Analysis method and device for crack network of rock

Abstract

The invention provides a rock crack network analysis method and device, and belongs to the field of seismic rock physics, and the method comprises the steps: obtaining a to-be-recognized rock image, carrying out the color filling of a crack in a to-be-recognized region of the rock image, and obtaining the crack color information of the to-be-recognized region; based on the crack color information of the plurality of to-be-identified areas, automatically searching spatial distribution characteristics in the whole area of the rock image by using a graphic algorithm; performing feature recognition and extraction on the spatial distribution features of the cracks by using a Hough transform method to obtain a whole crack network structure; performing multi-scale feature extraction on all the crack network structures to obtain multi-scale crack features; and analyzing the multi-scale fracture characteristics to obtain an analysis result. Through the method provided by the invention, the accuracy and comprehensiveness of rock crack feature recognition and extraction can be improved, the crack feature extraction efficiency is improved, and the cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of seismic rock physics, and in particular to a method and apparatus for analyzing fracture networks in rocks. Background Technology

[0002] The study of rock fracture characteristics is crucial for determining oil and gas reserves and reservoir quality. Rock fracture characteristics can be used not only to construct accurate reservoir models to simulate fluid migration and oil and gas extraction, thereby optimizing oil and gas exploration, development, and production operations; but also to establish the relationship between seismic wave response and reservoir properties, thus predicting and identifying potential reservoirs. Furthermore, rock fracture characteristics are significant for interpreting seismic profiles, identifying subsurface structures, and assessing oil and gas reservoirs, providing valuable information and guidance for geological exploration.

[0003] In the early stages, the identification of fracture and porosity characteristics in oil and gas reservoir rocks primarily relied on traditional geological observation and well logging data analysis. Through visual observation and optical microscopy analysis of core samples, scientists could obtain some basic information about rock porosity and fracture characteristics. This extraction method could leverage the experience and knowledge of experts, improving the accuracy and reliability of fracture feature extraction. However, due to differences in the opinions of different experts, subjectivity and inconsistency existed, leading to unstable results in fracture feature analysis; furthermore, the labor and time costs were also high. Summary of the Invention

[0004] The present invention provides a method and apparatus for analyzing the fracture network of rocks, so as to overcome or at least partially solve the above-mentioned problems.

[0005] A first aspect of the present invention provides a method for analyzing fracture networks in rocks, the method comprising:

[0006] A rock image to be identified is acquired, and cracks within the area to be identified in the rock image are filled with color to obtain crack color information for the area to be identified; wherein the filling color of the cracks is different from the existing colors in the rock image;

[0007] Based on the crack color information of multiple regions to be identified, a graphics algorithm is used to automatically search for the spatial distribution features of the entire region of the rock image; wherein, the spatial distribution features include the coordinate points of the main crack network structure within the entire region of the rock image;

[0008] The spatial distribution characteristics of the cracks are identified and extracted using the Hough transform method to obtain the complete crack network structure.

[0009] Multi-scale feature extraction is performed on all the crack network structures to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology;

[0010] The multi-scale crack characteristics were analyzed, and the analysis results were obtained.

[0011] Optionally, before acquiring the image of the rock to be identified and color-filling the cracks within the area to be identified in the rock image, the method further includes:

[0012] Obtain rock samples, and cut and grind the rock samples to obtain a smooth rock surface;

[0013] The rock cross-section is image acquired, and the acquired images are preprocessed to obtain rock images; wherein, the preprocessing includes noise reduction, contrast enhancement, brightness adjustment, and color balance adjustment;

[0014] The rock image is displayed, and the region to be identified is obtained based on the selection boxes marked on the displayed rock image.

[0015] Optionally, the step of obtaining the rock sample includes:

[0016] Select representative sampling points; these sampling points should reflect the rock composition and characteristics of the area; furthermore, areas that have been damaged or altered should be avoided to prevent human interference with the rock samples.

[0017] Based on the sampling points, rock samples are selected, and the orientation information of the rock samples is recorded; the orientation information is used for subsequent geological interpretation and rock analysis; wherein, the orientation information includes the geographical location, the coordinates of the sampling points, and the orientation and dip angle of the rock samples;

[0018] The covering on the surface of the rock sample is cleaned to obtain a clean and clear rock sample; the rock sample is used to accurately observe and describe the texture, structure and color of rock cracks.

[0019] Optionally, the step of color-filling the cracks within the area to be identified in the rock image to obtain the crack color information of the area to be identified includes:

[0020] Obtain the color distribution of the region to be identified;

[0021] Based on the color distribution in the area to be identified, the area where the crack is located is obtained;

[0022] Based on the selected target color, the target color is filled into the area where the crack is located to obtain the crack color information of the area to be identified.

