Simulation method, system and equipment for reservoir karst cave distribution and storage medium

Abstract

The embodiment of the invention provides a simulation method, system and equipment for reservoir karst cave distribution and a storage medium, and belongs to the technical field of oil-gas field development geology. The reservoir karst cave distribution simulation method comprises the steps that carbonate reservoir parameters are obtained, target karst cave pore space distribution is determined on the basis of the carbonate reservoir parameters through a sequential Gaussian simulation algorithm, and visual display is carried out according to the target karst cave pore space distribution. According to the embodiment of the invention, by determining the pore space distribution of the target karst cave, the problems that the karst cave distribution in the aperture-cavity stratum is not considered in the existing matrix acidification simulation and the effective action distance of the real acid liquid is difficult to predict are solved, and the karst cave distribution is visually displayed, so that the intuition is improved.

Description

Technical Field

[0001] The invention relates to the field of oil and gas field development geology, and in particular to a simulation method for reservoir cave distribution, a simulation system for reservoir cave distribution, an electronic device and a readable storage medium. Background Art

[0002] Fracture-cavity carbonate rocks are rich in oil and gas resources. In carbonate reservoirs with complex pores, fractures and caves, caves are the main storage space for oil and gas. Characterized by the combination of pores, fractures and caves, the reservoir is extremely heterogeneous, but has a certain correlation in space. For the development of this type of reservoir, the main purpose of matrix acidification of this type of reservoir is to communicate fractures and cave reservoirs. At present, the distribution characteristics of caves in reservoirs are comprehensively analyzed through geological exploration methods including drilling, logging and well logging. However, in order to simulate the distribution law of caves in actual formations and make it as close to the field survey characteristics as possible, a simulation method for simulating the distribution of caves in fracture-cavity carbonate reservoirs is formed. The study of the distribution law of caves in fracture-cavity carbonate reservoirs will directly affect the communication of target distances in acidification transformation. The porosity of reservoirs containing caves is higher than that of matrix reservoirs, which leads to a large amount of acid flowing to caves, shortening the distance of acid transformation and changes in dissolution morphology.

[0003] Existing cave simulation technology usually performs flow simulation based on the actual fracture distribution parameters and cave distribution parameters of fractures and caves in carbonate oil reservoir formations. However, this method does not take into account the randomness and spatial correlation of the actual distribution characteristics of caves in the formation, which makes the accuracy of cave simulation low. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a simulation method, system equipment and storage medium for reservoir cave distribution to solve the above technical problems.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for simulating reservoir cave distribution, the method comprising:

[0006] Acquiring carbonate reservoir parameters; wherein the carbonate reservoir parameters include matrix porosity parameters, cave porosity parameters and grid parameters;

[0007] Based on the carbonate reservoir parameters, the spatial distribution of the target cave pores is determined using a sequential Gaussian simulation algorithm;

[0008] The pore space distribution of the target cave is visualized.

[0009] Optionally, the determining of the target cave pore space distribution based on the carbonate reservoir parameters by using a sequential Gaussian simulation algorithm includes:

[0010] According to the carbonate reservoir parameters, using a sequential Gaussian simulation algorithm, an initial matrix pore space distribution is obtained;

[0011] According to the carbonate reservoir parameters, using a sequential Gaussian simulation algorithm, the initial cave pore space distribution is obtained;

[0012] Based on the initial matrix pore space distribution and the initial cave pore space distribution, the target cave pore space distribution is obtained.

[0013] Optionally, obtaining the initial matrix pore space distribution using a sequential Gaussian simulation algorithm according to the carbonate reservoir parameters includes:

[0014] Performing normal transformation on the matrix porosity parameter to obtain the matrix porosity parameter under normal distribution;

[0015] Based on the grid parameters, the target reservoir area is gridded, and the matrix porosity parameters under the normal distribution are assigned to the corresponding grids to obtain a matrix pore grid;

[0016] According to the matrix porosity parameter under normal distribution, a first empirical distribution function is determined;

[0017] Based on the first empirical distribution function, the initial matrix pore space distribution is determined.

