Method and device for predicting non-deposition condition of sand-carrying migration of tubular column

Abstract

The invention discloses a method and a device for predicting a non-deposition condition of sand-carrying migration of a tubular column. The method comprises the following steps: within preset calculation physical time, performing numerical simulation on a flow field in a tubular column under a single independent variable working condition by utilizing a pre-established sand migration simulation model, and monitoring sand mass flow at an inlet and an outlet of the tubular column to obtain simulation and monitoring results; based on the mass flow of the sand at the inlet and outlet of the tubular column in the simulation and monitoring results, obtaining the sand mass at the inlet and outlet of the tubular column; determining the discharge rate of the sand grains according to the mass of the sand grains at the inlet and the outlet of the tubular column, and determining the minimum sand carrying flow under the single independent variable working condition by combining a preset flow rule; and fitting the minimum sand carrying flow and the single independent variable to obtain a fitting relationship, and predicting a sand migration non-deposition condition under a given independent variable condition by utilizing the fitting relationship. The migration condition of sand grains in a pipe column can be accurately simulated, so that the migration non-deposition condition of the sand grains can be more scientifically and accurately determined, and smooth discharge of the sand grains is ensured.

Description

Technical Field

[0001] This invention relates to the field of oilfield production technology, and in particular to a method and apparatus for predicting conditions for sand transport without deposition in tubing. Background Technology

[0002] ESP (Electric Submersible Screw Pump) units are a crucial component of oilfield production systems, closely linked to the lifting of deep and ultra-deep wells. The efficiency and safety of lifting in these wells directly impact the cost and profitability of oilfield production. Therefore, the stability and safety of fluid transport within the ESP system tubing are paramount. Currently, some oilfields in exploration areas experience sand production issues, primarily caused by fracturing sand return. Severe sand production can lead to problems such as ESP blockage, pump wear, and pump burial, affecting oilfield production efficiency and even causing production reductions or shutdowns. Therefore, resolving the sand production problem in oilfields is of great significance.

[0003] To solve the problem of sand production in oil fields, it is necessary to prevent the deposition of oil and sand two-phase fluids during their transport within the tubing of an electric submersible screw pump. Most existing methods for studying the characteristics of sand transport and deposition involve building a wellbore two-phase or multiphase flow sand transport experimental device to observe the sand transport and deposition within the tubing and the discharge of sand particles, and then evaluating the sand-carrying performance of the tubing based on the experimental data. Summary of the Invention

[0004] Existing methods for studying sand transport and deposition characteristics using multiphase flow sand transport experimental devices do not provide a rigorous method for predicting non-deposition conditions of sand transport in tubing. The experimental results are random and cannot avoid errors caused by objective and subjective factors. Furthermore, these methods require expensive equipment and space to be built during field exploration, making them unsuitable for widespread use in exploration sites. Therefore, there is an urgent need to develop a predictive method for determining non-deposition conditions of sand transport in the field to ensure the smooth discharge of sand and guarantee efficient and stable production of shale oil wells.

[0005] In view of the above problems, the present invention is proposed to provide a method and apparatus for predicting sand transport and non-deposition conditions in tubular columns that overcomes or at least partially solves the above problems.

[0006] In a first aspect, embodiments of the present invention provide a method for predicting sand-carrying transport and non-deposition conditions in tubular casings, including:

[0007] Within a preset computational physical time, the flow field inside the tubing under a single independent variable condition is numerically simulated using a pre-established sand transport simulation model, and the sand mass flow rate at the inlet and outlet of the tubing is monitored to obtain the simulation and monitoring results under a single independent variable condition. The independent variables include at least one of the following: sand particle size, inlet velocity, pipe diameter, and tubing tilt angle.

[0008] Based on the mass flow rate of sand particles at the inlet and outlet of the tubing in the simulation and monitoring results, the mass of sand particles at the inlet and outlet of the tubing under the single independent variable condition is obtained.

[0009] The discharge rate of sand particles under the single independent variable operating condition is determined based on the sand particle mass at the inlet and outlet of the tubing under the single independent variable operating condition, and the minimum sand-carrying flow rate under the single independent variable operating condition is determined in combination with the preset flow rate rules.

[0010] The minimum sand-carrying flow rate and a single independent variable are fitted to obtain the fitting relationship between the single independent variable and the minimum sand-carrying flow rate. The fitting relationship is used to predict the sand transport and non-deposition conditions under given independent variable conditions.

[0011] In some optional embodiments, pre-establishing a sand grain transport simulation model includes:

[0012] Establish Euler-Euler models for the fluid phase and particulate phase within the tubular column;

[0013] A sand transport simulation model is obtained by introducing a sand transport-settling model and a turbulence model into the Euler-Euler model. The equation set of the sand transport simulation model includes the governing equations of the Euler-Euler model, the transport-settling model equations, and the turbulence model equations.

[0014] In some optional embodiments, the sand transport-sedimentation model includes particle dynamics equations and particle phase bulk viscosity equations;

[0015] The turbulence model is established based on the transport equations of turbulent kinetic energy and the transport equations of dissipation rate.

[0016] In some optional embodiments, the particle dynamics model is represented by the following expression:

[0017]

[0018] Among them, C s ρ is the volume fraction of the particulate phase. s Particle phase density, θ s Particle phase-like temperature, v s Particle phase velocity, P s For the particle phase pressure, τ s Let k be the shear stress tensor of the solid granular phase. θs φ is the energy diffusion coefficient of the particulate phase. ls This refers to the energy exchange between the particle phase and the fluid phase caused by the velocity pulsation of the particle phase. It is the gradient factor;

[0019] Particle phase pressure P s P is represented by the following expression: s =C s ρ s θ s+2ρ s C s g 0,ss (1+e)θ s Where e is the particle collision restitution coefficient, g 0,ss It is a radial distribution function;

[0020] Particle phase pseudo-temperature θ s This can be represented by the following expression: Among them, v s ′ represents particle velocity pulsation;

[0021] The bulk viscosity of the particulate phase is expressed by the following expression: Where d is the particle diameter.