[0023] Optionally, based on the crack color information of multiple regions to be identified, the step of automatically searching for the spatial distribution features of the entire area of ​​the rock image using a graphics algorithm includes:

[0024] S1: Based on the crack color information of multiple regions to be identified, the rock image is identified using graphics algorithms and automatic search technology to obtain the spatial location information of cracks within the entire region of the rock image; the spatial location information of cracks includes the coordinates of each location of the crack.

[0025] S2: Based on the selection operation of the crack color information, obtain the target coordinate point of the selected location;

[0026] S3: Save the selected target coordinates to the data buffer;

[0027] Repeat steps S1-S3 above to obtain the coordinates of the main crack network structure within the entire area of ​​the rock image;

[0028] Based on the coordinates of the main crack network structure within the entire area of ​​the rock image, the spatial distribution characteristics of the cracks are obtained.

[0029] Optionally, the graphics algorithm includes clustering method recognition, manual recognition, and color card recognition;

[0030] The clustering method identification includes:

[0031] Cluster analysis is performed on the colors of the area to be identified, and the crack colors are identified based on the cluster analysis results;

[0032] The manual identification includes:

[0033] Based on the crack color of the area to be identified selected by the user, all areas in the rock image that correspond to the crack color are automatically picked up.

[0034] The color card recognition includes:

[0035] The colors of the area to be identified are used to form a color bar, and the crack color is selected from the color bar.

[0036] Optionally, the step of using the Hough transform method to identify and extract the spatial distribution characteristics of the cracks to obtain the complete crack network structure includes:

[0037] Based on the spatial distribution characteristics of the cracks, the Hough transform method is used to identify crack features at the coordinate points of the main crack network structure.

[0038] Furthermore, edge detection and segmentation are performed on the crack edges of the main crack network structure to obtain all crack network features;

[0039] The Hough transform method is used to detect the geometric features of straight lines or curves.

[0040] Optionally, the step of extracting multi-scale features from all the crack network structures to obtain multi-scale crack features includes:

[0041] Multiple different scale parameters are obtained, which are used to indicate the field of view size for crack identification;

[0042] Based on multiple scale parameters, the entire crack network structure is divided to obtain multiple local regions of the entire crack network structure corresponding to the scale parameters.

[0043] The crack characteristics of multiple local regions of the entire crack network structure are calculated and statistically analyzed to obtain multi-scale crack characteristics.

[0044] The crack features include crack width, crack length, and crack shape.

[0045] A second aspect of the present invention provides an analysis apparatus for fracture networks in rocks, the analysis apparatus comprising:

[0046] A color filling module is used to acquire a rock image to be identified, and to fill the cracks in the area to be identified in the rock image with color to obtain the crack color information of the area to be identified; wherein, the filling color of the crack is different from the existing color in the rock image;

[0047] An automatic search module is used to automatically search for the spatial distribution features of the entire area of ​​the rock image based on the crack color information of multiple areas to be identified and using a graphics algorithm; wherein, the spatial distribution features include the coordinate points of the main crack network structure within the entire area of ​​the rock image;

[0048] The feature recognition module is used to identify and extract the spatial distribution features of the cracks using the Hough transform method to obtain the complete crack network structure.

[0049] The feature extraction module is used to extract multi-scale features from the entire crack network structure to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology.

[0050] The feature analysis module is used to analyze the multi-scale crack features and obtain the analysis results.

[0051] The method provided by this invention has the following main advantages:

[0052] The method provided by this invention introduces a graphical computation approach to accurately select crack colors and extract spatial distribution features of cracks. This ensures that the extracted crack features better reflect the actual conditions of rock cracks, avoiding the subjectivity and inconsistencies inherent in relying on expert experience and knowledge for crack analysis. Compared to traditional methods, it avoids identification errors caused by the similarity between crack and matrix colors in highly heterogeneous rock samples, improving the accuracy of crack identification. Based on the Hough transform method, multi-scale crack features of different lengths, widths, and shapes can be extracted, making feature extraction more comprehensive. Compared to traditional single-scale extraction methods, multi-scale feature extraction overcomes the problems caused by the heterogeneity and complexity of rock samples in crack feature extraction, improving the accuracy and reliability of feature extraction. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating the steps of a method for analyzing fracture networks in rock according to an embodiment of this application.

[0055] Figure 2 This is a schematic diagram of a limestone outcrop containing cracks, provided in an embodiment of the present invention.

[0056] Figure 3 This is a schematic diagram of a region to be identified within a rock image provided by an embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of crack color recognition in an area to be identified, provided by an embodiment of the present invention;

[0058] Figure 5 This is a schematic diagram of crack color filling in a region to be identified provided by an embodiment of the present invention;

[0059] Figure 6 This is a schematic diagram of the spatial distribution features within a rock image provided by an embodiment of the present invention;

[0060] Figure 7 This is a schematic diagram of a crack network structure within a rock image provided by an embodiment of the present invention;

[0061] Figure 8This is a schematic diagram of multi-scale crack features within a rock image provided by an embodiment of the present invention;

[0062] Figure 9 This is a schematic diagram of an analysis device for rock fracture networks provided in an embodiment of the present invention. Detailed Implementation

[0063] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to the viewer.

