Solid-containing oil-gas-water conveying calculation method

Abstract

The invention discloses a solid-containing oil-gas-water conveying calculation method, and belongs to the field of petroleum engineering. The invention aims to solve the problem that a traditional calculation method is difficult to comprehensively consider respective physical characteristics of oil-gas-water three phases, complex interphase interface action and influence of particles on overall flow. The method comprises the following steps: step 1, determining large-scale analytic particles and small-scale semi-analytic particles in the multiphase flow according to a preset fluid domain grid size; 2, constructing a multiphase flow-particle coupling numerical model based on a particle classification result in the step 1, and distinguishing a control equation of a fluid phase and two types of particle phases; 3, configuring relevant parameters of fluid attributes and particle parameters based on the coupling numerical model; 4, on the basis of the relevant parameters, independent solution and inter-phase coupling effect calculation of the fluid field and the particle field are achieved through a two-way coupling algorithm, and calculation result data are obtained; and 5, outputting a simulation result based on the calculation result data.

Description

Technical Field

[0001] This invention relates to the field of petroleum engineering technology, specifically to a calculation method for solid oil-gas-water transportation. Background Technology

[0002] In oil extraction, transportation, and processing, the coexistence of three-phase flows (oil, gas, and water) with particles is extremely common. For example, in oil reservoir extraction, formation sands and other particles are extracted along with the oil, gas, and water; in oil pipelines, rust, scale particles, and other contaminants also mix into the three-phase flow system. Accurately calculating the various parameters of this complex system is crucial for ensuring oil production safety, improving oil recovery rates, and optimizing pipeline transportation efficiency.

[0003] However, traditional calculation methods struggle to fully consider the individual physical properties of the oil, gas, and water phases, the complex interphase interactions, and the impact of particles on overall flow. Existing multiphase flow calculation models often neglect the presence of particles or oversimplify particle treatment when dealing with three-phase flow, failing to accurately reflect particle settling, aggregation, and changes in flow resistance. This leads to inaccurate assessments of pipeline blockage risks and unreasonable reservoir development plans in practical engineering applications, resulting in significant economic losses and safety hazards. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a calculation method for solid-oil-gas-water transportation, comprising the following steps: Step 1: Determine the large-scale analytical particles and small-scale semi-analytical particles in the multiphase flow based on the pre-set fluid domain grid size; Step 2: Based on the particle classification results in Step 1, construct a multiphase flow-particle coupled numerical model and distinguish the governing equations between the fluid phase and the two types of particle phases; Step 3: Based on the coupled numerical model, configure the relevant parameters of fluid properties and particle parameters; Step 4: Based on the relevant parameters, a two-way coupling algorithm is used to achieve independent solution of the fluid field and particle field and calculation of interphase coupling effect, and the calculation result data is obtained; Step 5: Output simulation results based on the calculation results.

[0005] As a further improvement of the present invention, the determination of large-scale analytical particles and small-scale semi-analytical particles in the multiphase flow in step 1 is specifically as follows: by comparing the particle size with the mesh size, particles with a particle size ratio of less than 0.1 are defined as analytical particles, and particles with a particle size ratio between 0.1 and 3 are defined as semi-analytical particles.

[0006] As a further improvement of the present invention, in step 2, the fluid phase adopts a multiphase flow model under the Euler framework, and the flow of oil, gas and water three phases is described by the Navier-Stokes equations, and the fluid volume method is used to track the gas-liquid interface. The particle phase can be divided into two categories: analytical particles and semi-analytical particles. The motion characteristics of each type of particle are analyzed in detail using the discrete element method (DEM) under the Lagrange framework, and the translational and rotational motions of each particle are accurately solved using the Newton-Euler equations.

[0007] As a further improvement of the present invention, the relevant parameters of the numerical model in step 3 include fluid property parameters and particle property parameters.

[0008] As a further improvement of the present invention, the fluid property parameters include: density, viscosity, surface tension coefficient and interphase interaction force parameters of oil, gas and water.

[0009] As a further improvement of the present invention, the particle property parameters include: particle size distribution, density, elastic modulus, Poisson's ratio, coefficient of friction, and non-spherical correction factor.

[0010] As a further improvement of the present invention, the calculation results include the spatial relationship between particles, various forces acting on the particles, the updated velocity and position of the particles, the changed velocity field and pressure field of the fluid, the physical information of the analytical particles, the physical information of the semi-analytical particles, the position of the oil-gas-water three-phase interface, and the porosity of each fluid domain grid cell.