[0018] Optionally, determining the initial matrix pore space distribution based on the first empirical distribution function includes:

[0019] Based on the grid parameters, a first random array is generated using a variogram; wherein the first random array has the same number of grids as the matrix pore grid, and each data in the first random array corresponds to a grid in the matrix pore grid;

[0020] Determining a grid calculation order according to a correspondence between the first random array and the matrix pore grid;

[0021] According to the grid calculation order, the simulated matrix porosity value corresponding to each grid in the matrix pore grid is calculated using the first empirical distribution function;

[0022] Each simulated matrix porosity value is assigned to the corresponding grid in the matrix pore grid to determine the initial matrix pore space distribution.

[0023] Optionally, the generating a first random array based on the grid parameters using a variogram includes:

[0024] Based on the grid parameters, a first random array is generated using a variogram; wherein the variogram is represented by formula (1):

[0025]

[0026] In formula (1), h is the lag distance, y(h) is the data with a lag distance of h in the first random array, E is the mathematical expectation, Z(x) is the observation value at the point, and x is a point in the grid space.

[0027] Optionally, obtaining the initial cave pore space distribution by using a sequential Gaussian simulation algorithm according to the carbonate reservoir parameters includes:

[0028] Performing normal transformation on the cave porosity parameter to obtain the cave porosity parameter under normal distribution;

[0029] Based on the grid parameters, the target reservoir area is gridded, and the cave porosity parameters under the normal distribution are assigned to the corresponding grids to obtain a cave pore grid;

[0030] According to the cave porosity parameters under normal distribution, the second empirical distribution function is determined;

[0031] Based on the second empirical distribution function, the initial cave pore space distribution is determined.

[0032] Optionally, obtaining the target cave pore space distribution based on the initial matrix pore space distribution and the initial cave pore space distribution includes:

[0033] Selecting a karst cave pore grid with a porosity greater than a preset porosity from the initial karst cave pore space distribution to obtain a target karst cave pore grid;

[0034] The target cave pore grid is projected onto the matrix pore grid corresponding to the initial matrix pore space distribution to obtain the target cave pore space distribution.

[0035] In a second aspect of an embodiment of the present invention, a system for simulating reservoir cave distribution is provided, the system comprising:

[0036] A data acquisition module, used to acquire carbonate reservoir parameters; wherein the carbonate reservoir parameters include matrix porosity parameters, cave porosity parameters and grid parameters;

[0037] A distribution determination module, for determining the spatial distribution of target cave pores based on the carbonate reservoir parameters using a sequential Gaussian simulation algorithm;

[0038] A display module is used to perform visual display according to the pore space distribution of the target cave.

[0039] A third aspect of the present application provides an electronic device configured to execute the above-mentioned method for simulating reservoir cave distribution.

[0040] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the above-mentioned method for simulating reservoir cave distribution.

[0041] The embodiment of the present invention obtains carbonate reservoir parameters, and based on the carbonate reservoir parameters, uses a sequential Gaussian simulation algorithm to determine the target cave pore space distribution, and further visualizes the target cave pore space distribution. That is, the embodiment of the present invention solves the problem that the existing matrix acidizing simulation does not consider the cave distribution in the pore-crack-cavity formation, and it is difficult to predict the effective action distance of the real acid solution, and the embodiment of the present invention visualizes the cave distribution, which improves the intuitiveness.

[0042] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0044] Figure 1 The embodiment of the present invention provides a flow chart of a method for simulating reservoir cave distribution;

[0045] Figure 2 is the initial matrix pore space distribution map;

[0046] Figure 3 (a) to Figure 3 (f) is the distribution diagram of cave pores under different correlation lengths;

[0047] Figure 4 (a) to Figure 4 (f) is the distribution diagram of cave pores under different area ratios;

[0048] Figure 5 (a) to Figure 5 (f) is the distribution diagram of cave pores under different coefficients of variation;

[0049] Figure 6 The embodiment of the present invention provides a functional module schematic diagram of a system for simulating reservoir cave distribution. DETAILED DESCRIPTION

[0050] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0052] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0053] Embodiment 1

[0054] Please refer to Figure 1 , Figure 1 It is a schematic flow chart of a method for simulating reservoir cave distribution provided in this embodiment.