[0064] A first aspect of the present invention provides a method for analyzing fracture networks in rocks, referring to... Figure 1 , Figure 1 A flowchart illustrating the steps of a method for analyzing fracture networks in rocks, as provided in this embodiment of the invention, is shown below. Figure 1 As shown, the method includes:

[0065] Step S101: Obtain the rock image to be identified, and fill the cracks in the area to be identified in the rock image with color to obtain the crack color information of the area to be identified; wherein, the filling color of the crack is different from the existing color in the rock image.

[0066] The area to be identified is the region where cracks need to be identified. This area typically contains many cracks, and their features need to be extracted for analysis. Generally, this area to be identified can be specified manually or identified by machine. The fill color of the cracks differs from the existing colors in the rock image. For example, if the initial identification color of the cracks in the rock image is dark gray, the target color for color filling can be red, yellow, green, etc. A brighter target color enhances the accuracy of crack feature extraction. It should be noted that the initial identification color is the color automatically assigned to the cracks by the computer software when displaying the image; the target color can be specified by the user in advance.

[0067] When filling the target color, you can either fill along the outline of the crack, or identify the coordinates of the crack and fill each coordinate point with color.

[0068] After the crack is filled with the target color, its color changes, and the crack color information of the area to be identified can be obtained. This crack color information can include information such as the shape and size of the crack filled with the target color.

[0069] Step S102: Based on the crack color information of multiple regions to be identified, use a graphics algorithm to automatically search for the spatial distribution features of the entire region of the rock image; wherein, the spatial distribution features include the coordinate points of the main crack network structure in the entire region of the rock image.

[0070] Specifically, based on the shape of the cracks within the area to be identified and the target color used to fill the crack areas, a graphics algorithm is used to automatically pick regions within the rock image with crack shapes similar to those in the area to be identified, and fill these regions with the target color, thus obtaining the crack regions and crack colors in the rock image. The crack colors within the rock image must match the target color of the area to be identified. The crack regions may include the coordinates of the areas where the cracks are located.

[0071] When filling the crack area of ​​a rock image with the target color, the coordinates of the main crack network structure of the rock image can be identified first based on the crack shape and color of the area to be identified, and saved to the data buffer. Then, the target color is filled into each coordinate point.

[0072] Based on the coordinates of the stored data cache, the spatial distribution characteristics of cracks in the rock image are obtained. These spatial distribution characteristics include the coordinates of the main crack network structures within the entire area of ​​the rock image.

[0073] The graphics algorithm is a method for processing graphic data using a computer, which can be used to control and manage the color of graphics.

[0074] Step 103: Use the Hough transform method to identify and extract the spatial distribution characteristics of the cracks to obtain the complete crack network structure.

[0075] The basic structure of the crack network is obtained by using the Hough transform method to identify the coordinate points already stored in the data cache. Then, the identified crack edges are detected and segmented to obtain the complete crack network structure. The Hough transform method is a feature extraction technique in image processing used to detect the geometric features of lines or curves.

[0076] In addition to acquiring the crack network structure, it can also identify the width range of large-scale cracks based on the crack network structure, which is an advantage that traditional methods do not have. Among them, large-scale cracks refer to cracks with larger dimensions in the crack network structure.

[0077] Step 104: Perform multi-scale feature extraction on the entire crack network structure to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology.

[0078] This process involves setting different scale parameters. Based on each scale parameter, the entire crack network structure is divided into regions, resulting in local regions of the entire crack network structure corresponding to each scale parameter. The scale parameter reflects the size of the divided local region; for example, a scale parameter of 3 corresponds to a local region size of 3×3; a scale parameter of 4 corresponds to a local region size of 4×4; a scale parameter of 5 corresponds to a local region size of 5×5, and so on.

[0079] Within each local region, crack features are extracted to obtain crack features corresponding to different scale parameters. These crack features may include crack width, crack length, and crack shape.

[0080] By fusing and statistically analyzing the crack features corresponding to each scale parameter, multi-scale crack features of the entire crack network structure are obtained.

[0081] Step 105: Analyze the multi-scale crack characteristics and obtain the analysis results.

[0082] Based on the extracted multi-scale fracture characteristic parameters, the fracture network structure of the rock samples was analyzed, and the analysis results were obtained. The obtained analysis results can be used in geological research, reservoir evaluation, and engineering design for oil and gas exploration and development.

[0083] This embodiment provides a method for analyzing rock fracture networks. By introducing human-computer interaction and graphical computation, it fully utilizes the knowledge and experience of human experts to accurately select fracture colors and extract spatial distribution features, ensuring that the extracted fracture features better reflect the actual conditions of rock fractures. Compared to traditional methods, the human-computer interaction method avoids identification errors caused by the similarity between fracture and matrix colors in highly heterogeneous rock samples, improving the accuracy of fracture identification. The Hough transform method can extract fracture features of different lengths, widths, and shapes, making feature extraction more comprehensive. Furthermore, multi-scale feature extraction overcomes the problems caused by the heterogeneity and complexity of rock samples in fracture feature extraction, improving the accuracy and reliability of feature extraction.

