Method and device for determining limit seepage distance of low-permeability reservoir

Abstract

The invention discloses a method and device for determining the limit seepage distance of a low-permeability reservoir. The method comprises the following steps: generating a numerical simulation grid of a target low-permeability reservoir according to reservoir parameters; a seepage field control equation is constructed in advance according to a mass conservation equation, a seepage motion equation and a permeability stress sensitive model; on the basis of the numerical simulation grid, reservoir parameters are substituted into the seepage field control equation to generate pressure distribution fields and seepage velocity distribution fields at different development times; implanting a plurality of virtual mass points in the numerical simulation grid, and determining the movement track of each virtual mass point according to the pressure distribution field and the seepage velocity distribution field; and generating limit seepage distances at different development times according to the motion trails of the virtual mass points. According to the scheme, the dynamic change of the limit seepage distance along with the development time can be accurately obtained, the limit seepage distance determination precision is improved, and an accurate data basis is provided for subsequent cluster spacing design, fracturing scale optimization and the like.

Description

Technical Field

[0001] This application relates to the field of exploration technology, specifically to a method, apparatus, computing device, computer storage medium, and computer program product for determining the ultimate seepage distance of a low-permeability reservoir. Background Technology

[0002] The ultimate seepage distance, also known as the effective utilization radius or economic seepage boundary, refers to the maximum distance that oil and gas can flow from the reservoir matrix to the production well under specific development conditions (such as formation pressure, development time, and bottom hole flowing pressure).

[0003] The ultimate seepage distance is one of the key parameters for the development of low-permeability reservoirs. The accuracy of determining the ultimate seepage distance directly or indirectly affects the development strategy and efficiency of low-permeability reservoirs.

[0004] However, the inventors discovered the following defects in the prior art during implementation: the commonly used method for determining the ultimate seepage distance in the prior art is usually based on Darcy's law. However, the ultimate seepage distance obtained by this method in the prior art deviates significantly from the actual seepage situation of low-permeability reservoirs, and the accuracy of determining the ultimate seepage distance is low. Summary of the Invention

[0005] In view of the above problems, this application is made in order to provide a method, apparatus, computing device, computer storage medium, and computer program product for determining the ultimate seepage distance of low-permeability reservoirs that overcomes or at least partially solves the above problems.

[0006] According to a first aspect of this application, a method for determining the ultimate seepage distance of a low-permeability reservoir is provided, comprising: Obtain reservoir parameters of the target low-permeability reservoir; wherein, the reservoir parameters include basic parameters and production parameters; A numerical simulation grid for the target low-permeability reservoir is generated based on the reservoir parameters. The seepage field control equations are constructed in advance based on the mass conservation equation, the seepage motion equation, and the permeability stress sensitivity model. Based on the numerical simulation grid, the reservoir parameters are substituted into the seepage field control equation to generate the pressure distribution field and seepage velocity distribution field under different development times. Multiple virtual particles are implanted in the numerical simulation grid, and the motion trajectory of each virtual particle is determined according to the pressure distribution field and the seepage velocity distribution field. The ultimate seepage distance is generated based on the motion trajectory of each virtual mass point under different development times.

[0007] In one optional implementation, implanting multiple virtual particles in the numerical simulation grid includes: Centered on the production well, multiple radial mass rings are arranged at first distance intervals along the radial direction; Multiple particles are uniformly distributed in any radial particle ring; And along the effective thickness direction of the reservoir, mass points are set at second distance intervals.

[0008] In one optional implementation, determining the motion trajectory of each virtual particle based on the pressure distribution field and the seepage velocity distribution field includes: For any virtual particle, calculate the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step. Based on the position of the virtual particle in the previous time step and the displacement, determine the position of the virtual particle in the current time step. Determine whether the virtual particle currently meets the conditions for stopping motion; If so, update the virtual particle's state to the stopped state; If not, then proceed to the step of calculating the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step.

[0009] In one optional implementation, the motion-stopping condition includes at least one of the following conditions: The magnitude of the current seepage velocity of the virtual particle is less than or equal to the preset seepage velocity; In addition, the pressure gradient of the current grid cell where the virtual particle is located is less than or equal to the starting pressure gradient; And the current position of the virtual particle is located at the boundary of the numerical simulation grid.

[0010] In one optional implementation, generating the limit seepage distance for different development times based on the motion trajectory of each virtual mass point includes: For any given development time, the target virtual mass that is in a stopped state at that development time is determined based on the motion trajectory of each virtual mass. Calculate the radial distance between each target virtual particle and the production well; The maximum value of the radial distances is taken as the limit seepage distance for that development time.

[0011] In one alternative implementation, the mass conservation equation is generated based on porosity, fluid density, and seepage velocity; The seepage motion equation is generated based on the non-Darcy seepage coefficient, the starting pressure gradient, the pressure gradient, and the seepage velocity; The permeability stress-sensitive model is generated based on the non-Darcy permeability coefficient, the starting pressure gradient, the pressure gradient, and the permeability velocity.