[0022] In some alternative embodiments, the transport equation for turbulent kinetic energy is expressed by the following expression:

[0023]

[0024] Where ρ is the mixed density of the fluid phase and the particle phase, k is the turbulent kinetic energy, ω is the dissipation rate, and v i For velocity, x j and x i Γ represents the x-axis component. k For the effective diffusion term of k, G k Y is the term generated by turbulent kinetic energy k. k S is the divergent term of k. k For k, the source term;

[0025] The dissipation rate transport equation is expressed by the following expression:

[0026]

[0027] Among them, Γ w For the effective diffusion term of w, G w Y is the term generated by the dissipation rate w. w S is the divergent term of w. w D is the source term of w. ω It is an orthogonal divergent term.

[0028] In some optional embodiments, within a preset computational physical time, a pre-established sand transport simulation model is used to numerically simulate the flow field inside the tubing under a single independent variable condition; including:

[0029] By changing the value of a selected single independent variable, the range of its numerical variation can be obtained.

[0030] Within the preset computational physical time, the sand particle transport simulation model under each numerical value is solved to obtain the solution results of the flow field inside the tube within the numerical range of a single independent variable. The solution results include the concentration distribution of each phase, the pressure distribution of each phase, the velocity distribution of each phase, the phase distribution, and the average concentration of each phase. The phase distribution includes the fluid phase distribution and the particle phase distribution.

[0031] The solution results are post-processed to obtain the distribution cloud map of the flow field inside the tube within a single independent variable range.

[0032] In some optional embodiments, the sand transport simulation model is solved, including:

[0033] Set the initial conditions and boundary conditions of the model. The boundary conditions include inlet boundary conditions, outlet boundary conditions and wall conditions. The initial conditions include at least fluid phase parameters, particle phase parameters and interphase forces.

[0034] Determine the computational domain of the tubing and then mesh the computational domain;

[0035] The set of equations included in the discretized sand particle transport model determines the preset time step of the computational physical time.

[0036] Set the solution control parameters and solve the discretized equations according to the preset solution algorithm to obtain the solution results;

[0037] Determine whether the solution meets the preset convergence criterion. If it does, output the solution.

[0038] If the conditions are not met, adjust the initial boundary conditions of the model and continue to perform the step of meshing the computational watershed until the solution meets the preset convergence criterion, and then output the solution.

[0039] In some optional embodiments, the mass of sand particles at the inlet and outlet of the tubing is obtained under a single independent variable condition based on the mass flow rate of the sand particles at the inlet and outlet of the tubing in the simulation and monitoring results, including:

[0040] By using the integral relationship between the mass flow rate of inlet and outlet sand particles and the calculation physical time for each single independent variable value, the mass of sand particles at the inlet and outlet of the tubing for each single independent variable value can be obtained.

[0041] The mass flow rate of sand particles is expressed by the following expression:

[0042]

[0043] Where ρ is the density of sand particles in the tubing per unit physical time. The velocity of sand particles inside the tubing is calculated per unit of physical time. The area of ​​a grid cell;

[0044] The mass of sand particles is expressed by the following expression:

[0045] m 砂 =∫M 砂 dt

[0046] Where, m 砂 M represents the mass of sand particles. 砂 t represents the mass flow rate of the sand particles, and t represents the physical time of the calculation.

[0047] In some optional embodiments, the discharge rate of sand particles under a single independent variable operating condition is determined based on the sand particle mass at the inlet and outlet of the tubing under that condition, and the minimum sand-carrying flow rate under that condition is determined in conjunction with a preset flow rate rule, including:

[0048] Based on the ratio of sand mass at the inlet to the outlet of the tubing under each single independent variable value, the discharge rate of sand under each single independent variable value is determined.

[0049] The discharge rate of sand particles is expressed by the following expression:

[0050]

[0051] Where R is the sand discharge rate, m 出 For the quality of exported sand, m 入 The mass of the sand particles at the inlet;

[0052] The inlet velocity of the tubing when the discharge rate meets the preset flow rules is taken as the minimum sand-carrying flow rate.

[0053] In some optional embodiments, the minimum sediment-carrying capacity is fitted to a single independent variable to obtain a fitting relationship between the independent variable and the minimum sediment-carrying capacity, including:

[0054] Obtain the minimum sand-carrying flow rate for each individual independent variable;

[0055] By fitting a function to the minimum sediment carrying capacity and a single independent variable, the fitting relationship between the minimum sediment carrying capacity and the single independent variable is obtained.

[0056] In some optional embodiments, the fitting relationship is used to predict sand transport and non-deposition conditions under given independent variables, including:

[0057] By utilizing the fitting relationship corresponding to the given independent variables, the minimum sand-carrying flow rate under each independent variable value is predicted. According to the preset rules, the final minimum sand-carrying flow rate is determined, and the final minimum sand-carrying flow rate is used as the sand grain migration and non-deposition condition under the given independent variable conditions.

[0058] Secondly, embodiments of the present invention provide a device for calculating the non-deposition conditions of sand transport in a tubular column, comprising:

[0059] The numerical simulation module is used to perform numerical simulation of the flow field inside the tubing under a single independent variable condition within a preset computational physical time using a pre-established sand transport simulation model, and to monitor the sand mass flow rate at the inlet and outlet of the tubing, so as to obtain the simulation and monitoring results under a single independent variable condition; the independent variables include sand particle size, inlet velocity, pipe diameter and tilt angle.

[0060] The quality acquisition module is used to obtain the mass of sand particles at the inlet and outlet of the tubing under a single independent variable operating condition based on the mass flow rate of sand particles at the inlet and outlet of the tubing in the simulation and monitoring results.

[0061] The sand-carrying flow rate fitting module is used to determine the sand discharge rate under a single independent variable operating condition based on the sand mass at the inlet and outlet of the tubing. Combined with preset flow rules, it determines the minimum sand-carrying flow rate under the single independent variable operating condition and fits the minimum sand-carrying flow rate with the single independent variable to obtain the fitting relationship between the independent variable and the minimum sand-carrying flow rate.

[0062] The sedimentation condition prediction module is used to predict the sand transport and deposition conditions under given independent variables by utilizing the fitting relationship between the independent variables and the minimum sand-carrying flow rate.

[0063] This invention also provides a computer storage medium storing computer-executable instructions. When these computer-executable instructions are executed by a processor, the aforementioned method for predicting sand transport and non-deposition conditions in tubing is implemented.

[0064] This invention also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for predicting the non-deposition conditions of sand transport in tubing.

