Shale reservoir anisotropy analysis method, device, equipment, medium and product

Abstract

The embodiment of the invention provides a shale reservoir anisotropy analysis method, device and equipment, a medium and a product, and is applied to the technical field of petroleum and natural gas engineering. The method comprises the following steps: collecting a plurality of microscopic images of a target shale sample in a bedding direction and a crossing direction, and classifying and storing the microscopic images according to directions corresponding to the microscopic images; aligning the plurality of microscopic images in the same direction, extracting pore throat structure parameters of each microscopic image after alignment, and performing data fusion on the plurality of pore throat structure parameters to obtain a fusion parameter value in the direction; on the basis of the fusion parameter value in the bedding direction and the fusion parameter value in the crossing direction, at least one differentiation ratio is obtained through calculation; and generating an anisotropy analysis report of the target shale sample based on the differentiation ratio. The technical effect of improving the reliability of anisotropy analysis of the shale reservoir is achieved.

Description

Technical Field

[0001] This application relates to the field of oil and gas engineering technology, and in particular to a method, apparatus, equipment, medium and product for anisotropy analysis of shale reservoirs. Background Technology

[0002] Due to the bedding structures formed during sedimentation and diagenesis, shale exhibits significant differences in its pore-throat structure along both the bedding and trans-bedding directions; this difference is known as anisotropy. Anisotropy directly affects the gas / oil storage capacity, permeability, and development scheme design of shale reservoirs. Therefore, anisotropy analysis of shale reservoirs is a crucial step in geological evaluation, development scheme optimization, and resource extraction efficiency improvement.

[0003] In existing technologies, the anisotropy analysis method for shale reservoirs relies on high-resolution imaging technology to obtain images of the pore-throat structure, and then uses visual observation or simple software tools to compare the characteristics of pore distribution, throat morphology, etc. in images from different directions to make qualitative judgments.

[0004] Because the analysis results in existing technologies are highly dependent on the operator's experience, there is a technical problem of low reliability in the anisotropy analysis of shale reservoirs. Summary of the Invention

[0005] This application provides methods, apparatus, equipment, media, and products for anisotropy analysis of shale reservoirs, aiming to improve the reliability of anisotropy analysis of shale reservoirs.

[0006] In a first aspect, embodiments of this application provide a method for anisotropy analysis of shale reservoirs, including:

[0007] Multiple microscopic images of the target shale sample were acquired along the bedding direction and across the bedding direction, and the images were classified and stored according to the direction corresponding to the microscopic images.

[0008] Alignment processing is performed on multiple microscopic images in the same direction, the pore throat structure parameters of each aligned microscopic image are extracted, and the multiple pore throat structure parameters are fused to obtain the fused parameter value in that direction.

[0009] Based on the fusion parameter values ​​in the in-layer direction and the fusion parameter values ​​in the trans-layer direction, at least one difference ratio value is calculated;

[0010] An anisotropy analysis report for the target shale sample is generated based on the differential ratio.

[0011] In one possible implementation, multiple microscopic images in the same direction are aligned, and the pore throat structure parameters of each aligned microscopic image are extracted, including:

[0012] The scale-invariant feature transform algorithm is used to register microscopic images in the same direction, resulting in multiple registered images with sub-pixel level alignment.

[0013] Image segmentation and skeletonization processes are performed on the registered images to extract the pore throat structure parameters of each registered image.

[0014] In one possible implementation, multiple pore throat structure parameters are fused to obtain fused parameter values ​​in that direction, including:

[0015] For a parameter array consisting of pore throat structure parameters from multiple microscopic images in the same direction, the median of the parameter array is extracted and used as the fusion parameter value for the direction.

[0016] In one possible implementation, a scale-invariant feature transform algorithm is used to register microscopic images in the same direction, resulting in multiple registered images with sub-pixel alignment, including:

[0017] Based on the preset Gaussian difference pyramid, the key point combination of each microscopic image is detected;

[0018] From multiple microscopic images in the same direction, a reference image is selected, and the remaining microscopic images in the multiple microscopic images are the images to be registered;

[0019] For each image to be registered, the similarity between the first keypoint combination of the image to be registered and the second keypoint combination of the reference image is calculated, and a set of matching points is obtained from the first keypoint combination and the second keypoint combination based on the similarity.

[0020] The spatial transformation matrix required to align the image to be registered and the reference image is calculated based on the set of matching points.

[0021] Geometric transformation and pixel resampling are performed on the image to be registered based on the spatial transformation matrix to obtain a registered image that is aligned to the reference image at the sub-pixel level.

[0022] In one possible implementation, image segmentation and skeletonization are performed on the registered images to extract the aperture throat structure parameters of each registered image, including:

[0023] Dynamic thresholding is performed on the registered image to obtain a preliminary binary image corresponding to the registered image;

[0024] Based on the initial binary image, noise filtering and hole filling optimization are performed to obtain the target binary image corresponding to the registered image.

[0025] Based on the target binary image, pore parameters and throat parameters are extracted, and the pore-throat structure parameters corresponding to the target binary image are generated based on the extracted pore parameters and throat parameters.

[0026] The pore parameters include the binary image of each pore, the equivalent diameter and shape factor of each pore, and the average pore diameter of multiple pores in the entire target binary image; the throat parameters include the throat centerline network, multiple independent throat segments, the length of each independent throat segment, and the throat bottleneck index; where the throat bottleneck index refers to the proportion of short throats in multiple independent throat segments.

[0027] In one possible implementation, at least one differentiation ratio is calculated based on the fusion parameter values ​​in the lamellar direction and the fusion parameter values ​​in the translaminar direction, including:

[0028] The average pore diameter ratio is calculated based on the average pore diameter in the fusion parameter values ​​along the layer direction and the average pore diameter in the fusion parameter values ​​across the layer direction.

[0029] The throat bottleneck index ratio is calculated based on the throat bottleneck index in the fusion parameter values ​​along the lamination direction and the throat bottleneck index in the fusion parameter values ​​across the lamination direction.

[0030] Accordingly, an anisotropy analysis report for the target shale sample is generated based on the difference ratio, including:

[0031] Generate an anisotropy analysis report that includes the average pore diameter ratio and throat bottleneck index ratio; the anisotropy analysis report also includes sample information of the target shale sample and fused parameter values ​​for the bedding direction and the cross-bedding direction.

[0032] Secondly, embodiments of this application provide a shale reservoir anisotropy analysis apparatus, comprising:

[0033] The acquisition module is used to collect multiple microscopic images of the target shale sample along the bedding direction and across the bedding direction, and to classify and store them according to the direction corresponding to the microscopic images.

[0034] The first processing module is used to perform alignment processing on multiple microscopic images in the same direction, extract the pore throat structure parameters of each microscopic image after alignment, and fuse multiple pore throat structure parameters to obtain the fusion parameter value in that direction.