[0012] According to a second aspect of this application, a device for determining the ultimate seepage distance of a low-permeability reservoir is provided, comprising: An acquisition module is used to acquire reservoir parameters of a target low-permeability reservoir; wherein, the reservoir parameters include basic parameters and production parameters; A grid generation module is used to generate a numerical simulation grid for the target low-permeability reservoir based on the reservoir parameters. The seepage field control module is used to pre-construct the seepage field control equations based on the mass conservation equation, the seepage motion equation, and the permeability stress sensitivity model. The distribution field generation module is used to substitute the reservoir parameters into the seepage field control equation based on the numerical simulation grid to generate pressure distribution fields and seepage velocity distribution fields at different development times. The trajectory generation module is used to implant multiple virtual particles in the numerical simulation grid and determine the motion trajectory of each virtual particle according to the pressure distribution field and the seepage velocity distribution field. The distance determination module is used to generate the limit seepage distance under different development times based on the motion trajectory of each virtual mass point.

[0013] In one optional implementation, the trajectory generation module is used to: arrange multiple radial mass rings at first distance intervals along the radial direction, centered on the production well; Multiple particles are uniformly distributed in any radial particle ring; And along the effective thickness direction of the reservoir, mass points are set at second distance intervals.

[0014] In one optional implementation, the trajectory generation module is used to: for any virtual particle, calculate the motion displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity of the virtual particle at the current time step and the time step length. Based on the position of the virtual particle in the previous time step and the displacement, determine the position of the virtual particle in the current time step. Determine whether the virtual particle currently meets the conditions for stopping motion; If so, update the virtual particle's state to the stopped state; If not, then proceed to the step of calculating the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step.

[0015] In one optional implementation, the motion-stopping condition includes at least one of the following conditions: The magnitude of the current seepage velocity of the virtual particle is less than or equal to the preset seepage velocity; In addition, the pressure gradient of the current grid cell where the virtual particle is located is less than or equal to the starting pressure gradient; And the current position of the virtual particle is located at the boundary of the numerical simulation grid.

[0016] In one optional implementation, the distance determination module is used to: for any given development time, determine the target virtual mass that is in a stopped state at that development time based on the motion trajectory of each virtual mass; Calculate the radial distance between each target virtual particle and the production well; The maximum value of the radial distances is taken as the limit seepage distance for that development time.

[0017] In one alternative implementation, the mass conservation equation is generated based on porosity, fluid density, and seepage velocity; The seepage motion equation is generated based on the non-Darcy seepage coefficient, the starting pressure gradient, the pressure gradient, and the seepage velocity; The permeability stress-sensitive model is generated based on the non-Darcy permeability coefficient, the starting pressure gradient, the pressure gradient, and the permeability velocity.

[0018] According to a third aspect of this application, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for determining the ultimate seepage distance of low-permeability reservoirs.

[0019] According to a fourth aspect of this application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction that causes a processor to perform the operation corresponding to the above-described method for determining the ultimate seepage distance of a low-permeability reservoir.

[0020] According to a fifth aspect of this application, a computer program product is provided, comprising at least one executable instruction that causes a processor to perform operations corresponding to the above-described method for determining the ultimate seepage distance of a low-permeability reservoir.

[0021] The method, apparatus, computing device, computer storage medium, and computer program product for determining the ultimate seepage distance of low-permeability reservoirs provided in this application construct the seepage field control equation based on the mass conservation equation, the seepage motion equation, and the permeability stress-sensitive model, thereby constructing a multi-factor coupled seepage field. Furthermore, based on the seepage field control equation, pressure distribution fields and seepage velocity distribution fields under different development times are generated. Multiple virtual particles are implanted in the numerical simulation grid, and the motion trajectory of each virtual particle is determined based on the pressure distribution field and the seepage velocity distribution field. Finally, the ultimate seepage distance under different development times is generated based on the motion trajectory of each virtual particle. This scheme constructs a multi-factor coupled seepage field and obtains the ultimate seepage distance by tracking the motion trajectory of virtual particles. This allows for accurate determination of the dynamic change of the ultimate seepage distance over development time, improving the accuracy of ultimate seepage distance determination. This provides an accurate data foundation for subsequent cluster spacing design and fracturing scale optimization, avoiding resource and development cost waste and improving the economic benefits of developing low-permeability reservoirs. Furthermore, this scheme can be adapted to different low-permeability reservoirs, such as low-permeability sandstone and low-permeability carbonate reservoirs, by adjusting reservoir parameters, thus expanding its applicability. Moreover, this scheme is simple and easy to implement, making it suitable for large-scale application and implementation.