[0065] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0066] The present invention provides a method for predicting sand transport and non-deposition conditions in a tubing. Within a preset computational physical time, a pre-established sand transport simulation model is used to numerically simulate the flow field within the tubing under a single independent variable condition. The mass flow rates of sand at the inlet and outlet of the tubing are monitored to obtain the simulation and monitoring results under the single independent variable condition. The independent variables include at least one of sand particle size, inlet velocity, pipe diameter, and tubing tilt angle. The sand transport simulation model, based on the object under study, can better simulate the motion relationship between oil and sand in two-phase flow or even multiphase flow within the tubing, providing technical support for subsequent research. When different single independent variables change, the sand transport state of the oil within the tubing will change. Therefore, during the numerical simulation of the model, a single-factor control variable method is used to progressively analyze the sand transport and settling state under the single independent variable condition, exploring the transport and settling law of sand within the tubing. Simultaneously, the outlet and inlet mass flow rates of the tubing are monitored to explore the influence of different single independent variable conditions on sand transport and deposition, laying the foundation for subsequently determining the non-deposition conditions of sand.

[0067] Based on the mass flow rate of sand particles at the inlet and outlet of the tubing string from simulation and monitoring results, the mass of sand particles at the inlet and outlet of the tubing string under a single independent variable operating condition is obtained. The discharge rate of sand particles under this single independent variable operating condition is determined based on their mass. Combined with a preset flow rate rule, the minimum sand-carrying flow rate under this single independent variable operating condition is determined, and the minimum sand-carrying flow rate is fitted to the single independent variable to obtain the fitting relationship between the independent variable and the minimum sand-carrying flow rate. This fitting relationship is used to predict the sand particle migration and non-deposition conditions of the independent variable. Sand particles are dynamically distributed in the tubing string flow field. The mass of sand particles at the inlet and outlet of the tubing string is obtained from the mass flow rate of the sand particles, thus yielding the sand particle discharge rate. Based on the sand particle discharge rate, the minimum sand-carrying flow rate of the tubing string under each operating condition is evaluated. A data fitting method is used to fit the minimum sand-carrying flow rate to the independent variable operating condition to predict the sand particle migration and non-deposition conditions, ensuring that sand particles can be smoothly discharged from the tubing string, avoiding pump jamming and burying phenomena in the ESP system, and improving on-site oil production efficiency.

[0068] This method uses numerical simulation to simulate the transport and deposition of sand particles within the tubing string, thereby studying the transport patterns of sand particles within the tubing string. Compared with the traditional method of building experimental devices, the simulation results of this method are more accurate and can avoid errors caused by subjective and objective factors in existing methods. This ensures efficient and stable production of shale oil wells and provides more accurate and efficient guidance for field oil production work, thus guaranteeing the smooth progress of oil production.

[0069] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0070] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0071] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0072] Figure 1 This is a flowchart of the method for predicting the non-deposition conditions of sand-carrying transport in tubular columns in an embodiment of the present invention;

[0073] Figure 2 This is a concentration distribution cloud map at different column tilt angles in an embodiment of the present invention;

[0074] Figure 3 This is a graph showing the curves of different sand particle sizes and corresponding minimum sand carrying capacity in embodiments of the present invention;

[0075] Figure 4 This is a diagram of the device for predicting sand transport and non-deposition conditions in a tubular column according to an embodiment of the present invention. Detailed Implementation

[0076] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0077] Existing methods observe the sand transport and deposition characteristics of the tubing using experimental setups. The sand-carrying capacity of the tubing can be assessed based on experimental data. However, in order to prevent sand deposition in the tubing and avoid phenomena such as pump jamming in the electric submersible screw pump, a rigorous method is needed to predict the sand transport and non-deposition conditions in the tubing, so as to ensure that the sand in the tubing can be discharged smoothly.

[0078] To address the problems of unpredictable sand migration and non-deposition conditions and inaccurate experimental data in existing technologies, this invention provides a method for predicting sand migration and non-deposition conditions in tubular columns, enabling accurate prediction of sand migration and non-deposition conditions and facilitating smooth discharge of sand particles.

[0079] This invention provides a method for calculating the conditions for sand transport without deposition in a tubular column, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0080] Step S101: Within the preset computational physical time, the flow field inside the tubing under a single independent variable condition is numerically simulated using a pre-established sand transport simulation model, and the sand mass flow rate at the inlet and outlet of the tubing is monitored to obtain the simulation and monitoring results under a single independent variable condition. The independent variable includes at least one of the following: sand particle size, inlet velocity, pipe diameter, and tubing tilt angle.

[0081] Step S102: Based on the mass flow rate of sand particles at the inlet and outlet of the tubing in the simulation and monitoring results, obtain the mass of sand particles at the inlet and outlet of the tubing under the single independent variable condition.

[0082] Step S103: Determine the sand discharge rate under the single independent variable condition based on the sand mass at the inlet and outlet of the tubing under the single independent variable condition, and determine the minimum sand-carrying flow rate under the single independent variable condition in combination with the preset flow rate rule. Fit the minimum sand-carrying flow rate with the single independent variable to obtain the fitting relationship between the single independent variable and the minimum sand-carrying flow rate. The fitting relationship is used to predict the sand transport and non-deposition conditions under the given independent variable conditions.

[0083] This invention proposes a sand particle transport simulation model. Based on this model, the transport state of sand particles in the tubing string is simulated, and a method for calculating the sand particle non-deposition conditions is calculated based on the simulation results. The model comprehensively considers the influence of factors such as flow velocity, sand particle size, pipe diameter, and tilt angle in the oil-sand two-phase transport, ensuring that the oil well can smoothly discharge sand at low fluid flow rates. This provides a basis for intelligent lifting of deep well electric submersible screw pump systems and ensures long-term, stable, and high-yield production operations of shale oil wells.

[0084] Optionally, in step S101 above, the pre-establishment of a sand particle transport simulation model includes:

[0085] An Euler-Euler model of the fluid phase and particle phase inside the tubular column was established. A sand transport-settling model and a turbulence model were introduced into the Euler-Euler model to obtain a sand transport simulation model. The equation set of the sand transport simulation model includes the governing equations of the Euler-Euler model, the transport-settling model equations, and the turbulence model equations.