[0035] The second processing module is used to calculate at least one difference ratio based on the fusion parameter values ​​in the in-layer direction and the fusion parameter values ​​in the trans-layer direction.

[0036] The third processing module is used to generate anisotropy analysis reports for the target shale sample based on the difference ratio.

[0037] In one possible implementation, the first processing module is further configured to:

[0038] The scale-invariant feature transform algorithm is used to register microscopic images in the same direction, resulting in multiple registered images with sub-pixel level alignment.

[0039] Image segmentation and skeletonization processes are performed on the registered images to extract the pore throat structure parameters of each registered image.

[0040] In one possible implementation, the first processing module is further configured to:

[0041] For a parameter array consisting of pore throat structure parameters from multiple microscopic images in the same direction, the median of the parameter array is extracted and used as the fusion parameter value for the direction.

[0042] In one possible implementation, the first processing module is further configured to:

[0043] Based on the preset Gaussian difference pyramid, the key point combination of each microscopic image is detected;

[0044] From multiple microscopic images in the same direction, a reference image is selected, and the remaining microscopic images in the multiple microscopic images are the images to be registered;

[0045] For each image to be registered, the similarity between the first keypoint combination of the image to be registered and the second keypoint combination of the reference image is calculated, and a set of matching points is obtained from the first keypoint combination and the second keypoint combination based on the similarity.

[0046] The spatial transformation matrix required to align the image to be registered and the reference image is calculated based on the set of matching points.

[0047] Geometric transformation and pixel resampling are performed on the image to be registered based on the spatial transformation matrix to obtain a registered image that is aligned to the reference image at the sub-pixel level.

[0048] In one possible implementation, the first processing module is further configured to:

[0049] Dynamic thresholding is performed on the registered image to obtain a preliminary binary image corresponding to the registered image;

[0050] Based on the initial binary image, noise filtering and hole filling optimization are performed to obtain the target binary image corresponding to the registered image.

[0051] Based on the target binary image, pore parameters and throat parameters are extracted, and the pore-throat structure parameters corresponding to the target binary image are generated based on the extracted pore parameters and throat parameters.

[0052] The pore parameters include the binary image of each pore, the equivalent diameter and shape factor of each pore, and the average pore diameter of multiple pores in the entire target binary image; the throat parameters include the throat centerline network, multiple independent throat segments, the length of each independent throat segment, and the throat bottleneck index; where the throat bottleneck index refers to the proportion of short throats in multiple independent throat segments.

[0053] In one possible implementation, the second processing module is further configured to:

[0054] The average pore diameter ratio is calculated based on the average pore diameter in the fusion parameter values ​​along the layer direction and the average pore diameter in the fusion parameter values ​​across the layer direction.

[0055] The throat bottleneck index ratio is calculated based on the throat bottleneck index in the fusion parameter values ​​along the lamination direction and the throat bottleneck index in the fusion parameter values ​​across the lamination direction.

[0056] Correspondingly, the third processing module is also used for:

[0057] Generate an anisotropy analysis report that includes the average pore diameter ratio and throat bottleneck index ratio; the anisotropy analysis report also includes sample information of the target shale sample and fused parameter values ​​for the bedding direction and the cross-bedding direction.

[0058] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0059] The memory stores the instructions that the computer executes;

[0060] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect above and various possible implementations of the first aspect.

[0061] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and various possible implementations thereof.

[0062] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and various possible implementations thereof.

[0063] This application provides a method, apparatus, equipment, medium, and product for anisotropy analysis of shale reservoirs. The method involves acquiring multiple microscopic images of a target shale sample along both the bedding and trans-bedding directions, and aligning these images for each direction to obtain multiple aligned microscopic images for each direction. Parameter extraction is performed on each aligned microscopic image to obtain pore-throat structure parameters. For each direction, the pore-throat structure parameters from the multiple microscopic images in that direction are fused to obtain a fused parameter value for that direction. Using the calculated fused parameter values ​​for the bedding and trans-bedding directions, at least one difference ratio is calculated between the fused parameter values ​​for the two directions. An anisotropy analysis report of the target shale sample is generated using the difference ratio. Compared with existing technologies, this application utilizes image alignment to eliminate spatial misalignment errors between images; it uses data fusion to suppress outlier interference in single images and generate representative parameter values ​​in different directions; and finally, by calculating the difference quantification ratio, the anisotropy of shale reservoirs is transformed from a qualitative description into an objective indicator, providing data support for subsequent geological evaluation, thereby achieving the technical effect of improving the reliability of shale reservoir anisotropy analysis. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] Figure 1 Flowchart of the shale reservoir anisotropy analysis method provided in this application Figure 1 ;

[0066] Figure 2 Flowchart of the shale reservoir anisotropy analysis method provided in this application Figure 2 ;

[0067] Figure 3 A schematic diagram of the structure of the shale reservoir anisotropy analysis system provided in this application;

[0068] Figure 4 Flowchart of the shale reservoir anisotropy analysis method provided in this application Figure 3 ;

[0069] Figure 5 A comparative schematic diagram of image registration before and after, provided for an embodiment of this application;

[0070] Figure 6 A radar image of anisotropy analysis of a shale sample provided in an embodiment of this application;

[0071] Figure 7 A line graph illustrating the anisotropy analysis of a shale sample provided in this application embodiment;

[0072] Figure 8 A schematic diagram of a parameter table for anisotropy analysis of a shale sample provided for an embodiment of this application;

[0073] Figure 9 A schematic diagram of the shale reservoir anisotropy analysis device provided in this application;

[0074] Figure 10 A schematic diagram of the structure of the electronic device provided in this application.

[0075] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0076] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0077] First, let me explain the terms used in this application:

[0078] Sauvola algorithm: refers to a local binarization algorithm that dynamically generates a threshold by calculating the mean and standard deviation of the gray values ​​of the current pixel's neighborhood. It is suitable for images with uneven lighting.

[0079] Niblack algorithm: refers to a local thresholding method that calculates the threshold based on the neighborhood mean, standard deviation and correction factor.

[0080] Watershed algorithm: refers to a morphological image segmentation method that treats the image as terrain and divides regions by simulating the process of water flowing from high to low.

[0081] Scale-Invariant Feature Transform (SIFT) algorithm: refers to a feature extraction algorithm that generates keypoints by detecting extreme points in the scale space of an image and calculates their position, scale, and orientation invariant descriptors.

[0082] In existing technologies, the main methods for anisotropy analysis of shale reservoirs are to rely on high-resolution imaging technology to obtain pore throat structure images and then perform qualitative or semi-quantitative analysis through manual or semi-automatic methods.

[0083] However, because existing technologies rely solely on manual qualitative judgment and lack objective quantitative indicators, the analysis results cannot be standardized, resulting in the technical problem of low reliability in shale reservoir anisotropy analysis.

