CN118862544B - A finite element modeling method and system for complex porous rock mass based on digital core - Google Patents
A finite element modeling method and system for complex porous rock mass based on digital coreInfo
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Abstract
The application discloses a method and a system for modeling finite elements of a complex pore rock mass based on a digital rock core, which belong to the technical field of computers, and the method comprises the steps of processing CT images after CT scanning is carried out on a complex pore rock mass sample to obtain a binary image; the method comprises the steps of establishing an original three-dimensional digital core model of a rock microstructure according to a binarization image, determining first prime data according to CT scanning parameters, the size of a CT image and the actual size of a complex pore rock mass sample, resampling the first prime data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, obtaining second prime data, further generating a first complex pore rock mass finite element model, and finally obtaining a target complex pore rock mass finite element model according to the accuracy difference between the three-dimensional digital core model and the first finite element model. The application reduces the modeling calculation amount through resampling, and improves the modeling efficiency and precision through adjusting the scaling factor.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a system for modeling finite elements of a complex pore rock mass based on a digital rock core.
Background
Along with the development of computer technology, the numerical method becomes an effective choice, is widely applied to the research of the mechanical behavior and the destruction process of rock, and is helpful for revealing the rule for explaining crack growth and the evolution process of the microscopic pore structure. The influence of the heterogeneity on the rock mechanical response, in order to make the constructed numerical model reflect the heterogeneity characteristics of the rock material more truly, one introduces the heterogeneity into the numerical model, assuming that the mechanical properties of the rock material obey the statistical distribution characteristics. However, due to the simplified spatial structure of the rock complex, and the random parameters introduced are uncertain, subjective, highly dependent on the statistical distribution parameters. This means that these numerical models reduce the accuracy of the numerical simulation results, and it is difficult to accurately repeat the petrophysical experimental results, which may not provide the phenomena and experimental effects expected by researchers in designing experiments. Therefore, the accuracy and adaptability of numerical simulation are greatly dependent on the established microscopic model, and the simulation result has theoretical and application values only when the structural space distribution of microscopic pores and the like of the model can reflect the real structural characteristics of the rock sample and the corresponding mechanical parameters are endowed with enough scientific basis, rather than only when subjective assumption is adopted. Since many rock masses have highly heterogeneous mesostructures, rock mesostructure characterization and theoretical modeling are limited. Therefore, establishing a three-dimensional accurate model capable of truly reflecting the development characteristics of micropores plays a vital role in numerical simulation.
The related technology adopts the traditional finite element method and discrete element method to reconstruct the three-dimensional structure of the microscopic pore. However, the complex pore rock mass has more pores and more complicated microscopic pore structure, and the pore diameter is mainly concentrated on the micrometer or even nanometer scale, so that millions or even hundreds of millions of pixel points often exist in the digital core model. Whether the modeling is performed by a traditional finite element method or a discrete element method, the numerical model reconstructed based on the digital core technology in the related technology is far smaller than the test size due to the limitation of computer computing power, and the method is only suitable for researching the mechanical behavior of a sample under the microscopic scale.
Disclosure of Invention
The embodiment of the application mainly aims to provide a method and a system for finite element modeling of a complex pore rock mass based on a digital rock core, which aim to reduce the calculated amount of modeling of the complex pore rock mass and improve the modeling efficiency and accuracy.
To achieve the above objective, an aspect of the embodiments of the present application provides a method for finite element modeling of a complex pore rock mass based on a digital core, the method comprising:
CT scanning is carried out on the rock mass sample with the complex aperture to obtain CT scanning data, wherein the CT scanning data comprises CT images and CT scanning parameters;
performing image processing on the CT image to obtain a binarized image;
establishing an original three-dimensional digital core model of the rock microstructure according to the binarized image;
determining first body data according to the CT scanning parameters, the size of the CT image and the actual size of the complex pore 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 pore rock mass finite element model according to the second voxel data;
And adjusting the scaling factor according to the precision difference between the three-dimensional digital core model and the first finite element model, returning 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 obtaining second voxel data until the precision difference between the three-dimensional digital core model and the first finite element model meets the preset condition, and obtaining the target complex pore rock mass finite element model.
