A finite element modeling method and system for complex porous rock mass based on digital core

Abstract

The present application discloses a method and system for finite element modeling of complex porous rock masses based on digital cores, belonging to the field of computer technology. The method comprises: performing a CT scan on a complex porous rock mass sample, processing the CT image to obtain a binary image; establishing an original three-dimensional digital core model of the rock microstructure based on the binary image; determining first voxel data based on CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock mass sample; resampling the first voxel data based on the nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, obtaining second voxel data, and then generating a first complex porous rock mass finite element model; adjusting the scaling factor based on the accuracy difference between the three-dimensional digital core model and the first finite element model, ultimately obtaining a target complex porous rock mass finite element model. The present application reduces the modeling computational complexity through resampling, and improves modeling efficiency and accuracy by adjusting the scaling factor.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a finite element modeling method and system for complex porous rock masses based on digital rock cores. Background Art

[0002] With the advancement of computer technology, numerical methods have become an effective option and are widely used to study the mechanical behavior and failure processes of rocks. They help reveal and explain the laws governing crack propagation and the evolution of microscopic pore structures. To better reflect the heterogeneity of rock materials, numerical models have been constructed to incorporate heterogeneity, assuming that the mechanical properties of rock materials follow statistical distributions. However, these models simplify the complex spatial structure of rock and introduce random parameters that are uncertain, subjective, and highly dependent on statistical distributions. This means that these numerical models reduce the accuracy of simulation results, making it difficult to accurately replicate rock physics experimental results. Experimental results may not reflect the phenomena and experimental effects that researchers anticipated when designing their experiments. Therefore, the accuracy and adaptability of numerical simulations depend heavily on the microscopic models. Simulation results are only of theoretical and practical value when the spatial distribution of microscopic pore structures and other structures in the model reflects the actual structural characteristics of the rock sample, and when the corresponding mechanical parameters are based on sufficient scientific evidence, rather than subjective assumptions. The highly heterogeneous mesostructures of many rock masses limit the characterization and theoretical modeling of rock mesostructures. Therefore, establishing a three-dimensional accurate model that can truly reflect the characteristics of micropore development will play a vital role in numerical simulation.

[0003] Related technologies use traditional finite element methods and discrete element methods to reconstruct mesoscopic pore structures in three dimensions. However, complex-porous rock masses have numerous pores and even more complex mesoscopic pore structures, with pore diameters primarily concentrated at the micrometer or even nanometer scale. This results in digital core models often containing tens or even hundreds of millions of pixels. Whether using traditional finite element or discrete element methods, due to limitations in computer computing power, the numerical models reconstructed using digital core technology in related technologies are far smaller than the experimental scale and are therefore only suitable for studying the mechanical behavior of specimens at the microscale. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a finite element modeling method and system for complex porous rock masses based on digital cores, aiming to reduce the computational complexity of complex porous rock mass modeling and improve modeling efficiency and accuracy.

[0005] To achieve the above objectives, one aspect of an embodiment of the present application provides a finite element modeling method for complex porous rock mass based on digital rock cores, the method comprising:

[0006] Performing CT scanning on a complex porous rock mass sample to obtain CT scanning data; wherein the CT scanning data includes a CT image and CT scanning parameters;

[0007] performing image processing on the CT image to obtain a binary image;

[0008] Establishing an original three-dimensional digital core model of rock microstructure based on the binarized image;

[0009] determining first voxel data according to the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock mass sample;

[0010] resampling the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels to obtain second voxel data;

[0011] generating a first complex porous rock mass finite element model according to the second voxel data;

[0012] The scaling factor is adjusted according to the accuracy difference between the three-dimensional digital core model and the first finite element model, and the step of resampling the first voxel data to reduce the number of voxels based on the nearest neighbor interpolation method and the scaling factor to obtain the second voxel data is returned to execution until the accuracy difference between the three-dimensional digital core model and the first finite element model meets the preset conditions, thereby obtaining the target complex porous rock mass finite element model.