[0084] To address the aforementioned technical problems, this application proposes the following technical concept: by automating the processing of multi-directional microscopic images and combining image registration, parameter extraction, and data fusion techniques, a systematic and quantitative analysis framework for shale reservoir anisotropy is constructed. This transforms anisotropy from a qualitative description into an objective indicator, thereby improving the reliability of shale reservoir anisotropy analysis.

[0085] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0086] Figure 1 Flowchart of the shale reservoir anisotropy analysis method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0087] S101. Collect multiple microscopic images of the target shale sample along the bedding direction and across the bedding direction, and classify and store them according to the direction corresponding to the microscopic images.

[0088] In this step, the bedding direction refers to the direction parallel to the shale bedding plane. It should be noted that shale is a layered sedimentary rock, and bedding planes are parallel surfaces formed by sedimentation. The trans-bedding direction refers to the direction perpendicular to the shale bedding plane. Microscopic images refer to high-resolution images that allow observation of the pore-throat structure of the shale, which require directional acquisition. Categorized storage refers to grouping and saving images according to the acquisition direction to avoid confusion.

[0089] For example, the microscopic images can be images taken by scanning electron microscopes or slice images taken by focused ion beam scanning electron microscopes, while ensuring that the image shooting direction is consistent with the laminar or translaminar direction.

[0090] Alternatively, one possible implementation of acquiring microscopic images and classifying and storing them is as follows:

[0091] a1. Sample orientation preparation: Cut the target shale sample from the target shale core, ensuring that the sample surface is parallel to the corresponding direction.

[0092] a2. Microscopic image acquisition: Place the target shale sample into the imaging device, adjust the sample orientation so that the imaging direction is perpendicular to the sample surface, and acquire at least 3 images in each direction.

[0093] a3. Categorized storage: After acquisition, image files are named according to the acquisition direction, image number, and imaging parameters, and stored in folders according to direction. At the same time, metadata such as imaging resolution and field of view are recorded for subsequent parameter conversion.

[0094] S102. Align multiple microscopic images in the same direction, extract the pore throat structure parameters of each aligned microscopic image, and fuse multiple pore throat structure parameters to obtain the fused parameter value in that direction.

[0095] In this step, alignment refers to eliminating spatial misalignment among multiple images in the same direction, ensuring that the aperture throat structure positions correspond. Spatial misalignment can be caused by slight translation or rotation during shooting.

[0096] For example, in three images along the same laminar direction, the pore positions shift by 2 to 5 pixels due to slight sample movement. After alignment, the pore center coordinate error is less than 0.5 pixels.

[0097] Pore-throat structural parameters refer to quantitative indicators characterizing the features of pores and throats. Data fusion refers to taking representative values ​​of the same parameter from multiple microscopic images in the same direction to suppress outlier interference from a single image.

[0098] Alternatively, one possible way to obtain the fusion parameter values ​​is as follows:

[0099] S1021. The scale-invariant feature transform algorithm is used to register the microscopic images in the same direction, resulting in multiple registered images with sub-pixel level alignment.

[0100] In this step, the scale-invariant feature transform algorithm refers to an image feature extraction and matching algorithm that can stably detect and match key points under scale scaling, rotation, and brightness changes. Registration refers to mapping the image to be registered to the coordinate system of the reference image through key point matching, eliminating spatial misalignment. Subpixel-level alignment refers to registration accuracy reaching below pixels, which is higher than ordinary pixel-level alignment.

[0101] It should be noted that the specific implementation method of image registration in this step is as follows. Figure 2 Further explanation will be provided in the embodiments shown, and will not be repeated here.

[0102] S1022. Perform image segmentation and skeletonization processing on the registered images to extract the pore throat structure parameters of each registered image.

[0103] In this step, image segmentation refers to converting the registered grayscale image into a binary image where the aperture equals the foreground and the matrix equals the background. Skeletonization refers to reducing the connected regions of the apertures to a single-pixel-width centerline, i.e., the geometric center trajectory of the throat. Aperture-throat structural parameters refer to a complete set of indicators that integrates aperture and throat parameters.

[0104] It should be noted that the extraction of pore throat structural parameters is as follows: Figure 2 Further explanation will be provided in the embodiments shown, and will not be repeated here.

[0105] S1023. For the parameter array composed of pore throat structure parameters of multiple microscopic images in the same direction, extract the median of the parameter array and determine the median as the fusion parameter value of the direction.

[0106] In this step, the parameter array refers to the numerical set of the same type of pore throat parameters from multiple registered images in the same direction. For example: the average pore diameter array of 5 images in the somatic direction: [98,102,105,110,115] (unit: nm); throat bottleneck index array: [25%,28%,30%,32%,35%].

[0107] The median is the middle value in a parameter array sorted from smallest to largest. For even-numbered data points, the median is the average of the two middle values. Choosing the median helps suppress outliers. For example, if the average pore diameter array above is sorted as [98, 102, 105, 110, 115], the median is 105 nm; if the array is [98, 102, 105, 110], the median is (102 + 105) / 2 = 103.5 nm.

[0108] The fusion parameter value refers to the parameter value corresponding to the median, serving as a representative indicator of the pore-throat structure in that direction. For example, the fusion parameter value in the lamellar direction is an average pore diameter of 105 nm and a throat bottleneck index of 30%; the fusion parameter value in the translaminar direction is an average pore diameter of 75 nm and a throat bottleneck index of 55%.

[0109] S103. Based on the fusion parameter values ​​in the in-layer direction and the fusion parameter values ​​in the trans-layer direction, at least one differential ratio is calculated.

[0110] In this step, the difference ratio refers to the ratio of the fusion parameter value in the in-layer direction to the fusion parameter value in the cross-layer direction, which is used to quantify the degree of difference between the two directions.

[0111] Alternatively, one possible implementation of the difference ratio calculation is as follows:

[0112] S1031. The average pore diameter ratio is calculated based on the average pore diameter in the fusion parameter values ​​along the layer direction and the average pore diameter in the fusion parameter values ​​across the layer direction.

[0113] In this step, the average pore diameter ratio refers to the ratio of the average pore diameter along the bedding layer to the average pore diameter across the bedding layer.

[0114] For example, the average pore diameter ratio can be calculated as follows: if the average pore diameter along the bedding plane is 105 nm and the average pore diameter across the bedding plane is 75 nm, then the average pore diameter ratio is 1.4, which reflects that the pore diameter along the bedding plane is 40% larger than that across the bedding plane.

[0115] S1032. The throat bottleneck index ratio is calculated based on the throat bottleneck index in the fusion parameter values ​​in the in-layer direction and the throat bottleneck index in the fusion parameter values ​​in the trans-layer direction.

[0116] In this step, the throat bottleneck index ratio refers to the ratio of the in-place throat bottleneck index to the trans-place throat bottleneck index.