In some embodiments, the image processing the CT image to obtain a binarized image includes the following steps:
Performing gray value optimization on the CT images, and adjusting the gray value range of each CT image to the gray value range of a transition region to obtain a first image;
Filtering out ring artifacts and impulse noise in the first image by adopting a median filtering method to obtain a second image;
and carrying out binarization processing on the second image by adopting a self-adaptive threshold segmentation algorithm to obtain a binarized image.
In some embodiments, the establishing the original three-dimensional digital core model of the rock microstructure from the binarized image comprises the steps of:
And constructing an original three-dimensional digital core model of the rock microstructure by adopting a volume rendering model according to the binarized image.
In some embodiments, the determining the first volumetric data based on the CT scan parameters, the size of the CT image, and the actual size of the complex pore rock mass sample comprises the steps of:
Determining the number of voxel points according to CT scanning parameters and the size of the CT image;
And determining first voxel information according to the number of the voxel points and the actual size of the complex pore rock mass sample, wherein the first voxel information comprises voxel values, the number of the voxel points and voxel positions.
In some embodiments, the resampling the first voxel data to reduce the number of voxels based on nearest neighbor interpolation and scaling factor to obtain second voxel data comprises the steps of:
and according to the scaling factor, directly mapping the voxel value in the binarized image to the nearest voxel position in the resampled image to obtain second voxel data, wherein the expression of voxel mapping is as follows:
wherein T is the target size, D is the original size, and f is the scaling factor.
In some embodiments, the generating a first complex pore rock mass finite element model from the second voxel data comprises the steps of:
converting the second body data into grid cell numbers and node information by a grid mapping method;
And generating an initial complex pore rock mass finite element model according to the grid cell numbers and the node information.
In some embodiments, the step of determining a difference in accuracy of the three-dimensional digital core model and the first finite element model comprises the steps of:
and calculating the precision difference of the three-dimensional digital core model and the first finite element model by adopting a statistical method and a structural similarity index algorithm.
To achieve the above object, another aspect of the embodiments of the present application proposes a complex pore rock mass finite element modeling system based on a digital core, the system comprising:
The first module is used for carrying out CT scanning on the rock mass sample with the complex aperture to obtain CT scanning data, wherein the CT scanning data comprises CT images and CT scanning parameters;
The second module is used for carrying out image processing on the CT image to obtain a binarized image;
the third module is used for establishing an original three-dimensional digital core model of the rock microstructure according to the binarization image;
A fourth module for determining a first volumetric data based on the CT scan parameters, the size of the CT image, and the actual size of the complex pore rock mass specimen;
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, to obtain second voxel data;
a sixth module for generating a first complex pore rock mass finite element model from the second voxel data;
And a seventh module, configured to adjust the scaling factor according to the difference in precision between the three-dimensional digital core model and the first finite element model, and return to perform 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, so as to obtain second voxel data, until the difference in precision between the three-dimensional digital core model and the first finite element model meets a preset condition, so as to obtain a target complex pore rock mass finite element model. To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor implements the method described above when executing the computer program.
To achieve the above object, another aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method described above.
The embodiment of the application at least has the beneficial effects that the method and the system for modeling the finite element of the complex pore rock mass based on the digital rock core have the advantages that the number of voxels is reduced by resampling the voxel data, the calculated amount is reduced, and the modeling efficiency is improved. And the scaling factors are adjusted according to the precision difference between the digital core model and the original model under different scaling factors, so that the relation between the calculation efficiency and the model precision is weighed, and the model precision is ensured while the modeling efficiency is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.
FIG. 1 is a step diagram of a finite element modeling method for a complex pore rock mass based on a digital core, provided by an embodiment of the application;
FIG. 2 is a flow chart of a finite element modeling method of a complex pore rock mass based on a digital core, which is provided by the embodiment of the application;
FIG. 3 is a schematic view of an original CT image of a limestone reef provided by an embodiment of the present application;
fig. 4 is a gray histogram of a reef limestone original CT image provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of the result of gray value optimization provided by an embodiment of the present application;
FIG. 6 is a graph showing the result of median filtering provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of the result of adaptive thresholding provided by an embodiment of the present application;
FIG. 8 is a three-dimensional digital core model at different scaling factors provided by an embodiment of the present application;
FIG. 9 is a statistical plot of equivalent pore radius distribution provided by an embodiment of the present application;
FIG. 10 is a structural similarity index line graph of a skeleton provided by an embodiment of the present application;
FIG. 11 is a schematic diagram of a reef limestone sample, a three-dimensional digital core model and a target complex pore rock mass finite element model of a reef limestone provided by an embodiment of the present application;
FIG. 12 is a block diagram of a finite element modeling system for a complex pore rock mass based on a digital core, provided by an embodiment of the present application;
fig. 13 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with embodiments of the application, but are merely examples of apparatuses and methods consistent with aspects of embodiments of the application as detailed in the accompanying claims.
