Cross-scale permeability calculation method, device, equipment and medium for core

Abstract

The embodiment of the present application provides a cross-scale permeability calculation method, device, equipment and medium for rock cores, which belongs to the field of artificial intelligence technology. The method includes: performing low-resolution scanning on the target sample to obtain a grayscale image; performing image processing on the grayscale image to obtain a multi-segmentation image; performing high-resolution scanning on the target area of ​​the target sample to obtain a structural data body; wherein the target area is the area in the target sample where the matrix component meets the preset component specifications; performing image processing on the structural data body to obtain a three-dimensional pore model; performing permeability calculation on the three-dimensional pore model according to a preset permeability calculation algorithm to obtain initial permeability; performing flow field simulation on the multi-segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to obtain the target permeability. The embodiment of the present application can deeply analyze the relationship between the microstructure and macroscopic permeability of different media, and improve the scale of core permeability calculation.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method and apparatus, equipment, and medium for calculating cross-scale permeability of a core. Background Art

[0002] Calculating cross-scale permeability of cores is primarily done through experimental measurements, but this approach is labor-intensive, costly, and often time-consuming. To address this issue, existing technologies have employed computer simulations to calculate the permeability of cores. However, these models, constructed by directly scanning the cores, have significant limitations regarding permeability scale requirements, hindering permeability calculations at higher scales. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a cross-scale permeability calculation method and device, equipment and medium for core rocks, aiming to achieve permeability calculation from millimeter scale to centimeter scale.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for calculating cross-scale permeability of a core, the method comprising:

[0005] Scan the target sample at low resolution to obtain a grayscale image;

[0006] performing image processing on the grayscale image to obtain a multi-element segmentation image;

[0007] Performing a high-resolution scan on a target region of the target sample to obtain a structural data volume; wherein the target region is a region in the target sample where the matrix component meets a preset component specification;

[0008] performing image processing on the structural data volume to obtain a three-dimensional pore model;

[0009] Performing permeability calculation on the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability;

[0010] A flow field simulation is performed on the multivariate segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to obtain a target permeability.

[0011] In some embodiments, performing image processing on the grayscale image to obtain a multi-element segmentation image includes:

[0012] Performing smoothing and noise reduction processing on the grayscale image to obtain a smoothed image;

[0013] Performing threshold segmentation processing on the smoothed image to obtain the multi-element segmentation image.

[0014] In some embodiments, performing image processing on the structural data volume to obtain a three-dimensional pore model includes:

[0015] Performing smoothing and noise reduction processing on the structural data body to obtain target data;

[0016] Performing threshold segmentation processing on the target data to obtain structural data;

[0017] The model is reconstructed according to the structural data to obtain the three-dimensional pore model.

[0018] In some embodiments, performing permeability calculation on the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability includes:

[0019] The permeability of the three-dimensional pore model is calculated according to a preset lattice Boltzmann algorithm to obtain the initial permeability.

[0020] In some embodiments, calculating the permeability of the three-dimensional pore model according to a preset lattice Boltzmann algorithm to obtain the initial permeability includes:

[0021] Initializing the flow field of the three-dimensional pore model according to preset flow field parameters to obtain an equilibrium distribution function;

[0022] Performing collision processing on the equilibrium distribution function according to a single relaxation model to obtain a collision function;

[0023] Performing migration calculation or rebound calculation on the collision function according to the pore points of the three-dimensional pore model to obtain an objective function;

[0024] Assigning preset boundary conditions to the objective function;

[0025] The permeability of the objective function is calculated according to the boundary conditions to obtain the initial permeability.

[0026] In some embodiments, performing flow field simulation on the multivariate segmentation image, the three-dimensional pore model, and the initial permeability according to a preset permeability analysis model to obtain the target permeability includes:

[0027] A flow field simulation is performed on the segmented image, the three-dimensional pore model and the initial permeability according to a preset steady-state DSB model to obtain the target permeability.

[0028] In some embodiments, performing flow field simulation on the multivariate segmentation image, the three-dimensional pore model, and the initial permeability according to a preset steady-state DSB model to obtain the target permeability includes:

[0029] extracting a first rock attribute of the multivariate segmented image;

[0030] extracting a second rock property of the three-dimensional pore model;

[0031] The target permeability is obtained by performing flow field simulation based on the preset steady-state DSB model using the first rock property, the second rock property, and the initial permeability; wherein the steady-state DSB model is:

[0032]

[0033]

[0034] where ε is the local porosity of each target area, p is the pressure, and K is the initial permeability of the target area.