[0117] For example, the throat bottleneck index ratio can be calculated as follows: if the in-layer throat bottleneck index is 30% and the trans-layer throat bottleneck index is 55%, then the throat bottleneck index ratio is 0.55, which reflects that the number of in-layer throat bottlenecks is only 55% of that of trans-layer throat bottlenecks.

[0118] It should be noted that this application uses the ratio of the pore diameter along the bedding plane to the pore diameter along the cross-bedding plane to calculate the throat bottleneck index ratio; the latter two ratios are calculated based on this method.

[0119] S104. Generate anisotropy analysis report of the target shale sample based on the differential ratio.

[0120] Optionally, one possible way to generate an anisotropy report is to generate an anisotropy analysis report that includes the average pore diameter ratio and the throat bottleneck index ratio; wherein, the anisotropy analysis report also includes sample information of the target shale sample and fused parameter values ​​for the bedding direction and the trans-bedding direction.

[0121] In this step, the anisotropy analysis report refers to a structured document that integrates sample information, merges parameter values, and differential ratios to quantitatively characterize the anisotropy of shale reservoirs.

[0122] For example, an anisotropy analysis report can be generated in the following ways:

[0123] b1. Collect core data and perform structured processing.

[0124] In this step, the core data includes basic sample information: sample number, acquisition location, imaging equipment and parameters; fusion parameter value table: average pore diameter, throat bottleneck index, shape factor, and average throat length in both in-laminar and translaminar directions; and differential ratios: average pore diameter ratio and throat bottleneck index ratio.

[0125] A structured approach can include: setting a report cover page, including the report title, preparation date, and preparer; setting sample information, detailing the sample source and imaging conditions; setting a fusion parameter table, primarily presenting the core parameters in both directions in tabular form; performing a differential ratio analysis, explaining the meaning of each ratio; and summarizing anisotropy conclusions, providing an evaluation of reservoir anisotropy intensity by combining the two ratios, for example, the greater the ratio deviates from 1, the stronger the anisotropy; it can also be correlated with geological significance, such as better pore throat development along the bedding direction, and horizontal well development should be deployed along the bedding direction.

[0126] b2. Output the report in document format, and key images can be attached to the report to enhance readability.

[0127] The anisotropy analysis method for shale reservoirs provided in this application involves acquiring multiple microscopic images of a target shale sample along both the bedding and trans-bedding directions. These images are then aligned for each direction to obtain multiple aligned microscopic images. Parameters are extracted from each aligned microscopic image to obtain pore-throat structure parameters. For each direction, the pore-throat structure parameters from the multiple microscopic images in that direction are fused to obtain a fused parameter value for that direction. Using the calculated fused parameter values ​​along the bedding and trans-bedding directions, at least one difference ratio is calculated between the fused parameter values ​​in the two directions. An anisotropy analysis report of the target shale sample is generated using this difference ratio. Compared with existing technologies, this application utilizes image alignment to eliminate spatial misalignment errors between images; it uses data fusion to suppress outlier interference in single images and generate representative parameter values ​​in different directions; and finally, by calculating the difference quantification ratio, the anisotropy of shale reservoirs is transformed from a qualitative description into an objective indicator, providing data support for subsequent geological evaluation, thereby achieving the technical effect of improving the reliability of shale reservoir anisotropy analysis.

[0128] Figure 2 Flowchart of the shale reservoir anisotropy analysis method provided in this application Figure 2 ,like Figure 2 As shown, the method includes:

[0129] S201. Based on the preset Gaussian difference pyramid, the key point combination of each microscopic image is detected.

[0130] In this step, the Gaussian difference pyramid consists of a Gaussian pyramid and difference images. The Gaussian pyramid is a multi-layered image obtained by scaling and blurring the image using different Gaussian blur coefficients; the difference image is the difference image between two adjacent Gaussian images. For example, there are pre-set Gaussian pyramids with 6 groups, each with 5 layers, and Gaussian blur coefficients of 1.6, 1.6√2, 1.6×2, and 1.6×2√2. The difference images consist of 6 groups multiplied by 4 layers, totaling 24 layers.

[0131] Keypoint combinatorial refers to the set of feature points obtained after optimizing the extreme points detected in all difference images. For example, a 512×512 pixel microscopic image may contain 250 keypoints detected through the difference of Gaussian pyramid, forming the keypoint combinatorial for that image.

[0132] For example, the keypoint combination of each microscopic image can be obtained as follows:

[0133] c1. For a single microscopic image, scale it according to a preset scale factor to obtain image layers of different resolutions; blur each image layer with a Gaussian filter with different Gaussian blur coefficients to form a Gaussian pyramid with a group-layer structure.

[0134] c2. Subtract the images of adjacent two layers in each group of the Gaussian pyramid to obtain the difference image, which is used to highlight the edges and feature points of the image.

[0135] c3. For each pixel in each difference image, compare it with its own 8-neighbor pixels and the corresponding 9-neighbor pixels in the difference images above and below. If it is a local maximum or minimum value, mark it as a potential key point.

[0136] c4. Calculate the grayscale interpolation of potential key points, remove pseudo key points with low contrast and edge response, and obtain the final key point combination.

[0137] c5. Assign a 128-dimensional feature description vector to each key point based on its local image gradient direction. This vector is robust to changes in illumination, rotation, and viewpoint.

[0138] S202. From multiple microscopic images in the same direction, select one reference image, and the remaining microscopic images in the multiple microscopic images are the images to be registered.

[0139] In this step, the reference image refers to the image with the highest clarity, the most complete pore throat structure, and the largest number of key points among multiple microscopic images in the same direction, and is used as the reference for registration. When multiple images meet this condition, one is selected as the reference image.

[0140] For example, among the eight images in the planar direction, the fourth image is unblurred, with clear edges of pores and throats, and detects 320 key points, so it can be selected as the reference image.

[0141] The image to be registered refers to other images in the same direction besides the reference image, which need to be mapped to the coordinate system of the reference image through registration.

[0142] For example, the remaining 7 images in the layer direction are all images to be registered and need to be aligned with the reference image respectively.

[0143] S203. For each image to be registered, calculate the similarity between the first key point combination of the image to be registered and the second key point combination of the reference image, and select a set of matching points from the first key point combination and the second key point combination based on the similarity.

[0144] In this step, keypoint similarity refers to the distance between the keypoint description vectors of the image to be registered and the keypoint description vectors of the reference image. The smaller the distance, the higher the similarity. A nearest neighbor search strategy is typically used, which considers the two points with the smallest Euclidean distance as a matching pair.

[0145] For example, if the Euclidean distance between the key point description vector A of the image to be registered and the key point description vector B of the reference image is 20, and the distance between A and the key point description vector C is 80, then A and B have a higher similarity.

[0146] The matching point set refers to the pairs of key points that correspond to each other after similarity filtering and removal of incorrect matches.

