A method for predicting the amount of gel particle blockage in randomly packed porous media

Abstract

The present invention discloses a method for predicting the amount of gel particle blockage in a randomly stacked porous medium, which belongs to the technical field of oil and gas extraction and is used for predicting the amount of gel particle blockage. The gel particle kinematic model and the porous medium fluid flow model are coupled to establish a fluid-gel particle fluid-solid coupling microscopic seepage model. According to the constructed three-dimensional randomly stacked porous medium pore structure, the particle information and flow field information in the fluid-gel particle fluid-solid coupling microscopic seepage model are initialized, and the amount of gel particle blockage at different times is calculated. The unified growth mode of the gel particle blockage change curve is fitted, and the prediction formula of the blockage amount of gel particles of different particle sizes is further obtained, which realizes the accurate prediction of the blockage effect of the porous medium after the gel particles act, and provides a basis for guiding the formulation of gel particle injection on site.

Description

Technical Field

[0001] The invention discloses a method for predicting the blocking amount of gel particles in a randomly stacked porous medium, and belongs to the technical field of oil and natural gas exploitation. Background Art

[0002] Gel particles play an important role in profile control, water plugging and improving oilfield recovery due to their excellent rheological properties and channeling ability. Particle plugging concentration is an important parameter to measure the plugging effect of gel particles, and particle size is a key factor affecting particle plugging concentration. There are significant differences in the flow characteristics and plugging ability of gel particles of different particle sizes in porous media: particles with smaller particle sizes can penetrate tiny pores, while particles with larger particle sizes form more stable blockages at the pores. At present, the research on the plugging concentration of gel particles in porous media mainly focuses on experimental observations, lacks systematic theoretical analysis and model building, resulting in a large uncertainty in how to reasonably select the particle size and injection amount of gel particles in practical applications. The computational fluid dynamics-discrete element fluid-solid coupling microscopic simulation method is currently an important method for simulating gel particle transmission and blockage phenomena, which can achieve accurate characterization of fluid flow, particle transmission and fluid-solid interaction. Summary of the invention

[0003] The purpose of the present invention is to provide a method for predicting the amount of gel particle blockage in randomly stacked porous media, so as to solve the problem in the prior art that it is difficult to accurately characterize the gel particle transmission and blockage mechanism at the pore scale in field and indoor experiments of oil reservoirs.

[0004] A method for predicting the amount of gel particle blockage in a randomly stacked porous medium, comprising:

[0005] Step 1: construct a gel particle kinematic model based on the discrete element method, construct a porous medium fluid flow model based on the computational fluid dynamics method, couple the gel particle kinematic model and the porous medium fluid flow model, and establish a fluid-gel particle fluid-solid coupling microscopic seepage model;

[0006] Step 2: construct a three-dimensional randomly stacked porous medium pore structure, initialize the particle information and flow field information in the fluid-gel particle fluid-solid coupling microscopic seepage model, and use the fluid-gel particle fluid-solid coupling microscopic seepage model to calculate the particle and fluid information at different times, and observe the change of gel particle blockage;

[0007] Step 3, changing the particle size of the injected gel particles, and using the fluid-gel particle fluid-solid coupling microscopic seepage model to simulate the gel particle transmission under different injection particle size conditions, and obtaining the gel particle blockage amount change curve under different injection particle size conditions;

[0008] Step 4: Based on the existing data curve fitting, a unified growth pattern of the gel particle blockage change curve is obtained, and the change of the blockage change curve fitting parameters under different particle size conditions is obtained, and further a gel particle blockage prediction formula is obtained;

[0009] Step 5: Use the fluid-gel particle fluid-solid coupling microscopic seepage model to simulate the operating status of gel particles and fluid under different injection parameter conditions, simulate the transmission of gel particles, and combine the gel particle blockage prediction formula to predict the gel particle blockage amount, so as to further guide the optimization of gel particle injection parameters at the oil field site.

[0010] Step 1 includes analyzing the force conditions of gel particles in the fluid based on the discrete element method, constructing a kinematic model of gel particles, simulating the microscopic movement process of gel particles in the fluid using the discrete element method, and determining the tangential contact force between gel particles, the normal contact force between gel particles, the tangential contact force between gel particles and rock walls, the normal contact force between gel particles and rock walls, and the fluid forces acting on the gel particles, wherein the fluid forces include drag force, buoyancy, and lift.