Although functional block diagrams are depicted in system diagrams, logical orders of magnitude are depicted in flowchart form, in some cases, the steps shown or described may be performed in a different order than the block diagrams in the system. The terms first/S100, second/S200, and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
It is to be understood that the terms "first," "second," and the like, as used herein, may be used to describe various concepts, but are not limited by these terms unless otherwise specified. These terms are only used to distinguish one concept from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present application. The words "if", as used herein, may be interpreted as "when" or "in response to a determination", depending on the context.
The terms "at least one", "a plurality", "each", "any" and the like as used herein, at least one includes one, two or more, a plurality includes two or more, each means each of the corresponding plurality, and any one means any of the plurality.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
In the related art, a finite element method and a discrete element method are adopted to reconstruct the three-dimensional structure of the microscopic pore. However, the complex pore rock mass has more pores and more complicated microscopic pore structure, and the pore diameter is mainly concentrated on the micrometer or even nanometer scale, so that millions or even hundreds of millions of pixel points often exist in the digital core model. Whether the modeling is carried out by a traditional finite element method or a discrete element method, because of the limitation of computer calculation, the numerical model reconstructed based on the digital core technology is far smaller than the test size, and the method is only suitable for researching the mechanical behavior of the sample under the microscopic scale.
In view of the above, the application provides a method and a system for finite element modeling of a complex pore rock mass based on a digital core, which utilize a resampling technology to reduce the number of voxels by sacrificing part of pore-skeleton structure details, reduce the calculated amount and improve the modeling efficiency. And the statistical method and the SSIM algorithm (structural similarity index) are adopted to quantify the precision difference between the digital core model and the original model under different scaling factors, so that the relation between the calculation efficiency and the model precision is weighed, and the model precision is ensured while the modeling efficiency is improved.
The embodiment of the application provides a complex pore rock mass finite element modeling method based on a digital rock core, and relates to the technical field of computers. The method for modeling the complex pore rock mass finite element based on the digital rock core provided by the embodiment of the application can be applied to a terminal, a server and software running in the terminal or the server. In some embodiments, the terminal may be, but not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle terminal, etc., the server may be configured as an independent physical server, may be configured as a server cluster or a distributed system formed by a plurality of physical servers, may be configured as a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligence platforms, and the server may also be a node server in a blockchain network, and the software may be an application for implementing a digital core-based complex pore rock finite element modeling method, etc., but is not limited to the above forms.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. Such as a personal computer, a server computer, a hand-held or portable device, a tablet device, a multiprocessor system, a microprocessor-based system, a set top box, a programmable consumer electronics, a network PC, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Referring to fig. 1 and 2, fig. 1 is an optional step diagram of a method for modeling a complex pore rock mass finite element based on a digital core according to an embodiment of the present application, and fig. 2 is an optional flowchart of a modeling method according to an embodiment of the present application. The method of FIG. 1 may include, but is not limited to, steps S100-S700.
And step S100, CT scanning is carried out on the rock mass sample with the complex pore to obtain CT scanning data, wherein the CT scanning data comprise CT images and CT scanning parameters.
And step 200, performing image processing on the CT image to obtain a binarized image.
And step S300, establishing an original three-dimensional digital core model of the rock microstructure according to the binarized image.
And step S400, determining a first prime number according to the CT scanning parameters, the size of the CT image and the actual size of the complex pore rock mass sample.
And step S500, resampling the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, and obtaining second voxel data.
And S600, generating a first complex pore rock finite element model according to the second voxel data.
And step S700, adjusting the scaling factor according to the precision difference of the three-dimensional digital core model and the first finite element model, and returning to the step S500 until the precision difference of the three-dimensional digital core model and the first finite element model meets the preset condition, so as to obtain the target complex pore rock mass finite element model.