[0035] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a device for calculating cross-scale permeability of a core, the device comprising:

[0036] A first scanning module is used to scan the target sample at a low resolution to obtain a grayscale image;

[0037] A first image processing module, configured to perform image processing on the grayscale image to obtain a multi-element segmentation image;

[0038] A second scanning module is configured to perform high-resolution scanning on a target region of the target sample to obtain a structural data volume; wherein the target region is a region in the target sample where the matrix component meets a preset component specification;

[0039] a second image processing module, configured to perform image processing on the structural data volume to obtain a three-dimensional pore model;

[0040] A calculation module, configured to calculate the permeability of the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability;

[0041] The simulation module is used to perform flow field simulation on the multivariate segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to obtain a target permeability.

[0042] To achieve the above-mentioned objectives, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the method described in the first aspect above is implemented.

[0043] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in the first aspect above.

[0044] The present application proposes a cross-scale permeability calculation method, device, equipment and medium for rock cores, which calculate the permeability of micropores according to a three-dimensional pore model using a preset permeability calculation algorithm to obtain an initial permeability, and then perform flow field simulation on the multi-element segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to carry out permeability scale upgrade calculation to obtain the target permeability, thereby upgrading the permeability calculation from the millimeter scale to the centimeter scale, and deeply analyzing the relationship between the microstructure and macroscopic permeability of different media, thereby improving the scale of core permeability calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a cross-scale permeability calculation method for a core provided in an embodiment of the present application;

[0046] Figure 2 yes Figure 1 Flowchart of step S102 in FIG.

[0047] Figure 3 yes Figure 1 Flowchart of step S104 in FIG.

[0048] Figure 4 is a flow chart of a cross-scale permeability calculation method for a core provided by another embodiment of the present application;

[0049] Figure 5 is a flow chart of a cross-scale permeability calculation method for a core provided by another embodiment of the present application;

[0050] Figure 6 Schematic diagram of the structure of the cross-scale permeability calculation device for a core provided in an embodiment of the present application;

[0051] Figure 7 This is a schematic diagram of the hardware structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0055] First, let’s analyze some of the terms used in this application:

[0056] Artificial intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0057] Rock permeability: Rock permeability refers to the ability of a rock to allow fluid to pass through it under a certain pressure differential. Permeability is often expressed in terms of permeability, which is distinctly directional. Unlike porosity, permeability is a vector. This means that permeability varies significantly in different directions and is generally categorized as horizontal permeability (Kh) and vertical permeability (Kv).

[0058] Image noise reduction: Real-world digital images are often affected by interference from the imaging device and the external environment during the digitization and transmission process, resulting in noisy images. Image noise reduction, sometimes also called image denoising, is the process of reducing noise in digital images.

[0059] Threshold segmentation: Threshold segmentation is a region-based image segmentation technique that divides image pixels into several categories. Image thresholding is a traditional and most commonly used image segmentation method. Due to its simple implementation, low computational complexity, and relatively stable performance, it has become the most basic and widely used segmentation technique in image segmentation. It is particularly suitable for images where the target and background occupy different grayscale ranges. It not only greatly compresses the data volume but also greatly simplifies the analysis and processing steps. Therefore, in many cases, it is a necessary image preprocessing step before image analysis, feature extraction, and pattern recognition. The purpose of image thresholding is to divide the pixel set according to grayscale. Each subset formed by the obtained grayscale forms a region corresponding to the real scene. Each region has consistent attributes, while adjacent regions do not have these consistent attributes. This division can be achieved by selecting one or more thresholds based on the grayscale.

[0060] Lattice Boltzmann method: Based on a microscopic model and the Boltzmann kinetic theory of gases, the Lattice Boltzmann method discretizes the Boltzmann equation to obtain the discrete Boltzmann equation as its theoretical foundation. This equation can be recovered to the macroscopic Navier-Stokes equation through asymptotic function theory.

[0061] Darcy flow regime: Flow regimes that conform to Darcy's law, generally laminar. Darcy's law is the primary experimental principle and fundamental law for studying groundwater movement. It is derived from numerous experiments on water seepage through uniform sand layers. The steady flow rate Q of groundwater per unit time through a sand layer is proportional to the hydraulic gradient J and the cross-sectional area W through which water flows, i.e., Q = KJW, where K is the proportionality factor, or permeability coefficient. The permeability velocity V = Q / W, thus V = KJ, indicating a linear relationship between permeability and hydraulic gradient.