[0084] In some optional examples, the step of acquiring a rock sample, prior to acquiring an image of the rock to be identified and color-filling cracks within the area to be identified in the rock image, includes:

[0085] Obtain rock samples, and cut and grind the rock samples to obtain a smooth rock surface;

[0086] The rock cross-section is image acquired, and the acquired images are preprocessed to obtain rock images; wherein, the preprocessing includes noise reduction, contrast enhancement, brightness adjustment, and color balance adjustment;

[0087] The rock image is displayed, and the region to be identified is obtained based on the selection boxes marked on the displayed rock image.

[0088] In this embodiment, a rock sample is obtained, and the rock sample is cut and ground to obtain a smooth rock cross-section. This is achieved by obtaining the rock sample using a rock collection device, and then cutting and grinding the rock sample to obtain a smooth rock cross-section. The rock cross-section is a plane parallel to the direction of the internal cracks in the rock, dividing the cracks in two.

[0089] Images of the rock cross-section were acquired and preprocessed to obtain rock images, which were then used as a reference. Figure 2 This involves using digital cameras or high-resolution microscopes to acquire images of rock cross-sections containing cracks, and then preprocessing these images to improve image clarity and crack feature recognition. Image acquisition refers to the process of obtaining images of cracked rocks through manual observation of crack distribution on the rock cross-section and through photographic capture. Preprocessing includes denoising, contrast enhancement, brightness adjustment, and color balance adjustment. Denoising can employ image filtering algorithms, such as median filtering or Gaussian filtering, to remove noise interference from the rock image. Contrast enhancement can use methods such as histogram equalization to optimize crack edges and details. Adjusting brightness and color balance corrects brightness and color deviations in the image, improving the accuracy of crack color recognition.

[0090] The rock image is displayed, and the region to be identified is obtained based on the selection boxes marked on the displayed rock image, with reference to... Figure 3 The rock image can be opened and displayed using computer software, and a selection box can be marked on the rock image using a mouse to obtain the area to be identified. The area to be identified refers to the region in the rock image containing cracks.

[0091] In some optional embodiments, for outcrop rock samples obtained in the field, the step of obtaining the rock samples includes:

[0092] Select representative sampling points; these sampling points should reflect the rock composition and characteristics of the area; furthermore, areas that have been damaged or altered should be avoided to prevent human interference with the rock samples.

[0093] Based on the sampling points, rock samples are selected, and the orientation information of the rock samples is recorded; the orientation information is used for subsequent geological interpretation and rock analysis; wherein, the orientation information includes the geographical location, the coordinates of the sampling points, and the orientation and dip angle of the rock samples;

[0094] The covering on the surface of the rock sample is cleaned to obtain a clean and clear rock sample; the rock sample is used to accurately observe and describe the texture, structure and color of rock cracks.

[0095] In this embodiment, representative sampling points are selected based on factors such as geological background, rock type, and research objectives. These sampling points reflect the rock composition and characteristics of the area. Furthermore, areas that have been damaged or altered should be avoided to prevent human interference with the rock samples.

[0096] Based on the sampling points, rock samples are selected, and their orientation information is recorded. This involves selecting rock samples within the chosen sampling points and obtaining their orientation information using accurate GPS or map surveying tools, then recording this information. This orientation information is used for subsequent geological interpretation and rock analysis. The orientation information includes the geographical location, the coordinates of the sampling point, and the orientation and dip angle of the rock sample. It is important to note that rock sample selection should avoid damaging the samples to preserve their integrity and original appearance as much as possible.

[0097] Cleaning the surface of the rock sample to obtain a clean and clear sample involves removing soil, vegetation, or other coverings to facilitate observation and study of the rock's characteristics. Care should be taken to avoid leaving any human contamination or traces during the cleaning process. Human contamination or traces include fingerprints, chemicals, etc. The rock sample is used to accurately observe and describe the texture, structure, and color of rock fissures.

[0098] In one optional embodiment, the step of color-filling the cracks within the area to be identified in the rock image to obtain crack color information of the area to be identified includes:

[0099] Obtain the color distribution of the region to be identified;

[0100] Based on the color distribution in the area to be identified, the area where the crack is located is obtained;

[0101] Based on the selected target color, the target color is filled into the area where the crack is located to obtain the crack color information of the area to be identified.

[0102] In this embodiment, the color distribution of the region to be identified is obtained, referring to... Figure 4 The method involves identifying the crack color in the region to be identified through clustering. Specifically, the clustering method performs cluster analysis on the colors of the region to be identified and identifies the crack color based on the cluster analysis results.