In the steps S100 to S700 shown in the embodiment of the application, the number of voxels is reduced by resampling the voxel data, the calculated amount is reduced, and the modeling efficiency is improved. And the scaling factors are adjusted according to the precision difference between the digital core model and the original model under different scaling factors, so that the relation between the calculation efficiency and the model precision is weighed, and the model precision is ensured while the modeling efficiency is improved.
In step S100 of some embodiments, a CT scan test is performed on a complex pore rock mass. Specifically, a rock core with complex pores is cut into a standard sample, then a CT scanner is used for scanning the rock core with complex pores, and after scanning, continuous section images z sheets of the rock are obtained, and the pixel size of each section is x multiplied by y. The gray value distribution range of the image is 0-255, wherein two main gray levels of the gray histogram respectively represent the pores and skeleton matrix of the rock, the part with the gray value close to 0 is black representing the pores of the rock, and the part with the gray value close to 255 is white representing the matrix of the rock.
In some embodiments, step S200 may include, but is not limited to, steps S210-S230:
step S210, gray value optimization is performed on the CT images, and the gray value range of each CT image is adjusted to the gray value range of the transition area, so that a first image is obtained.
And step S220, filtering out ring artifacts and impulse noise in the first image by adopting a median filtering method to obtain a second image.
And step S230, performing binarization processing on the second image by adopting an adaptive threshold segmentation algorithm to obtain a binarized image.
In step S210 of some embodiments, an adaptive threshold segmentation algorithm may be employed to determine the gray threshold between the two components of the rock matrix and the pore using image recognition techniques. In order to further distinguish the rock matrix and the pores in the transition region, gray value optimization is carried out, and the gray value range of each CT picture is adjusted from original 0-255 to the gray value range of the transition region.
In step S220 of some embodiments, a median filtering method is used to remove ringing artifacts and impulse noise in the image. The median filtering method considers the neighborhood N p pixel values around each pixel point, sorts the pixel values in the neighborhood and selects the median as a new pixel value.
In step S230 of some embodiments, an adaptive threshold segmentation method is used to process the problems of image ringing and luminance non-uniformity and perform image binarization. The principle of the method is that the average value or the median value of gray values in a pixel point neighborhood N p is used as a threshold value of a central pixel, if the gray value of the pixel is larger than the threshold value, the pixel is white (rock matrix) in an output image, otherwise, the output is black (rock pore).
In some embodiments, step S300 may include, but is not limited to including step S310:
and step S310, constructing an original three-dimensional digital core model of the rock microstructure by adopting a volume rendering model according to the binarized image.
In step S310 of some embodiments, after the operations of image enhancement, denoising and threshold segmentation, the pore-skeleton structural features of the rock limestone are retained to the greatest extent and a Volume Rendering model (Volume Rendering) is used to build a three-dimensional digital core model of the rock microstructure.
In some embodiments, step S400 may include, but is not limited to, steps S410-S420:
step S410, determining the number of voxel points according to CT scanning parameters and the size of the CT image.
And step S420, determining first voxel information according to the number of the voxel points and the actual size of the complex pore rock mass sample, wherein the first voxel information comprises voxel values, the number of the voxel points and voxel positions.
In step S410 of some embodiments, the complex pore rock mass digitized image size parameter is x y x z, i.e., sharing x y x z voxels, according to the CT scan parameter.
In step S420 of some embodiments, the actual size of the combined rock sample is available, with voxels as side lengthsWherein voxel value v p is 0 (pore) or 1 (skeleton), respectively, representing in vivo region information. It should be noted that the voxel values may be set to other values to distinguish between the pores and the skeleton, which is not limited in the embodiment of the present application.
In some embodiments, step S500 may include, but is not limited to including, step S510 of:
And step S510, according to the scaling factor, the voxel value in the binarized image is directly mapped to the nearest voxel position in the resampled image, so as to obtain second voxel data.
Wherein, the expression of voxel mapping is:
wherein T is the target size, D is the original size, and f is the scaling factor.