[0011] The beneficial effects of this invention are: By importing the calculation results into the post-processing tool, users can perform detailed analysis and visualization of the results. The output includes, but is not limited to, key data such as particle motion trajectories, fluid phase velocity fields, pressure distribution fields, temperature distribution fields, and phase interface changes. Through comprehensive analysis of this data, a precise understanding of the oil-gas-water miscible flow characteristics can be achieved, assisting users in optimizing the design of oil and gas transportation equipment, assessing pipeline blockage risks, and formulating reservoir development plans. It also supports the optimization of oil and gas transportation system design, pipeline and equipment anti-blockage analysis, pump station layout and operation plan adjustments, and the scientific evaluation and optimization of reservoir development strategies.

[0012] It can support multi-scheme comparison and sensitivity analysis, providing data support for oilfield development scheme selection, improving oil and gas extraction efficiency and reducing safety and operational risks.

[0013] By correcting the fluid velocity at the interface, the problem of large-scale particles being constantly surrounded by a single phase during the process of crossing the interface is effectively avoided, thereby improving the accuracy and realism of particle motion simulation in multiphase flow fields. Attached Figure Description

[0014] Figure 1 This is a flowchart of the calculation method for solid-oil-gas-water transportation in this invention; Figure 2 This is a flowchart of the multiphase flow and DEM coupling algorithm of the solid-oil-gas-water transportation calculation method of the present invention; Figure 3 This is a schematic diagram of analytical particles and semi-analytical particles; Figure 4 This is a schematic diagram of semi-analytical and analytical particle fields and VOF fields. Detailed Implementation

[0015] like Figure 1 As shown, a calculation method for solid-oil-gas-water transportation includes the following steps: Step 1: Determine the large-scale analytical particles and small-scale semi-analytical particles in the multiphase flow based on the pre-set fluid domain grid size; like Figure 3 As shown, specifically, by comparing particle size with mesh size, particles with a particle size ratio less than 0.1 are defined as analytical particles, and particles with a particle size ratio between 0.1 and 3 are defined as semi-analytical particles. Step 2: Based on the particle classification results in Step 1, construct a multiphase flow-particle coupled numerical model and distinguish the governing equations between the fluid phase and the two types of particle phases; like Figure 4 As shown, the fluid phase adopts a multiphase flow model under the Euler framework, and the flow of oil, gas and water three phases is described by the Navier-Stokes equations. The volume of fluid (VOF) method is used to track the gas-liquid interface. The particle phase can be divided into two categories: analytical particles and semi-analytical particles. The kinematic characteristics of each type of particle are analyzed in detail using the Discrete Element Method (DEM) within the Lagrange framework. In this method, the translational and rotational motions of each particle are accurately solved using the Newton-Euler equations, thereby achieving an accurate description and simulation of the particle phase behavior.

[0016] Step 3: Based on the coupled numerical model, configure the relevant parameters of fluid properties and particle parameters; The relevant parameters of the numerical model include fluid property parameters and particle property parameters: Fluid property parameters: density, viscosity, surface tension coefficient, and interphase interaction force parameters of oil, gas, and water; Particle property parameters: particle size distribution, density, elastic modulus, Poisson's ratio, coefficient of friction, and non-spherical correction factor; Note: The physical property parameters of particles and fluids need to be modified and set according to the actual local engineering conditions.

[0017] Step 4: Based on the relevant parameters, a two-way coupling algorithm is used to achieve independent solution of the fluid field and particle field and calculation of interphase coupling effect, and the calculation result data is obtained; like Figure 2 As shown, the specific steps for achieving independent solutions for the fluid field and particle field and calculating interphase coupling effects using a two-way coupling algorithm include: Step 4.1: Perform particle localization and inter-particle contact identification, and calculate the spatial relationship between particles; Particle localization and inter-particle contact identification are fundamental to calculating the spatial relationships between particles. They determine the spatial coordinates of all particles at the current moment and, based on this, accurately identify the contact states between particles and between particles and boundaries. Explicit time integration can be used to update particle velocity and position. ; ; In the formula, This represents the particle's velocity at the next time step t+1; This represents the velocity of the particle at the current time step t; The time step represents the interval between two consecutive time points; Indicates the mass of the particle; This represents the hydrodynamic force acting on a particle; This represents the contact force acting on a particle, specifically the force generated when the particle collides or comes into contact with other objects. This indicates the position of the particle at the next time step t+1; This indicates the position of the particle at the current time step t.