[0055] Step S100: Acquire carbonate reservoir parameters; wherein the carbonate reservoir parameters include matrix porosity parameters, cave porosity parameters and grid parameters.

[0056] Matrix porosity parameters include matrix average porosity, matrix variation coefficient, and dimensionless correlation length of matrix porosity in radial and axial directions.

[0057] The cave porosity parameters include the coefficient of variation of caves, the proportion of caves, the average porosity of caves, and the dimensionless correlation length of the radial and axial directions of cave porosity.

[0058] The grid parameters may include the number of grids, the size of the unit grid, the overall area of ​​the grid, and other parameters, which may be set according to actual simulation needs and are not limited in this embodiment.

[0059] Step S200: Based on carbonate reservoir parameters, the pore space distribution of the target cave is determined using a sequential Gaussian simulation algorithm.

[0060] In this step, the initial matrix pore space distribution is obtained according to the carbonate reservoir parameters using the sequential Gaussian simulation algorithm, and then the initial cave pore space distribution is obtained according to the carbonate reservoir parameters using the sequential Gaussian simulation algorithm. Finally, based on the initial matrix pore space distribution and the initial cave pore space distribution, the target cave pore space distribution is obtained.

[0061] According to the carbonate reservoir parameters, the initial matrix pore space distribution is obtained by using the sequential Gaussian simulation algorithm, which specifically includes: performing a normal transformation on the matrix porosity parameters to obtain the matrix porosity parameters under normal distribution; based on the grid parameters, meshing the target reservoir area, and assigning the matrix porosity parameters under normal distribution to the corresponding grids to obtain the matrix pore grid; determining the first empirical distribution function according to the matrix porosity parameters under normal distribution; further based on the grid parameters, generating the first random array by using the variation function [Formula (1)]; determining the grid calculation order according to the correspondence between the first random array and the matrix pore grid; according to the grid calculation order, calculating the simulated matrix porosity value corresponding to each grid in the matrix pore grid by using the first empirical distribution function; assigning each simulated matrix porosity value to the corresponding grid in the matrix pore grid, and determining the initial matrix pore space distribution (such as Figure 2 As shown). The first random array has the same number of grids as the matrix pore grid, and each data in the first random array corresponds to a grid in the matrix pore grid; according to the grid calculation order, the simulated matrix porosity value corresponding to each grid in the matrix pore grid is calculated using the first empirical distribution function, specifically including: according to the dimensionless correlation length of the matrix porosity in the radial and axial directions, the matrix porosity parameter and the number of matrix porosity parameters within the correlation length of any grid node in the matrix pore grid are searched based on the correlation length, the mean and variance of the first empirical distribution function at the grid node are determined by the Kriging interpolation method, and a simulated value is randomly selected in the first empirical distribution function as the data value of the grid node, that is, the simulated matrix porosity value corresponding to the grid; Formula (1) is as follows:

[0062] Based on the grid parameters, a first random array is generated using a variogram; wherein the variogram is represented by formula (1):

[0063]

[0064] In formula (1), h is the lag distance, y(h) is the data with lag distance h in the first random array, E is the mathematical expectation, Z(x) is the observation value at that point, and x is a point in the grid space. The various characteristics of the data in the first random array that change with lag distance h express the various spatial heterogeneity characteristics of the regionalized variable, including the size of the affected area, the degree of spatial heterogeneity, and the continuity of the variable in space.