[0062] Traditionally, cross-scale permeability calculations of cores are performed manually through experimental measurements. This requires not only manual simulation of the core environment for measurement but also specialized instruments, which is labor-intensive and increases the cost of core measurement. With the advancement of computer technology, researchers have begun to scan cores to construct core models and use computers to simulate the core model's environment to calculate the core's permeability. However, current calculations of core permeability are limited to the millimeter scale, which limits the scope of core permeability calculations.

[0063] Based on this, an embodiment of the present application provides a cross-scale permeability calculation method, device, equipment and medium for rock cores, which performs a low-resolution scan on the target sample to obtain a grayscale image, and then processes the grayscale image to obtain a multi-segmentation image. Then, a high-resolution scan is performed on the target area of ​​the target sample to obtain a structural data body, and the structural data body is image processed to obtain a pore three-dimensional model. The permeability of the pore three-dimensional model is calculated to obtain an initial permeability, and the initial permeability is a high-resolution rock property. The permeability is then calculated based on the initial permeability, the multi-segmentation image and the pore three-dimensional model to consider the rock properties obtained by high-resolution modeling and the rock properties of the large-scale results obtained by low-resolution imaging, so as to obtain the target permeability through scale upgrading calculation, so as to upgrade the permeability calculation from the millimeter scale to the centimeter scale.

[0064] The cross-scale permeability calculation method, device, equipment and medium of the core provided in the embodiments of the present application are specifically illustrated by the following examples. First, the permeability calculation method in the embodiments of the present application is described.

[0065] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0066] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0067] The permeability calculation method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The permeability calculation method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the permeability calculation method, etc., but is not limited to the above forms.

[0068] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0069] Figure 1 This is an optional flow chart of the permeability calculation method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0070] Step S101, performing a low-resolution scan on a target sample to obtain a grayscale image;

[0071] Step S102, performing image processing on the grayscale image to obtain a multi-element segmentation image;

[0072] Step S103, performing high-resolution scanning on a target region of the target sample to obtain a structural data volume; wherein the target region is a region in the target sample where the matrix components meet preset component specifications;

[0073] Step S104, performing image processing on the structural data volume to obtain a three-dimensional pore model;

[0074] Step S105, performing permeability calculation on the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability;

[0075] Step S106 , performing flow field simulation on the multi-element segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to obtain a target permeability.

[0076] In steps S101 to S106, as shown in the embodiment of the present application, a low-resolution scan of the target sample is performed to obtain a grayscale image, a high-resolution scan of the target area of ​​the target sample is performed to obtain a structural data volume, the grayscale image is processed to obtain a multi-element segmentation image, and the structural data volume is processed to obtain a three-dimensional pore model. A preset permeability calculation algorithm is used to calculate the permeability of micropores based on the three-dimensional pore model to obtain an initial permeability. A flow field simulation is then performed on the multi-element segmentation image, the three-dimensional pore model, and the initial permeability based on a preset permeability analysis model to perform a permeability scale-up calculation to obtain a target permeability. This upgrades the permeability calculation from the millimeter scale to the centimeter scale, and allows for in-depth analysis of the relationship between the microstructure and macroscopic permeability of different media, thereby improving the scale of core permeability calculations.

[0077] In step S101 of some embodiments, the target sample is a plunger sample, and a low-resolution scan is performed on the entire core of the plunger sample to obtain a grayscale image. The entire core is scanned at a first predetermined resolution, which is between 20 and 35 μm, and the diameter of the entire core is 2.54 cm. Therefore, even when scanning large-scale cores, a clear grayscale image can be obtained using low-resolution scanning.

[0078] In step S102 of some embodiments, after obtaining the grayscale image, image processing is performed on the grayscale image to segment the grayscale image into multi-segmented images, so as to calculate the permeability of the core according to the multi-segmented images.

[0079] In some embodiments, step S103 determines the target region of the target sample based on the grayscale image quality. Specifically, the region of the plunger sample where the matrix composition meets a preset composition specification is selected, and the preset composition rule indicates a region with a high matrix composition. Specifically, the region with a high matrix composition is scanned at high resolution. The target region is preferably no larger than 4 mm × 4 mm × 4 mm, and the resolution of the high-resolution scan is between 1 and 2 μm. Therefore, a high-resolution scan of the target region is used to obtain a clear structural data volume.