[0011] The gel particle kinematic model is based on Newton's second law:

[0012] ;

[0013] ;

[0014] In the formula, For gel particles Quality, For gel particles speed, For the moment, For gel particles The contact force, is the acceleration due to gravity, is the fluid force on the gel particles, For gel particles The moment of inertia, For gel particles The angular velocity of For gel particles The contact force on the gel particles includes the normal contact force and the tangential contact force.

[0015] The normal contact force on the gel particles is:

[0016] ;

[0017] ; ;

[0018] In the formula, for The normal contact force on the gel particles at time , for The normal contact force on the gel particles at time , is the time step, is the loading contact stiffness coefficient, is the unloading contact stiffness coefficient, for The normal overlap displacement of the gel particles at time t, for The normal overlap displacement of the gel particles at time , for The variation of the internal normal overlap displacement, is a dimensionless constant used to ensure that when the normal overlap displacement is reduced to zero, the normal contact force also returns to zero;

[0019] The tangential contact force on the gel particles is:

[0020] ;

[0021] ;

[0022] In the formula, for The pure elastic tangential contact force on the gel particles at the moment is: for The tangential contact force on the gel particles at time , is the tangential stiffness coefficient, is the time step The relative displacement in the inner direction, for The complete tangential contact force on the gel particles at the moment is is the friction coefficient, for The purely elastic tangential contact force on the gel particles at this moment.

[0023] When calculating the normal contact force between gel particles, and for:

[0024] ;

[0025] ; ;

[0026] In the formula, For gel particles The loading contact stiffness coefficient is and Gel particles and gel particles The loading contact stiffness coefficient is For gel particles Young's modulus, For gel particles of particle size.

[0027] When calculating the normal contact force between the gel particles and the rock wall, and for:

[0028] ; ; ;

[0029] In the formula, is the loading contact stiffness coefficient of the rock wall, is the Young's modulus of the rock wall.

[0030] The porous media fluid flow model is the fluid control equation, which includes the mass conservation equation, momentum conservation equation, and energy conservation equation;

[0031] The mass conservation equation is:

[0032] ;

[0033] The momentum conservation equation is:

[0034] ;

[0035] The energy conservation equation is:

[0036] ;

[0037] In the formula, is the fluid density, is the gradient, is the fluid velocity, is the fluid dynamic viscosity, is the fluid pressure, is the additional external force acting on the fluid, is the specific heat capacity of the fluid at constant pressure, is the temperature, is the thermal conductivity of the fluid; The rate at which heat is introduced into the fluid for viscous dissipation and other sources.

[0038] The drag force on the gel particles is:

[0039] ;

[0040] ;

[0041] ;

[0042] In the formula, is the drag force on the gel particles, For particles The pressure field near the location, is the volume of the gel particles, is the momentum transfer coefficient between the fluid phase and the particle phase, is the volume fraction of the particle phase in the computational grid, For particles The velocity field near the position, is the fluid volume fraction, is the relative velocity of the fluid and gel particles, is the drag force coefficient, is the Reynolds number of the fluid;

[0043] The buoyancy of the gel particles is:

[0044] ;

[0045] In the formula, is the buoyancy of the gel particles;

[0046] The lift force on the gel particles is:

[0047] ;

[0048] In the formula, is the lift force on the gel particles, is the lift coefficient, is the projected area of ​​the gel particles in the direction of fluid flow rate.

[0049] In step 2, the fluid-gel particle fluid-solid coupling microscopic seepage model is used to calculate the particle and fluid information at different times, and the movement information of the particles in the fluid is used to accurately determine whether the particles are blocked and count the number of blocked particles.