[0018] Step 4.2: Calculate the various forces acting on the particles based on the spatial relationships between them; By analyzing the spatial relationships between particles, we can calculate the interaction forces between particles (including normal contact force, tangential contact force, and damping force) and the field forces acting on the particles (such as fluid drag force and pressure gradient force). We can then perform vector synthesis of these forces to provide input for the subsequent integral solution of the Navier-Stokes equations.

[0019] When solids collide with each other, contact forces are generated between discrete elements. According to the Hertz-Mindlin contact model in the DEM, the contact force... It can be divided into normal contact force. and tangential force The calculation is as follows: Normal contact force The following formula can be used to calculate: ; In the formula, is the normal stiffness coefficient (unit: N / m³ / ²); n is the normal unit vector, pointing towards the center of particle b; This is the normal damping coefficient (unit: N·s / m). The normal relative velocity (unit: m / s) is the relative velocity between two particles or between a particle and a wall in the normal direction. This is the normal phase shift.

[0020] Tangential force The following formula can be used to calculate: ; In the formula, Tangential stiffness coefficient (unit: N / m); Tangential displacement (unit: m); It is a tangential unit vector, parallel to the contact surface; is the tangential damping coefficient (unit: N·s / m); The coefficient of sliding friction (dimensionless) is determined by the surface roughness of the material. The tangential relative velocity (unit: m / s).

[0021] Regarding the frictional force generated between particles and the wall due to flow. The following formula can be used to calculate: ; In the formula, is the coefficient of rolling friction (dimensionless). The equivalent radius (unit: m). If a particle collides with a planar wall, ,but .

[0022] The drag force on the particles generated by the fluid field The following formula can be used to calculate: ; In the formula, For fluid density; The projected area of ​​the particle; The velocity vector of the flow field; Particle velocity vector; For the drag coefficient Schiller-Naumann model, the values ​​are required as follows: ; Pressure gradient force The following formula can be used to solve it: ; In the formula, Indicates particle volume; This represents the fluid pressure gradient (which can be obtained from the fluid field).

[0023] Virtual mass force The following formula can be used to calculate: ; In the formula, This represents the virtual quality coefficient (default value is 0.5). This represents the mass derivative of a fluid.

[0024] Step 4.3: Based on the various forces acting on the particles, calculate the particle motion equations and update the particle velocity and position; Based on the calculated forces acting on each particle, the kinematic equations of the particles (satisfying Newton's second law) are solved by integration, as follows: ; In the formula, Let be the mass of the particle; x is the position vector of the particle. External force; This refers to the contact force between particles; This refers to the drag force between the particles and the fluid.

[0025] After the calculation is completed, the particle velocity and position are updated using the formula in step 4.1, which serves as the input for subsequent fluid field iterative calculations.

[0026] Step 4.4: Calculate the porosity of each fluid domain grid cell; Porosity calculation is closely related to the fluid velocity field and particle distribution. The porosity of each grid cell is calculated using the following formula: ; In the formula, This represents the porosity of the j-th grid cell; This represents the volume of the particle within the j-th grid cell; This represents the total volume of the j-th grid cell.

[0027] Step 4.5: Solve for particle physical information; Considering the existence of analytical and semi-analytical particles, this step uses a threshold method to distinguish between analytical and semi-analytical particles, and then specifically solves the physical information of the two types of particles.

[0028] For fully analytical particles, the particle is divided into z sub-blocks, and the volume fraction at the corresponding grid of each sub-block is calculated to obtain the volume fraction at the background of the analytical particle.

[0029] For semi-analyzed particles, the volume averaging method is used to obtain the concentration distribution of the semi-analyzed particles in space, which is the particle concentration field.

[0030] Analytical particle processing (mesh size ratio < 0.1): For particles with a mesh size ratio less than 0.1, accurate analysis is performed. The flow field information is solved using the Navier-Stokes equations. The submerged boundary method (IBM) is used to describe the particle boundaries and solve for the particle physical information, as detailed below: First, the motion of an incompressible viscous fluid with a free surface is described using the Navier-Stokes equations: ; ; In the formula, For mixed density; Mixing speed; To share the pressure field (assuming equal pressure in each phase); Virtual force density introduced by particle boundaries (calculated by IBM); External volume forces (such as gravity).