[0103] The region where the crack is located is obtained based on the color distribution in the region to be identified. This is achieved by extracting the spatial coordinates of the crack from the color distribution in the region to be identified.

[0104] Based on the selected target color, the target color is filled into the area where the crack is located to obtain the crack color information of the area to be identified, referring to... Figure 5 This method involves manually selecting a target color and filling it into the area where the crack is located to obtain crack color information. The fill color for the crack differs from the existing colors in the rock image. The target color is manually selected to fill the crack area. The crack color information refers to the color information of the crack area after the manually selected target color is filled. Human experts can select the most suitable target color based on experience and domain knowledge to avoid identification errors caused by the similarity between crack and rock matrix colors in heterogeneous rock samples, thereby improving the accuracy of crack identification.

[0105] In an optional embodiment, the step of automatically searching for the spatial distribution features of the entire area of ​​the rock image based on the crack color information of multiple areas to be identified, using a graphics algorithm, includes:

[0106] S1: Based on the crack color information of multiple regions to be identified, the rock image is identified using graphics algorithms and automatic search technology to obtain the spatial location information of cracks within the entire region of the rock image; the spatial location information of cracks includes the coordinates of each location of the crack.

[0107] S2: Based on the selection operation of the crack color information, obtain the target coordinate point of the selected location;

[0108] S3: Save the selected target coordinates to the data buffer;

[0109] Repeat steps S1-S3 above to obtain the coordinates of the main crack network structure within the entire area of ​​the rock image;

[0110] Next, based on the coordinates of the main crack network structure within the entire area of ​​the rock image, the spatial distribution characteristics of the cracks are obtained.

[0111] In this embodiment, based on the crack color information of multiple areas to be identified, a graphics algorithm and automatic search technology are used to identify the rock image, obtaining the spatial location information of cracks within the entire area of ​​the rock image. This is achieved by automatically searching and extracting the spatial location information of cracks within the entire area of ​​the rock image based on the color information corresponding to the crack locations in the areas to be identified. The spatial location information of the cracks includes the coordinates of each location of the crack. The graphics algorithm is a method for processing graphic data using a computer and can be used to control and manage the color of graphics.

[0112] Based on the selection operation of the crack color information, the target coordinate point of the selected location is obtained. This selection operation can be achieved by using the mouse to select the target coordinate point in the crack area and confirming the selection, thus obtaining the target coordinate point of the selected location. The target coordinate point selected by the mouse is displayed in green, while the target coordinate point selected and confirmed by the mouse is displayed in red.

[0113] The selected target coordinates are saved to the data buffer. This means that all selected target coordinates will be saved to the data buffer for subsequent crack feature identification. It is important to note that selected coordinates that have not been confirmed (i.e., those displayed in green) will not be saved to the data buffer.

[0114] Repeat steps S1-S3 above to obtain the coordinates of the main crack network structure within the entire area of ​​the rock image, referring to... Figure 6 This involves combining the coordinates of multiple regions to be identified within the rock image, selected and saved to the data buffer, to obtain the coordinates of the main fracture network structure constituting the entire region of the rock image. Figure 6 As shown in the image on the right.

[0115] Based on the coordinates of the main fracture network structure within the entire area of ​​the rock image, the spatial distribution characteristics of the fractures are obtained, referring to... Figure 6 The coordinates of the main crack network structure can be used to form the spatial distribution characteristics of cracks throughout the entire area of ​​the rock image.

[0116] In one optional embodiment, the graphics algorithm includes clustering method recognition, manual recognition, and color card recognition;

[0117] The clustering method identification includes:

[0118] Cluster analysis is performed on the colors of the area to be identified, and the crack colors are identified based on the cluster analysis results.

[0119] Specifically, clustering methods can be used for color recognition of cracks in the area to be identified.

[0120] The manual identification includes:

[0121] Based on the crack color of the area to be identified selected by the user, all areas within the rock image corresponding to the crack color are automatically picked up.

[0122] Specifically, manual identification can be used for color filling of cracks in the area to be identified. The color to be filled for the cracks differs from the existing colors in the rock image. For example, if the initial identification color of the cracks in the rock image is dark gray, the target color for color filling can be red, yellow, green, etc. The brighter the target color, the more accurate the crack feature extraction can be.

[0123] The color card recognition includes:

[0124] The colors of the area to be identified are used to form a color bar, and the crack color is selected from the color bar.

[0125] Specifically, based on the color card recognition method, the distribution of cracks can be obtained by dragging the mouse to select the color to fill the cracks in the color bar and identifying the coordinates of the area where the cracks are located.

[0126] In an optional embodiment, the step of using the Hough transform method to identify and extract the spatial distribution characteristics of the cracks to obtain the complete crack network structure includes:

[0127] Based on the spatial distribution characteristics of the cracks, the Hough transform method is used to identify crack features at the coordinate points of the main crack network structure.