[0013] In some embodiments, performing image processing on the CT image to obtain a binary image includes the following steps:

[0014] Optimizing the grayscale values ​​of the CT images, adjusting the grayscale value range of each CT image to the grayscale value range of the transition zone, and obtaining a first image;

[0015] Using a median filtering method to filter out ring artifacts and impulse noise in the first image to obtain a second image;

[0016] The second image is binarized using an adaptive threshold segmentation algorithm to obtain a binarized image.

[0017] In some embodiments, establishing an original three-dimensional digital core model of the rock microstructure based on the binarized image comprises the following steps:

[0018] Based on the binarized image, an original three-dimensional digital core model of the rock microstructure is constructed using a volume rendering model.

[0019] In some embodiments, determining the first voxel data according to the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock sample comprises the following steps:

[0020] determining the number of voxel points according to CT scanning parameters and the size of the CT image;

[0021] First voxel information is determined according to the number of voxel points and the actual size of the complex porous rock sample; wherein the first voxel information includes voxel value, number of voxel points and voxel position.

[0022] In some embodiments, resampling the first voxel data to reduce the number of voxels based on the nearest neighbor interpolation method and the scaling factor to obtain the second voxel data includes the following steps:

[0023] According to the scaling factor, the voxel value in the binary image is directly mapped to the nearest neighbor voxel position in the resampled image to obtain the second voxel data; wherein the expression of the voxel mapping is:

[0024]

[0025] Where T is the target size; D is the original size; and f is the scaling factor.

[0026] In some embodiments, generating a first complex porous rock mass finite element model based on the second voxel data comprises the following steps:

[0027] converting the second voxel data into grid unit numbers and node information by a grid mapping method;

[0028] An initial complex porous rock mass finite element model is generated according to the grid unit numbers and the node information.

[0029] In some embodiments, the step of determining the accuracy difference between the three-dimensional digital core model and the first finite element model comprises the following steps:

[0030] The accuracy difference between the three-dimensional digital core model and the first finite element model is calculated using a statistical method and a structural similarity index algorithm.

[0031] To achieve the above objectives, another aspect of the present application provides a finite element modeling system for complex porous rock mass based on digital cores, the system comprising:

[0032] The first module is used to perform CT scanning on a complex porous rock sample to obtain CT scanning data; wherein the CT scanning data includes a CT image and CT scanning parameters;

[0033] The second module is used to perform image processing on the CT image to obtain a binary image;

[0034] The third module is used to establish an original three-dimensional digital core model of the rock microstructure based on the binary image;

[0035] A fourth module is configured to determine first voxel data based on the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock sample;

[0036] a fifth module, configured to resample the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, thereby obtaining second voxel data;

[0037] A sixth module is used to generate a first complex porous rock mass finite element model based on the second voxel data;

[0038] The seventh module is configured to adjust the scaling factor based on the accuracy difference between the three-dimensional digital core model and the first finite element model, and return to the step of resampling the first voxel data to reduce the number of voxels based on the nearest neighbor interpolation method and the scaling factor to obtain the second voxel data, until the accuracy difference between the three-dimensional digital core model and the first finite element model meets a preset condition, thereby obtaining the target complex porous rock mass finite element model. To achieve the above objectives, another aspect of an embodiment of the present application provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the aforementioned method when executing the computer program.

[0039] To achieve the above objectives, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0040] The embodiments of the present application include at least the following beneficial effects: This application provides a method and system for finite element modeling of complex porous rock masses based on digital cores. This solution reduces the number of voxels and computational complexity by resampling voxel data, thereby improving modeling efficiency. Furthermore, the scaling factor is adjusted based on the accuracy difference between the digital core model and the original model at different scaling factors, balancing computational efficiency and model accuracy. This improves modeling efficiency while ensuring model accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0042] Figure 1 This is a step diagram of a finite element modeling method for complex porous rock mass based on digital cores provided in an embodiment of the present application;

[0043] Figure 2 A flowchart of a finite element modeling method for complex porous rock mass based on digital rock cores provided in an embodiment of the present application;

[0044] Figure 3 This is a schematic diagram of the original CT image of reef limestone provided in the embodiment of the present application;