In step S510 of some embodiments, to enable rock digital core finite element modeling and simulation at laboratory scale, voxel data is resampled based on nearest neighbor interpolation, reducing the number of voxels by sacrificing part of the pore-skeleton structure details. The basic principle of nearest neighbor interpolation is to map voxel values in the original image directly to nearest neighbor voxel locations in the resampled image. If one voxel position in the resampled image is (x ′,y′,z′), then the value of that position is closest from the original imageWherein f x,fy,fz is a scaling factor in three dimensions.
In some embodiments, step S600 may include, but is not limited to, steps S610-S620:
Step S610, converting the second volumetric data into grid cell numbers and node information by a grid mapping method.
And step S620, generating an initial complex pore rock finite element model according to the grid cell numbers and the node information.
In steps S610 to S620 of some embodiments, the read voxel information is converted into grid cell number and node information by a grid mapping method, and a corresponding finite element model may be generated in finite element software according to the grid cell number and node information.
In some embodiments, step S700 may include, but is not limited to including step S710:
Step S710, calculating the accuracy difference between the three-dimensional digital core model and the first finite element model by using a statistical method and a structural similarity index algorithm.
For step S700, specifically, when resampling is performed using nearest neighbor interpolation, a relationship between the calculation efficiency and the model accuracy needs to be weighed. In order to further verify and improve the model precision, a statistical method and an SSIM algorithm (structural similarity index) are respectively adopted to quantify the precision difference between the digital core model and the original model under different scaling factors, and when SSIM is more than 0.65, the resampling model has higher consistency with the original model in visual characteristics, and the shape and the boundary of the framework structure are better reserved in the scaling process.
Based on the above-mentioned trade-off analysis of computational efficiency and model accuracy, voxel information is resampled with a suitable scaling factor f, and a finite element model is built accordingly. For any voxel cell v i whose position in the three-dimensional voxel array is (x vi,yvi,zvi), considering that each vertex (dx i,dyi,dzi) of a voxel can be obtained by adding 0 or 1 in three dimensions, the v i vertex relative positions are as follows:
Thus, for each vertex of v i (dx i,dyi,dzi), its corresponding actual physical space coordinates (x i,yi,zi) are:
xi＝(xvi+dxi)×fx′,
yi＝(yvi+dyi)×fy′,
zi＝(zvi+dzi)×fz′,
wherein f x′、fy′、fz' is the scaling factor for converting v i coordinates from voxel space to actual physical space, and deleting the unit with v p =0 can obtain the target complex pore rock finite element model of the porous medium microscopic three-dimensional structure.
The scheme of the embodiment of the application is described and illustrated in detail below by combining a specific complex pore rock mass modeling scene:
in the embodiment of the application, a finite element modeling method for a complex pore rock body based on a digital core is provided, the method can be applied to accurately modeling the complex pore rock body, and the steps of the method can comprise CT scanning, image processing, digital core model construction and three-dimensional reconstruction based on finite element software.
Illustratively, taking the finite element modeling of a complex pore structure of the reef limestone as an example, the reef limestone sample is taken from a shallow layer area of an island, is white in color, has pores in rough surface and is brittle. And (3) drilling a cylindrical sample with the diameter phi=50 mm and the height h=25 mm on the rock of the reef limestone, cutting two ends to be flat, and then performing a CT scanning test, wherein the CT resolution is 31.25um. The sample is scanned layer by layer to obtain 800 CT image slices, and the pixel size of each slice is 1600 multiplied by 1600. As shown in fig. 3 and fig. 4, the distribution range of gray values of the CT image is 0-255, wherein two main gray levels of the gray histogram respectively represent the pores and the skeleton matrix of the rock, the portion with the gray value close to 0 is black to represent the pores of the rock, and the portion with the gray value close to 255 is white to represent the rock matrix.
Based on the characteristics of the CT images of the rock reef, the Python software is used for carrying out batch processing on the CT images. As can be seen from fig. 4, the reef limestone CT image has two peaks of pore and matrix gray scale, and the gray scale value between the two peaks represents the transition region between the rock matrix and the pore. In order to further distinguish the rock matrix and the pores in the transition region, gray value optimization is performed first, and the gray value range of each CT picture is adjusted from original 0-255 to the gray value range of the transition region, and the result is shown in FIG. 5. Then, a median filtering method is adopted to remove ring artifacts and impulse noise in the image. The median filtering method considers the neighbor Np pixel values around each pixel point, sorts the pixel values in the neighbor and selects the median as the new pixel value, and the result is shown in fig. 6. And finally, adopting an adaptive threshold segmentation method to treat the problems of image ring artifacts and uneven brightness and binarizing the image. The principle of the method is that the average value or the median value of gray values in the neighborhood Np of the pixel point is taken as the threshold value of a central pixel, if the gray value of the pixel is larger than the threshold value, the pixel is white (rock matrix) in an output image, otherwise, the output is black (rock pore), and the result is shown in fig. 7.