[0022] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of the embodiments of this application are described below. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for determining the ultimate seepage distance in a low-permeability reservoir, as provided in an embodiment of this application, is shown. Figure 2 This paper shows a planar schematic diagram of a numerical simulation mesh provided in an embodiment of this application; Figure 3 A schematic flowchart of a method for generating a pressure distribution field and a seepage velocity distribution field according to an embodiment of this application is shown. Figure 4 A flowchart illustrating a method for determining the motion trajectory of a virtual mass according to an embodiment of this application is shown. Figure 5This application provides an embodiment of a curve showing the variation of the limiting seepage distance with development time. Figure 6 This illustration shows a schematic diagram of a device for determining the ultimate seepage distance in a low-permeability reservoir, provided in an embodiment of this application. Figure 7 A schematic diagram of the structure of a computing device provided in an embodiment of this application is shown. Detailed Implementation

[0024] 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.

[0025] Figure 1 A flowchart illustrating a method for determining the ultimate seepage distance in a low-permeability reservoir, as provided in an embodiment of this application, is shown.

[0026] Specifically, such as Figure 1 As shown, the method includes the following steps: Step S101: Obtain the reservoir parameters of the target low-permeability reservoir.

[0027] The target low-permeability reservoir is the low-permeability reservoir for which the ultimate seepage distance is to be determined. The low-permeability reservoir described in the embodiments of this application refers to an oil and gas reservoir with a permeability lower than a certain threshold (such as 100mD).

[0028] This step obtains the reservoir parameters of the target low-permeability reservoir, including basic parameters and production parameters.

[0029] Among them, basic parameters are parameters used to describe the properties of the target low-permeability reservoir. For example, basic parameters may include: reservoir permeability. (Unit: mD), porosity (Dimensionless), Effective Thickness (Unit: m) Rock compressibility coefficient (Unit: MPa⁻¹) Fluid viscosity (Unit: mPa·s) Fluid compressibility coefficient (Unit: MPa⁻¹) Starting pressure gradient (Unit: MPa / m), Permeability stress sensitivity coefficient (Unit: MPa⁻¹), and / or fluid density (Unit: kg / m³), etc.

[0030] Production parameters are used to describe the relevant parameters for producing the target low-permeability reservoir. For example, production parameters may include: bottomhole flowing pressure of the production well. (Unit: MPa) Original formation pressure of the reservoir (Unit: MPa), Development Time (Unit: d), and / or single well production (Unit: m³ / d), etc.

[0031] Taking a low-permeability reservoir as an example, the reservoir parameters obtained may include: Basic reservoir parameters: , , , , , , , .

[0032] Development parameters: , (Production pressure difference) Development time .

[0033] In this embodiment of the invention, the specific method of obtaining reservoir parameters is not limited. For example, reservoir parameters can be obtained through laboratory core experiments (such as permeability testing, starting pressure gradient experiments, etc.), well logging interpretation (such as porosity interpretation, effective thickness interpretation, etc.), and collecting mine production data (such as bottom hole flowing pressure data, production data, etc.).

[0034] Step S102: Generate a numerical simulation grid for the target low-permeability reservoir based on the reservoir parameters.

[0035] Numerical simulation is performed based on the reservoir parameters of the target low-permeability reservoir obtained in step S101 to numerically mesh the target low-permeability reservoir, resulting in a numerical simulation mesh for the target low-permeability reservoir. This transforms the analysis of the target low-permeability reservoir into the analysis of a series of discrete grid cells; specifically, the numerical simulation mesh in this step contains a series of grid cells with different spatial locations.

[0036] The numerical simulation grid can be obtained through a corresponding gridded numerical simulation algorithm. For example, based on the reservoir parameters in step S101, a low-permeability reservoir PEBI (Perpendicular Bisector Grid) numerical simulation grid can be constructed using the finite volume method.

[0037] Specifically, firstly, based on the geometry (e.g., length, width, height) and wellbore location of the target low-permeability reservoir, a PEBI numerical simulation grid is generated using the finite volume method. Then, the reservoir parameters obtained in step S101 are assigned as initial attribute values ​​to each grid cell. Corresponding boundary conditions are set, such as setting the production well grid as the wellbore boundary and setting bottomhole flowing pressure boundary conditions. The numerical simulation grid generated in this embodiment can accurately characterize the reservoir property distribution and wellbore location of the target low-permeability reservoir.

[0038] like Figure 2 As shown, the generated numerical simulation grid is a network composed of a series of unstructured grids centered on the wellbore, with corresponding boundaries, and containing a finite number of grid cells.

[0039] Step S103: Construct the seepage field control equation in advance based on the mass conservation equation, seepage motion equation, and permeability stress sensitivity model.

[0040] In this embodiment, the seepage field control equation is pre-constructed based on the mass conservation equation, the seepage motion equation, and the permeability stress sensitivity model.