[0086] When establishing an Eulerian-Eulerian model for both the particulate and fluid phases, it is crucial to fully consider the physical properties of both phases and the interphase forces, such as drag, lift, and turbulent dispersion forces. Governing equations for each phase in the Eulerian-Eulerian model should be established separately. Based on the Eulerian-Eulerian framework, the multiphase flow motion within the tubing is simulated. The Eulerian-Eulerian model is a mathematical model used to describe the motion of particles or bubbles in multiphase flow. It can be used to analyze the motion, distribution, and final deposition of sand particles in a fluid. The fluid in the tubing can be oil, and the particulate phase can be sand. During the momentum exchange between the oil and sand phases in the tubing, the effects of drag, lift, and turbulent dispersion forces on the two-phase flow must typically be considered. The momentum exchange M between the phases can be described by the following expression:

[0087]

[0088] Where K is the interphase momentum exchange coefficient, C L C is the lift coefficient. T C is the turbulent dispersion force coefficient. s Where is the volume fraction of the particulate phase, and M is the momentum exchange between phases; For particle phase velocity vectors; ρ is the liquid phase velocity vector; f The density of the fluid phase; C is the gradient factor; f It represents the volume fraction of the fluid phase; the interphase momentum exchange coefficient should take into account the volume fractions of the fluid phase and the particle phase, as well as the particle Reynolds number.

[0089] The governing equations for the fluid phase are expressed as follows:

[0090]

[0091] Among them, C l ρ is the volume fraction of the fluid phase. l v is the density of the fluid phase. l The velocity of the fluid phase, τ is the gradient factor. l Let be the shear stress tensor of the fluid phase, P be the shared pressure, M be the momentum exchange between phases, and g be the gravitational acceleration.

[0092] The governing equations for the granular phase are expressed as follows:

[0093]

[0094] Among them, C s ρ is the volume fraction of the particulate phase. s v is the density of the granular phase. s P is the velocity of the particulate phase. s For the particle phase pressure, τs Let be the shear stress tensor of the granular phase.

[0095] Optionally, the above sand transport-sedimentation model includes particle dynamics equations and particle phase bulk viscosity equations; the turbulence model is established based on turbulent kinetic energy transport equations and dissipation rate transport equations.

[0096] The particle dynamics equation is expressed by the following expression:

[0097]

[0098] Among them, C s ρ is the volume fraction of the particulate phase. s Particle phase density, θ s Particle phase-like temperature, v s The velocity of the particulate phase, P s For the particle phase pressure, τ s Let k be the shear stress tensor of the solid granular phase. θs φ is the energy diffusion coefficient of the particulate phase. ls This refers to the energy exchange between the particle phase and the fluid phase caused by the velocity pulsation of the particle phase. It is the gradient factor;

[0099] Particle phase pressure P s P is represented by the following expression: s =C s ρ s θ s +2ρ s C s g 0,ss (1+e)θ s Where e is the particle collision restitution coefficient, g 0,ss It is a radial distribution function;

[0100] Particle phase pseudo-temperature θ s This can be represented by the following expression: Among them, v s ′ represents particle velocity pulsation;

[0101] The above equation for the bulk viscosity of the particulate phase is expressed by the following expression: Where d is the particle diameter.

[0102] To better simulate and control the flow behavior, mixing effect, and performance of multiphase flows, it is necessary to consider turbulent kinetic energy and dissipation rate during multiphase flow transport, fully utilize the beneficial effects of turbulence, and reduce unnecessary energy losses during multiphase flow transport.

[0103] The transport equation for turbulent kinetic energy in the turbulence model is expressed by the following expression:

[0104]

[0105] Where ρ is the mixed density of the fluid phase and the particle phase, k is the turbulent kinetic energy, ω is the dissipation rate, and v i For velocity, x j and x i Γ represents the x-axis component. k For the effective diffusion term of k, G k Y is the term generated by turbulent kinetic energy k. k S is the divergent term of k. k For k, the source term;

[0106] The dissipation rate transport equation of the turbulent model is expressed by the following expression:

[0107]

[0108] Among them, Γ w For the effective diffusion term of w, G w Y is the term generated by the dissipation rate w. w S is the divergent term of w. w D is the source term of w. ω It is an orthogonal divergent term.

[0109] Turbulence models provide the flow context for particle transport-deposition models. Therefore, particle transport-deposition models need to be combined with turbulence models to more accurately describe the forces and trajectories of particles in turbulent flow. Eulerian-Eulerian models, by incorporating relevant theories and methods from particle transport-deposition models, can more comprehensively describe the transport and deposition processes of particles and accurately predict their deposition location and amount. Particle dynamics models can describe the transport state of sand particles in oil. In the governing equations of the particle phase included in the Eulerian-Eulerian model, the particle phase shear stress tensor τ... s The particle phase bulk viscosity λ in the particle transport-deposition model s , particle phase shear viscosity μ s and particle phase pressure P s Closely related to this, the radial distribution function is used to correct the collision probability between particles.

[0110] Optionally, in step S101 above, within a preset computational physical time, a pre-established sand transport simulation model is used to numerically simulate the flow field inside the tubing under a single independent variable condition, and the sand mass flow rate at the inlet and outlet of the tubing is monitored to obtain the simulation and monitoring results under a single independent variable condition; including:

[0111] By changing the value of a selected single independent variable, the range of its numerical variation can be obtained.

[0112] Within a preset computational physical time, the simulation model of sand particle transport under each numerical value is solved to obtain the solution results of the flow field inside the column within the numerical range of a single independent variable. The solution results include the flow field, pressure field, velocity field and phase distribution, and the phase distribution includes fluid phase distribution and particle phase distribution.

[0113] The solution results are post-processed to obtain the distribution cloud map of the flow field inside the tube within a single independent variable range.