After the operations of image enhancement, denoising and threshold segmentation, the characteristics of pore-skeleton structures of the reef limestone are reserved to the greatest extent, and a Volume Rendering model (Volume Rendering) is adopted to build a three-dimensional digital core model of the rock microstructure. According to CT scanning parameters, the size parameters of the reef limestone digital image are 1600 multiplied by 800, namely 2.048 multiplied by 10 9 individual pixels are shared. The actual dimensions of the combined reef limestone sample are available, and the voxels are cubes with a side length of l v =31.25 um, wherein the voxel value v p is 0 (pore) or 1 (skeleton) respectively, and the voxel value represents in-vivo region information. At this time, the read voxel information is converted into grid cell number and node information by a grid mapping method, and a corresponding finite element model is generated in finite element software according to the grid cell number and node information.
In order to realize the finite element modeling and simulation of the rock digital core at the laboratory scale, the voxel data is resampled based on the nearest neighbor interpolation method, and the number of voxels is reduced by sacrificing part of pore-skeleton structure details. Figure 8 shows a three-dimensional digital core model of a reef limestone pore-skeleton structure with the size of 2 multiplied by 2cm in a core data volume under different scaling factors, f in the figure represents the scale factor size of each model. As can be seen from an examination of fig. 8, when f=0.20, a partially continuous skeleton is discretized into isolated solids, and when f=0.15, the loss of detailed information of the pore-skeleton is more pronounced.
In order to further verify the model accuracy, a statistical method and an SSIM algorithm (structural similarity index) are respectively adopted to quantify the accuracy difference between the digital core model and the original model under different scaling factors, as shown in FIG. 9, FIG. 9 is the probability distribution of equivalent pore radius of the model, and when f is more than or equal to 0.25, the pore radius distribution accords with the lognormal distribution. And f is less than or equal to 0.20, the model has more information loss of the micro-pore structure, so that the model can not meet the logarithmic normal distribution. Fig. 10 shows the structural similarity index of the skeleton on the XY plane at the position of model z=1.00 cm, when f is greater than or equal to 0.25, SSIM is greater than 0.65, and at this time, the resampling model has higher consistency with the original model in visual characteristics, and the shape and the boundary of the skeleton structure are better reserved in the scaling process.
Based on the above analysis, a finite element model was built using a resampled digital core model with f=0.25. For any voxel cell v i whose position in the three-dimensional voxel array is (x vi,yvi,zvi), considering that each vertex (dx i,dyi,dzi) of a voxel can be obtained by adding 0 or 1 in three dimensions, the v i vertex relative positions are as follows:
Thus, for each vertex of v i (dx i,dyi,dzi), its corresponding actual physical space coordinates (x i,yi,zi) are:
xi＝(xvi+dxi)×fx′,
yi＝(yvi+dyi)×fy′,
zi＝(zvi+dzi)×fz′,
Where f x′、fy′、fz' is the scaling factor that converts the v i coordinates from voxel space to real physical space, respectively. In this example, the actual dimensions of the sample are cylinders of phi 50mm x25 mm, and the parameters of the digitized image of the resampled reef limestone are 400 x 200, corresponding to f x′＝fy′＝fz′＝0.125mm-1. Further, deleting the v p =0 unit can then obtain the fine target complex pore rock mass finite element model of the rock reef limestone. The final modeling result obtained by the above-described method can be shown in fig. 11.
In summary, the embodiment of the application has at least the following beneficial effects:
1. The embodiment of the application provides a method for establishing a fine three-dimensional model of a fine pore structure of a complex pore rock mass, aiming at the characteristics that the complex pore rock mass has high porosity and complex pore structure, and the pore diameter is mainly concentrated on a micrometer scale.
