Metamorphic rock surface porosity analysis and evaluation method, device, equipment and medium

Abstract

The invention discloses a metamorphic rock surface porosity analysis and evaluation method, device, equipment and medium, and relates to the technical field of petroleum logging reservoir evaluation, and the method comprises the steps: preprocessing a rock debris sample, scanning the preprocessed rock debris sample, and obtaining a rock debris sample image; performing identification detection on mineral and background color edges in the rock debris sample image and rock debris gap image segmentation to obtain rock debris particles, and determining the total area of the particles; performing threshold truncation on the rock debris particles by adopting a threshold segmentation method, calculating pore areas in the rock debris particles, identifying cracks in the rock debris particles by adopting a maximum likelihood method, calculating crack areas in the rock debris particles, and calculating pore and crack areas by utilizing the pore areas and the crack areas; and calculating the surface porosity based on the total particle area and the pore and crack area, and analyzing and evaluating the imaging logging crack interpretation result and the surface porosity to obtain an analysis and evaluation result. The metamorphic rock surface porosity analysis and evaluation accuracy and efficiency can be improved, and metamorphic rock surface porosity analysis and evaluation complexity can be reduced.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum logging reservoir evaluation, and in particular to a metamorphic rock surface porosity analysis and evaluation method, device, equipment and medium. Background Art

[0002] At present, the main methods used are grayscale threshold truncation and edge detection to extract porosity pixels from rock scanning electron microscope photos to achieve pore identification and pore ratio calculation. The current method for crack identification is mainly based on projecting different colors of light sources on the core casting thin sections to make the difference between the crack color and the matrix color more obvious, thereby realizing the calculation of the crack ratio. This is time-consuming and the thin section sample is more susceptible to damage and distortion. The main limitations of the existing technology include: (1) When using rock cuttings for sampling, due to the different sizes of particles, there is a large amount of space between particles, which can interfere with the identification of pores; (2) The existing crack extraction method is based on optical microscopy, and its quantitative calculation is difficult and time-consuming. At the same time, the casting thin sections used as samples are more easily damaged and distorted, resulting in a large amount of time and manpower consumption, and low efficiency when processing a large number of samples.

[0003] It can be seen from the above that how to improve the accuracy and efficiency of metamorphic rock surface fraction analysis and evaluation and reduce the complexity of metamorphic rock surface fraction analysis and evaluation is a problem to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a metamorphic rock surface ratio analysis and evaluation method, device, equipment and medium, which can improve the accuracy and efficiency of metamorphic rock surface ratio analysis and evaluation and reduce the complexity of metamorphic rock surface ratio analysis and evaluation. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a method for analyzing and evaluating the porosity of metamorphic rocks, comprising:

[0006] Screening rock chip samples of metamorphic rocks, pre-processing the rock chip samples to obtain the pre-processed rock chip samples, and scanning the pre-processed rock chip samples to obtain rock chip sample images;

[0007] Identify and detect the mineral and background color edges in the rock cuttings sample image and perform rock cuttings gap image segmentation to obtain rock cuttings particles and determine the total particle area of ​​the rock cuttings particles;

[0008] Performing threshold truncation on the rock cutting particles by using a threshold segmentation method to calculate the pore area in the rock cutting particles, identifying cracks in the rock cutting particles by using a maximum likelihood method to calculate the crack area in the rock cutting particles, and calculating the pore and crack areas by using the pore area and the crack area;

[0009] The surface porosity of the metamorphic rock is calculated based on the total area of ​​the particles and the pore and fracture areas, an imaging logging fracture interpretation result corresponding to the surface porosity is obtained, and the imaging logging fracture interpretation result and the surface porosity are analyzed and evaluated to obtain an analysis and evaluation result.

[0010] Optionally, the screening of rock chip samples of metamorphic rocks and preprocessing the rock chip samples to obtain the preprocessed rock chip samples include:

[0011] Obtaining an initial rock chip sample of metamorphic rock, filtering and screening the initial rock chip sample to obtain the filtered and screened rock chip sample;

[0012] The rock cutting machine is used to cut the rock cutting sample, and the rock cutting sample is polished and ground to obtain the pre-treated rock cutting sample.

