the particle motion control equations, and the normal / tangential contact forces between particles.

[0134] Step 6, nanoparticle parameter optimization and performance evaluation: input the initial parameters such as nanoparticle radius, concentration, injection flow rate, etc. into the established porous medium multiphase seepage microscopic three-dimensional model, optimize the nanoparticle parameters according to the change of oil saturation and the diffusion capacity of nanoparticles, evaluate the oil displacement capacity of the nanofluid composite system, and improve the ultimate recovery rate of shale oil reservoir-nanofluid displacement;

[0135] According to the preset nanoparticle injection scheme, a microscopic three-dimensional model of multiphase seepage in porous media is set up. The flow field and particle properties are initialized to simulate the operation status of nanoparticles and fluids under different porous media, oil phase parameters, and nanofluid parameters. The migration distance of nanoparticles, the distribution of residual oil in the model, and the saturation at different time steps are simulated, and a comparative analysis and performance evaluation of different nanofluid composite system oil displacement schemes are carried out.

[0136] In order to prove the feasibility of the present invention, the following comparative experiment is given.

[0137] According to the established nanoparticle / oil / water multiphase microscopic seepage coupling model, the nanoparticle diameters were set to 5nm, 10nm, and 15nm for microscopic oil displacement simulation, and the other initial conditions of the model (matrix porosity, fluid injection rate, etc.) were kept consistent for single factor analysis. The schematic diagram of the local simulation results of nanoparticle / oil / water multiphase distribution (10nm) is shown in the figure. Figure 3 As shown in the figure, the migration distance of nanoparticles, model residual oil distribution and saturation were simulated. The oil displacement schemes of three nanofluid composite systems with different particle diameters of 5nm, 10nm and 15nm were compared and analyzed and their performance was evaluated. The specific results are shown in the figure below. Figure 4 As shown, it can be seen that the oil displacement efficiency of nanofluid increases with the increase of particle diameter, and after reaching a certain simulation time, the oil displacement efficiency increases slowly and tends to be stable.

[0138] It can be seen that the method of the present invention can predict the oil displacement of the nanofluid composite system under different injection conditions, which is helpful for guiding field work.

[0139] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A DEM-based microscopic simulation method for nanofluid flooding in shale oil reservoirs, characterized in that: The steps include: Step 1: Extract typical pore throat structure, initialize flow field and particle properties, and define various parameters; Step 2: Establish an oil / water two-phase flow simulation model, which includes an oil / water two-phase flow Euler model and an oil / water two-phase turbulence model. The specific process is as follows: Step 2.1, establish the Euler model of oil / water two-phase flow; the Euler model of oil / water two-phase flow includes the average mass conservation equation and the average momentum conservation equation, which are as follows: Average mass conservation equation: The mean momentum conservation equation is: Where, t represents time; α f represents the fluid volume fraction; ρ f represents the fluid density; v f represents the fluid velocity; represents the gradient operator; Γ q represents the mass exchange term between phases; p represents pressure; μ eff represents effective viscosity; T represents transposition; g represents gravitational acceleration vector; M f represents the momentum exchange term between phases; When calculating with the particle phase, the interphase mass exchange term Γ q The value of the phase momentum exchange term M is zero. f The value of F is the force F exerted by the particles on the fluid phase p→f , the specific equation form is as follows: Among them, V c represents the volume of the calculation unit, N represents the number of particles in the calculation unit, and F f→p represents the fluid force exerted on the nanoparticles; Step 2.2, define the oil / water two-phase turbulence model; use the RNG k-ε model to simulate the turbulence phenomenon in the oil / water two-phase flow and update the fluid velocity field; the RNG k-ε model includes the turbulent kinetic energy equation and the dissipation rate equation, which are as follows: Turbulent kinetic energy equation: Dissipation rate equation: where k is the turbulent kinetic energy; x i and x j are the horizontal and vertical coordinates in the coordinate direction; μ i is the fluid at x i The velocity component in the direction; α k is the turbulent Prandtl number of the turbulent kinetic energy equation; G k is the turbulent kinetic energy generated by the laminar velocity gradient; G b is the turbulent kinetic energy generated by buoyancy; represents the arithmetic mean of the two-phase density; ε is the turbulent kinetic energy dissipation rate; α ε is the turbulent Prandtl number for the dissipation rate equation; C 1ε , C 2ε , C 3ε For different constants; Step 3, establishing a CFD module, in which computational fluid dynamics is used to simulate the oil-water two-phase flow, and the particle phase data is calculated by the CFD module, and the particle phase data includes the fluid force exerted on the nanoparticles, the resistance of the liquid phase fluid, the pressure gradient force, the increased virtual mass force, and the lift; Step 4, establishing a DEM module, in which the discrete element method is used to establish the solid-phase control equation of nanoparticle motion, the normal contact force model between particles, and the tangential contact force model between particles; Step 5: Establish a coupling module to couple the data obtained by the CFD module and the DEM module; Step 6: Nanoparticle parameter optimization and performance evaluation.