[0114] A preset computational physical time is set as the flow time of multiphase flow in the simulated tubing. The motion distribution of the multiphase flow field in the tubing is observed over a period of time to study the sand grain transport and deposition patterns. In numerical simulations, the single-factor control variable method is used to study the influence of each independent variable on the sand grain migration and deposition state. The numerical simulation process is also the model solving process. When solving the model, different sand grain sizes, different inlet velocities, different pipe diameters, and different pipe column inclination angles can be set to numerically simulate the flow field inside the pipe column under different sand grain sizes, different inlet velocities, different pipe diameters, and different pipe column inclination angles. In this way, the concentration distribution, velocity distribution, pressure distribution, phase distribution, and average concentration of each phase inside the pipe column under different grain sizes can be analyzed step by step, and the sand grain migration and deposition state in the pipe column can be obtained. Correspondingly, the flow field distribution, pressure field distribution, velocity field distribution, and phase distribution under different inlet velocities, different pipe diameters, and different pipe column inclination angles can also be analyzed one by one. When controlling the change of a single independent variable, other independent variables can remain unchanged, thus enabling the study of the influence of single factors on the sand grain migration and deposition process.

[0115] After obtaining the solution results, post-processing is performed to generate flow field distribution contour maps, including concentration, velocity, pressure, and phase distribution contour maps. These contour maps provide a more intuitive analysis of the distribution of each phase within the tubing. For example, concentration distribution contour maps at different tubing tilt angles can be found here. Figure 2 As shown, in Figure 2 In this study, the flow field of tubular columns with tilt angles of 30°, 45°, and 60° was simulated and post-processed to obtain concentration distribution cloud maps for three different tilt angles. The red portion represents the concentration distribution of the particulate phase. Figure 2 Analysis shows that the greater the inclination angle of the tubing, the lower the concentration of sand particles, and the less likely sedimentation will occur.

[0116] Optionally, the process of solving the sand grain transport simulation model includes:

[0117] Set the initial conditions and boundary conditions of the model. The boundary conditions include inlet boundary conditions, outlet boundary conditions and wall conditions. The initial conditions include at least fluid phase parameters, particle phase parameters and interphase forces.

[0118] Determine the fluid computation domain of the tubing and mesh the fluid computation domain;

[0119] The set of equations included in the discretized sand particle transport model determines the preset time step of the computational physical time.

[0120] Set the solution control parameters and solve the discretized equations according to the preset solution algorithm to obtain the solution results;

[0121] Determine whether the solution meets the preset convergence criterion. If it does, output the solution.

[0122] If the conditions are not met, adjust the initial boundary conditions of the model and continue to perform the step of meshing the fluid computational domain until the solution meets the preset convergence criterion, and then output the solution.

[0123] Initially, the oil and sand phases are uniformly distributed throughout the tubing. In solving the above model, the finite volume method (CFD) can be used. When setting the model boundary conditions, the outlet boundary condition can be free flow, the inlet boundary condition can be inlet velocity, and the wall condition can be a smooth wall. During the solution process, there is a force relationship between the wall and the phases; therefore, the wall roughness will affect the simulation results. The initial fluid phase parameters can include fluid density, fluid viscosity, and fluid velocity. The particle phase parameters can include sand density, sand viscosity, sand velocity, sand volume fraction, and sand particle size. When the selected sand particle size is a single independent variable, the sand particle size does not need to be set again.

[0124] When meshing the fluid computational domain, the settings need to be configured according to the actual simulation requirements. The Mesh module of Fluent software can be used for mesh generation. To capture the flow conditions near the pipe wall more precisely, the boundary layer near the wall of the tubing can be refined. Refinement refers to improving the accuracy of the mesh. The near-wall boundary layer refers to the region with a large velocity gradient and significant viscous forces, for example, but not limited to, a boundary layer of 5 layers with a growth rate of 0.272 mm.

[0125] When discretizing the equations of a system of equations, a first-order discretization scheme can be used. During the numerical solution of the model, different physical processes, such as convection, diffusion, and reaction, may have different time scales. To accurately simulate these processes, it is necessary to set an appropriate step size according to their time scales. Setting a suitable step size allows for more accurate monitoring of the mass flow rate of sand particles at each time step, improving the accuracy of the simulation results. The choice of step size directly affects the stability of the computation. An excessively large step size may cause the numerical solution to diverge, meaning the calculated results no longer approach the true solution, or even deviate completely. By appropriately reducing the step size, the stability of the computation can be improved, allowing the numerical solution of the model to converge to the true solution.

[0126] When solving the system of equations, a preset solution algorithm can be used. This preset algorithm is an iterative algorithm, such as the phase-coupled SIMPLE algorithm under pressure and velocity coupling. This algorithm is used to numerically calculate the flow field inside the tubing of an electric submersible screw pump unit. When solving the model's equations using an iterative algorithm, each solution needs to be updated and adjusted. Therefore, solution control parameters can be set to control the update amount of the solution in each iteration. These parameters can be relaxation factors, used to balance the update rate and stability of the solution. During the iterative solution process, after each numerical solution is obtained, it needs to be checked whether it meets the preset convergence criterion. If not, the boundary conditions need to be adjusted until the solution converges, thus obtaining the true solution. The convergence criterion can be that the residual on the residual curve is at 10... -3 The following conditions are considered as convergence of the numerical solution.

[0127] Optionally, in step S102 above, based on the mass flow rate of sand particles at the inlet and outlet of the tubing in the simulation and monitoring results, the mass of sand particles at the inlet and outlet of the tubing under the single independent variable condition is obtained, including:

[0128] By using the integral relationship between the mass flow rate of inlet and outlet sand particles and the calculation physical time for each single independent variable value, the mass of sand particles at the inlet and outlet of the tubing for each single independent variable value can be obtained.

[0129] The mass flow rate of sand particles is expressed by the following expression:

[0130]

[0131] Where ρ is the density of sand particles in the tubing per unit physical time. The velocity of sand particles inside the tubing is calculated per unit of physical time. The area of ​​a grid cell;

[0132] The mass of sand particles is expressed by the following expression:

[0133] m砂 =∫M 砂 dt

[0134] Where, m 砂 M represents the mass of sand particles. 砂 t represents the mass flow rate of the sand particles, and t represents the physical time of the calculation.

[0135] The mass flow rate of sand particles at each time step is obtained by multiplying and integrating the sand particle density, velocity, and grid cell area at the local cross section. The mass flow rate of sand particles represents the mass of sand particles passing through a certain cross section per unit computational physical time. Although sand particles are dynamically distributed in the oil pipe flow field, the mass of sand particles at the oil pipe inlet and outlet can be obtained by integrating the mass flow rate of sand particles with computational physical time.