[0080] It should be noted that when determining the target area of ​​the target sample, the selected target area should exclude the holes / cracks inside the fracture-cavity rock and the large gravels in the sandstone.

[0081] In step S104 of some embodiments, image processing is performed on the structural data volume to reconstruct a three-dimensional pore model based on the structural data volume.

[0082] In step S105 of some embodiments, a preset permeability calculation algorithm is used to simulate and calculate the permeability of the micropores in the matrix using a three-dimensional pore model. The permeabilities of the micropores of multiple target samples are then averaged to obtain an initial permeability, thereby making the permeability calculation more accurate. The initial permeability obtained is the permeability in the target area of ​​the target sample, i.e., the permeability of the micropores, and is a millimeter-level permeability calculation.

[0083] In some embodiments, in step S106, after obtaining the permeability of the micropores, it is necessary to calculate the permeability of the entire core pores, that is, to upgrade the permeability calculation from the millimeter level to the centimeter level. By performing permeability upscaling based on the preset permeability analysis model, the 3D pore model, the multi-element segmentation image, and the initial permeability, the flow field simulation is performed within the micropores and pores / fractures of the target sample core to obtain the target permeability, thereby improving the cross-scale permeability calculation scale of the core.

[0084] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S202:

[0085] Step S201, performing smoothing and noise reduction processing on the grayscale image to obtain a smoothed image;

[0086] Step S202: performing threshold segmentation processing on the smoothed image to obtain a multi-element segmentation image.

[0087] In step S201 of some embodiments, because the grayscale image obtained by directly scanning the entire core may contain noise signals, the grayscale image is subjected to noise reduction processing, and smoothing noise reduction processing is used to remove the noise signals in the grayscale image to obtain a smoothed image. Therefore, subsequent processing by smoothing the image can reduce the problem of inaccurate image processing caused by noise signals.

[0088] In step S202 of some embodiments, after obtaining a smoothed image, the smoothed image is subjected to threshold segmentation processing to obtain a multi-segmented image. If the target sample is a fractured rock, the resulting multi-segmented image is a binary segmented image, with one portion representing fractures and pores and another portion representing impermeable coarse particles. If the target sample is a conglomerate, the resulting multi-segmented image is a ternary segmented image, with the first portion representing impermeable coarse particles, the second portion representing small particle aggregates including fine sand, silt, and clay, and the third portion representing macropores.

[0089] Please refer to Figure 3 In some embodiments, step 104 may include but is not limited to steps S301 to S303:

[0090] Step S301, performing smoothing and noise reduction processing on the structure data volume to obtain target data;

[0091] Step S302, performing threshold segmentation processing on the target data to obtain structure data;

[0092] Step S303: reconstruct the model according to the structural data to obtain a three-dimensional pore model.

[0093] In some embodiments, step S301 involves performing a high-resolution scan of a core region with a high matrix content to obtain a structural data volume. Because the structural data volume obtained by directly scanning the core region contains noise, a smoothing and noise reduction process is performed on the structural data volume to remove the noise and obtain the target data. Therefore, model construction based on the target data is more accurate.

[0094] In step S302 of some embodiments, after obtaining the target data, the target data is threshold segmented to obtain structural data by segmenting the pores and pore skeletons, so as to more accurately construct a pore three-dimensional model based on the result data.

[0095] In some embodiments, in step S303, after obtaining the structural data, a model is reconstructed based on the structural data to obtain a three-dimensional pore model, and the three-dimensional pore model corresponds to the three-dimensional pore structure. Therefore, by scanning the core area with a high matrix component to obtain a structural data volume, the structural data volume is image processed and then reconstructed to obtain the three-dimensional pore model. Therefore, the three-dimensional pore model is easily constructed.

[0096] In some embodiments, the preset permeability calculation algorithm is a preset lattice Boltzmann algorithm, and step S105 may include but is not limited to: performing permeability calculation on the pore three-dimensional model according to the preset lattice Boltzmann algorithm to obtain an initial permeability.