[0041] Specifically, the mass conservation equation is generated based on porosity, fluid density, and seepage velocity. It can be illustrated as shown in Equation 1: (Formula 1) in, Indicates porosity; Indicates fluid density; Indicates time; Indicates the seepage velocity (unit: m / s); This represents the divergence operator.

[0042] The seepage motion equation is generated based on the non-Darcy seepage coefficient, the initiation pressure gradient, the pressure gradient, and the seepage velocity. This allows for full consideration of the initiation pressure gradient and non-Darcy seepage effects in low-permeability reservoirs, improving the accuracy of subsequent determination of the ultimate seepage distance. The specific seepage motion equation is shown in Equation 2: (Formula 2) in, Indicates the seepage velocity; Indicates penetration rate; Indicates fluid viscosity; Indicates the pressure gradient (unit: MPa / m); Indicates the initiation pressure gradient; Indicates the non-Darcy seepage coefficient (unit: m⁻¹); This indicates the fluid density.

[0043] The permeability stress-sensitive model is generated based on the non-Darcy flow coefficient, the initiation pressure gradient, the pressure gradient, and the seepage velocity. The specific permeability stress-sensitive model can be represented as shown in Equation 3: (Formula 3) in, Indicates penetration rate; Indicates the original permeability (unit: mD); This represents the permeability stress sensitivity coefficient; Indicates grid node pressure (unit: MPa); This represents the original formation pressure.

[0044] The mass conservation equation, the seepage motion equation, and the permeability stress-sensitive model together constitute the seepage field control equation. That is, the seepage field control equation is a combined equation consisting of the above formulas 1, 2, and 3.

[0045] Step S104: Based on the numerical simulation grid, the reservoir parameters are substituted into the seepage field control equation to generate the pressure distribution field and seepage velocity distribution field under different development times.

[0046] Based on the numerical simulation grid generated in step S102, the relevant reservoir parameters from step S101 are substituted into the flow field control equations constructed in step S103 to obtain the pressure distribution field and the flow velocity distribution field under different development times. The pressure distribution field describes the pressure of different grid cells at the corresponding development time; the flow velocity distribution field describes the flow velocity (vector) of different grid cells at the corresponding development time.

[0047] The pressure distribution field can be represented as: The seepage velocity distribution field can be expressed as .in,( ) represents the spatial coordinates of the grid cell, and t represents the development time.

[0048] Specifically, it can be adopted Figure 3 The steps shown yield the pressure distribution field and the seepage velocity distribution field: S1041, determine the development time of the current time step.

[0049] The development time range and time step size are obtained in advance, thus determining the development time for each time step. In the initial state, the development time of each time step is the initial value. After obtaining the pressure distribution field and seepage velocity distribution field of the current time step, the time step is updated, that is, the next time step is taken as the current time step, thus entering the calculation process of the pressure distribution field and seepage velocity distribution field under the next development time.

[0050] S1042, for any grid cell, substitute the pressure distribution and reservoir parameters of the previous time step of the grid cell into the seepage field control equation, and use the preset solution algorithm to obtain the pressure distribution and seepage velocity distribution of the grid cell at the current time step.

[0051] For each grid cell, based on the pressure distribution of the grid cell in the previous time step and the reservoir parameters in step S101 of the seepage field control equation, a preset solution algorithm is used to obtain the pressure distribution and seepage velocity distribution of the grid cell in the current time step.

[0052] For example, IMPES (Implicit Pressure Explicit Saturation) and SEQ (Sequential Implicit) can be used to solve the seepage field control equations to obtain the pressure distribution and seepage velocity distribution of the grid cell at the current time step.

[0053] S1043, determine whether the termination condition is met; if yes, proceed to step S1044; if no, proceed to step S1041.

[0054] If the termination condition is met (e.g., the pressure distribution and seepage velocity distribution of each grid cell at each time step have been obtained), then step S1045 is executed; if the termination condition is not met, then step S1041 is executed to update the time step and perform the calculation for the next time step.

[0055] S1044 generates pressure distribution fields and seepage velocity distribution fields under different development times.

[0056] After the termination condition is met, the pressure distribution field and seepage velocity distribution field at different development times are obtained based on the pressure distribution and seepage velocity distribution of each grid cell at each time step that has been generated.

[0057] For example, the time step can be set to 10d, thereby obtaining the pressure distribution field and seepage velocity distribution field under different development times such as t=100d, 500d, 1000d.

[0058] Step S105: Multiple virtual particles are implanted in the numerical simulation grid, and the motion trajectory of each virtual particle is determined according to the pressure distribution field and the seepage velocity distribution field.

[0059] Multiple virtual particles are implanted into the numerical simulation grid generated in step S102. The virtual particles are used to simulate fluid micro-elements. Then, the motion trajectory of each virtual particle is determined according to the pressure distribution field and the seepage velocity distribution field. The motion of the fluid micro-elements is simulated by the motion trajectory.