[0045] Figure 4 is a grayscale histogram of the original CT image of the reef limestone provided in the embodiment of the present application;

[0046] Figure 5 This is a schematic diagram of the grayscale value optimization results provided by the embodiment of the present application;

[0047] Figure 6 This is a schematic diagram of the results of median filtering provided in an embodiment of the present application;

[0048] Figure 7 is a schematic diagram of the result of adaptive threshold segmentation provided by an embodiment of the present application;

[0049] Figure 8 The three-dimensional digital core model under different scaling factors provided in the embodiment of the present application is provided;

[0050] Figure 9 This is a statistical diagram of the equivalent pore radius distribution provided in the embodiments of the present application;

[0051] Figure 10 is a line graph of the structural similarity index of the skeleton provided in the examples of the present application;

[0052] Figure 11 Schematic diagram of a reef limestone sample, a three-dimensional digital core model, and a target complex porous rock mass finite element model provided in an embodiment of the present application;

[0053] Figure 12 This is a module structure diagram of a complex porous rock mass finite element modeling system based on digital cores provided in an embodiment of the present application;

[0054] Figure 13 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0056] Although the system diagrams illustrate functional modules and the flowcharts illustrate a logical sequence, in some cases, the steps shown or described may be performed in a different order than the module divisions in the system or the order in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar items and are not necessarily used to describe a specific order or sequence.

[0057] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0058] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0059] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] 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.

[0061] Related technologies employ the finite element method (FEM) and discrete element method (DEM) to reconstruct microscopic pore structures in three dimensions. However, complex-porous rock masses have numerous pores and even more complex microscopic pore structures, with pore diameters primarily concentrated at the micrometer or even nanometer scale. This results in digital core models often containing tens or even hundreds of millions of pixels. Whether using traditional FEM or DEM modeling, numerical models reconstructed using digital core technology are significantly smaller than the experimental scale due to computing power limitations, making them suitable only for studying the mechanical behavior of specimens at the microscale.

[0062] In light of this, this application proposes a finite element modeling method and system for complex porous rock masses based on digital cores. This method utilizes resampling technology to reduce the number of voxels by sacrificing some pore-skeleton structural details, thereby reducing computational effort and improving modeling efficiency. Furthermore, the application proposes using statistical methods and the SSIM algorithm (Structural Similarity Index) to quantify the accuracy differences between the digital core model and the original model at different scaling factors, balancing computational efficiency and model accuracy, thereby improving modeling efficiency while ensuring model accuracy.

[0063] The embodiment of the present application provides a finite element modeling method for complex porous rock mass based on digital cores, which relates to the field of computer technology. The finite element modeling method for complex porous rock mass based on digital cores provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; 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 server can also be a node server in a blockchain network; the software can be an application that implements a finite element modeling method for complex porous rock mass based on digital cores, etc., but is not limited to the above forms.

[0064] 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 electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. 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, and the like 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.

[0065] Reference Figure 1 and Figure 2 , Figure 1 This is an optional step diagram of a finite element modeling method for complex porous rock mass based on digital cores provided in an embodiment of the present application. Figure 2 This is an optional flowchart of the modeling method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S100 to S700.

[0066] Step S100 , performing CT scanning on a complex porous rock mass sample to obtain CT scanning data; wherein the CT scanning data includes a CT image and CT scanning parameters.

[0067] Step S200: performing image processing on the CT image to obtain a binary image.

[0068] Step S300: establishing an original three-dimensional digital core model of the rock microstructure based on the binarized image.

[0069] Step S400: determining a first voxel number according to the CT scanning parameters, the size of the CT image and the actual size of the complex porous rock mass sample.

[0070] Step S500 : resampling the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, thereby obtaining second voxel data.

[0071] Step S600: generating a first complex porous rock mass finite element model according to the second voxel data.

[0072] In step S700, the scaling factor is adjusted according to the accuracy difference between the three-dimensional digital core model and the first finite element model, and the process returns to step S500 until the accuracy difference between the three-dimensional digital core model and the first finite element model meets a preset condition, thereby obtaining a target complex porous rock mass finite element model.