[0097] It should be noted that the lattice Boltzmann algorithm is a fluid dynamics calculation method based on the kinetic theory of gases. Through the Chapman-Enskog first-order expansion, the discrete velocity Boltzmann equation can be restored to the incompressible Nevill-Stokes equation in moment space. Its systematic error is the cube of the Mach number. Therefore, the lattice Boltzmann algorithm is suitable for simulating low-speed incompressible flows. Therefore, the lattice Boltzmann algorithm is used to simulate the three-dimensional pore model to obtain the permeability of the micropores in the matrix and the initial permeability.

[0098] See also Figure 4 In some embodiments, the permeability of the three-dimensional pore model is calculated according to a preset lattice Boltzmann algorithm to obtain the initial permeability, which may include but is not limited to steps S401 to S405:

[0099] Step S401, initializing the flow field of the three-dimensional pore model according to preset flow field parameters to obtain an equilibrium distribution function;

[0100] Step S402, performing collision processing on the equilibrium distribution function according to the single relaxation model to obtain a collision function;

[0101] Step S403, performing migration calculation or rebound calculation on the collision function according to the pore points of the pore three-dimensional model to obtain the target function;

[0102] Step S404, assigning preset boundary conditions to the objective function;

[0103] Step S405 , performing permeability calculation on the objective function according to the boundary conditions to obtain the initial permeability.

[0104] In step S401 of some embodiments, the equilibrium distribution function is obtained by initializing the flow field of the three-dimensional pore model. Specifically, the equilibrium distribution function is determined based on the preset flow field parameters, which include density and velocity, and the three-dimensional pore model. For example, if the preset density ρ = 1 and the velocity U = (0, 0, 0), and the distribution function fi is initialized to the equilibrium distribution function, that is, as shown in formula (1):

[0105] f i =g i ≡ρω i [1+3U·c i +9(U·c i ) 2 / 2-3c i 2 / twenty one)

[0106] Where, ω 1-6 =1 / 9,ω 7-14 =1 / 72,ω 15 =2 / 9, c i is the discrete speed. The discrete speed values ​​are shown in Table 1:

[0107] Table 1

[0108] x y z <![CDATA[c1]]> 1 0 0 <![CDATA[c2]]> -1 0 0 <![CDATA[c3]]> 0 1 0 <![CDATA[c4]]> 0 -1 0 <![CDATA[c5]]> 0 0 1 <![CDATA[c6]]> 0 0 -1 <![CDATA[c7]]> 1 1 1 <![CDATA[c8]]> -1 -1 -1 <![CDATA[c9]]> 1 1 -1 <![CDATA[c 10 ]]> -1 -1 1 <![CDATA[c 11 ]]> 1 -1 1 <![CDATA[c 12 ]]> -1 1 -1 <![CDATA[c 13 ]]> 1 -1 -1 <![CDATA[c 14 ]]> -1 1 1 <![CDATA[c 15 ]]> 0 0 0

[0109] Therefore, the equilibrium distribution function is determined by inputting the preset flow field parameters into formula (1) and taking the discrete velocity values ​​according to Table 1.

[0110] In step S402 of some embodiments, after obtaining the equilibrium distribution function, the velocity in the equilibrium distribution function needs to be corrected to U * =∑ i c if i +ρaΔt / 2, where a is the external acceleration. Then the equilibrium distribution function is subjected to collision processing to obtain the collision function, which is shown in formula (2):

[0111] f i * =f i -(f i -g i ) / τ+3ρa·c i Δt / 2 (2)

[0112] Wherein, τ is the relaxation factor, and the relationship between the relaxation factor τ and the fluid viscosity is μ = ρ(τ-1 / 2) / 3.

[0113] In step S403 of some embodiments, after obtaining the collision function, it is necessary to perform corresponding operations on the collision function according to the pore points of the pore three-dimensional model to obtain the target function. If the downstream grid point (x, y, z) + ciΔt of the current pore point (x, y, z) along the i direction is a pore point, the collision function is migrated, where the migration process is shown in formula (3):

[0114] f i ((x,y,z)+c i Δt)=f i * (x, y, z) (3)

[0115] If the downstream grid point (x, y, z) + c iΔt of the current pore point (x, y, z) along the i direction is not a pore point, then the collision function is subjected to rebound processing, as shown in formula (4):

[0116] f ι (x, y, z) = f i * (x, y, z) (4)

[0117] Here, ι is the opposite direction of i.