2. The DEM-based shale oil reservoir nanofluid flooding microscopic simulation method according to claim 1, characterized in that: The specific process of step 1 is as follows: extract typical pore throat structure, establish a microscopic three-dimensional model of multiphase seepage in porous media, and use the microscopic three-dimensional model of multiphase seepage in porous media as the geometric boundary of the simulation, define the no-slip wall boundary condition, and use the standard wall function for near-wall treatment; initialize the flow field and particle properties, define the initial volume fraction of the flow field, the liquid phase injection velocity, and the velocity, number, and diameter of the nanoparticles, and define the inlet and outlet of the fluid as the pressure outlet boundary condition and the velocity inlet boundary condition, respectively.

3. The DEM-based shale oil reservoir nanofluid flooding microscopic simulation method according to claim 1, characterized in that: The specific process of step 3 is as follows: The fluid forces exerted on nanoparticles can be divided into two categories: resistance and non-resistance, namely: Under the condition of fluid-solid coupling, the movement of the particle phase is subject to the resistance F from the liquid phase. D , the expression is as follows: Among them, C D is the drag coefficient; A′ is the projected area of ​​the particle in the flow direction; v p is the particle velocity; Re is the relative Reynolds number between the fluid and solid phases; In non-resistance, there is a pressure gradient force The added virtual mass force F VM , lift F L , the expressions are as follows: F VM =C VM r f v p a r (10); Among them, V p is the volume of the particle; is the local pressure gradient; C VM is the virtual quality coefficient; a r is the relative acceleration of the particle; C L is the lift coefficient; v rel is the relative velocity between the fluid and the particles.

4. The DEM-based shale oil reservoir nanofluid flooding microscopic simulation method according to claim 3 is characterized in that: The specific process of step 4 is as follows: Step 4.1, establish the solid phase control equation of nanoparticle motion as follows: Among them, m p is the mass of nanoparticles; F c is the contact force between nanoparticles and between particles and the wall; F f→p is the fluid force exerted on the nanoparticles; J p is the moment of inertia and inertia tensor; w p is the angular velocity vector; M c is the net torque generated by the tangential force causing the particle to rotate; M f→p is the additional torque caused by the liquid phase velocity gradient; Step 4.2, establish a normal contact force model between particles; The normal contact force between particles adopts a linear hysteresis spring model as follows: in, and are the normal elastic-plastic contact forces at the current time t and the previous time t-Δt, respectively, Δt is the time step; K nl and K nu are the contact stiffness values ​​during collision compression and collision recovery respectively; Δs n is the change in contact normal overlap during the current time; and are the normal overlap values ​​at the current time t and the previous time t-Δt, respectively; λ is a small dimensionless constant; Step 4.3, establishing a tangential contact force model between particles; the tangential contact force model between particles adopts a linear spring Coulomb limit model; Pure elastic tangential force at current time t for: in, is the tangential force at the previous time t-Δt; Δs τ is the tangential relative displacement of the particle in the time period; K τ is the tangential stiffness; Complete tangential force at current time t for: Where μ is the friction coefficient; is the normal force.

5. The DEM-based shale oil reservoir nanofluid flooding microscopic simulation method according to claim 4 is characterized in that: In step 5, the coupling module performs coupling data interaction based on computational fluid dynamics-discrete element method, and the specific process is as follows: Step a, establishing a nanoparticle motion simulation model and an oil / water two-phase flow Euler model, initializing the flow field and particle properties; the nanoparticle motion simulation model includes a particle-to-particle normal contact force model and a particle-to-particle tangential contact force model; Step b, entering the iteration process, during which it is determined whether the current simulation time has been reached. When the simulation time has been reached, the entire data interaction process is terminated; otherwise, the liquid-solid phase interaction force, the particle motion control equation, and the particle-particle normal / tangential contact force are calculated in sequence; the liquid-solid phase interaction force includes the force generated by the particle relative to the fluid phase corresponding to formula (3) and the fluid force applied to the nanoparticles corresponding to formula (6); Step c: Determine nT DEM Is it equal to T CFD ; n is a multiple; T DEM is the time step of the DEM module; T CFD is the time step of the CFD module; When nT DEM =T CFD When the particle position, velocity and pressure are updated, the particle phase data and the phase interaction force data are calculated and input into the oil / water two-phase flow simulation model, the oil / water two-phase turbulence model is calculated, the fluid velocity field is updated and it is judged whether the convergence conditions are met. When the convergence conditions are met, the fluid phase data is calculated and the DEM flow field force is updated, the time step is updated, and then the step b is returned to continue to judge whether the simulation time is reached; When the convergence condition is not met, the oil / water two-phase flow simulation model is returned to perform calculations again; When nT DEM ≠T CFD When , it returns to recalculate the liquid-solid interaction force, the particle motion control equations, and the normal / tangential contact forces between particles.

6. The DEM-based shale oil reservoir nanofluid flooding microscopic simulation method according to claim 5, characterized in that: In step 6, a porous medium multiphase seepage microscopic three-dimensional model is set according to a preset nanoparticle injection scheme, and the flow field and particle properties are initialized to simulate the operation state of nanoparticles and fluids under different porous media, oil phase parameters, and nanofluid parameter conditions. The migration distance of nanoparticles, the model residual oil distribution, and the saturation at different time steps are simulated, and a comparative analysis and performance evaluation of different nanofluid composite system oil displacement schemes are performed.

Images