[0050] The gel particle blockage prediction formula includes the definition of dimensionless blockage , dimensionless time , the particle size ratio of gel particles to matrix particles :

[0051] ; ; ; ;

[0052] In the formula, is the actual blocking concentration of gel particles, is the saturated plugging concentration of gel particles, is the characteristic time, is the particle size of the matrix particles, is the original porosity;

[0053] The relationship between particle blockage and time:

[0054] ;

[0055] In the formula, is the average slope of the rising phase of the curve, describing the average overall blocking rate of particle blocking before reaching the saturation value of blocking amount. is the critical time of the curve growth rate, and when hour, , indicating that the particle blockage is zero at the beginning. is a natural constant.

[0056] Compared with the prior art, the present invention has the following beneficial effects: it realizes accurate prediction of the plugging effect of porous media after the action of gel particles of different particle sizes, provides a basis for guiding the field application of gel particles and the optimization of gel particle injection parameters, and improves the oil reservoir recovery rate and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of a method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to the present invention;

[0058] Figure 2 Schematic diagram of the transmission of gel particles in a randomly stacked porous medium in an embodiment of the present invention;

[0059] Figure 3 This is an image of the gel particle blockage amount at time t=0.3 in an embodiment of the present invention;

[0060] Figure 4 This is an image of the gel particle blockage amount at time t=0.6 in an embodiment of the present invention;

[0061] Figure 5 This is an image of the gel particle blockage amount at time t=0.9 in an embodiment of the present invention;

[0062] Figure 6 This is an image of the gel particle blockage amount at time t=1.2 in an embodiment of the present invention;

[0063] Figure 7 is a normalized curve diagram of the blocking amount of gel particles with different particle sizes in an embodiment of the present invention;

[0064] Figure 8 This is a diagram showing the variation of fitting parameters of the saturated blocking concentration curve of gel particles with different particle sizes in an embodiment of the present invention;

[0065] Fig. 9 It is a curve fitting parameter variation diagram of the average slope of the critical time of the dimensionless time curve of the curve growth rate of gel particles with different particle sizes in the embodiment of the present invention in the rising stage;

[0066] Fig.10 This is a diagram showing the variation of fitting parameters of the saturated blocking concentration curve of gel particles with different particle sizes in an embodiment of the present invention;

[0067] Fig.11 This is an empirical curve of the blockage amount of large-diameter gel particles in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] 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.

[0069] A method for predicting the amount of gel particle blockage in randomly stacked porous media, the technical process is as follows Figure 1 As shown, including:

[0070] Step 1: construct a gel particle kinematic model based on the discrete element method, construct a porous medium fluid flow model based on the computational fluid dynamics method, couple the gel particle kinematic model and the porous medium fluid flow model, and establish a fluid-gel particle fluid-solid coupling microscopic seepage model;

[0071] Step 2: construct a three-dimensional randomly stacked porous medium pore structure, initialize the particle information and flow field information in the fluid-gel particle fluid-solid coupling microscopic seepage model, and use the fluid-gel particle fluid-solid coupling microscopic seepage model to calculate the particle and fluid information at different times, and observe the change of gel particle blockage;

[0072] Step 3, changing the particle size of the injected gel particles, and using the fluid-gel particle fluid-solid coupling microscopic seepage model to simulate the gel particle transmission under different injection particle size conditions, and obtaining the gel particle blockage amount change curve under different injection particle size conditions;

[0073] Step 4: Based on the existing data curve fitting, a unified growth pattern of the gel particle blockage change curve is obtained, and the change of the blockage change curve fitting parameters under different particle size conditions is obtained, and further a gel particle blockage prediction formula is obtained;

[0074] Step 5: Use the fluid-gel particle fluid-solid coupling microscopic seepage model to simulate the operating status of gel particles and fluid under different injection parameter conditions, simulate the transmission of gel particles, and combine the gel particle blockage prediction formula to predict the gel particle blockage amount, so as to further guide the optimization of gel particle injection parameters at the oil field site.

[0075] Step 1 includes analyzing the force conditions of gel particles in the fluid based on the discrete element method, constructing a kinematic model of gel particles, simulating the microscopic movement process of gel particles in the fluid using the discrete element method, and determining the tangential contact force between gel particles, the normal contact force between gel particles, the tangential contact force between gel particles and rock walls, the normal contact force between gel particles and rock walls, and the fluid forces acting on the gel particles, wherein the fluid forces include drag force, buoyancy, and lift.