[0118] In some embodiments, in step S404, after determining the objective function, boundary conditions need to be assigned to the objective function. Permeability calculations can then be performed based on these boundary conditions to obtain an accurate initial permeability. Specifically, the assigned boundary conditions are periodic, meaning that the unknown distribution function of the incoming flow field on one side is assigned a collision function of the outgoing flow field on the buffer layer on the other side, after collision. Therefore, assigning boundary conditions allows for more accurate permeability calculations based on the objective function.

[0119] In step S405 of some embodiments, the permeability of the objective function is calculated according to the boundary conditions as follows:

[0120] Set the grid resolution to D, take the z direction as the main flow direction, the number of grids in the z direction to L, and the cross-sectional area in the xy direction to A grid square. First, calculate the average flow Q = D 2 ∑ 孔隙点 [∑ i c i (z)f i +ρa(z)Δt / 2] / (ρL), and then calculate the pressure difference ΔP=Lρa(z)Δt, and finally calculate the permeability as shown in formula (5):

[0121] k=QμL / ΔPA=(τ-1 / 2)D 2 ∑ 孔隙点 [∑ i c i (z)f i +ρa(z)Δt / 2] / (3ALa(z)Δt)(5)

[0122] Therefore, the initial permeability is calculated by formula (5), and the initial permeability is the permeability of micropores, so the permeability at the millimeter scale can be obtained.

[0123] It should be noted that when calculating the initial permeability, in order to improve the accuracy of the initial permeability, it is necessary to calculate the relative error of the permeability. The relative error of the permeability is ε=|k-k0| / k0. If ε meets certain conditions, the calculation is stopped to determine the initial permeability k=k / (9.869233×10 -16 ), otherwise, set k0=k and execute step S402 again. Therefore, the initial permeability is determined by calculating the relative error of the permeability to meet certain conditions, so that the initial permeability is calculated accurately.

[0124] In some embodiments, step S106 may include, but is not limited to, performing flow field simulation on the segmented image, the three-dimensional pore model, and the initial permeability according to a preset steady-state DSB model to obtain a target permeability.

[0125] It should be noted that since the initial permeability is based on the permeability of millimeter-scale micropores, it is not possible to calculate the centimeter-scale permeability of the core. Therefore, a pre-set steady-state DSB model is used to perform flow field simulation on the segmented image, 3D pore model, and initial permeability to achieve centimeter-scale permeability calculation, that is, the permeability of the entire core. The steady-state DSB model is the Darcy-Stokes-Brinkman model. The steady-state DSB model is used to perform permeability upscaling, simulating the flow field within the micropores and pore / fracture structures of the plug-like digital core. Because the flow field simulation is performed on the segmented image and 3D pore model, the calculation process includes rock properties derived from high-resolution imaging and modeling, as well as rock properties from large-scale structures derived from low-resolution imaging. Flow field simulation is also known as multiscale modeling, and is a two-step process: Darcy flow in micropores within the matrix and Stokes flow in larger pores, pores, and fractures. For target samples with strong heterogeneity, the permeability obtained from the target samples is very different from the permeability results obtained by the laboratory pressure drop method. However, the target permeability calculated by scale upscaling is highly consistent with the experimental results. Therefore, the calculated target permeability has higher accuracy.

[0126] See also Figure 5 In some embodiments, a flow field simulation is performed on the multivariate segmentation image, the three-dimensional pore model, and the initial permeability according to a preset steady-state DSB model to obtain a target permeability, including but not limited to steps S501 to S503:

[0127] Step S501, extracting the first rock attribute of the multi-element segmentation image;

[0128] Step S502, extracting a second rock attribute of the three-dimensional pore model;

[0129] Step S503: Perform flow field simulation based on the preset steady-state DSB model using the first rock property, the second rock property, and the initial permeability to obtain the target permeability. The steady-state DSB model is represented by formula (6):

[0130]

[0131]

[0132] where ε is the local porosity of each target area, p is the pressure, and K is the initial permeability of the target area.

[0133] In step S501 of some embodiments, by extracting the first rock attribute of the multi-element segmented image, that is, the structure of pores, holes, and cracks, the Stokes flow in the cracks and holes can be restored according to the steady-state DSB model.

[0134] In step S502 of some embodiments, by extracting the second rock property of the three-dimensional pore model, the second rock property includes a microporous matrix, and thus Darcy flow of the microporous matrix is ​​simulated according to a steady-state DSB model to calculate the target permeability.