[0147] For example, the image to be registered and the reference image are initially matched to obtain 150 point pairs. After removing incorrect matches, 120 correct matching points remain, forming a set of matching points.

[0148] S204. Calculate the spatial transformation matrix required to align the image to be registered with the reference image based on the matching point set.

[0149] In this step, the spatial transformation matrix refers to the matrix that describes the geometric transformation relationship between the image to be registered and the reference image, including parameters such as translation, rotation, scaling, and shearing.

[0150] For example, 2D images often use a 3×3 homography matrix or a 2×3 affine matrix, such as the affine matrix: [1.02,0.01,-2.5;0.005,1.03,-1.8], where (-2.5,-1.8) is the translation amount, 1.02 and 1.03 are the scaling factors, and 0.01 and 0.005 are the shearing coefficients.

[0151] Alternatively, the spatial transformation matrix can be calculated in the following ways:

[0152] d1. Selecting a transformation model: Spatial misalignment in microscopic images is mainly due to translation, rotation, and scaling. An affine transformation model or a similarity transformation model can be selected.

[0153] d2. Solve for the spatial transformation matrix: Based on the matching point set, use the least squares method to minimize the transformation error between the key point coordinates of the image to be registered and the key point coordinates of the reference image, and solve for the parameters of the spatial transformation matrix.

[0154] d3. Verify the reliability of the matrix: Calculate the average reprojection error of the matching point set, which is the average distance between the points in the image to be registered and the corresponding points in the reference image after transformation. If the error is less than 0.5 pixels, the matrix is ​​valid; otherwise, re-select the matching point pairs and solve again.

[0155] It should be noted that, in order to further improve the registration accuracy, a random sampling consensus algorithm can be used to estimate the transformation model between images by randomly sampling a small number of matching point pairs and to calculate the number of local points that conform to the model. Through iterative optimization, the correct set of matching points is finally selected, and the optimal spatial transformation matrix is ​​calculated accordingly.

[0156] S205. Based on the spatial transformation matrix, perform geometric transformation and pixel resampling on the image to be registered to obtain a registered image with sub-pixel level alignment between the image to be registered and the reference image.

[0157] In this step, geometric transformation refers to mapping the pixel coordinates of the image to be registered according to the spatial transformation matrix to correct misalignments such as translation, rotation, and scaling.

[0158] For example, the image to be registered has a 3-pixel translation and a 2° rotation. After geometric transformation, the pixel coordinates are mapped to the coordinate system of the reference image, thus correcting the misalignment.

[0159] Pixel resampling refers to the process of using interpolation algorithms to supplement grayscale values ​​when the coordinates of some pixels are non-integer after geometric transformation, thus ensuring continuous and clear images.

[0160] For example, bilinear interpolation can be used for pixel resampling, which calculates the gray value of the target pixel by weighting the gray values ​​of the four pixels surrounding it at non-integer coordinates, balancing accuracy and efficiency.

[0161] A subpixel-level aligned registered image refers to an image whose spatial error with the reference image is less than 1 pixel after geometric transformation and resampling.

[0162] For example, after registration, the error between the aperture center coordinates of the image to be registered and the reference image is 0.3 pixels, which meets the sub-pixel level alignment requirement.

[0163] S206. Perform dynamic thresholding on the registered image to obtain a preliminary binary image corresponding to the registered image.

[0164] In this step, dynamic thresholding refers to adaptively adjusting the segmentation threshold according to the gray-level characteristics of local areas of the image, rather than fixing the threshold globally. This method is suitable for images with uneven gray levels.

[0165] For example, if there are shadows at the edges of a microscopic image, a dynamic threshold will lower the segmentation threshold for the shadowed areas to prevent pores from being misidentified as matrix.

[0166] The preliminary binary image refers to the pore-matrix binary image obtained after segmentation. It has not undergone noise filtering and pore filling, and may contain a small amount of noise and black holes.

[0167] For example, in the initial binary image, the pores are white and the matrix is ​​black, but there are sporadic white noise spots in the matrix and small black holes inside the pores.

[0168] Alternatively, the initial binary image can be generated in the following ways:

[0169] e1. For microscopic images, choose either the Sauvola algorithm or the Niblack algorithm for dynamic thresholding segmentation.

[0170] e2. Define the local neighborhood window size as 5×5 or 7×7 pixels. The larger the window, the better it adapts to local grayscale changes, but the boundary accuracy decreases slightly.

[0171] e3. For each pixel in the registered image, calculate the mean and standard deviation of grayscale within its local window, and calculate the segmentation threshold for that pixel according to the algorithm formula.

[0172] The Sauvola threshold formula is as follows: Where k=0.5 refers to the threshold adjustment coefficient; R=128 refers to the grayscale dynamic range coefficient; This refers to the standard deviation of grayscale values ​​in a local window; This refers to the average grayscale value of a local window.

[0173] e4. If the pixel gray level is greater than or equal to the segmentation threshold, it is determined to be a pore; otherwise, it is matrix, and a preliminary binary image is output.

[0174] S207. Based on the preliminary binary image, noise filtering and hole filling optimization are performed to obtain the target binary image corresponding to the registered image.

[0175] In this step, noise filtering refers to removing isolated noise points from the initial binary image, such as small white dots in the matrix or small black dots in the pores.

[0176] For example, white noise in the matrix with a diameter of less than 2 pixels is removed after filtering; black dots with a diameter of less than 2 pixels in the pores are filled.

[0177] Pore ​​filling refers to filling the closed black hole inside the pore area. The closed black hole refers to a small matrix area completely surrounded by pores, which belongs to the segmentation error.

[0178] For example, a closed black hole with an internal diameter of less than 3 pixels becomes a pore after being filled, and the pore region is continuous and complete.

[0179] The target binary image refers to the optimized pure binary image, which is free of noise and pores, with clear boundaries between pores and matrix, and can be directly used for parameter extraction.

[0180] S208. Extract pore parameters and throat parameters based on the target binary image, and generate pore-throat structure parameters corresponding to the target binary image based on the extracted pore parameters and throat parameters.

[0181] In this step, the pore parameters include the binary image of each pore, the equivalent diameter and shape factor of each pore, and the average pore diameter of multiple pores in the entire target binary image; the throat parameters include the throat centerline network, multiple independent throat segments, the length of each independent throat segment, and the throat bottleneck index; where the throat bottleneck index refers to the proportion of short throats in multiple independent throat segments.

[0182] For example, the pore parameter extraction method can be:

[0183] f1. Perform distance transformation on the pore region of the target binary image and detect local maxima as seed points.

[0184] f2. Use the watershed algorithm to segment the adhesive pores and obtain a binary image of each independent pore.

[0185] Specifically, the gradient of the distance transformation map is used as the terrain surface, and a watershed algorithm is executed in conjunction with seed points. This algorithm simulates the process of water injection starting from each seed point and stopping when different catchment area boundaries meet, thereby accurately dividing the contiguous porous regions into independent units. Here, the catchment area refers to the porous region.