[0128] Furthermore, edge detection and segmentation are performed on the crack edges of the main crack network structure to obtain all crack network features;

[0129] The Hough transform method is used to detect the geometric features of straight lines or curves.

[0130] In this embodiment, refer to Figure 7 Based on the spatial distribution characteristics of the cracks, the Hough transform method is used to identify crack features at the coordinate points of the main crack network structure. The Hough transform method is a feature extraction technique in image processing, possessing the advantage of scale invariance.

[0131] Furthermore, edge detection and segmentation are performed on the crack edges of the main crack network structure to obtain all crack network features. The boundary range of large-scale cracks is identified using the Hough transform method (see reference). Figure 7(The red edge line in the image). Based on the Hough transform method, the boundary range of large-scale cracks can be identified, which is an advantage that traditional methods do not have.

[0132] The Hough transform method is used to detect the geometric features of straight lines or curves.

[0133] In one optional embodiment, the step of extracting multi-scale features from the entire crack network structure to obtain multi-scale crack features includes:

[0134] Multiple different scale parameters are obtained, which are used to indicate the field of view size for crack identification;

[0135] Based on multiple scale parameters, the entire crack network structure is divided to obtain multiple local regions of the entire crack network structure corresponding to the scale parameters.

[0136] The crack characteristics of multiple local regions of the entire crack network structure are calculated and statistically analyzed to obtain multi-scale crack characteristics.

[0137] The crack features include crack width, crack length, and crack shape.

[0138] In this embodiment, multiple different scale parameters are obtained, meaning different scale parameters are set. These scale parameters are used to indicate the field of view size for crack identification.

[0139] Based on multiple scale parameters, the entire fracture network structure is divided into multiple local regions corresponding to the scale parameters. This involves dividing the fracture network structure within the rock image into multiple local regions based on different set scale parameters. The scale parameters can represent the size of each local region.

[0140] For example, when the scale parameter is set to 3, the crack network structure within the rock image is divided into multiple 3×3 local networks; when the scale parameter is set to 5, the crack network structure within the rock image is divided into multiple 5×5 local regions. Then, based on these local regions, crack features are extracted and analyzed for the cracks within each local region. For some cracks with complex shapes and small scales, local region crack feature recognition can improve the accuracy of the recognition.

[0141] Reference Figure 8 The crack features of multiple local regions in the entire crack network structure are calculated and statistically analyzed to obtain multi-scale crack features. Based on the multi-scale feature recognition method, cracks of different sizes are identified, which has significant advantages over traditional single-scale recognition methods.

[0142] The crack characteristics include crack width, crack length, and crack shape. The crack shape can be used to describe characteristics such as crack direction and degree of curvature.

[0143] A second aspect of the present invention provides an analysis apparatus for fracture networks in rocks, the analysis apparatus comprising:

[0144] A color filling module is used to acquire a rock image to be identified, and to fill the cracks in the area to be identified in the rock image with color to obtain the crack color information of the area to be identified; wherein, the filling color of the crack is different from the existing color in the rock image;

[0145] An automatic search module is used to automatically search for the spatial distribution features of the entire area of ​​the rock image based on the crack color information of multiple areas to be identified and using a graphics algorithm; wherein, the spatial distribution features include the coordinate points of the main crack network structure within the entire area of ​​the rock image;

[0146] The feature recognition module is used to identify and extract the spatial distribution features of the cracks using the Hough transform method to obtain the complete crack network structure.

[0147] The feature extraction module is used to extract multi-scale features from the entire crack network structure to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology.

[0148] The feature analysis module is used to analyze the multi-scale crack features and obtain the analysis results.

[0149] In this embodiment, refer to Figure 9 , Figure 9 This is a schematic diagram of the structure of an analysis device for rock fracture networks provided in an embodiment of the present invention; as shown. Figure 9 As shown, the analysis device for rock fracture networks includes:

[0150] The color filling module 201 is used to acquire a rock image to be identified, and to fill the cracks in the area to be identified in the rock image with color to obtain the crack color information of the area to be identified; wherein, the filling color of the crack is different from the existing color in the rock image;

[0151] The automatic search module 202 is used to automatically search for the spatial distribution features of the entire area of ​​the rock image based on the crack color information of multiple areas to be identified and using a graphics algorithm; wherein, the spatial distribution features include the coordinate points of the main crack network structure in the entire area of ​​the rock image.

[0152] The feature recognition module 203 is used to identify and extract the spatial distribution features of the cracks using the Hough transform method to obtain the complete crack network structure.

[0153] The feature extraction module 204 is used to perform multi-scale feature extraction on the entire crack network structure to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology.

[0154] The feature analysis module 205 is used to analyze the multi-scale crack features and obtain the analysis results.

[0155] Optionally, the device further includes:

[0156] The cutting module is used to acquire rock samples and cut and grind the rock samples to obtain a smooth rock surface.