[0031] Interpolate the velocity field on the Eulerian grid to the particle boundary points (Lagrange points). superior: ; In the formula, Particle boundary point The speed at that location; Euler mesh nodes The speed at that location; For interpolation kernel functions; Euler mesh nodes The corresponding control volume.

[0032] On the other hand, the target velocity on the particle surface is determined by rigid body motion: ; In the formula, and These are the translational velocity and angular velocity of the particle's center of mass, respectively. The coordinates of the particle's centroid; Let be the coordinates of a point on the particle surface. This formula represents the velocity of a point on the particle surface under ideal no-slip boundary conditions.

[0033] The velocity difference can be obtained by comparing the interpolated fluid velocity with the target boundary velocity. : ; This difference characterizes the degree of deviation between the fluid velocity and the particle boundary conditions. To eliminate this deviation, a corrected force density needs to be applied at the Lagrange point: ; In the formula, To correct the force density; For fluid density; The correlation coefficient (related to the characteristics of the interpolation and correction process); For time step.

[0034] The physical meaning of this corrected force density is to push the fluid velocity toward the particle boundary velocity.

[0035] Then, the correction force at the Lagrange point is diffused back to the Eulerian grid: ; In the formula, The distributed volume forces after diffusion into the Eulerian grid; It is an interpolation function; Coordinates of discrete points on the particle surface; Let be the area of ​​discrete surface elements on the particle surface. This introduces distributed body forces into the fluid momentum equation. This allows the fluid solution to gradually satisfy the boundary conditions.

[0036] Finally, according to the law of conservation of momentum, the body forces acting on the fluid must produce a reaction force acting on the particle. By integration, the total resultant force and resultant torque of the fluid on the particle can be obtained: , ; In the formula, The hydrodynamic force exerted on the particles by the fluid, The torque applied to the fluid. These quantities are transmitted to the DEM module for updating the translational and rotational states of the particles.

[0037] Among them, for the behavior of large-scale particles crossing the interface, a fluid velocity correction method at the interface is adopted to prevent the particles from always being surrounded by one phase. Specifically, when the volume fraction of oil, gas, or water phase in the flow field is between (0, 1), a volume fraction between (0.01, 0.99) can be used. The fluid velocity at the particle-covered grid (particle volume fraction greater than 0.99) is corrected to the reverse velocity of the particle velocity to account for the influence of surface tension. The velocity correction factor can be taken as -0.5. For the velocity correction at the particle boundary (particle volume fraction 0.01-0.99), the velocity correction scheme of the IB submerged boundary method is still used, and a delay factor is introduced. The factor can be taken as 0.9 to prevent errors in the calculation of particle force, while allowing particle surface wetting.

[0038] Semi-analytical particle processing (mesh size ratio 0.1~3): For particles with a mesh size ratio of 0.1 to 3, a fluid porosity field is introduced into the Navier-Stokes equations, and particle velocities and forces are mapped to the local flow field through local averaging theory: Instead of precisely analyzing the flow state around each particle, the fluid porosity field is introduced into the Navier-Stokes equations, and the particle velocity and force are mapped to the local flow field through local averaging theory: ; ; In the formula, The fluid porosity; For fluid density; For fluid velocity; It is the acceleration due to gravity; The force acting on a fluid per unit volume is expressed as: ; In the formula, N is the number of particles in the grid cell; For the mesh volume; Particles The drag force it experiences; and Particles The pressure gradient force and viscous force it is subjected to; and Particles The virtual mass force and Basset force acting on it; and Particles It is subjected to the Saffman force and the Magnus force.

[0039] interparticle lubrication force Represented as: ; In the formula, Let be the particle diameter. If the interaction occurs between the particle and the wall, then... , This is the closest distance between the surfaces of the two particles; Let be the relative velocity between the two particles at their center, i.e. ; This is the particle velocity vector; Particles The center of the ball points to the particle. The unit vector at the center of the sphere; This is the spatial position vector of the particle.