[0135] In step S503 of some embodiments, the steady-state DSB model is shown in formula (5), where ε is the initial porosity of the target area, p is the pressure, and K is the local permeability. In order to simulate the multi-scale fluid flow in the low-resolution plug-like image, the boundary values ​​of initial permeability ε = 1 and ε = 0 ≡ 0.01 are introduced based on the steady-state DSB model, representing the distinguished macropores and impermeable particle regions, and 0 < ε < 1 represents the unidentified and resolved microporous regions below the image resolution. According to these physical definitions, the steady-state DSB model can use a single momentum equation to simulate Darcy flow in the microporous matrix and restore Stokes flow in cracks and pores. The initial permeability can restore the almost zero velocity inside the particles and the no-slip velocity condition on the particle surface. Therefore, the use of a single field equation can ensure that the velocity and stress are continuous throughout the entire computational domain. The numerical solver of the steady-state DSB model is based on the open source CFD software OpenFOAM and the finite volume virtual machine (FVM), and the pressure-velocity coupling is handled by the prediction-correction algorithm. The pressure and velocity equations are then discretized to produce a set of algebraic equations. The algebraic equations are solved using the generalized geometric-algebraic multigrid (GAMG) method, and pressure Dirichlet boundary conditions are applied at the inlet and outlet during the simulation according to the steady-state DSB model. When the steady-state DSB model is used to solve the velocity distribution in the computational domain, the permeability can be given by It is deduced that the driving force becomes the pressure difference between the inlet and outlet boundaries. Therefore, by calculating the permeability of the entire core according to formula (5) to obtain the target permeability, the permeability calculation is upgraded from the millimeter scale to the centimeter scale, which enables in-depth analysis of the relationship between the microstructure and macroscopic permeability of different media, thereby improving the scale range of permeability calculation.

[0136] See also Figure 6 The present application also provides a device for calculating cross-scale permeability of a core, which can implement the above-mentioned cross-scale permeability calculation method of the core. The device includes:

[0137] The first scanning module 601 is used to scan the target sample at a low resolution to obtain a grayscale image;

[0138] The first image processing module 602 is used to perform image processing on the grayscale image to obtain a multi-element segmentation image;

[0139] The second scanning module 603 is used to perform high-resolution scanning on a target area of ​​the target sample to obtain a structural data volume; wherein the target area is an area in the target sample where the matrix components meet the preset component specifications;

[0140] The second image processing module 604 is used to perform image processing on the structure data volume to obtain a pore three-dimensional model;

[0141] The calculation module 605 is used to calculate the permeability of the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability;

[0142] The simulation module 606 is used to perform flow field simulation on the multi-element segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to obtain a target permeability.

[0143] The specific implementation of the cross-scale permeability calculation device for a rock core is substantially the same as the specific embodiment of the cross-scale permeability calculation method for a rock core described above, and will not be described in detail here.

[0144] The present application also provides a computer device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, the aforementioned electronic device method is implemented. The computer device may be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0145] See also Figure 7 , Figure 7 The hardware structure of a computer device according to another embodiment is shown. The computer device includes:

[0146] The processor 701 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0147] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 701 to execute the cross-scale permeability calculation method for the core of the embodiment of this application;

[0148] Input / output interface 703, used to implement information input and output;

[0149] Communication interface 704, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0150] Bus 705 , which transmits information between various components of the device (e.g., processor 701 , memory 702 , input / output interface 703 , and communication interface 704 );

[0151] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0152] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned cross-scale permeability calculation method of the core.

[0153] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0154] The cross-scale permeability calculation method, cross-scale permeability calculation device, computer equipment and storage medium of the rock core provided in the embodiments of the present application obtain a grayscale image by performing a low-resolution scan on the entire rock core, and then smoothing, denoising and threshold segmenting the grayscale image to obtain a multi-segmented image. Then, a high-resolution scan is performed on the portion of the rock core whose matrix composition meets the preset requirements to obtain a structural data body, and the structural data body is smoothed, denoised and threshold segmented to obtain a pore three-dimensional model. By performing permeability calculation on the pore three-dimensional model according to a preset permeability calculation algorithm, the initial permeability of the micropores is obtained, and the initial permeability is a millimeter-scale permeability. Then, a flow field simulation is performed on the multi-segmented image, the pore three-dimensional model and the initial permeability according to the preset permeability analysis model to carry out permeability scale upgrade, and the permeability is increased from the millimeter scale to the centimeter scale, thereby increasing the scale range of the permeability calculation.