[0157] The image acquisition module is used to acquire images of the rock cross-section and preprocess the acquired images to obtain rock images; wherein, the preprocessing includes noise reduction, contrast enhancement, brightness adjustment and color balance adjustment;

[0158] The annotation module is used to display the rock image and obtain the region to be identified based on the selection boxes annotated on the displayed rock image.

[0159] Optionally, the device further includes:

[0160] The sampling module is used to select representative sampling points; wherein the sampling points can reflect the rock composition and characteristics of the area; in addition, it is necessary to avoid selecting areas that have been damaged or modified, so as to avoid human interference with the rock samples;

[0161] The information recording module selects rock samples based on the sampling points and records the orientation information of the rock samples; the orientation information is used for subsequent geological interpretation and rock analysis; wherein, the orientation information includes the geographical location, the coordinates of the sampling points, and the orientation and dip angle of the rock samples;

[0162] The cleaning module is used to clean the covering on the surface of the rock sample to obtain a clean and clear rock sample; the rock sample is used to accurately observe and describe the texture, structure and color of rock cracks.

[0163] Optionally, the color filling module includes:

[0164] The first acquisition module is used to acquire the color distribution of the region to be identified;

[0165] The second acquisition module acquires the region where the crack is located based on the color distribution in the region to be identified;

[0166] The third acquisition module fills the area where the crack is located with the selected target color to obtain the crack color information of the area to be identified.

[0167] Optionally, the automatic search module includes:

[0168] The fourth acquisition module is used to execute step S1, which uses graphics algorithms and automatic search technology to identify rock images based on the crack color information of multiple areas to be identified, and obtains the spatial location information of cracks in the entire area; the spatial location information of cracks includes the coordinate points of each location of the cracks.

[0169] The fifth acquisition module is used to execute step S2, which is to acquire the target coordinate point of the selected location based on the selection operation of the crack color information;

[0170] The sixth acquisition module is used to execute step S3 and save the selected target coordinate point to the data buffer;

[0171] The seventh acquisition module is used to repeatedly execute the above steps S1-S3 to obtain the coordinate points of the main crack network structure in the entire area of ​​the rock image;

[0172] The eighth acquisition module is used to obtain the spatial distribution characteristics of cracks based on the coordinates of the main crack network structure within the entire area of ​​the rock image.

[0173] Optionally, the feature recognition module includes:

[0174] The crack feature recognition module is used to identify crack features of the coordinate points of the main crack network structure based on the spatial distribution characteristics of the cracks and using the Hough transform method.

[0175] The crack edge cutting module is used to perform edge detection and segmentation on the crack edges of the main crack network structure to obtain all crack network features.

[0176] The Hough transform method is used to detect the geometric features of straight lines or curves.

[0177] Optionally, the feature extraction module includes:

[0178] The parameter setting module is used to acquire multiple different scale parameters, which are used to indicate the field of view size for crack identification;

[0179] The region division module is used to divide the entire crack network structure based on multiple scale parameters to obtain multiple local regions of the entire crack network structure corresponding to the scale parameters.

[0180] The calculation and statistics module is used to calculate and statistically analyze the crack features of multiple local regions of the entire crack network structure to obtain multi-scale crack features;

[0181] The crack features include crack width, crack length, and crack shape.

[0182] The rock fracture network analysis device provided in this embodiment, by combining human-computer interaction and graphical calculation methods, can reduce the workload and time consumption of manual operation and improve work efficiency. Furthermore, based on this analysis device, color recognition and Hough transform methods can be combined to fully utilize color and geometric information for multi-scale analysis of fracture features, improving the accuracy and comprehensiveness of feature extraction. This provides more accurate data support for geological research, reservoir evaluation, and engineering design for oil and gas exploration and development.

[0183] In a third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the rock fracture network analysis method described in the first aspect of the present invention.

[0184] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0185] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of an apparatus according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0186] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0187] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or terminal device that includes said element.

[0188] The above provides a detailed description of the method and apparatus for analyzing rock fracture networks provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the apparatus and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for analyzing fracture networks in rocks, characterized in that, The method includes: A rock image to be identified is acquired, and cracks within the area to be identified in the rock image are filled with color to obtain crack color information for the area to be identified; wherein the filling color of the cracks is different from the existing colors in the rock image; Based on the crack color information of multiple regions to be identified, a graphics algorithm is used to automatically search for the spatial distribution features of the entire region of the rock image; wherein, the spatial distribution features include the coordinate points of the main crack network structure within the entire region of the rock image; The spatial distribution characteristics of the cracks are identified and extracted using the Hough transform method to obtain the complete crack network structure. Multi-scale feature extraction is performed on all the crack network structures to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology; The multi-scale crack characteristics were analyzed, and the analysis results were obtained.