[0073] In steps S100 to S700, as shown in the embodiment of this application, voxel data is resampled to reduce the number of voxels, thus reducing the computational effort and improving modeling efficiency. Furthermore, the scaling factor is adjusted based on the accuracy difference between the digital core model and the original model at different scaling factors, balancing computational efficiency and model accuracy. This improves modeling efficiency while ensuring model accuracy.

[0074] In step S100 of some embodiments, a CT scanning test is performed on a complex porous rock mass. Specifically, a core of the complex porous rock mass is cut into a standard sample, which is then scanned using a CT scanner. Z continuous cross-sectional images of the rock mass are obtained, with each slice having a pixel size of x × y. The grayscale value distribution of the image ranges from 0 to 255, where the grayscale histogram has two main grayscale levels, representing the rock's pores and matrix, respectively. Grayscale values ​​close to 0 are black, representing the rock's pores, and grayscale values ​​close to 255 are white, representing the rock's matrix.

[0075] In some embodiments, step S200 may include but is not limited to steps S210 to S230:

[0076] Step S210 , performing grayscale value optimization on the CT images, adjusting the grayscale value range of each CT image to the grayscale value range of the transition zone, and obtaining a first image.

[0077] Step S220 : Using a median filtering method to filter out ring artifacts and impulse noise in the first image to obtain a second image.

[0078] Step S230 : binarizing the second image using an adaptive threshold segmentation algorithm to obtain a binarized image.

[0079] In step S210 of some embodiments, image recognition technology can be used to employ an adaptive threshold segmentation algorithm to determine the grayscale threshold between the rock matrix and pore components. To further distinguish the rock matrix and pores within the transition region, grayscale value optimization is performed, adjusting the grayscale value range of each CT image from the original 0-255 to the grayscale value range of the transition region.

[0080] In step S220 of some embodiments, a median filtering method is used to remove ring artifacts and impulse noise in the image. The median filtering method considers the neighborhood N around each pixel.p Pixel value, sort the pixel values ​​in the neighborhood and select the median as the new pixel value.

[0081] In step S230 of some embodiments, an adaptive threshold segmentation method is used to deal with the image ring artifacts and uneven brightness problems and perform image binarization. The principle of this method is to use the pixel neighborhood N p The mean or median of the inner grayscale value is used as the threshold of the central pixel. If the grayscale value of the pixel is greater than the threshold, the pixel is white (rock matrix) in the output image, otherwise it is black (rock pores).

[0082] In some embodiments, step S300 may include, but is not limited to, step S310:

[0083] Step S310: constructing an original three-dimensional digital core model of the rock microstructure using a volume rendering model based on the binarized image.

[0084] In step S310 of some embodiments, after image enhancement, denoising, and threshold segmentation operations, the pore-skeleton structural characteristics of the reef limestone are retained to the greatest extent and a three-dimensional digital core model of the rock microstructure is established using a volume rendering model.

[0085] In some embodiments, step S400 may include but is not limited to steps S410 to S420:

[0086] Step S410 : determining the number of voxel points according to CT scanning parameters and the size of the CT image.

[0087] Step S420 : determining first voxel information according to the number of voxel points and the actual size of the complex porous rock sample; wherein the first voxel information includes voxel value, number of voxel points and voxel position.

[0088] In step S410 of some embodiments, according to CT scanning parameters, the size parameters of the digital image of the complex porous rock mass are x×y×z, that is, there are x×y×z individual pixels in total.

[0089] In step S420 of some embodiments, it is possible to obtain that the voxel is the side length of the rock sample in combination with the actual size of the rock sample. A cube with a voxel value v p The values ​​of voxel ⁻¹ and voxel ⁻¹ are 0 (pore) or 1 (skeleton), respectively, representing the internal region information. It should be noted that the above voxel values ​​can also be set to other values ​​to distinguish pores from skeletons, and this embodiment of the application does not limit this.

[0090] In some embodiments, step S500 may include but is not limited to the following steps S510:

[0091] Step S510 : According to the scaling factor, directly map the voxel value in the binary image to the nearest neighbor voxel position in the resampled image to obtain second voxel data.