[0040] Contact force Calculated using the Hertz-Mindlin model: ; In the formula, the first set of parentheses represents the normal contact force, and the second set of parentheses represents the tangential contact force; , , , It is the elastic constant; , , These represent the overlap, tangential vector, and tangential relative velocity of the two particles, respectively. The rotation of the particles is described by the following equation: ; In the formula, and Let be the moment of inertia and angular velocity of the particle.

[0041] Semi-analyzed particles The covered grid area extends to an outer radius of In a spherical region, using a Gaussian function as the mesh weighting function, the particles are reconstructed using the entire flow field mesh within the extended spherical region. Background flow field information: ; ; In the formula, The Gaussian kernel function is used; N is the radius. The total number of grid cells within the spherical region; The semi-analyzed particle diameter; and Particles Center and the The position vector of each grid center; For the first Porosity of each grid cell; For the first The volume of each grid cell; For the first Flow field information for each grid.

[0042] Although the kernel function approximation error decreases with increasing extended region radius, increasing the extended region radius leads to a rapid increase in the number of grids covered by each particle.

[0043] Background flow field porosity at particles Calculated using the following formula: ; It should be noted that the porosity calculated here is only used for the force exerted by the fluid on the particles and cannot be used for the governing equations.

[0044] Dragging force plays a dominant role among the various forces exerted by fluids on particles, making the accuracy of its computational model crucial. The drag force of a single particle is calculated using the following formula: ; In the formula, Particles speed; Particles The diameter; This is the drag force coefficient.

[0045] 4.6: The oil-gas-water three-phase interface is traced and the location of the phase interface is determined by calculating the volume fraction of each phase fluid in each fluid domain grid cell; The problem of the oil-gas-water three-phase interface can be traced using the VOF (Volume of Fluid) method. This method tracks the phase interface by calculating the volume fraction of each fluid phase in each grid cell. When the volume fraction in a grid is 1, the grid is completely filled with a certain fluid phase; if it is 0, it contains no fluid of that phase; and if it is between 0 and 1, it indicates that the phase interface passes through the grid. As time progresses, the volume fraction is updated according to the fluid flow and motion equations, thus determining the location of the phase interface.

[0046] Specifically, the governing equations for the phase fraction are written as follows: ; In the formula, represents the volume fraction of the k-th phase fluid in the grid cell; To improve the speed of interface reconstruction.

[0047] If we consider *a* phases, then conform to ; ; In the formula, when =1 indicates that the interface sharpening factor is enabled. =0 indicates that the interface sharpening factor is turned off.

[0048] Since the VOF method treats all fluids as a continuous body, the volume-averaged momentum equation is: ; ; In the formula, The volume fraction of phase c; The velocity vector of a certain phase; It is an interphase force; Volume forces other than gravity; For shear force tensor; For the velocity gradient tensor; This is the transpose of the velocity gradient; and The density and viscosity of a certain phase fluid can be calculated using the following formula: ; ; Surface tension This can be described by the CSF method and can be calculated using the following formula: ; ; In the formula, It is the surface tension coefficient; For the interface curvature.

[0049] Step 4.7: The particle phase interaction forces are fed back to the fluid field, changing the fluid's velocity and pressure fields; The forces (drag force and reaction force of pressure gradient force) of the particle phase acting on the fluid phase obtained in step 4.2 are fed back to the fluid dynamics computational domain as the momentum source phase: Momentum source term: Averages the particle forces across the fluid mesh.

[0050] ; In the formula, The volume of the fluid mesh; The force exerted by a single particle on the fluid phase.

[0051] Volume fraction correction: Update the fluid phase fraction based on the volume occupied by the particles. ; The momentum source term represents the force exerted by the particle on the fluid when the particle interacts with the fluid, including the drag and lift of the particle. These forces change the momentum distribution of the fluid. The volume fraction correction takes into account the space occupied by the particle, affecting the continuity and compressibility of the fluid, and thus changing the velocity and pressure field of the fluid.

[0052] In the above calculation process, the particle phase time step is adaptively adjusted according to the particle collision frequency and contact stiffness to ensure that the Cron number C under CFL (Courant-Friedrichs-Lewy) conditions is maintained within the range of 1 to 2. Its expression is: ; In the formula, Characteristic velocity, For time step, This represents the spatial step size.

[0053] The velocity field is obtained by scanning the computational domain and the local Cron number is calculated. The new time step is determined based on the most stringent stability condition to ensure computational stability and accuracy.