2. The method for analyzing fracture networks in rocks according to claim 1, characterized in that, Before acquiring the image of the rock to be identified and color-filling the cracks within the area to be identified in the rock image, the method further includes: Obtain rock samples, and cut and grind the rock samples to obtain a smooth rock surface; The rock cross-section is image acquired, and the acquired images are preprocessed to obtain rock images; wherein, the preprocessing includes noise reduction, contrast enhancement, brightness adjustment, and color balance adjustment; The rock image is displayed, and the region to be identified is obtained based on the selection boxes marked on the displayed rock image.

3. The method for analyzing fracture networks in rocks according to claim 2, characterized in that, The steps for obtaining rock samples include: Select representative sampling points; these sampling points should reflect the rock composition and characteristics of the area; furthermore, areas that have been damaged or altered should be avoided to prevent human interference with the rock samples. Based on the sampling points, rock samples are selected, and the orientation information of the rock samples is recorded; the orientation information is used for subsequent geological interpretation and rock analysis; wherein, the orientation information includes the geographical location, the coordinates of the sampling points, and the orientation and dip angle of the rock samples; The covering on the surface of the rock sample is cleaned to obtain a clean and clear rock sample; the rock sample is used to accurately observe and describe the texture, structure and color of rock cracks.

4. The method for analyzing fracture networks in rocks according to claim 1, characterized in that, The step of color-filling the cracks within the target area of ​​the rock image to obtain crack color information for the target area includes: Obtain the color distribution of the region to be identified; Based on the color distribution in the area to be identified, the area where the crack is located is obtained; Based on the selected target color, the target color is filled into the area where the crack is located to obtain the crack color information of the area to be identified.

5. The method for analyzing fracture networks in rocks according to claim 1, characterized in that, Based on the crack color information of multiple regions to be identified, a graphics algorithm is used to automatically search for the spatial distribution features of the entire region of the rock image, including: S1: Based on the crack color information of multiple regions to be identified, the rock image is identified using graphics algorithms and automatic search technology to obtain the spatial location information of cracks within the entire region of the rock image; the spatial location information of cracks includes the coordinates of each location of the crack. S2: Based on the selection operation of the crack color information, obtain the target coordinate point of the selected location; S3: Save the selected target coordinates to the data buffer; Repeat steps S1-S3 above to obtain the coordinates of the main crack network structure within the entire area of ​​the rock image; Based on the coordinates of the main crack network structure within the entire area of ​​the rock image, the spatial distribution characteristics of the cracks are obtained.

6. The method for analyzing fracture networks in rocks according to claim 5, characterized in that, The graphics algorithms include clustering method recognition, manual recognition, and color card recognition; The clustering method identification includes: Cluster analysis is performed on the colors of the area to be identified, and the crack colors are identified based on the cluster analysis results; The manual identification includes: Based on the crack color of the area to be identified selected by the user, all areas in the rock image that correspond to the crack color are automatically picked up. The color card recognition includes: The colors of the area to be identified are used to form a color bar, and the crack color is selected from the color bar.

7. The method for analyzing fracture networks in rocks according to claim 1, characterized in that, The spatial distribution characteristics of the cracks are identified and extracted using the Hough transform method to obtain the complete crack network structure, including: Based on the spatial distribution characteristics of the cracks, the Hough transform method is used to identify crack features at the coordinate points of the main crack network structure. Furthermore, edge detection and segmentation are performed on the crack edges of the main crack network structure to obtain all crack network features; The Hough transform method is used to detect the geometric features of straight lines or curves.

8. The method for analyzing fracture networks in rocks according to claim 1, characterized in that, The step of extracting multi-scale features from all the crack network structures to obtain multi-scale crack features includes: Multiple different scale parameters are obtained, which are used to indicate the field of view size for crack identification; Based on multiple scale parameters, the entire crack network structure is divided to obtain multiple local regions of the entire crack network structure corresponding to the scale parameters. The crack characteristics of multiple local regions of the entire crack network structure are calculated and statistically analyzed to obtain multi-scale crack characteristics. The crack features include crack width, crack length, and crack shape.

9. An analytical apparatus for analyzing fracture networks in rocks, characterized in that, The analytical apparatus includes: A color filling module is used to acquire a rock image to be identified, and to fill the cracks in the area to be identified in the rock image with color to obtain the crack color information of the area to be identified; wherein, the filling color of the crack is different from the existing color in the rock image; An automatic search module is used to automatically search for the spatial distribution features of the entire area of ​​the rock image based on the crack color information of multiple areas to be identified and using a graphics algorithm; wherein, the spatial distribution features include the coordinate points of the main crack network structure within the entire area of ​​the rock image; The feature recognition module is used to identify and extract the spatial distribution features of the cracks using the Hough transform method to obtain the complete crack network structure. The feature extraction module is used to extract multi-scale features from the entire crack network structure to obtain multi-scale crack features; wherein, the multi-scale crack features include crack length, width, branching, and pore morphology. The feature analysis module is used to analyze the multi-scale crack features and obtain the analysis results.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method for analyzing the fracture network of rock according to any one of claims 1 to 8.

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