[0060] In one optional implementation, virtual particles can be uniformly implanted in the following manner to improve the accuracy of subsequent determination of the ultimate seepage distance: Multiple radial particle rings are set radially at first distance intervals, centered on the production well; multiple particles are uniformly set in any radial particle ring; and particles are set at second distance intervals along the effective thickness direction of the reservoir. For example, a radial particle ring is set every 5m along the radial direction centered on the production well, where the radial distance range can be 1m to 5000m to cover the potential seepage boundary, such as radial particle rings at 1m, 6m, ..., 4996m; 36 particles are uniformly set in each radial particle ring on the radial plane, with adjacent particles in each radial particle ring spaced 10° apart, thus ensuring full coverage of the reservoir planar seepage area; vertically, one particle is set every 0.5m along the effective thickness direction of the reservoir, i.e., a radial plane containing a particle is set at certain distances, thus facilitating the capture of vertical seepage differences. Therefore, 999 (radial ring number) × 36 (planar mass number) × 16 (vertical mass number) = 575424 virtual mass points can be implanted.

[0061] In one alternative implementation, the Lagrange method can be used to track the motion of a particle; specifically, it can be used... Figure 4 The steps shown are used to determine the trajectory of any virtual particle: S1051, Calculate the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step.

[0062] Based on the development time range, different development times are set for different time steps, resulting in different time steps. ( Thus, this embodiment obtains the particle position, seepage velocity, etc. of any virtual particle at each time step.

[0063] Specifically, the displacement of the virtual particle from the previous time step to the current time step is calculated, and this displacement = the seepage velocity of the virtual particle * the time step length. Here, based on the particle position obtained in the previous time step, the grid cell containing that position is determined; this grid cell is called the original grid cell of the virtual particle in the current time step. Further, based on the seepage velocity distribution field obtained in step S104, the seepage velocity of this original grid cell at the development time corresponding to the current time step is determined; this seepage velocity is the seepage velocity of the virtual particle in the current time step. Therefore, the displacement = the seepage velocity of the virtual particle in the current time step * the time step length.

[0064] Optionally, when determining the seepage velocity of the virtual particle in the current time step, if it is determined that cross-grid movement has occurred, i.e., the particle moves from the original grid cell to an adjacent grid cell within the current time step, then linear interpolation is used to redetermine the seepage velocity of the virtual particle in the current time step, thereby ensuring the continuity of displacement calculation. Specifically, the seepage velocity of the original grid cell at the development time corresponding to the current time step is determined as the candidate seepage velocity. If the particle position in the current time step and the particle position in the previous time step are determined to be in different grid cells according to the candidate seepage velocity, then the seepage velocity of the virtual particle in the current time step is obtained again by interpolation of the seepage velocities of the two different grid cells, and then the motion displacement in the current time step is obtained again based on the redetermined seepage velocity in the current time step.

[0065] S1052, Based on the position of the virtual particle in the previous time step and the displacement of the virtual particle from the previous time step to the current time step, determine the position of the virtual particle in the current time step.

[0066] Specifically, the sum of the virtual particle's position at the previous time step and the motion displacement obtained in S1051 is taken as the virtual particle's position at the current time step.

[0067] Therefore, the position of the virtual particle at the current time step can be determined using the following formula 4: (Formula 4) in, This indicates the position of the particle at the current time step n; This indicates the position of the particle at the previous time step n-1; This represents the time step from the previous time step n-1 to the current time step n, which is usually less than or equal to 1 day. This represents the seepage velocity of the virtual particle at the current time step; This represents the motion and displacement of a virtual particle from the previous time step to the current time step.

[0068] S1053, determine whether the virtual particle currently meets the motion stopping condition; if yes, proceed to step S1054; if no, proceed to step S1051.

[0069] The virtual particle remains in motion until the motion-stopping condition is met. Each time the particle's position at the current time step is obtained, it is determined whether the virtual particle meets the motion-stopping condition. If the virtual particle currently meets the motion-stopping condition, step S1054 is executed to end the particle's motion; if the virtual particle currently does not meet the motion-stopping condition, it is determined that the virtual particle is still in motion, and step S1051 is then executed to perform motion tracking for the next time step.

[0070] The motion-stopping conditions include at least one of the following: Motion stopping condition one: The magnitude of the current seepage velocity of the virtual particle is less than the preset seepage velocity. Specifically, determine the current position of the virtual particle, and determine the seepage velocity of the grid cell where the particle position is located at the current time based on the seepage velocity distribution field. This seepage velocity is taken as the current seepage velocity of the virtual particle. If the magnitude of the current seepage velocity of the virtual particle is less than or equal to the preset seepage velocity (e.g., ... If the velocity is m / s, it indicates that the virtual particle has stopped flowing, thus determining that the virtual particle meets the motion cessation condition.