[0054] To adapt to the spatiotemporal variations of the flow field's characteristic velocities and avoid the inefficiency caused by a fixed global time step, the algorithm dynamically adjusts the time step through the following steps: 1. Scan the computational domain to obtain the current velocity field or wave velocity field; 2. Calculate the local Cron number for each grid cell; 3. Determine the new time step based on the most stringent stability condition, i.e.: ; In the formula, The time step size to be used in the next time step; Maximum allowed crown number; Let j be the size of the j-th grid cell; Let be the characteristic velocity magnitude of the j-th grid cell.

[0055] Step 5: Output simulation results based on the calculation results.

[0056] The calculation method described above can effectively solve the state of fluids and particles. After the calculation is completed, the user can output the various types of data obtained to a specified data file or import them into post-processing software for further analysis. The calculation results include the spatial relationships between particles, various forces acting on the particles, the updated particle velocity and position, the changed velocity and pressure fields of the fluid, the physical information of the analytical particles, the physical information of the semi-analytical particles, the position of the oil-gas-water three-phase interface, and the porosity of each fluid domain grid cell.

[0057] By importing various types of data into the post-processing tool, users can perform in-depth analysis and visualization of the simulation results. The output includes key information such as particle motion trajectories, fluid phase velocity and pressure distribution cloud maps, oil-gas-water phase interface distribution maps, and particle stress analysis diagrams. Based on this visualized data, the migration and sedimentation patterns of particles can be further analyzed, and the erosion risk of particle wear on the pipe wall can be assessed. This provides a reliable basis for optimizing the design of oil and gas transportation equipment, accurately assessing pipeline blockage risks, and scientifically formulating reservoir development plans.

[0058] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A calculation method for solid oil-gas-water transportation, characterized in that, Includes the following steps: Step 1: Determine the large-scale analytical particles and small-scale semi-analytical particles in the multiphase flow based on the pre-set fluid domain grid size; Step 2: Based on the particle classification results in Step 1, construct a multiphase flow-particle coupled numerical model and distinguish the governing equations between the fluid phase and the two types of particle phases; Step 3: Based on the coupled numerical model, configure the relevant parameters of fluid properties and particle parameters; Step 4: Based on the relevant parameters, a two-way coupling algorithm is used to achieve independent solution of the fluid field and particle field and calculation of interphase coupling effect, and the calculation result data is obtained; Step 5: Output simulation results based on the calculation results.

2. The calculation method for solid-oil-gas-water transportation according to claim 1, characterized in that, In step 1, the determination of large-scale analytical particles and small-scale semi-analytical particles in the multiphase flow is specifically as follows: by comparing the particle size with the mesh size, particles with a particle size ratio of less than 0.1 are defined as analytical particles, and particles with a particle size ratio between 0.1 and 3 are defined as semi-analytical particles.

3. The calculation method for solid-oil-gas-water transportation according to claim 1, characterized in that, In step 2, the fluid phase adopts a multiphase flow model under the Euler framework, and the flow of oil, gas and water is described by the Navier-Stokes equations. The fluid volume method is used to track the gas-liquid interface. The particle phase can be divided into two categories: analytical particles and semi-analytical particles. The motion characteristics of each type of particle are analyzed in detail using the discrete element method (DEM) under the Lagrange framework, and the translational and rotational motions of each particle are accurately solved using the Newton-Euler equations.

4. The calculation method for solid-oil-gas-water transportation according to claim 1, characterized in that, The relevant parameters of the numerical model in step 3 include fluid property parameters and particle property parameters.

5. The calculation method for solid-oil-gas-water transportation according to claim 4, characterized in that, Fluid property parameters include: density, viscosity, surface tension coefficient, and interphase interaction force parameters of oil, gas, and water.

6. The calculation method for solid-oil-gas-water transportation according to claim 4, characterized in that, Particle properties include: particle size distribution, density, elastic modulus, Poisson's ratio, coefficient of friction, and non-spherical correction factor.

7. The calculation method for solid-oil-gas-water transportation according to claim 1, characterized in that, The calculation results include the spatial relationships between particles, various forces acting on the particles, the updated particle velocity and position, the changed velocity and pressure fields of the fluid, the physical information of the analytical particles, the physical information of the semi-analytical particles, the position of the oil-gas-water three-phase interface, and the porosity of each fluid domain grid cell.

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