[0136] Optionally, in step S103 above, the discharge rate of sand particles under the single independent variable operating condition is determined based on the sand particle mass at the inlet and outlet of the tubing under the single independent variable operating condition, and the minimum sand-carrying flow rate under the single independent variable operating condition is determined in conjunction with the preset flow rate rule, including:

[0137] Based on the ratio of sand mass at the inlet to the outlet of the tubing under each single independent variable value, the discharge rate of sand under each single independent variable value is determined.

[0138] The discharge rate of sand particles is expressed by the following expression:

[0139]

[0140] Where R is the sand discharge rate, m 出 For the quality of exported sand, m 入 The mass of the sand particles at the inlet;

[0141] The inlet velocity of the tubing when the discharge rate meets the preset flow rules is taken as the minimum sand-carrying flow rate.

[0142] By setting the sand particle size, pipe diameter, inlet velocity, and tubing inclination angle, under this operating condition, the sand discharge rate during shale oil sand transport within the tubing can be obtained by calculating the ratio of sand mass at the inlet and outlet of the tubing over a certain physical time interval. This allows us to determine the sand discharge rate for each individual independent variable. By changing the value of a single independent variable while keeping other variables constant, we can obtain the influence of that single variable on the sand discharge rate and further study the impact of each individual independent variable on the sand discharge rate. After obtaining the sand discharge rate, based on a preset flow rate rule, the minimum sand-carrying flow rate for each individual independent variable can be determined. This preset flow rate rule could be that the inlet velocity of the tubing when the sand discharge rate is 85% is used as the minimum sand-carrying flow rate for that single independent variable. For example, the sand discharge rate for different particle sizes can be calculated, and the inlet velocity of the sand with a discharge rate of 85% can be used as the minimum sand-carrying flow rate for the corresponding particle size. The minimum sand-carrying capacity refers to the minimum fluid flow rate required to ensure the effective transport of sand or other solid particles in a pipeline. This flow rate needs to be large enough to prevent sand from settling at the bottom of the pipeline, thus ensuring that the sand is transported out with the fluid.

[0143] Optionally, in step S103 above, the minimum sediment-carrying flow rate and a single independent variable are fitted to obtain the fitting relationship between the independent variable and the minimum sediment-carrying flow rate, including:

[0144] Obtain the minimum sediment-carrying flow rate for each individual independent variable; fit the minimum sediment-carrying flow rate to the individual independent variable to obtain the fitting relationship between the minimum sediment-carrying flow rate and the individual independent variable.

[0145] Optionally, the above fitting relationship can be used to predict sand grain migration and non-deposition conditions under given independent variables, including:

[0146] By utilizing the fitting relationship corresponding to the given independent variables, the minimum sand-carrying capacity under each single independent variable value is predicted. Based on the preset correction rules, the final minimum sand-carrying capacity is determined and used as the sand transport and non-deposition condition under the given independent variable conditions. The value of the given independent variable is determined from the range of variation of the single independent variable values. The preset correction rules include: taking the minimum or maximum value of the minimum sand-carrying capacity under each single independent variable condition, determining the average value of the minimum sand-carrying capacity under each single independent variable condition, and obtaining the weighted average value of the minimum sand-carrying capacity under each single independent variable condition.

[0147] Optionally, based on the values ​​of the given independent variables and the corresponding fitting relationships, the minimum sediment-carrying flow rate under the given independent variable values ​​is predicted, including:

[0148] For a single independent variable included in the given independent variables, based on the value of the single independent variable and the fitting relationship corresponding to the single independent variable, predict the minimum sediment carrying capacity under the value of the single independent variable.

[0149] For example, taking the investigation of the anti-deposition conditions of different sand particle sizes in the tubing as an example, the sand particle sizes are set to 250μm (60 mesh), 315μm (50 mesh), 400μm (40 mesh), and 500μm (30 mesh), while other independent variables remain unchanged. The sand particle transport simulation model of this embodiment is used to simulate the two-phase transport state of oil and sand in the tubing and to monitor the sand particle mass flow rate at the tubing outlet and inlet. The outlet mass and inlet mass of sand particles are determined based on the sand particle mass flow rate, and the sand particle discharge rate is then clarified. The flow rate corresponding to a sand particle discharge rate of more than 85% is regarded as the minimum sand-carrying flow rate to prevent sand particle deposition. Different sand particle sizes are fitted with the minimum sand-carrying flow rate to obtain the functional relationship between different sand particle sizes and the minimum sand-carrying flow rate. This relationship can be used to predict the minimum sand-carrying flow rate to prevent sand particle deposition for any particle size between 250μm and 500μm. The minimum sand-carrying flow rate is the condition to prevent sand particle deposition. The different sand particle sizes and their corresponding minimum sand carrying capacity can be plotted as curves. See [link / reference]. Figure 3 As shown, according to Figure 3 The relationship between sand particle size and minimum sand-carrying flow rate is given by the formula V = 0.0091d + 0.1197, where V is the minimum flow rate to prevent solid deposition, in m³. 3 / d; d is the particle size of the sand, in μm.

[0150] When making predictions at the exploration site, based on the given independent variables within the EDP (Electric Submersible Pump) string, the sand transport and deposition conditions under that operating condition can be predicted. For example, given independent variables such as sand particle size, pipe diameter, inlet flow velocity, and string inclination angle, based on the value of each individual independent variable and the corresponding fitting relationship, the minimum sand-carrying flow rate under specific sand particle sizes, specific inlet flow velocities, specific pipe diameters, and specific string inclination angles within the EDP string can be predicted. The minimum sand-carrying flow rate under each individual independent variable is different. The final minimum sand-carrying flow rate can be determined according to preset rules. This final minimum sand-carrying flow rate is used as the condition for sand transport and non-deposition under given independent variable conditions within the electric submersible screw pump string. Preset correction rules include, but are not limited to, taking the minimum or maximum value of the minimum sand-carrying flow rate under each single independent variable condition, averaging the minimum sand-carrying flow rate under each single independent variable condition, assigning corresponding weights to the minimum sand-carrying flow rate under each single independent variable condition, and then performing a weighted average. When assigning weights, different weights can be assigned based on the degree of influence of each single independent variable on the minimum sand-carrying flow rate. Although the process of determining the fitting relationship explores the relationship between a single independent variable and the minimum sand-carrying flow rate, in practical applications, multiple single independent variables may be correlated and affect the minimum sand-carrying flow rate. Therefore, after obtaining the minimum sand-carrying flow rate under each single independent variable condition, correction rules can be set according to actual needs to determine the final minimum sand-carrying flow rate, ensuring that sand particles can be smoothly discharged from the string.