2. The embodiment of the application provides a resampling technology when finite element software modeling is adopted, and reduces the number of voxels by sacrificing part of pore-skeleton structure details, reduces the calculated amount and improves the modeling efficiency.
3. When the number of voxels is reduced by sacrificing part of pore-skeleton structure details through a resampling technology, a statistical method and an SSIM algorithm (structural similarity index) are adopted to quantify the precision difference between a digital core model and an original model under different scaling factors, the relation between the calculation efficiency and the model precision is weighed, and the model precision is ensured while the modeling efficiency is improved.
4. The method for establishing the fine three-dimensional model of the fine pore structure of the complex pore rock mass, provided by the embodiment of the application, can meet the fine three-dimensional modeling requirement of the fine pore structure of the complex pore rock mass with multiple scales, and is not limited to the fine scale.
Referring to fig. 12, the embodiment of the present application further provides a complex pore rock mass finite element modeling system based on a digital core, which can implement the complex pore rock mass finite element modeling method based on the digital core, where the system includes:
the first module 101 is used for performing CT scanning on the rock mass sample with the complex aperture to obtain CT scanning data, wherein the CT scanning data comprises CT images and CT scanning parameters;
a second module 102, configured to perform image processing on the CT image to obtain a binarized image;
a third module 103, configured to establish an original three-dimensional digital core model of the rock microstructure according to the binarized image;
A fourth module 104, configured to determine a first body data according to the CT scan parameters, the size of the CT image, and the actual size of the complex pore rock mass sample;
a fifth module 105, configured to resample the first voxel data based on a nearest neighbor interpolation method and a scaling factor to reduce the number of voxels, to obtain a second voxel data;
A sixth module 106, configured to generate a first complex pore rock finite element model according to the second voxel data;
And a seventh module 107, configured to adjust the scaling factor according to the difference in precision between the three-dimensional digital core model and the first finite element model, and return to perform 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, so as to obtain second voxel data, until the difference in precision between the three-dimensional digital core model and the first finite element model meets a preset condition, so as to obtain a target complex pore rock mass finite element model.
It can be understood that the content in the above method embodiment is applicable to the system embodiment, and the functions specifically implemented by the system embodiment are the same as those of the above method embodiment, and the achieved beneficial effects are the same as those of the above method embodiment.
The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the complex pore rock finite element modeling method based on the digital rock core when executing the computer program. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
It can be understood that the content in the above method embodiment is applicable to the embodiment of the present apparatus, and the specific functions implemented by the embodiment of the present apparatus are the same as those of the embodiment of the above method, and the achieved beneficial effects are the same as those of the embodiment of the above method.
Referring to fig. 13, fig. 13 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:
The processor 201 may be implemented by a general purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solution provided by the embodiments of the present application;
The Memory 202 may be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 202 may store an operating system and other application programs, and when the technical scheme provided by the embodiments of the present disclosure is implemented by software or firmware, relevant program codes are stored in the memory 202, and the processor 201 invokes a complex pore rock mass finite element modeling method based on a digital core to perform the embodiments of the present disclosure;
An input/output interface 203 for implementing information input and output;
The communication interface 204 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);
a bus 205 for transferring information between various components of the device (e.g., processor 201, memory 202, input/output interface 203, and communication interface 204);
Wherein the processor 201, the memory 202, the input/output interface 203 and the communication interface 204 are communicatively coupled to each other within the device via a bus 205.
The embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the complex pore rock mass finite element modeling method based on the digital rock core when being executed by a processor.
It can be understood that the content of the above method embodiment is applicable to the present storage medium embodiment, and the functions of the present storage medium embodiment are the same as those of the above method embodiment, and the achieved beneficial effects are the same as those of the above method embodiment.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by persons skilled in the art that the embodiments of the application are not limited by the illustrations, and that more or fewer steps than those shown may be included, or certain steps may be combined, or different steps may be included.
The system embodiments described above are merely illustrative, in that the units illustrated as separate components may or may not be physically separate, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" is used to describe an association relationship of an associated object, and indicates that three relationships may exist, for example, "a and/or B" may indicate that only a exists, only B exists, and three cases of a and B exist simultaneously, where a and B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b or c may represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the above elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. The storage medium includes various media capable of storing programs, such as a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.