[0065] According to the carbonate reservoir parameters, the initial cave pore space distribution is obtained by using the sequential Gaussian simulation algorithm. It can be understood that the analysis and determination of the initial cave pore space distribution are consistent with the steps of the initial matrix pore space distribution, specifically including: performing a normal transformation on the cave porosity parameters to obtain the cave porosity parameters under the normal distribution; based on the grid parameters, the target reservoir area is gridded, and the cave porosity parameters under the normal distribution are assigned to the corresponding grids to obtain the cave pore grid; according to the cave porosity parameters under the normal distribution, a second empirical distribution function is determined; based on the second empirical distribution function, the initial cave pore space distribution is determined. It should be understood that this embodiment provides three initial cave pore space distributions for analyzing the distribution characteristics of caves under different conditions. The first initial cave pore space distribution is determined according to the above calculation and analysis steps based on the coefficient of variation of the caves, the proportion of the caves and the average porosity of the caves; the second initial cave pore space distribution is determined according to the above calculation and analysis steps based on the dimensionless correlation length of the cave porosity in the radial and axial directions and the average porosity of the caves; the third initial cave pore space distribution is determined according to the above calculation and analysis steps based on the proportion of the cave area, the coefficient of variation and the average porosity of the caves.

[0066] Based on the initial matrix pore space distribution and the initial cave pore space distribution, the target cave pore space distribution is obtained, specifically including: selecting the cave pore grid with a porosity greater than the preset porosity from the initial cave pore space distribution to obtain the target cave pore grid, and projecting the target cave pore grid onto the matrix pore grid corresponding to the initial matrix pore space distribution to obtain the target cave pore space distribution. The target cave pore grid is selected, for example, when the radial and axial dimensionless correlation lengths of the first initial cave pore space distribution are 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3 respectively, the cave pore grids (such as Figure 3 As shown in Figure 2, when the proportion of the cave area in the second initial cave pore space distribution to the total area of ​​the simulation area is 0.05, 0.15, 0.25 and 0.35, the cave pore grids with the porosity of the top 5%, 15%, 25% and 35% in the second initial cave pore space distribution are selected (as shown in Figure 2). Figure 4 As shown in Figure 2, when the coefficients of variation of the third initial cave pore space distribution are 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3 respectively, the cave pore grids with the top 15% of the porosity in the third initial cave pore space distribution are selected (as shown in Figure 2). Figure 5 shown).

[0067] Step S300: Visualizing the pore space distribution of the target cave.

[0068] This embodiment obtains carbonate reservoir parameters, and based on the carbonate reservoir parameters, uses a sequential Gaussian simulation algorithm to determine the target cave pore space distribution, and further performs a visual display based on the target cave pore space distribution. That is, the embodiment of the present invention solves the problem that the existing matrix acidizing simulation does not consider the cave distribution in the pore-fracture-cavity formation, and it is difficult to predict the effective action distance of the real acid solution, and the embodiment of the present invention visualizes the cave distribution, which improves the intuitiveness.

[0069] Embodiment 2

[0070] Please refer to Figure 6 , Figure 6 It is a schematic diagram of the functional modules of a reservoir cave distribution simulation system 200 provided in an embodiment of the present application.

[0071] The data acquisition module 210 is used to acquire carbonate reservoir parameters; wherein the carbonate reservoir parameters include matrix porosity parameters, cave porosity parameters and grid parameters;

[0072] The distribution determination module 220 is used to determine the spatial distribution of the target cave pores based on the carbonate reservoir parameters using a sequential Gaussian simulation algorithm;

[0073] The display module 230 is used to perform visual display according to the pore space distribution of the target cave.

[0074] It should be understood that the system corresponds to the above-mentioned reservoir cave distribution simulation method embodiment, and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the system can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here. The system includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0075] Embodiment 3

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0079] Embodiment 4

[0080] An embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, and the instructions are used to execute a program of steps of a simulation method for reservoir cave distribution when executed by a processor.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0086] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0087] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0088] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for simulating reservoir cave distribution, characterized in that: The method comprises: Acquiring carbonate reservoir parameters; wherein the carbonate reservoir parameters include matrix porosity parameters, cave porosity parameters and grid parameters; Based on the carbonate reservoir parameters, the spatial distribution of the target cave pores is determined using a sequential Gaussian simulation algorithm; The pore space distribution of the target cave is visualized.