[0151] The method for predicting non-deposition conditions of sand transport in tubing provided in this invention uses a sand transport simulation model to simulate the transport state of oil and sand phases in the tubing. The model combines the Euler-Euler model, the sand transport-deposition model, and the turbulence model, which can more accurately capture the flow of oil and sand phases in the tubing. It comprehensively considers the influence of inlet velocity, particle size, pipe diameter, and tubing tilt angle on the deposition state during sand transport. By controlling a single variable, the deposition law of sand particles under each factor is studied step by step. Finally, the minimum sand-carrying flow rate of sand particles is determined based on the sand discharge rate under each influencing factor. The minimum sand-carrying flow rate is used as the non-deposition condition of sand transport, ensuring that horizontal and directional wells can smoothly discharge sand at low flow rates. This provides a basis for intelligent lifting of deep well electric submersible screw pump systems and ensures long-term, stable, and high-yield production operations of shale oil wells.

[0152] Based on the same inventive concept, embodiments of the present invention also provide a device for predicting sand transport and non-deposition conditions in tubular columns. This device can be installed in a device capable of processing computer instructions, and its structure is as follows. Figure 4 As shown, it includes:

[0153] The numerical simulation module 11 is used to perform numerical simulation of the flow field inside the tube under a single independent variable condition using a pre-established sand transport simulation model within a preset computational physical time, and to monitor the sand mass flow rate at the inlet and outlet of the tube, so as to obtain the simulation and monitoring results under a single independent variable condition; the independent variables include sand particle size, inlet velocity, tube diameter and tilt angle.

[0154] The quality acquisition module 12 is used to obtain the mass of sand particles at the inlet and outlet of the tubing under a single independent variable operating condition based on the mass flow rate of sand particles at the inlet and outlet of the tubing in the simulation and monitoring results.

[0155] The sand-carrying flow fitting module 13 is used to determine the sand discharge rate under the single independent variable working condition based on the sand mass at the inlet and outlet of the tubing under the single independent variable working condition, and combined with the preset flow rules, to determine the minimum sand-carrying flow rate under the single independent variable working condition and fit the minimum sand-carrying flow rate with the single independent variable to obtain the fitting relationship between the single independent variable and the minimum sand-carrying flow rate.

[0156] The sedimentation condition prediction module 14 is used to predict the sand transport and deposition conditions under given independent variable conditions by using the fitting relationship between a single independent variable and the minimum sand-carrying flow rate.

[0157] Regarding the string-carrying sand transport and non-deposition condition prediction device in the above embodiments, the specific operation of each module has been described in detail in the embodiments of the relevant method, and will not be elaborated here.

[0158] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0159] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0160] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0161] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0162] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0163] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0164] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for predicting a sand-conveying non-deposition condition of a pipe string, characterized by, The method comprises the following steps: numerically simulating the flow field in the pipe string under a single independent variable condition by using a pre-established sand migration simulation model within a preset calculation physical time and monitoring the mass flow rate of sand at the inlet and outlet of the pipe string, to obtain simulation and monitoring results under the single independent variable condition; the independent variable includes at least one of sand particle size, inlet flow rate, pipe diameter and pipe string inclination angle; obtaining the mass of sand at the inlet and outlet of the pipe string under the single independent variable condition based on the mass flow rate of sand at the inlet and outlet of the pipe string in the simulation and monitoring results; determining the discharge rate of sand under the single independent variable condition according to the mass of sand at the inlet and outlet of the pipe string under the single independent variable condition, and determining the minimum sand-carrying flow rate under the single independent variable condition in combination with a preset flow rate rule; fitting the minimum sand-carrying flow rate and the single independent variable to obtain a fitting relationship between the single independent variable and the minimum sand-carrying flow rate, and the fitting relationship is used to predict the sand migration non-deposition condition under a given independent variable condition.

2. The method of claim 1, wherein, The pre-established sand migration simulation model comprises: establishing an Euler-Euler model of fluid phases and particle phases in the pipe string; introducing a sand migration-settling model and a turbulence model into the Euler-Euler model to obtain a sand migration simulation model, and the equation set of the sand migration simulation model includes control equations of the Euler-Euler model, equations of the migration-settling model and equations of the turbulence model.

3. The method of claim 2, wherein, The sand migration-settling model includes a particle kinetics equation and a particle phase volume viscosity equation; The turbulence model is established based on a transport equation of turbulent kinetic energy and a transport equation of dissipation rate.

4. The method of claim 3, wherein, The particle kinetics model is expressed by the following expression: where C s is the volume fraction of the particulate phase, p s is the density of the particulate phase, Q s is the granular temperature of the particulate phase, v s is the velocity of the particulate phase, P s is the pressure of the particulate phase, T s is the shear stress tensor of the solid particulate phase, k θs is the energy diffusion coefficient of the particulate phase, Q ls is the energy exchange between the particulate and fluid phases due to the fluctuations of the particulate phase velocity, and is the gradient factor; The particle phase pressure P s is expressed by the following expression: s = C s ρ s θ s + 2ρ s C s g 0,ss (1 + e)θ s ; where e is a particle collision restitution coefficient, g 0,ss is a radial distribution function; The particle phase pseudo temperature θ s is expressed by the following expression: where v' s is the particle velocity fluctuation; The volume viscosity of the particle phase is represented by the following expression: where d is the particle diameter.

5. The method of claim 3, wherein, The transport equation of turbulent kinetic energy is expressed by the following expression: where p is the mixture density of the fluid and particle phases, k is the turbulent kinetic energy, w is the dissipation rate, v i is the velocity, x j is the x i component of the x-axis, G k is the effective diffusion term for k, F k is the production term for the turbulent kinetic energy k, Y k is the divergence term for k, S k is the source term for k; The transport equation of dissipation rate is expressed by the following expression: where Γ w is the effective diffusion term for w, G w is the dissipation rate w production term, Y w is the divergence term for w, S w is the source term for w, D ω is the orthogonal divergence term.