[0092] The expression of voxel mapping is:

[0093]

[0094] Where T is the target size; D is the original size; and f is the scaling factor.

[0095] In step S510 of some embodiments, in order to achieve laboratory-scale digital rock core finite element modeling and simulation, voxel data is resampled based on the nearest neighbor interpolation method, and the number of voxels is reduced by sacrificing some pore-skeleton structure details. The basic principle of nearest neighbor interpolation is to directly map the voxel value in the original image to the nearest voxel position in the resampled image. If a voxel position in the resampled image is (x ′ ,y ′ ,z ′ ), the value at that position will come from the closest voxels, where f x ,f y ,f z is the scaling factor in three dimensions.

[0096] In some embodiments, step S600 may include but is not limited to steps S610 to S620:

[0097] Step S610 : converting the second voxel data into grid unit numbers and node information through a grid mapping method.

[0098] Step S620: generating an initial complex porous rock mass finite element model according to the grid unit number and the node information.

[0099] In steps S610 to S620 of some embodiments, the read voxel information is converted into grid unit numbers and node information through a grid mapping method, and a corresponding finite element model can be generated in finite element software based on the information.

[0100] In some embodiments, step S700 may include, but is not limited to, step S710:

[0101] Step S710: Calculate the accuracy difference between the three-dimensional digital core model and the first finite element model using a statistical method and a structural similarity index algorithm.

[0102] Specifically, in step S700, when resampling using nearest neighbor interpolation, a trade-off between computational efficiency and model accuracy is necessary. To further verify and improve model accuracy, statistical methods and the SSIM algorithm (Structural Similarity Index) were used to quantify the accuracy differences between the digital core model and the original model at different scaling factors. When SSIM > 0.65, the resampled model exhibited high consistency in visual features with the original model, indicating that the scaling process effectively preserved the shape and boundaries of the skeletal structure.

[0103] Based on the above trade-off analysis between computational efficiency and model accuracy, an appropriate scaling factor f is used to resample the voxel information, and then the finite element model is established accordingly. i Its position in the three-dimensional voxel array is (x vi ,y vi ,z vi ), considering that each vertex of the voxel (dx i ,dy i ,dz i ) can be obtained by adding 0 or 1 in three dimensions, then v i The relative positions of the vertices are as follows:

[0104]

[0105] Therefore, for v i Each vertex (dx i ,dy i ,dz i ), the corresponding actual physical space coordinate (x i ,y i ,z i )for:

[0106] x i =(x vi +d xi )×f x ′,

[0107] y i =(y vi +d yi )×f y ′,

[0108] z i =(z vi +d zi )×f z ′,

[0109] Where, f x ′、f y ′、f z ′ are respectively iThe scaling factor for transforming coordinates from voxel space to actual physical space. Further, remove v p = 0, the target complex porous rock mass finite element model of the porous medium microscopic three-dimensional structure can be obtained.

[0110] Below, the solution of the embodiment of the present application is described in detail and explained in conjunction with a specific complex porous rock mass modeling scenario:

[0111] In an embodiment of the present application, a finite element modeling method for complex porous rock masses based on digital cores is provided. The method can be applied to accurately model complex porous rock masses. The steps of the method may include CT scanning, image processing, construction of a digital core model, and three-dimensional reconstruction based on finite element software.

[0112] For example, take the finite element modeling of the complex pore structure of reef limestone as an example. The reef limestone sample was taken from the shallow area of ​​a certain island reef. It is white in color, rough on the surface, has pores, and is brittle. A cylindrical sample with a diameter of Ф=50mm and a height of h=25mm was drilled from the reef limestone rock. After the two ends were cut flat, a CT scanning test was performed. The CT resolution was 31.25um. After the sample was scanned layer by layer, 800 CT image slices were obtained, and the pixel size of each slice was 1600×1600. Figure 3 and Figure 4 As shown in Figure 1, the grayscale value distribution range of the CT image is 0 to 255, where there are two main grayscale levels in the grayscale histogram, representing the pores and skeleton matrix of the rock respectively. The part with a grayscale value close to 0 is black, representing the pores of the rock, and the part with a grayscale value close to 255 is white, representing the rock matrix.