[0071] Motion stopping condition two: The pressure gradient of the current grid cell where the virtual particle is located is less than or equal to the starting pressure gradient. Specifically, the current position of the virtual particle is determined, and then the current grid cell where the virtual particle is located is determined based on the current particle position. Then, the pressure gradient of the current grid cell is obtained based on the pressure distribution field obtained in step S104 (such as using the finite difference method or gradient reconstruction method to obtain the pressure gradient based on the pressure distribution field). If the pressure gradient ≤ G, then the pressure gradient is insufficient to drive fluid flow, thus determining that the virtual particle meets the motion stopping condition.

[0072] Motion stopping condition three: The current position of the virtual particle is located at the boundary of the numerical simulation grid. If the current position of the virtual particle is located at the boundary of the numerical simulation grid, it indicates that the virtual particle has moved to the simulation boundary and exceeded the effective range of the reservoir, thus determining that the virtual particle meets the motion stopping condition.

[0073] S1054, Update the virtual particle state to the stopped state.

[0074] If the virtual particle currently meets the motion stopping condition, then the virtual particle's state is changed from a moving state to a stopped state, and the motion tracking of the virtual particle ends.

[0075] By implementing the above steps, the motion trajectory of each virtual particle can be obtained. This motion trajectory can include the particle position, seepage velocity, pressure gradient, etc. of the virtual particle at each development time.

[0076] Step S106: Generate the limit seepage distance for different development times based on the motion trajectory of each virtual mass point.

[0077] Specifically, for any given development time, the target virtual particle that is in a stopped state at that development time is determined based on the motion trajectory of each virtual particle; the radial distance between each target virtual particle and the production well is further calculated; the maximum value of the radial distances is taken as the limiting seepage distance at that development time. If there is no target virtual particle in a stopped state at a certain development time, the radial distance of the farthest particle in the numerical simulation area is taken as the temporary limiting seepage distance.

[0078] For example, development time If the maximum radial distance among the target virtual particles in the stationary state is 7.88m, then the limiting seepage distance is... ; Development time If the maximum radial distance among the target virtual particles in the stationary state is 26.78 m, then the limiting seepage distance is... ; Development time If the maximum radial distance among the target virtual particles in the stationary state is 44.55m, then the limiting seepage distance is... .

[0079] Therefore, this embodiment allows us to obtain the limiting seepage distance corresponding to different development times, and thus obtain the curve of the limiting seepage distance changing with development time. Figure 5 As shown, the ultimate seepage distance at a specified development time can be obtained from the curve of the ultimate seepage distance changing with development time.

[0080] Furthermore, obtaining the ultimate seepage distance under different development times can provide a basis for cluster spacing design and fracturing scale optimization. For example, if the development time is 1000 days and the ultimate seepage distance is 44.55m, the reasonable fracture spacing of the reservoir should be controlled at around 89.1m (i.e., twice the ultimate seepage distance in the stable stage).

[0081] Therefore, the method for determining the ultimate seepage distance of low-permeability reservoirs provided in this application constructs the seepage field control equation based on the mass conservation equation, the seepage motion equation, and the permeability stress-sensitive model, thereby constructing a multi-factor coupled seepage field; furthermore, it generates the pressure distribution field and seepage velocity distribution field under different development times based on the seepage field control equation, implants multiple virtual particles in the numerical simulation grid, determines the motion trajectory of each virtual particle based on the pressure distribution field and the seepage velocity distribution field, and finally generates the ultimate seepage distance under different development times based on the motion trajectory of each virtual particle. This scheme constructs a multi-factor coupled seepage field and obtains the ultimate seepage distance by tracking the trajectory of virtual particles. This allows for accurate determination of the dynamic change of the ultimate seepage distance over development time, improving the accuracy of ultimate seepage distance determination. This provides an accurate data foundation for subsequent cluster spacing design and fracturing scale optimization, avoiding resource and development cost waste and improving the economic benefits of low-permeability reservoir development. Furthermore, this scheme can be adapted to different low-permeability reservoirs, such as low-permeability sandstone and low-permeability carbonate reservoirs, by adjusting reservoir parameters, thus expanding its applicability. Moreover, this scheme is simple and easy to implement, making it suitable for large-scale application and implementation.

[0082] Figure 6A schematic diagram of a device for determining the ultimate seepage distance of a low-permeability reservoir provided in an embodiment of this application is shown.

[0083] Specifically, such as Figure 6 As shown, the device 600 includes: an acquisition module 610, a mesh generation module 620, a seepage field control module 630, a distribution field generation module 640, and a trajectory generation module 650.