6. The method of claim 1, wherein, The numerical simulation of the flow field in the pipe string under the single independent variable condition by using the pre-established sand migration simulation model within the preset calculation physical time comprises: changing the numerical value of the selected single independent variable to obtain a numerical value range of the single independent variable; solving the sand migration simulation model under each numerical value within the preset calculation physical time to obtain a solution result of the flow field in the pipe string within the numerical value range of the single independent variable, and the solution result includes concentration distribution of each phase, pressure distribution of each phase, velocity distribution of each phase, phase distribution and average concentration of each phase, and the phase distribution includes fluid phase distribution and particle phase distribution; post-processing the solution result to obtain a distribution cloud diagram of the flow field in the pipe string within the single independent variable range.

7. The method of claim 6, wherein, Solving the sand migration simulation model comprises: setting initial conditions and boundary conditions of the model, the boundary conditions include inlet boundary conditions, outlet boundary conditions and wall conditions, and the initial conditions include at least fluid phase parameters, particle phase parameters and interphase action force; determining a calculation flow domain of the pipe string, and performing grid division on the calculation flow domain; discretizing the equation set included in the sand migration model, and determining a time step of the preset calculation physical time; setting a solution control parameter, and solving the discretized equation set according to a preset solution algorithm to obtain a solution result; judging whether the solution result meets a preset convergence criterion, and if yes, outputting the solution result; If not satisfied, adjust the initial boundary conditions of the model, continue to perform the step of carrying out grid division on the calculation flow domain until the solution result satisfies the preset convergence criterion, and output the solution result.

8. The method of claim 6, wherein, Based on the mass flow of the sand particles at the inlet and outlet of the pipe string in the simulation and monitoring results, the mass of the sand particles at the inlet and outlet of the pipe string under the single-variable condition is obtained, including: The mass of the sand particles at the inlet and outlet of the pipe string under each single-variable value is obtained by using the integral relationship between the mass flow of the sand particles at the inlet and outlet and the calculation physical time under each single-variable value; The mass flow of the sand particles is represented by the following expression: where p is the sand density in the pipe column per unit of calculated physical time, is the velocity of the sand in the pipe column per unit of calculated physical time, is the cell area of the grid; The mass of the sand particles is represented by the following expression: m 砂 = ∫M 砂 dt where m 砂 represents the mass of sand particles, M 砂 represents the mass flow of sand particles, t represents the calculated physical time.

9. The method of claim 6, wherein, The discharge rate of the sand particles under the single-variable condition is determined according to the mass of the sand particles at the inlet and outlet of the pipe string under the single-variable condition, and the minimum sand-carrying flow rate under the single-variable condition is determined in combination with the preset flow rate rule, including: The discharge rate of the sand particles under each single-variable value is determined according to the proportional relationship between the mass of the sand particles at the inlet and the mass of the sand particles at the outlet of the pipe string under each single-variable value. The discharge rate of the sand particles is represented by the following expression: where R is the discharge rate of the sand, m 出 is the mass of sand exiting, m 入 is the mass of sand entering; The inlet flow rate of the pipe string under the condition that the discharge rate meets the preset flow rate rule is taken as the minimum sand-carrying flow rate.

10. The method of claim 9, wherein, The fitting relationship between the variable and the minimum sand-carrying flow rate is obtained by fitting the minimum sand-carrying flow rate and the single variable, including: The minimum sand-carrying flow rate under each single-variable value is obtained; The fitting relationship between the minimum sand-carrying flow rate and the single variable is obtained by function fitting the minimum sand-carrying flow rate and the single variable.

11. The method of claim 10, wherein, The sand particle migration non-deposition condition under a given variable condition is predicted by using the fitting relationship, including: The minimum sand-carrying flow rate under the given variable value is predicted based on the value of the given variable and the fitting relationship corresponding to the given variable, and the final minimum sand-carrying flow rate is determined according to the preset correction rule, which is taken as the sand particle migration non-deposition condition under the given variable condition; the value of the given variable is determined from the variable value range of the single variable; The preset correction rule includes: taking the minimum value or the maximum value of the minimum sand-carrying flow rate under each single-variable condition, determining the average value of the minimum sand-carrying flow rate under each single-variable condition, and obtaining the weighted average value of the minimum sand-carrying flow rate under each single-variable condition.

12. The method of claim 11, wherein, The minimum sand-carrying flow rate under the given variable value is predicted based on the value of the given variable and the fitting relationship corresponding to the given variable, including: For the single variable included in the given variable; The minimum sand-carrying flow rate under the single-variable value is predicted based on the value of the single variable and the fitting relationship corresponding to the single variable.

13. A device for calculating a sand-conveying non-deposition condition of a pipe string, characterized by Including: The numerical simulation module is configured to perform numerical simulation on the flow field in the pipe string under the single-variable condition and monitor the mass flow of the sand particles at the inlet and outlet of the pipe string within a preset calculation physical time by using a pre-established sand particle migration simulation model, and obtain simulation and monitoring results under the single-variable condition; the variable includes the sand particle size, the inlet flow rate, the pipe diameter and the inclination angle. a quality obtaining module configured to obtain the mass of the sand particles at the inlet and outlet of the pipe string under the single independent variable condition based on the simulation and the mass flow of the sand particles at the inlet and outlet of the pipe string in the monitoring result; a sand-carrying flow fitting module configured to determine the discharge rate of the sand particles under the single independent variable condition according to the mass of the sand particles at the inlet and outlet of the pipe string under the single independent variable condition, determine the minimum sand-carrying flow under the single independent variable condition in combination with a preset flow rule, and fit the minimum sand-carrying flow and the single independent variable to obtain a fitting relationship between the single independent variable and the minimum sand-carrying flow; a deposition condition predicting module configured to predict the sand particle migration and non-deposition condition under a given independent variable condition by using the fitting relationship between the single independent variable and the minimum sand-carrying flow.

14. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the pipe string sand-carrying migration and non-deposition condition predicting method in any one of claims 1-12.

15. A computer device, comprising: The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the pipe string sand-carrying migration and non-deposition condition predicting method in any one of claims 1-12. The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the pipe string sand-carrying migration and non-deposition condition predicting method in any one of claims 1-12.

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