[0076] The gel particle kinematic model is based on Newton's second law:

[0077] ;

[0078] ;

[0079] In the formula, For gel particles Quality, For gel particles speed, For the moment, For gel particles The contact force, is the acceleration due to gravity, is the fluid force on the gel particles, For gel particles The moment of inertia, For gel particles The angular velocity of For gel particles The contact force on the gel particles includes the normal contact force and the tangential contact force.

[0080] The normal contact force on the gel particles is:

[0081] ;

[0082] ; ;

[0083] In the formula, for The normal contact force on the gel particles at time , for The normal contact force on the gel particles at time , is the time step, is the loading contact stiffness coefficient, is the unloading contact stiffness coefficient, for The normal overlap displacement of the gel particles at time t, for The normal overlap displacement of the gel particles at time , for The variation of the internal normal overlap displacement, is a dimensionless constant used to ensure that when the normal overlap displacement is reduced to zero, the normal contact force also returns to zero;

[0084] The tangential contact force on the gel particles is:

[0085] ;

[0086] ;

[0087] In the formula, for The pure elastic tangential contact force on the gel particles at the moment is: for The tangential contact force on the gel particles at time , is the tangential stiffness coefficient, is the time step The relative displacement in the inner direction, for The complete tangential contact force on the gel particles at the moment is is the friction coefficient, for The purely elastic tangential contact force on the gel particles at this moment.

[0088] When calculating the normal contact force between gel particles, and for:

[0089] ;

[0090] ; ;

[0091] In the formula, For gel particles The loading contact stiffness coefficient is and Gel particles and gel particles The loading contact stiffness coefficient is For gel particles Young's modulus, For gel particles of particle size.

[0092] When calculating the normal contact force between the gel particles and the rock wall, and for:

[0093] ; ; ;

[0094] In the formula, is the loading contact stiffness coefficient of the rock wall, is the Young's modulus of the rock wall.

[0095] The porous media fluid flow model is the fluid control equation, which includes the mass conservation equation, momentum conservation equation, and energy conservation equation;

[0096] The mass conservation equation is:

[0097] ;

[0098] The momentum conservation equation is:

[0099] ;

[0100] The energy conservation equation is:

[0101] ;

[0102] In the formula, is the fluid density, is the gradient, is the fluid velocity, is the fluid dynamic viscosity, is the fluid pressure, is the additional external force acting on the fluid, is the specific heat capacity of the fluid at constant pressure, is the temperature, is the thermal conductivity of the fluid; The rate at which heat is introduced into the fluid for viscous dissipation and other sources.

[0103] The drag force on the gel particles is:

[0104] ;

[0105] ;

[0106] ;

[0107] In the formula, is the drag force on the gel particles, For particles The pressure field near the location, is the volume of the gel particles, is the momentum transfer coefficient between the fluid phase and the particle phase, is the volume fraction of the particle phase in the computational grid, For particles The velocity field near the position, is the fluid volume fraction, is the relative velocity of the fluid and gel particles, is the drag force coefficient, is the Reynolds number of the fluid;

[0108] The buoyancy of the gel particles is:

[0109] ;

[0110] In the formula, is the buoyancy of the gel particles;

[0111] The lift force on the gel particles is:

[0112] ;

[0113] In the formula, is the lift force on the gel particles, is the lift coefficient, is the projected area of ​​the gel particles in the direction of fluid flow rate.

[0114] In step 2, the fluid-gel particle fluid-solid coupling microscopic seepage model is used to calculate the particle and fluid information at different times, and the movement information of the particles in the fluid is used to accurately determine whether the particles are blocked and count the number of blocked particles.

[0115] The gel particle blockage prediction formula includes the definition of dimensionless blockage , dimensionless time , the particle size ratio of gel particles to matrix particles :

[0116] ; ; ; ;

[0117] In the formula, is the actual blocking concentration of gel particles, is the saturated plugging concentration of gel particles, is the characteristic time, is the particle size of the matrix particles, is the original porosity;

[0118] The relationship between particle blockage and time:

[0119] ;

[0120] In the formula, is the average slope of the rising phase of the curve, describing the average overall blocking rate of particle blocking before reaching the saturation value of blocking amount. is the critical time of the curve growth rate, and when hour, , indicating that the particle blockage is zero at the beginning. is a natural constant.