[0084] The acquisition module 610 is used to acquire reservoir parameters of the target low-permeability reservoir; wherein, the reservoir parameters include basic parameters and production parameters; The grid generation module 620 is used to generate a numerical simulation grid of the target low-permeability reservoir based on the reservoir parameters. The seepage field control module 630 is used to pre-construct the seepage field control equation based on the mass conservation equation, the seepage motion equation, and the permeability stress sensitivity model. The distribution field generation module 640 is used to substitute the reservoir parameters into the seepage field control equation based on the numerical simulation grid to generate pressure distribution fields and seepage velocity distribution fields at different development times. The trajectory generation module 650 is used to implant multiple virtual particles in the numerical simulation grid and determine the motion trajectory of each virtual particle according to the pressure distribution field and the seepage velocity distribution field. The distance determination module 660 is used to generate the limit seepage distance under different development times based on the motion trajectory of each virtual mass point.

[0085] In one alternative implementation, the trajectory generation module 650 is used to: arrange multiple radial mass rings at first distance intervals along the radial direction with the production well as the center; Multiple particles are uniformly distributed in any radial particle ring; And along the effective thickness direction of the reservoir, mass points are set at second distance intervals.

[0086] In one optional implementation, the trajectory generation module 650 is used to: for any virtual particle, calculate the motion displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step; Based on the position of the virtual particle in the previous time step and the displacement, determine the position of the virtual particle in the current time step. Determine whether the virtual particle currently meets the conditions for stopping motion; If so, update the virtual particle's state to the stopped state; If not, then proceed to the step of calculating the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step.

[0087] In one optional implementation, the motion-stopping condition includes at least one of the following conditions: The magnitude of the current seepage velocity of the virtual particle is less than or equal to the preset seepage velocity; In addition, the pressure gradient of the current grid cell where the virtual particle is located is less than or equal to the starting pressure gradient; And the current position of the virtual particle is located at the boundary of the numerical simulation grid.

[0088] In one optional implementation, the distance determination module 660 is used to: for any given development time, determine the target virtual mass that is in a stopped state at that development time based on the motion trajectory of each virtual mass; Calculate the radial distance between each target virtual particle and the production well; The maximum value of the radial distances is taken as the limit seepage distance for that development time.

[0089] In one alternative implementation, the mass conservation equation is generated based on porosity, fluid density, and seepage velocity; The seepage motion equation is generated based on the non-Darcy seepage coefficient, the starting pressure gradient, the pressure gradient, and the seepage velocity; The permeability stress-sensitive model is generated based on the non-Darcy permeability coefficient, the starting pressure gradient, the pressure gradient, and the permeability velocity.

[0090] Therefore, the device for determining the ultimate seepage distance of low-permeability reservoirs provided in this application embodiment constructs the seepage field control equation based on the mass conservation equation, the seepage motion equation, and the permeability stress-sensitive model, thereby constructing a multi-factor coupled seepage field; and further generates the pressure distribution field and seepage velocity distribution field under different development times based on the seepage field control equation, implants multiple virtual particles in the numerical simulation grid, determines the motion trajectory of each virtual particle based on the pressure distribution field and the seepage velocity distribution field, and finally generates the ultimate seepage distance under different development times based on the motion trajectory of each virtual particle. This scheme constructs a multi-factor coupled seepage field and obtains the ultimate seepage distance by tracking the trajectory of virtual particles. This allows for accurate determination of the dynamic change of the ultimate seepage distance over development time, improving the accuracy of ultimate seepage distance determination. This provides an accurate data foundation for subsequent cluster spacing design and fracturing scale optimization, avoiding resource and development cost waste and improving the economic benefits of low-permeability reservoir development. Furthermore, this scheme can be adapted to different low-permeability reservoirs, such as low-permeability sandstone and low-permeability carbonate reservoirs, by adjusting reservoir parameters, thus expanding its applicability. Moreover, this scheme is simple and easy to implement, making it suitable for large-scale application and implementation.

[0091] This application provides a non-volatile computer storage medium storing at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the method for determining the ultimate seepage distance of low-permeability reservoirs in any of the above method embodiments.

[0092] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the method for determining the ultimate seepage distance of low-permeability reservoirs in any of the above method embodiments.

[0093] Figure 7 The diagram shows a structural schematic of a computing device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the computing device.

[0094] like Figure 7 As shown, the computing device may include: a processor 702, a communications interface 704, a memory 706, and a communications bus 708.

[0095] The processor 702, communication interface 704, and memory 706 communicate with each other via communication bus 708. Communication interface 704 is used to communicate with other network elements, such as clients or other servers. Processor 702 executes program 710, specifically performing the relevant steps in the embodiment of the method for determining the ultimate seepage distance of low-permeability reservoirs for calculating equipment.

[0096] Specifically, program 710 may include program code that includes computer operation instructions.

[0097] The processor 702 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0098] Memory 706 is used to store program 710. Memory 706 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0099] Specifically, program 710 can be used to cause processor 702 to execute the method for determining the ultimate seepage distance of low-permeability reservoirs in any of the above method embodiments. The specific implementation of each step in program 710 can be found in the corresponding descriptions of the steps and units in the above embodiments of the method for determining the ultimate seepage distance of low-permeability reservoirs, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0100] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the contents of the embodiments of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best implementation of the embodiments of this application.