2. The method for analyzing reservoir cave distribution according to claim 1, characterized in that: The method of determining the target cave pore space distribution based on the carbonate reservoir parameters and using a sequential Gaussian simulation algorithm includes: According to the carbonate reservoir parameters, using a sequential Gaussian simulation algorithm, an initial matrix pore space distribution is obtained; According to the carbonate reservoir parameters, using a sequential Gaussian simulation algorithm, the initial cave pore space distribution is obtained; Based on the initial matrix pore space distribution and the initial cave pore space distribution, the target cave pore space distribution is obtained.

3. The method for analyzing reservoir cave distribution according to claim 2, characterized in that: The method of obtaining the initial matrix pore space distribution according to the carbonate reservoir parameters by using a sequential Gaussian simulation algorithm includes: Performing normal transformation on the matrix porosity parameter to obtain the matrix porosity parameter under normal distribution; Based on the grid parameters, the target reservoir area is gridded, and the matrix porosity parameters under the normal distribution are assigned to the corresponding grids to obtain a matrix pore grid; According to the matrix porosity parameter under normal distribution, a first empirical distribution function is determined; Based on the first empirical distribution function, the initial matrix pore space distribution is determined.

4. The method for simulating reservoir cave distribution according to claim 3, characterized in that: The step of determining the initial matrix pore space distribution based on the first empirical distribution function comprises: Based on the grid parameters, a first random array is generated using a variogram; wherein the first random array has the same number of grids as the matrix pore grid, and each data in the first random array corresponds to a grid in the matrix pore grid; Determining a grid calculation order according to a correspondence between the first random array and the matrix pore grid; According to the grid calculation order, the simulated matrix porosity value corresponding to each grid in the matrix pore grid is calculated using the first empirical distribution function; Each simulated matrix porosity value is assigned to the corresponding grid in the matrix pore grid to determine the initial matrix pore space distribution.

5. The method for simulating reservoir cave distribution according to claim 4, characterized in that: The method of generating a first random array based on the grid parameters and using a variogram comprises: Based on the grid parameters, a first random array is generated using a variogram; wherein the variogram is represented by formula (1): In formula (1), h is the lag distance, y(h) is the data with a lag distance of h in the first random array, E is the mathematical expectation, Z(x) is the observation value at the point, and x is a point in the grid space.

6. The method for simulating reservoir cave distribution according to claim 3, characterized in that: The method of obtaining the initial cave pore space distribution according to the carbonate reservoir parameters by using a sequential Gaussian simulation algorithm includes: Performing normal transformation on the cave porosity parameter to obtain the cave porosity parameter under normal distribution; Based on the grid parameters, the target reservoir area is gridded, and the cave porosity parameters under the normal distribution are assigned to the corresponding grids to obtain a cave pore grid; According to the cave porosity parameters under normal distribution, the second empirical distribution function is determined; Based on the second empirical distribution function, the initial cave pore space distribution is determined.

7. The method for simulating reservoir cave distribution according to claim 6, characterized in that: The step of obtaining a target cave pore space distribution based on the initial matrix pore space distribution and the initial cave pore space distribution comprises: Selecting a karst cave pore grid with a porosity greater than a preset porosity from the initial karst cave pore space distribution to obtain a target karst cave pore grid; The target cave pore grid is projected onto the matrix pore grid corresponding to the initial matrix pore space distribution to obtain the target cave pore space distribution.

8. A simulation system for reservoir cave distribution, characterized in that: The system comprises: A data acquisition module, used to acquire carbonate reservoir parameters; wherein the carbonate reservoir parameters include matrix porosity parameters, cave porosity parameters and grid parameters; A distribution determination module, for determining the spatial distribution of target cave pores based on the carbonate reservoir parameters using a sequential Gaussian simulation algorithm; A display module is used to perform visual display according to the pore space distribution of the target cave.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method for simulating reservoir cave distribution according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions for causing a machine to execute the method for simulating reservoir cave distribution according to any one of claims 1 to 7.

Images