[0113] Based on the characteristics of reef limestone CT images, Python software is used to batch process CT images. Figure 4 It can be seen that the reef limestone CT image has two peaks of pore and matrix grayscale, and the grayscale value between the two peaks represents the transition area between the rock matrix and the pores. In order to further distinguish the rock matrix and pores in the transition area, the grayscale value optimization is first performed, and the grayscale value range of each CT image is adjusted from the original 0 to 255 to the grayscale value range of the transition area. The results are shown in Figure 2. Figure 5 As shown. Then the median filter method is used to remove the ring artifacts and impulse noise in the image. The median filter method considers the Np pixel values ​​of the neighborhood around each pixel point, sorts the pixel values ​​in the neighborhood and selects the median as the new pixel value. The result is as shown in Figure 6As shown. Finally, the adaptive threshold segmentation method is used to deal with the problems of image ring artifacts and uneven brightness and perform image binarization. The principle of this method is to use the mean or median of the grayscale value within the pixel neighborhood Np as the threshold of the central pixel. If the grayscale value of the pixel is greater than the threshold, the pixel is white (rock matrix) in the output image, otherwise it is black (rock pores). The result is shown as follows: Figure 7 shown.

[0114] After image enhancement, denoising, and threshold segmentation, the pore-skeleton structural characteristics of the reef limestone are retained to the greatest extent possible, and a three-dimensional digital core model of the rock microstructure is established using volume rendering. According to CT scanning parameters, the digital image size of the reef limestone is 1600×1600×800, which means a total of 2.048×10 9 Individual voxel. Combined with the actual size of the reef limestone sample, the voxel has a side length of l v =31.25um cube, where the voxel value is v p The voxel information is converted into mesh unit numbers and node information through the mesh mapping method, and the corresponding finite element model is generated in the finite element software based on this.

[0115] In order to realize the finite element modeling and simulation of rock digital cores at laboratory scale, the voxel data is resampled based on the nearest neighbor interpolation method, and the number of voxels is reduced by sacrificing some pore-skeleton structure details. Figure 8 The following are three-dimensional digital core models of the pore-skeleton structure of reef limestone with a size of 2×2×2 cm under different scaling factors. The f in the figure represents the scaling factor of each model. Figure 8 It can be seen that when f = 0.20, part of the continuous skeleton is discretized into isolated solids, and when f = 0.15, the loss of pore-skeleton detail information is more obvious.

[0116] To further verify the accuracy of the model, statistical methods and SSIM algorithm (structural similarity index) were used to quantify the accuracy differences between the digital core model and the original model under different scaling factors. Figure 9 As shown, Figure 9 is the probability distribution of the equivalent pore radius of the model. When f ≥ 0.25, the pore radius distribution conforms to the lognormal distribution. However, when f ≤ 0.20, the model loses a lot of microscopic pore structure information, which makes it no longer conform to the lognormal distribution. Figure 10The figure shows the structural similarity index of the skeleton on the XY plane at Z = 1.00 cm of the model. When f ≥ 0.25, SSIM > 0.65. At this time, the resampled model has a high consistency with the original model in visual features, and the scaling process better preserves the shape and boundary of the skeleton structure.

[0117] Based on the above analysis, the resampled digital core model with f = 0.25 is used to establish the finite element model. i Its position in the three-dimensional voxel array is (x vi ,y vi ,z vi ), considering that each vertex of the voxel (dx i ,dy i ,dz i ) can be obtained by adding 0 or 1 in three dimensions, then v i The relative positions of the vertices are as follows:

[0118]

[0119] Therefore, for v i Each vertex (dx i ,dy i ,dz i ), the corresponding actual physical space coordinate (x i ,y i ,z i )for:

[0120] x i =(x vi +d xi )×f x ′,

[0121] y i =(y vi +d yi )×f y ′,

[0122] z i =(z vi +d zi )×f z ′,

[0123] Where, f x ′、f y ′、f z ′ are respectively i The scaling factor for converting coordinates from voxel space to actual physical space. In this embodiment, the actual size of the sample is a cylinder of Ø50mm×25mm, and the size parameters of the resampled reef limestone digital image are 400×400×200. The corresponding f x′=f y ′=f z ′=0.125mm -1 . Further, delete v p = 0, the target complex porous rock mass finite element model of reef limestone can be obtained. The final modeling result obtained by the above method can be shown as follows. Figure 11 shown.