Claims (10)
1. The finite element modeling method for the complex pore rock mass based on the digital rock core is characterized by comprising the following steps of:
CT scanning is carried out on the rock mass sample with the complex aperture to obtain CT scanning data, wherein the CT scanning data comprises CT images and CT scanning parameters;
performing image processing on the CT image to obtain a binarized image;
establishing an original three-dimensional digital core model of the rock microstructure according to the binarized image;
determining first body data according to the CT scanning parameters, the size of the CT image and the actual size of the complex pore 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 pore rock mass finite element model according to the second voxel data;
And adjusting the scaling factor according to the precision difference of the three-dimensional digital core model and the first complex pore rock mass finite element model, and returning 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 so as to obtain second voxel data until the precision difference of the three-dimensional digital core model and the first complex pore rock mass finite element model accords with a preset condition, so as to obtain the target complex pore rock mass finite element model.
2. The method according to claim 1, wherein the image processing of the CT image to obtain a binarized image comprises the steps of:
Performing gray value optimization on the CT images, and adjusting the gray value range of each CT image to the gray value range of a transition region to obtain a first image;
Filtering out ring artifacts and impulse noise in the first image by adopting a median filtering method to obtain a second image;
and carrying out binarization processing on the second image by adopting a self-adaptive threshold segmentation algorithm to obtain a binarized image.
3. The method of claim 1, wherein said creating an original three-dimensional digital core model of a rock microstructure from said binarized image comprises the steps of:
And constructing an original three-dimensional digital core model of the rock microstructure by adopting a volume rendering model according to the binarized image.
4. The method according to claim 1, wherein said determining first volumetric data based on said CT scan parameters, the size of said CT image and the actual size of said complex pore rock mass sample comprises the steps of:
Determining the number of voxel points according to CT scanning parameters and the size of the CT image;
And determining first voxel information according to the number of the voxel points and the actual size of the complex pore rock mass sample, wherein the first voxel information comprises voxel values, the number of the voxel points and voxel positions.
5. The method according to claim 1, wherein resampling the first voxel data to reduce the number of voxels based on nearest neighbor interpolation and scaling factor to obtain second voxel data comprises the steps of:
and according to the scaling factor, directly mapping the voxel value in the binarized image to the nearest voxel position in the resampled image to obtain second voxel data, wherein the expression of voxel mapping is as follows:
wherein T is the target size, D is the original size, and f is the scaling factor.
6. The method of claim 1, wherein generating a first complex pore rock mass finite element model from the second voxel data comprises the steps of:
converting the second body data into grid cell numbers and node information by a grid mapping method;
And generating an initial complex pore rock mass finite element model according to the grid cell numbers and the node information.
7. The method of claim 1, wherein the step of determining a difference in accuracy of the three-dimensional digital core model and the first complex pore rock mass finite element model comprises the steps of:
And calculating the precision difference of the three-dimensional digital core model and the first complex pore rock mass finite element model by adopting a statistical method and a structural similarity index algorithm.
8. A digital core-based complex pore rock mass finite element modeling system, comprising:
The first module is used for carrying out CT scanning on the rock mass sample with the complex aperture to obtain CT scanning data, wherein the CT scanning data comprises CT images and CT scanning parameters;
The second module is used for carrying out image processing on the CT image to obtain a binarized image;
the third module is used for establishing an original three-dimensional digital core model of the rock microstructure according to the binarization image;
A fourth module for determining a first volumetric data based on the CT scan parameters, the size of the CT image, and the actual size of the complex pore rock mass specimen;
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, to obtain second voxel data;
a sixth module for generating a first complex pore rock mass finite element model from the second voxel data;
And a seventh module, configured to adjust the scaling factor according to the difference in precision between the three-dimensional digital core model and the first complex pore rock mass finite element model, and return to perform the step of resampling the first prime data based on the nearest neighbor interpolation method and the scaling factor to reduce the number of voxels and obtain second prime data until the difference in precision between the three-dimensional digital core model and the first complex pore rock mass finite element model meets a preset condition, thereby obtaining a target complex pore rock mass finite element model.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
The processor executing the program implements the method of any one of claims 1 to 7.
10. A computer storage medium in which a processor executable program is stored, characterized in that the processor executable program is for implementing the method according to any one of claims 1 to 7 when being executed by the processor.
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