[0186] f3. For each individual aperture, calculate the pixel area A and perimeter P, and convert them to the actual area A × resolution² and the actual perimeter P × resolution. Calculate the equivalent diameter as 2√(A / π) and the shape factor as 4πA / P².

[0187] f4. Calculate the arithmetic mean of the equivalent diameters of all independent pores to obtain the global average pore diameter. This parameter value can be used as the core indicator of pore parameters.

[0188] The laryngeal parameters can be extracted in the following ways:

[0189] g1. Iteratively skeletonize the target binary image to obtain a throat centerline network with a single pixel width.

[0190] g2. Detect branch points in the skeleton network and disconnect the network at these points to obtain multiple independent throat segments. Branch points in the skeleton network can be pixels with more than 3 neighbor connections.

[0191] g3. Obtain the pixel length of each independent throat segment and convert it into the actual length.

[0192] g4. Set the short larynx threshold to 50nm, and obtain the number of short larynxes based on this threshold. Calculate the larynx bottleneck index using the formula: BI = (number of short larynxes / total number of larynxes) × 100%.

[0193] The pore throat structure parameters can be generated by integrating the above-mentioned pore parameters and throat parameters to form the pore throat structure parameters of the binary image of the target.

[0194] Figure 3 This is a schematic diagram of the structure of the shale reservoir anisotropy analysis system provided in this application, as shown below. Figure 3 As shown, the system includes: an image management module 301; an intra-directional fusion module 302; an inter-directional parameter comparison module 303; a result output module 304; and a user interface 305. The intra-directional fusion module 302, the inter-directional parameter comparison module 303, and the user interface 305 are sequentially connected. One end of the image management module 301 is connected to the intra-directional fusion module 302, and the other end of the image management module 301 is connected to the user interface 305. One end of the result output module 304 is connected to the intra-directional fusion module 302, and the other end of the result output module 304 is connected to the user interface 305.

[0195] The image management module 301 is used to acquire images of the observation surfaces along the bedding direction and the observation surfaces across the bedding direction of the same shale sample. At least three images are acquired for each direction, and the images are automatically classified and stored according to the direction.

[0196] The intra-directional fusion module 302 is used to align images in the same direction in the in-layer direction image set and the trans-layer direction image set, respectively, extract multiple pore throat structure parameters, perform data fusion calculations, and obtain fusion parameter values.

[0197] Optionally, in one possible implementation, the intra-direction fusion module 302 includes an image registration submodule, a parameter extraction submodule, and a data fusion submodule. The image registration submodule is used to align images in an image set in the same direction; the parameter extraction submodule is used to extract the aperture throat structure parameters of the aligned and registered images; and the data fusion submodule is used to perform fusion calculations on multiple aperture throat structure parameters in the same direction to obtain fusion parameter values.

[0198] The inter-direction parameter comparison module 303 is used to calculate one or more difference quantification ratios between the fusion parameter values ​​in the in-layer direction and the fusion parameter values ​​in the trans-layer direction.

[0199] The results output module 304 is used to output an anisotropy analysis report containing the quantified ratios of differences, and to store the report in a database.

[0200] User interface 305 is used to display the generated anisotropy analysis report and receive control commands issued by the user based on the interface to control the operation of various modules within the system.

[0201] exist Figure 3 Based on the embodiments shown, this application proposes a shale reservoir anisotropy analysis method based on a shale reservoir anisotropy analysis system. Figure 4 Flowchart of the shale reservoir anisotropy analysis method provided in this application Figure 3 ,like Figure 4 As shown, the shale sample used in this embodiment has been pre-prepared and is designated MH-01. The observation surfaces along the bedding planes and across the bedding planes of this shale sample have been precisely polished. The anisotropy analysis methods for shale sample MH-01 include:

[0202] A1. A dual-beam electron microscope system was used to acquire three backscattered electron images on each observation surface.

[0203] In this step, all images have a resolution of 2048×2048 pixels, and the physical pixel size is calibrated to 5.34nm. Image files in the same direction as the layer are named MH-01S01.tif, MH-01S02.tif, and MH-01S03.tif; image files in the cross-layer direction are named MH-01C01.tif, MH-01C02.tif, and MH-01C03.tif.

[0204] A2. Import the 6 images collected in step A1 into the shale reservoir anisotropy analysis system through the user interface and start the analysis process.

[0205] In this step, after the above 6 images are imported into the system, the image management module automatically scans the file names and identifies the prefixes "S" and "C", thereby accurately classifying the images into two independent image sets: "in-line" and "through-layer", preparing for subsequent processing.

[0206] A3. Based on the intra-directional fusion module, the image combination of the two directions is processed independently to obtain four representative parameter values ​​after fusion for the two directions.

[0207] For example, Figure 5 A comparative diagram of image registration before and after is provided for an embodiment of this application, as shown below. Figure 5 As shown, the following explanation uses three images from a composite image along the layer direction as an example. Based on the image registration submodule, MH-01S01.tif is used as the reference image, and MH-01S02.tif and MH-01S03.tif are registered sequentially.

[0208] Specifically:

[0209] Step 1: Use the SIFT algorithm to detect and describe feature points in each image, match the extracted feature points, and remove mismatched point pairs to achieve accurate image registration.

[0210] Step 2: Based on the correct combination of matching points, calculate the affine transformation matrix from the image to be registered to the reference image, and perform resampling transformation on MH-01S02.tif and MH-01S03.tif to achieve sub-pixel level alignment. The resulting three registered images are: MH-01S01.tif, MH-01S02.tif, and MH-01S03.tif.

[0211] Step 3: For the three registered images mentioned above, the pore-throat structure parameters of the three registered images are extracted using the parameter extraction submodule. Each registered image is a 16-bit grayscale image. The pore-throat structure parameter extraction specifically involves: linearly mapping the 16-bit grayscale image to the range of 0-255, segmenting it using a dynamic threshold to generate a binary image, and then using morphological opening operations to eliminate noise in the binary image to obtain the target binary image. The target binary image is then segmented, and batch parameter calculations are performed on each segmented independent pore region and throat network. While calculating the average pore diameter and throat bottleneck index, other pore-throat structure parameters also need to be extracted, including but not limited to: pore shape factor, pore equivalent diameter, pore-throat connectivity, porosity, and throat bottleneck index.

[0212] After processing, the core parameter values ​​of the three images are as follows: average pore diameter (D): D1=169.7nm, D2=174.8nm, D3=166.1nm. Throat bottleneck index (BI): BI1=38.01%, BI2=38.61%, BI3=37.63%.

[0213] It should be noted that the specific values ​​of the auxiliary parameters such as shape factor, connectivity, and porosity are not listed here, but the process of obtaining them is exactly the same as that of the core parameters.