[0124] In summary, the embodiments of the present application have at least the following beneficial effects:

[0125] 1. The embodiment of the present application proposes a method for establishing a refined three-dimensional model of the mesoscopic pore structure of a complex porous rock mass, in view of the characteristics of the complex porous rock mass with high porosity, complex pore structure, and pore diameters mainly concentrated in the micron scale.

[0126] 2. The embodiment of the present application proposes a resampling technology when using finite element software for modeling, which reduces the number of voxels by sacrificing some pore-skeleton structure details, reduces the amount of calculation, and improves modeling efficiency.

[0127] 3. When sacrificing some pore-skeleton structure details and reducing the number of voxels through resampling technology, it is proposed to use statistical methods and the SSIM algorithm (structural similarity index) to quantify the accuracy difference between the digital core model and the original model under different scaling factors, balance the relationship between computational efficiency and model accuracy, and ensure model accuracy while improving modeling efficiency.

[0128] 4. The method for establishing a refined three-dimensional model of the mesoscopic pore structure of a complex porous rock mass proposed in the embodiment of the present application can meet the requirements of refined three-dimensional modeling of the mesoscopic pore structure of a complex porous rock mass at multiple scales, and is not limited to the microscopic scale.

[0129] See also Figure 12 The present application also provides a digital core-based complex porous rock mass finite element modeling system, which can implement the above-mentioned digital core-based complex porous rock mass finite element modeling method. The system includes:

[0130] The first module 101 is used for performing CT scanning on a complex porous rock sample to obtain CT scanning data; wherein the CT scanning data includes a CT image and CT scanning parameters;

[0131] The second module 102 is used to perform image processing on the CT image to obtain a binary image;

[0132] The third module 103 is used to establish an original three-dimensional digital core model of the rock microstructure based on the binary image;

[0133] The fourth module 104 is configured to determine first voxel data based on the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock sample;

[0134] A fifth module 105 is configured to resample the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, thereby obtaining second voxel data.

[0135] A sixth module 106 is configured to generate a first complex porous rock mass finite element model based on the second voxel data;

[0136] The seventh module 107 is used to adjust the scaling factor according to the accuracy difference between the three-dimensional digital core model and the first finite element model, and return to execute the step of resampling the first voxel data based on the nearest neighbor interpolation method and the scaling factor to reduce the number of voxels and obtain the second voxel data, until the accuracy difference between the three-dimensional digital core model and the first finite element model meets the preset conditions, thereby obtaining the target complex porous rock mass finite element model.

[0137] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0138] An embodiment of the present application further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, implements the aforementioned method for finite element modeling of complex porous rock masses based on digital cores. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0139] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0140] See also Figure 13 , Figure 13 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0141] The processor 201 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 configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0142] The memory 202 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 202 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 202 and is called by the processor 201 to execute the finite element modeling method for complex porous rock mass based on digital rock cores in the embodiments of this application.

[0143] Input / output interface 203, used to implement information input and output;

[0144] Communication interface 204, 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.);

[0145] bus 205 , which transmits information between the various components of the device (e.g., processor 201 , memory 202 , input / output interface 203 , and communication interface 204 );

[0146] The processor 201 , the memory 202 , the input / output interface 203 and the communication interface 204 are connected to each other in communication within the device via the bus 205 .

[0147] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned finite element modeling method for complex porous rock mass based on digital core.

[0148] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0149] 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.

[0150] 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.

[0151] Those skilled in the art will understand that 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 shown in the figures, or a combination of certain steps, or different steps.