[0121] In the present invention, the finite volume method is used to discretize and solve the fluid control equations, following the following steps: the Euler method is used to discretize the time, and the finite volume method is used to discretize the equations in the control volume, so as to realize the division of the calculation area into several small volumes (control volumes), that is, each control volume corresponds to a discrete node. The conservation equations are converted into algebraic equations, and the SIMPLE pressure-velocity coupling algorithm is used to update the velocity and pressure field multiple times to realize iterative solution. In addition, the first-order upwind format is used to calculate the momentum, turbulent kinetic energy, volume fraction and turbulent dissipation rate, and the convergence of the calculation is ensured by setting the threshold and monitoring the residual and the change of physical quantities. Finally, the kinematic model of the gel particles is coupled with the single-phase water fluid flow model in the porous medium to establish a fluid-gel particle fluid-solid coupling microscopic seepage model.

[0122] The effect of fluid on gel particles is mainly reflected in the fluid force, which will also react on the fluid and be reflected by the additional force term in the fluid control equation. Finally, a two-way coupling cycle between gel particles and fluid is realized. The main calculation cycle process is: solve the force of the gel particle system in the particle discrete element solver to determine the speed, displacement and other information of the gel particles; transfer the interaction force between the gel particles and the fluid and the momentum information of the system to the fluid solver; solve the fluid control equation to determine the parameter value and state of the fluid phase; determine the fluid force exerted by the fluid on the particles and update it back to the particle discrete element solver.

[0123] In step 2, spherical matrix particles are randomly stacked to simulate the porous medium structure produced by gravity in nature. Initialize the particle information and flow field information in the fluid-gel particle fluid-solid coupling microscopic seepage model. Use the gel particle kinematic model to simulate the force conditions between gel particles, adjacent gel particles, and gel particles and fluids, and use the fluid-gel particle fluid-solid coupling microscopic seepage model to calculate the particle and fluid information at different times, and observe the changes in the gel particle blockage at different times.

[0124] In the embodiment, spherical matrix particles are randomly stacked to simulate the porous medium structure produced by gravity in nature. In this embodiment, a 30 μm × 30 μm × 60 μm area is constructed, the number of stacked matrix particles is 513, the diameter of the matrix particles is 5 μm, and the mass density is 2600 kg / m 3 The average pore size of the constructed pore space is 1 μm, and the original porosity is 37.82%. The schematic diagram is shown in Figure 2 shown.

[0125] Initialize the particle information and flow field information in the fluid-gel particle fluid-solid coupling microscopic seepage model. In this embodiment, the particle size of the gel particles is 0.5 μm and the density is 1.2 g / cm 3 The volume fraction of injected gel particles is 0.438%, the Darcy velocity of injected fluid is 0.265 mm / s, and the fluid density is 1000 kg / m 3 The dynamic viscosity of the fluid is 1 mPa·s. The gel particle kinematic model is used to simulate the forces between gel particles, adjacent gel particles, and gel particles and fluids, and the fluid-gel particle fluid-solid coupling microscopic seepage model is used to calculate the particle and fluid information at different times.

[0126] Then observe the change of gel particle blockage at different times. In this embodiment, the gel particle blockage judgment condition is defined as the absolute particle transmission speed is less than 10 -6 m / s. Figure 3 , Figure 4 , Figure 5 , Figure 6 It can be seen that the change of the blockage amount over time is very significant, and once blockage occurs, the sites originally occupied by the blockage particles are prone to continue to be blocked, causing the blockage group to gradually become larger.

[0127] In step 3, the present embodiment changes the particle size of the gel particles in the fluid-gel particle fluid-solid coupling microscopic seepage model to 0.25 μm-0.6 μm, that is, The fluid-gel particle fluid-solid coupling microscopic seepage model was used to simulate the blockage change curve under different injection particle size conditions. After further data processing, the normalized blockage change curve was obtained as follows: Figure 7 shown.