[0214] Step 4: Based on the array of parameters received by the data fusion submodule, the median of the average pore diameter array [169.7, 174.8, 166.1] is taken to obtain the representative parameter value D_laminated = 169.7 nm in the laminar direction. The median of the throat bottleneck index array [38.01%, 38.61%, 37.63] is taken to obtain the representative parameter value BI_laminated = 38.01% in the laminar direction. Thus, the fused parameter value in the laminar direction is obtained.

[0215] The same procedure was applied to the three images MH-01C01.tif, MH-01C02.tif, and MH-01C03.tif in the transdermal direction image group to obtain representative parameter values ​​for the transdermal direction: D transdermal = 128.79nm, BI transdermal = 43.81%.

[0216] A4. Based on the inter-directional comparison module, read the representative parameter values ​​of the above four fusions and calculate the pore diameter ratio and throat bottleneck index ratio between the two directions.

[0217] In this step, the pore diameter ratio is automatically calculated: Rd = D_parallel / D_cross = 169.7 / 128.79 ≈ 1.31. The throat bottleneck index ratio is automatically calculated: R BI =BI cross-layer / BI in-layer = 43.81% / 38.01%≈1.15. The pore diameter ratio and throat bottleneck index ratio can be calculated by dividing the cross-layer direction by the in-layer direction, or by dividing the in-layer direction by the cross-layer direction.

[0218] A5. Combining the calculated pore diameter ratio, throat bottleneck index ratio, basic information of the shale sample, and auxiliary parameters related to the shale sample obtained during the calculation process: shape factor, connectivity, and porosity, generate an anisotropy analysis report for the shale sample.

[0219] For example, Figure 6 A radar image of anisotropy analysis of a shale sample provided in an embodiment of this application, such as... Figure 6 As shown, taking the MH-01 sample as an example, the comprehensive difference pattern of multiple parameters, including average pore diameter, shape factor, bottleneck index, connectivity, and porosity, in the bedding and trans-bedding directions is intuitively displayed, revealing the anisotropy of shale bedding structure.

[0220] For example, Figure 7 A line graph illustrating the anisotropy analysis of a shale sample provided in this application embodiment, such as... Figure 7 As shown: Taking the MH-01 sample as an example, the comparison of the pore diameter ratio and throat bottleneck index ratio of this sample and two other representative samples, MH-02 and MH-03, provides an intuitive basis for the differences in the degree of anisotropy of different reservoirs.

[0221] A6. Display and generate anisotropy analysis reports on the user interface through the results output module.

[0222] For example, Figure 8 A schematic diagram of a parameter table for anisotropy analysis of a shale sample provided in this application embodiment is shown below. Figure 8 As shown, taking sample MH-01 as an example, the basic information of the sample and the data analysis results are presented. The basic information includes [Sample number: MH-01; Sampling point: Geographic location A; Analysis date: 20XX-XX-XX; Stratigraphic position: Permian Fengcheng Formation; Number of images: three images in the bedding direction and three images in the trans-bedding direction; Pixel size: 6.23nm / pixel]. The data analysis results include: [Porosity along the bedding direction (%): 5.27±0.3; Porosity across the bedding direction (%): 5.04±0.2; Average pore diameter along the bedding direction (nm): 169.70±5.1; Average pore diameter across the bedding direction (nm): 128.79±3.7; Throat bottleneck index along the bedding direction (%): 38.01±0.6; Throat bottleneck index across the bedding direction (%): 43.81±0.8; Connectivity along the bedding direction (%): 17.42±0.4; Connectivity across the bedding direction (%): 7.20±0.2; Shape factor along the bedding direction: 0.58±0.02; Shape factor across the bedding direction: 0.66±0.03]. Correspondingly, the average pore diameter along the bedding direction relative to the cross-bedding direction is used to obtain the average pore diameter ratio R. D The throat bottleneck index is 1.31, which is obtained by dividing the throat bottleneck index in the trans-layer direction by the throat bottleneck index in the bedding direction. BI It is 1.15.

[0223] based on Figure 8 The analysis of the sample data shown yielded the following conclusions regarding the anisotropy of the MH-01 sample: Ratio R D =1.31>1, and much greater than 1, quantitatively indicating that the pore system along the bedding plane of this sample is slightly more developed than that along the trans-bedding plane. Ratio R BI =1.15>1, which quantitatively indicates that the seepage bottleneck effect in the cross-layer direction of this sample is much stronger than that in the parallel-layer direction, that is, there is strong seepage anisotropy.

[0224] Figure 9 This is a schematic diagram of the shale reservoir anisotropy analysis device provided in this application, as shown below. Figure 9 As shown, the shale reservoir anisotropy analysis device provided in this embodiment includes:

[0225] The acquisition module 901 is used to acquire multiple microscopic images of the target shale sample along the bedding direction and across the bedding direction, and to classify and store them according to the direction corresponding to the microscopic images.

[0226] The first processing module 902 is used to perform alignment processing on multiple microscopic images in the same direction, extract the pore throat structure parameters of each microscopic image after alignment, and fuse multiple pore throat structure parameters to obtain the fusion parameter value in that direction.

[0227] The second processing module 903 is used to calculate at least one difference ratio based on the fusion parameter values ​​in the in-layer direction and the fusion parameter values ​​in the trans-layer direction.

[0228] The third processing module 904 is used to generate an anisotropy analysis report of the target shale sample based on the difference ratio.

[0229] Optionally, in one possible implementation, the first processing module 902 is further configured to:

[0230] The scale-invariant feature transform algorithm is used to register microscopic images in the same direction, resulting in multiple registered images with sub-pixel alignment.

[0231] Image segmentation and skeletonization processes are performed on the registered images to extract the pore throat structure parameters of each registered image.

[0232] Optionally, in one possible implementation, the first processing module 902 is further configured to:

[0233] For a parameter array consisting of pore throat structure parameters from multiple microscopic images in the same direction, the median of the parameter array is extracted and used as the fusion parameter value for the direction.

[0234] Optionally, in one possible implementation, the first processing module 902 is further configured to:

[0235] Based on the preset Gaussian difference pyramid, the key point combination of each microscopic image is detected.

[0236] From multiple microscopic images in the same direction, a reference image is selected, and the remaining microscopic images in the multiple microscopic images are the images to be registered.

[0237] For each image to be registered, the similarity between the first keypoint combination of the image to be registered and the second keypoint combination of the reference image is calculated, and a set of matching points is obtained from the first keypoint combination and the second keypoint combination based on the similarity.

[0238] The spatial transformation matrix required to align the image to be registered and the reference image is calculated based on the set of matching points.

[0239] Geometric transformation and pixel resampling are performed on the image to be registered based on the spatial transformation matrix to obtain a registered image that is aligned to the reference image at the sub-pixel level.

[0240] Optionally, in one possible implementation, the first processing module 902 is further configured to:

[0241] Dynamic thresholding is performed on the registered image to obtain a preliminary binary image corresponding to the registered image.