[0152] The system embodiment described above is 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 system embodiments described above are merely schematic. For example, the division of the above 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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 finite element modeling method for complex porous rock mass based on digital rock core, characterized in that: The following steps are involved: Performing CT scanning on a complex porous rock mass sample to obtain CT scanning data; wherein the CT scanning data includes a CT image and CT scanning parameters; performing image processing on the CT image to obtain a binary image; Establishing an original three-dimensional digital core model of rock microstructure based on the binarized image; determining first voxel data according to the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock mass sample; resampling the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels to obtain second voxel data; generating a first complex porous rock mass finite element model according to the second voxel data; The scaling factor is adjusted according to the accuracy difference between the three-dimensional digital core model and the first complex porous rock mass finite element model, and the step of resampling the first voxel data to reduce the number of voxels based on the nearest neighbor interpolation method and the scaling factor to obtain the second voxel data is returned to execution until the accuracy difference between the three-dimensional digital core model and the first complex porous rock mass finite element model meets the preset conditions, thereby obtaining the target complex porous rock mass finite element model.

2. The method according to claim 1, characterized in that The step of performing image processing on the CT image to obtain a binary image comprises the following steps: Optimizing the grayscale values ​​of the CT images, adjusting the grayscale value range of each CT image to the grayscale value range of the transition zone, and obtaining a first image; Using a median filtering method to filter out ring artifacts and impulse noise in the first image to obtain a second image; The second image is binarized using an adaptive threshold segmentation algorithm to obtain a binarized image.

3. The method according to claim 1, characterized in that The method of establishing an original three-dimensional digital core model of the rock microstructure based on the binary image comprises the following steps: Based on the binarized image, an original three-dimensional digital core model of the rock microstructure is constructed using a volume rendering model.

4. The method according to claim 1, wherein Determining first voxel data according to the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock sample comprises the following steps: determining the number of voxel points according to CT scanning parameters and the size of the CT image; First voxel information is determined according to the number of voxel points and the actual size of the complex porous rock sample; wherein the first voxel information includes voxel value, number of voxel points and voxel position.

5. The method according to claim 1, wherein The method of resampling the first voxel data based on the nearest neighbor interpolation method and the scaling factor to reduce the number of voxels to obtain the second voxel data includes the following steps: According to the scaling factor, the voxel value in the binary image is directly mapped to the nearest neighbor voxel position in the resampled image to obtain the second voxel data; wherein the expression of the voxel mapping is: Where, T is the target size; D is the original size; f is the scaling factor.

6. The method according to claim 1, wherein Generating a first complex porous rock mass finite element model according to the second voxel data comprises the following steps: converting the second voxel data into grid unit numbers and node information by a grid mapping method; An initial complex porous rock mass finite element model is generated according to the grid unit numbers and the node information.

7. The method according to claim 1, characterized in that The step of determining the accuracy difference between the three-dimensional digital core model and the first complex porous rock mass finite element model comprises the following steps: The accuracy difference between the three-dimensional digital core model and the first complex porous rock mass finite element model is calculated using a statistical method and a structural similarity index algorithm.

8. A finite element modeling system for complex porous rock mass based on digital rock core, characterized by: include: The first module is used to perform CT scanning on a complex porous rock sample to obtain CT scanning data; wherein the CT scanning data includes a CT image and CT scanning parameters; The second module is used to perform image processing on the CT image to obtain a binary image; The third module is used to establish an original three-dimensional digital core model of the rock microstructure based on the binary image; A fourth module is configured to determine first voxel data based on the CT scanning parameters, the size of the CT image, and the actual size of the complex porous rock sample; a fifth module, configured to resample the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, thereby obtaining second voxel data; A sixth module is used to generate a first complex porous rock mass finite element model based on the second voxel data; The seventh module is used to adjust the scaling factor according to the accuracy difference between the three-dimensional digital core model and the first complex porous rock mass finite element model, return to execute the step of resampling the first voxel data based on the nearest neighbor interpolation method and the scaling factor to reduce the number of voxels and obtain the second voxel data, until the accuracy difference between the three-dimensional digital core model and the first complex porous rock mass finite element model meets the preset conditions, and the target complex porous rock mass finite element model is obtained.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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