[0128] Depend on Figure 7 It can be seen that the amount of gel particle blockage increases with time and eventually tends to reach a stable platform. In addition, there is a turning point in the change of blockage rate. Based on this critical time point, the blocking process can be divided into three stages, including: rapid blocking stage, when At this stage, the particle blockage phenomenon is obvious, and the blockage rate gradually accelerates and reaches its maximum value when The slow blockage stage, in which the particle blockage phenomenon still continues, but the blockage rate slows down significantly; the stable stage, in which the particle blockage rate approaches 0, which makes the particle blockage amount finally reach a relatively stable platform. For particles of different particle sizes, there are obvious differences in the speed at which they reach the platform. Small particle size particles will reach their own blockage platform faster than large particle size particles.

[0129] In step 4, based on the fitted curve, as expected, the curve will eventually approach 1 infinitely, indicating that the value of the blocking particles will eventually reach saturation. When the particle size is small, the particles reach the blocking saturation stable value at a faster rate. As the particle size increases, the saturation platform gradually moves backward, and the growth rate of the early part of the curve gradually slows down. Based on the results of the fitting parameters, analyze how the particle size affects the fitting function, such as Figure 8 , Fig. 9 , Fig.10 As shown, it can be seen that the curve parameters are There are significant mutations or transitions before and after. ,when hour, Follow This means that the relative saturation of gel particle blocking is limited by the relative size of the gel particles and the pores; When it is greater than 0.08, The growth rate has accelerated significantly. ,when hour, Follow Increase and gradually decrease; when When it is greater than 0.08, will gradually approach a stable value. This shows that when When the particle size is much smaller than the matrix size, the particle blockage curve will quickly reach the final saturation platform. As the critical time increases, the particle blockage saturation platform will gradually lag behind and eventually tend to appear near a stable value. , showing the same There is a positive correlation, that is, the larger the injected particle size, the later the maximum point of particle blockage velocity appears. In addition, When it approaches 0.08, It shows an obvious tendency of increasing mutation.

[0130] In the embodiment, when When the blocking curves of larger particles show a good overlap, it shows that the blocking law of gel particles has good normalization and universality. A master curve can be used to describe the blocking law of larger particles. Fig.11 As shown, the parameters ; The master curve can be regarded as an empirical formula for the change law of gel particle blockage under the particle size condition.

[0131] 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 predicting the amount of gel particle blockage in a randomly stacked porous medium, characterized in that: include: Step 1: construct a gel particle kinematic model based on the discrete element method, construct a porous medium fluid flow model based on the computational fluid dynamics method, couple the gel particle kinematic model and the porous medium fluid flow model, and establish a fluid-gel particle fluid-solid coupling microscopic seepage model; Step 2: construct a three-dimensional randomly stacked porous medium pore structure, initialize the particle information and flow field information in the fluid-gel particle fluid-solid coupling microscopic seepage model, and use the fluid-gel particle fluid-solid coupling microscopic seepage model to calculate the particle and fluid information at different times, and observe the change of gel particle blockage; Step 3, changing the particle size of the injected gel particles, and using the fluid-gel particle fluid-solid coupling microscopic seepage model to simulate the gel particle transmission under different injection particle size conditions, and obtaining the gel particle blockage amount change curve under different injection particle size conditions; Step 4: Based on the existing data curve fitting, a unified growth pattern of the gel particle blockage change curve is obtained, and the change of the blockage change curve fitting parameters under different particle size conditions is obtained, and further a gel particle blockage prediction formula is obtained; Step 5: Use the fluid-gel particle fluid-solid coupling microscopic seepage model to simulate the operating status of gel particles and fluid under different injection parameter conditions, simulate the transmission of gel particles, and combine the gel particle blockage prediction formula to predict the gel particle blockage amount, so as to further guide the optimization of gel particle injection parameters at the oil field site.

2. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 1, characterized in that: Step 1 includes analyzing the force conditions of gel particles in the fluid based on the discrete element method, constructing a kinematic model of gel particles, simulating the microscopic movement process of gel particles in the fluid using the discrete element method, and determining the tangential contact force between gel particles, the normal contact force between gel particles, the tangential contact force between gel particles and rock walls, the normal contact force between gel particles and rock walls, and the fluid forces acting on the gel particles, wherein the fluid forces include drag force, buoyancy, and lift.

3. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 2, characterized in that: The gel particle kinematic model is based on Newton's second law: ； ； In the formula, For gel particles Quality, For gel particles speed, For the moment, For gel particles The contact force, is the acceleration due to gravity, is the fluid force on the gel particles, For gel particles The moment of inertia, For gel particles The angular velocity of For gel particles The contact force on the gel particles includes the normal contact force and the tangential contact force.

4. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 3, characterized in that: The normal contact force on the gel particles is: ； ； ； In the formula, for The normal contact force on the gel particles at time , for The normal contact force on the gel particles at time , is the time step, is the loading contact stiffness coefficient, is the unloading contact stiffness coefficient, for The normal overlap displacement of the gel particles at time t, for The normal overlap displacement of the gel particles at time , for The variation of the internal normal overlap displacement, is a dimensionless constant used to ensure that when the normal overlap displacement is reduced to zero, the normal contact force also returns to zero; The tangential contact force on the gel particles is: ； ； In the formula, for The pure elastic tangential contact force on the gel particles at the moment is: for The tangential contact force on the gel particles at time , is the tangential stiffness coefficient, is the time step The relative displacement in the inner tangential direction, for The complete tangential contact force on the gel particles at the moment is is the friction coefficient, for The purely elastic tangential contact force on the gel particles at time .

5. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 4, characterized in that: When calculating the normal contact force between gel particles, and for: ； ； ； In the formula, For gel particles The loading contact stiffness coefficient is and Gel particles and gel particles The loading contact stiffness coefficient is For gel particles Young's modulus, For gel particles of particle size.

6. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 4, characterized in that: When calculating the normal contact force between the gel particles and the rock wall, and for: ； ； ； In the formula, is the loading contact stiffness coefficient of the rock wall, is the Young's modulus of the rock wall.

7. A method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 6, characterized in that: The porous media fluid flow model is the fluid control equation, which includes the mass conservation equation, momentum conservation equation, and energy conservation equation; The mass conservation equation is: ； The momentum conservation equation is: ； The energy conservation equation is: ； In the formula, is the fluid density, is the gradient, is the fluid velocity, is the fluid dynamic viscosity, is the fluid pressure, is the additional external force acting on the fluid, is the specific heat capacity of the fluid at constant pressure, is the temperature, is the thermal conductivity of the fluid; The rate at which heat is introduced into the fluid for viscous dissipation and other sources.

8. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 7, characterized in that: The drag force on the gel particles is: ； ； ； In the formula, is the drag force on the gel particles, For particles The pressure field near the location, is the volume of the gel particles, is the momentum transfer coefficient between the fluid phase and the particle phase, is the volume fraction of the particle phase in the computational grid, For particles The velocity field near the position, is the fluid volume fraction, is the relative velocity of the fluid and gel particles, is the drag force coefficient, is the Reynolds number of the fluid; The buoyancy of the gel particles is: ； In the formula, is the buoyancy of the gel particles; The lift force on the gel particles is: ； In the formula, is the lift force on the gel particles, is the lift coefficient, is the projected area of ​​the gel particles in the direction of fluid flow rate.

9. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 8, characterized in that: In step 2, the fluid-gel particle fluid-solid coupling microscopic seepage model is used to calculate the particle and fluid information at different times, and the movement information of the particles in the fluid is used to accurately determine whether the particles are blocked and count the number of blocked particles.

10. The method for predicting the amount of gel particle blockage in a randomly stacked porous medium according to claim 9, characterized in that: The gel particle blockage prediction formula includes the definition of dimensionless blockage , dimensionless time , gel particle to matrix particle size ratio : ； ； ； ； In the formula, is the actual blocking concentration of gel particles, is the saturated plugging concentration of gel particles, is the characteristic time, is the particle size of the matrix particles, is the original porosity; The relationship between particle blockage and time: ； In the formula, is the average slope of the rising phase of the curve, describing the average overall blocking rate of particle blocking before reaching the saturation value of blocking amount. is the critical time of the curve growth rate, and when hour, , indicating that the particle blockage is zero at the beginning. is a natural constant.

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