[0101] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0102] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, features of the embodiments of this application are sometimes grouped together in a single embodiment, figure, or description thereof in the foregoing description of exemplary embodiments of the present application. However, this approach to disclosure should not be construed as reflecting an intention that the claimed embodiments of the present application require more features than expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the present application.

[0103] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0104] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the embodiments of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0105] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of this application. The embodiments of this application can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0106] It should be noted that the above embodiments are illustrative of the embodiments of this application and not limiting of the embodiments of this application, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of this application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for determining the limiting seepage distance of a low-permeability reservoir, characterized in that, include: Obtain reservoir parameters of the target low-permeability reservoir; wherein, the reservoir parameters include basic parameters and production parameters; A numerical simulation grid for the target low-permeability reservoir is generated based on the reservoir parameters. The seepage field control equations are constructed in advance based on the mass conservation equation, the seepage motion equation, and the permeability stress sensitivity model. Based on the numerical simulation grid, the reservoir parameters are substituted into the seepage field control equation to generate the pressure distribution field and seepage velocity distribution field under different development times. Multiple virtual particles are implanted in the numerical simulation grid, and the motion trajectory of each virtual particle is determined according to the pressure distribution field and the seepage velocity distribution field. The ultimate seepage distance is generated based on the motion trajectory of each virtual mass point under different development times.

2. The method according to claim 1, characterized in that, The process of implanting multiple virtual particles in the numerical simulation grid includes: Centered on the production well, multiple radial mass rings are arranged at first distance intervals along the radial direction; Multiple particles are uniformly distributed in any radial particle ring; And along the effective thickness direction of the reservoir, mass points are set at second distance intervals.

3. The method according to claim 1, characterized in that, Determining the motion trajectory of each virtual particle based on the pressure distribution field and the seepage velocity distribution field includes: For any virtual particle, calculate the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step. Based on the position of the virtual particle in the previous time step and the displacement, determine the position of the virtual particle in the current time step. Determine whether the virtual particle currently meets the conditions for stopping motion; If so, update the virtual particle's state to the stopped state; If not, then proceed to the step of calculating the displacement of the virtual particle from the previous time step to the current time step based on the seepage velocity and time step length of the virtual particle at the current time step.

4. The method according to claim 3, characterized in that, The motion-stopping condition includes at least one of the following conditions: The magnitude of the current seepage velocity of the virtual particle is less than or equal to the preset seepage velocity; In addition, the pressure gradient of the current grid cell where the virtual particle is located is less than or equal to the starting pressure gradient; Furthermore, the current position of the virtual particle is located at the boundary of the numerical simulation grid.

5. The method according to claim 3, characterized in that, The process of generating the limit seepage distance for different development times based on the motion trajectory of each virtual mass point includes: For any given development time, the target virtual mass that is in a stopped state at that development time is determined based on the motion trajectory of each virtual mass. Calculate the radial distance between each target virtual particle and the production well; The maximum value of the radial distances is taken as the limit seepage distance for that development time.

6. The method according to any one of claims 1-5, characterized in that, The mass conservation equation is generated based on porosity, fluid density, and seepage velocity; The seepage motion equation is generated based on the non-Darcy seepage coefficient, the starting pressure gradient, the pressure gradient, and the seepage velocity; The permeability stress-sensitive model is generated based on the non-Darcy permeability coefficient, the starting pressure gradient, the pressure gradient, and the permeability velocity.

7. A device for determining the ultimate seepage distance in a low-permeability reservoir, characterized in that, include: An acquisition module is used to acquire reservoir parameters of a target low-permeability reservoir; wherein, the reservoir parameters include basic parameters and production parameters; A grid generation module is used to generate a numerical simulation grid for the target low-permeability reservoir based on the reservoir parameters. The seepage field control module is used to pre-construct the seepage field control equations based on the mass conservation equation, the seepage motion equation, and the permeability stress sensitivity model. The distribution field generation module is used to substitute the reservoir parameters into the seepage field control equation based on the numerical simulation grid to generate pressure distribution fields and seepage velocity distribution fields at different development times. The trajectory generation module is used to implant multiple virtual particles in the numerical simulation grid and determine the motion trajectory of each virtual particle according to the pressure distribution field and the seepage velocity distribution field. The distance determination module is used to generate the limit seepage distance under different development times based on the motion trajectory of each virtual mass point.

8. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method for determining the ultimate seepage distance of a low-permeability reservoir as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the method for determining the ultimate seepage distance of a low-permeability reservoir as described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes at least one executable instruction that causes the processor to perform the operation corresponding to the method for determining the ultimate seepage distance of a low-permeability reservoir as described in any one of claims 1-6.

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