[0155] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0156] It will be understood by those skilled in the art that Figure 1-5 The technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or a combination of certain steps, or different steps.

[0157] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0158] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0159] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0160] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0162] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0165] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A cross-scale permeability calculation method for cores, characterized in that: The method comprises: Scan the target sample at low resolution to obtain a grayscale image; performing image processing on the grayscale image to obtain a multi-element segmentation image; Performing a high-resolution scan on a target region of the target sample to obtain a structural data volume; wherein the target region is a region in the target sample where the matrix component meets a preset component specification; performing image processing on the structural data volume to obtain a three-dimensional pore model; Performing permeability calculation on the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability; Performing flow field simulation on the multivariate segmentation image, the three-dimensional pore model, and the initial permeability according to a preset permeability analysis model to obtain a target permeability; The performing flow field simulation on the multivariate segmentation image, the three-dimensional pore model, and the initial permeability according to a preset permeability analysis model to obtain a target permeability includes: A flow field simulation is performed on the segmented image, the three-dimensional pore model and the initial permeability according to a preset steady-state DSB model to obtain the target permeability, wherein the steady-state DSB model is a Darcy-Stokes-Brinkman model.

2. The method according to claim 1, characterized in that The performing image processing on the grayscale image to obtain a multi-element segmentation image includes: Performing smoothing and noise reduction processing on the grayscale image to obtain a smoothed image; Performing threshold segmentation processing on the smoothed image to obtain the multi-element segmentation image.

3. The method according to claim 1, characterized in that The performing image processing on the structural data volume to obtain a three-dimensional pore model includes: Performing smoothing and noise reduction processing on the structural data body to obtain target data; Performing threshold segmentation processing on the target data to obtain structural data; The model is reconstructed according to the structural data to obtain the three-dimensional pore model.

4. The method according to any one of claims 1 to 3, characterized in that The performing permeability calculation on the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability includes: The permeability of the three-dimensional pore model is calculated according to a preset lattice Boltzmann algorithm to obtain the initial permeability.

5. The method according to claim 4, characterized in that The performing permeability calculation on the three-dimensional pore model according to a preset lattice Boltzmann algorithm to obtain the initial permeability includes: Initializing the flow field of the three-dimensional pore model according to preset flow field parameters to obtain an equilibrium distribution function; Performing collision processing on the equilibrium distribution function according to a single relaxation model to obtain a collision function; Performing migration calculation or rebound calculation on the collision function according to the pore points of the three-dimensional pore model to obtain an objective function; Assigning preset boundary conditions to the objective function; The permeability of the objective function is calculated according to the boundary conditions to obtain the initial permeability.

6. The method according to claim 1, characterized in that The performing flow field simulation on the multivariate segmentation image, the three-dimensional pore model, and the initial permeability according to the preset steady-state DSB model to obtain the target permeability includes: extracting a first rock attribute of the multivariate segmented image; extracting a second rock property of the three-dimensional pore model; The target permeability is obtained by performing flow field simulation based on the preset steady-state DSB model using the first rock property, the second rock property, and the initial permeability; wherein the steady-state DSB model is: Where, is the local porosity of each target area, p is the pressure, and K is the initial permeability of the target area.

7. A cross-scale permeability calculation device for a core, characterized in that: The device applies the cross-scale permeability calculation method of a core according to any one of claims 1 to 6, and the device comprises: A first scanning module is used to scan the target sample at a low resolution to obtain a grayscale image; A first image processing module, configured to perform image processing on the grayscale image to obtain a multi-element segmentation image; A second scanning module is configured to perform high-resolution scanning on a target region of the target sample to obtain a structural data volume; wherein the target region is a region in the target sample where the matrix component meets a preset component specification; a second image processing module, configured to perform image processing on the structural data volume to obtain a three-dimensional pore model; A calculation module, configured to calculate the permeability of the three-dimensional pore model according to a preset permeability calculation algorithm to obtain an initial permeability; The simulation module is used to perform flow field simulation on the multivariate segmentation image, the three-dimensional pore model and the initial permeability according to a preset permeability analysis model to obtain a target permeability.

8. A computer device, characterized in that: The computer device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A storage medium, which is a computer-readable storage medium and is used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of claims 1 to 6.

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