[0242] Noise filtering and hole filling optimization are performed on the initial binary image to obtain the target binary image corresponding to the registered image.

[0243] Pore ​​and throat parameters are extracted from the target binary image, and pore and throat structure parameters corresponding to the target binary image are generated based on the extracted pore and throat parameters.

[0244] The pore parameters include the binary image of each pore, the equivalent diameter and shape factor of each pore, and the average pore diameter of multiple pores in the entire target binary image; the throat parameters include the throat centerline network, multiple independent throat segments, the length of each independent throat segment, and the throat bottleneck index; where the throat bottleneck index refers to the proportion of short throats in multiple independent throat segments.

[0245] Optionally, in one possible implementation, the second processing module 903 is further configured to:

[0246] The average pore diameter ratio is calculated based on the average pore diameter in the fusion parameter values ​​along the layer direction and the average pore diameter in the fusion parameter values ​​across the layer direction.

[0247] The throat bottleneck index ratio is calculated based on the throat bottleneck index in the fusion parameter values ​​along the lamellar direction and the throat bottleneck index in the fusion parameter values ​​across the lamellar direction.

[0248] Correspondingly, the third processing module 904 is also used for:

[0249] Generate an anisotropy analysis report that includes the average pore diameter ratio and throat bottleneck index ratio; the anisotropy analysis report also includes sample information of the target shale sample and fused parameter values ​​for the bedding direction and the cross-bedding direction.

[0250] The apparatus provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0251] Figure 10 A schematic diagram of the structure of the electronic device provided in this application. Figure 10 As shown, the electronic device provided in this embodiment includes at least one processor 1001 and a memory 1002. Optionally, the device further includes a communication component 1003. The processor 1001, memory 1002, and communication component 1003 are connected via a bus 1004.

[0252] In the specific implementation process, at least one processor 1001 executes computer execution instructions stored in memory 1002, causing at least one processor 1001 to execute the above-mentioned shale reservoir anisotropy analysis method or approach.

[0253] The specific implementation process of processor 1001 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0254] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0255] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0256] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0257] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0258] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0259] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0260] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0261] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0262] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0263] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0264] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0265] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0266] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for anisotropy analysis of shale reservoirs, characterized in that, include: Multiple microscopic images of the target shale sample along the bedding direction and across the bedding direction are acquired, and the samples are classified and stored according to the direction corresponding to the microscopic images. Alignment processing is performed on multiple microscopic images in the same direction, the pore throat structure parameters of each aligned microscopic image are extracted, and the multiple pore throat structure parameters are fused to obtain the fused parameter value in that direction. Based on the fusion parameter values ​​in the in-layer direction and the fusion parameter values ​​in the trans-layer direction, at least one difference ratio value is calculated; An anisotropy analysis report of the target shale sample is generated based on the aforementioned difference ratio.

2. The method according to claim 1, characterized in that, The alignment process for multiple microscopic images in the same direction, and the extraction of pore throat structure parameters for each aligned microscopic image, includes: The scale-invariant feature transform algorithm is used to register microscopic images in the same direction, resulting in multiple registered images with sub-pixel level alignment. Image segmentation and skeletonization processes are performed on the registered images to extract the pore throat structure parameters of each registered image.

3. The method according to claim 1, characterized in that, The step of fusing multiple pore throat structure parameters to obtain fused parameter values ​​in that direction includes: For a parameter array consisting of pore throat structure parameters from multiple microscopic images in the same direction, the median of the parameter array is extracted, and the median is determined as the fusion parameter value for the direction.

4. The method according to claim 2, characterized in that, The scale-invariant feature transform algorithm is used to register microscopic images in the same direction, resulting in multiple registered images with sub-pixel alignment, including: Based on a preset Gaussian difference pyramid, key point combinations for each microscopic image are detected; From multiple microscopic images in the same direction, a reference image is selected, and the remaining microscopic images in the multiple microscopic images are the images to be registered; For each image to be registered, the similarity between the first key point combination of the image to be registered and the second key point combination of the reference image is calculated, and a set of matching points is obtained by filtering from the first key point combination and the second key point combination based on the similarity. The spatial transformation matrix required to align the image to be registered and the reference image is calculated based on the set of matching points. Based on the spatial transformation matrix, the image to be registered is subjected to geometric transformation and pixel resampling to obtain a registered image that is subpixel aligned with the reference image.

5. The method according to claim 2, characterized in that, The process of image segmentation and skeletonization of the registered images, extracting the aperture throat structure parameters of each registered image, includes: Dynamic thresholding is performed on the registered image to obtain a preliminary binary image corresponding to the registered image; Based on the preliminary binary image, noise filtering and hole filling optimization are performed to obtain the target binary image corresponding to the registered image; Based on the target binary image, pore parameters and throat parameters are extracted, and based on the extracted pore parameters and throat parameters, pore-throat structure parameters corresponding to the target binary image are generated. The pore parameters include a binary image of each pore, the equivalent diameter and shape factor of each pore, and the average pore diameter of multiple pores in the entire target binary image; the throat parameters include a throat centerline network, multiple independent throat segments, the length of each independent throat segment, and a throat bottleneck index; wherein, the throat bottleneck index refers to the proportion of short throats in multiple independent throat segments.

6. The method according to any one of claims 1-5, characterized in that, The calculation of at least one difference ratio based on the fusion parameter value in the in-laminar direction and the fusion parameter value in the translaminar direction includes: The average pore diameter ratio is calculated based on the average pore diameter in the fusion parameter values ​​along the lamination direction and the average pore diameter in the fusion parameter values ​​across the lamination direction. The throat bottleneck index ratio is calculated based on the throat bottleneck index in the fusion parameter values ​​in the in-laminar direction and the throat bottleneck index in the fusion parameter values ​​in the translaminar direction. Accordingly, generating the anisotropy analysis report of the target shale sample based on the difference ratio includes: Generate an anisotropy analysis report that includes the average pore diameter ratio and the throat bottleneck index ratio; wherein, the anisotropy analysis report also includes sample information of the target shale sample and fused parameter values ​​for the bedding direction and the cross-bedding direction.

7. A device for analyzing the anisotropy of shale reservoirs, characterized in that, include: The acquisition module is used to acquire multiple microscopic images of the target shale sample along the bedding direction and across the bedding direction, and to classify and store them according to the direction corresponding to the microscopic images; The first processing module is used to perform alignment processing on multiple microscopic images in the same direction, extract the pore throat structure parameters of each aligned microscopic image, and fuse multiple pore throat structure parameters to obtain the fused parameter value in that direction. The second processing module is used to calculate at least one difference ratio value based on the fusion parameter value in the in-layer direction and the fusion parameter value in the trans-layer direction. The third processing module is used to generate an anisotropy analysis report of the target shale sample based on the difference ratio.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

Images