[0013] Optionally, scanning the pre-processed rock cuttings sample to obtain a rock cuttings sample image includes:

[0014] The pre-processed rock cuttings sample is preliminarily scanned by using a scanning electron microscope and setting scanning parameters to screen a scanning area, and a high-resolution scan is performed on the pre-processed rock cuttings sample according to the scanning area to obtain a rock cuttings sample image.

[0015] Optionally, the identifying and detecting the mineral and background color edges in the rock cuttings sample image and segmenting the rock cuttings gap image to obtain rock cuttings particles includes:

[0016] Using an edge detection algorithm to identify and detect the edges of minerals and background colors in the rock cuttings sample image, and splicing the pixels in the rock cuttings sample image to obtain pixel edge lines;

[0017] The rock cutting sample image is decomposed and filled and the rock cutting gap image is segmented according to the pixel edge lines to obtain rock cutting particles.

[0018] Optionally, the using of an edge detection algorithm to identify and detect the edges of minerals and background colors in the rock cuttings sample image, and splicing pixels in the rock cuttings sample image to obtain pixel edge lines, includes:

[0019] An edge detection algorithm is used to perform Gaussian blur processing on the rock cuttings sample image, and the gradient of the processed rock cuttings sample image is calculated using the Sobel operator. The local maximum value of each pixel point in the processed rock cuttings sample image along the gradient direction is retained to identify the edges of the detected minerals and background colors, and the pixels in the rock cuttings sample image are spliced ​​to obtain pixel edge lines.

[0020] Optionally, the using a threshold segmentation method to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles, and using a maximum likelihood method to identify cracks in the rock cutting particles to calculate the crack area in the rock cutting particles includes:

[0021] A threshold segmentation method is adopted and a fixed gray value is used as a threshold to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles;

[0022] The fracture probability in the rock cutting particles is calculated by using a preset priori sample and adopting a maximum likelihood method, and the fractures in the rock cutting particles are identified based on the fracture probability to calculate the fracture area.

[0023] Optionally, the metamorphic rock porosity analysis and evaluation method further includes:

[0024] Get the built-in functions of the subprocess toolkit in the preset Python code;

[0025] The built-in function is used to realize the process of multi-threaded parallel acquisition of rock cuttings sample images and surface surface rate analysis and evaluation.

[0026] In a second aspect, the present application discloses a metamorphic rock porosity analysis and evaluation device, comprising:

[0027] A sample processing module, used for screening rock chip samples of metamorphic rocks, preprocessing the rock chip samples to obtain the preprocessed rock chip samples, and scanning the preprocessed rock chip samples to obtain rock chip sample images;

[0028] An identification and segmentation module, used for identifying and detecting the edges of minerals and background colors in the rock cuttings sample image and performing rock cuttings gap image segmentation to obtain rock cuttings particles and determine the total particle area of ​​the rock cuttings particles;

[0029] a pore and crack calculation module, for performing threshold truncation on the rock cutting particles by using a threshold segmentation method to calculate the pore area in the rock cutting particles, identifying cracks in the rock cutting particles by using a maximum likelihood method to calculate the crack area in the rock cutting particles, and calculating the pore and crack areas by using the pore area and the crack area;

[0030] The analysis and evaluation module is used to calculate the surface area of ​​the metamorphic rock based on the total area of ​​the particles and the pore and fracture areas, obtain the imaging logging fracture interpretation results corresponding to the surface area, analyze and evaluate the imaging logging fracture interpretation results and the surface area to obtain the analysis and evaluation results.

[0031] In a third aspect, the present application discloses an electronic device, comprising:

[0032] Memory, used to store computer programs;

[0033] A processor is used to execute the computer program to implement the aforementioned metamorphic rock porosity analysis and evaluation method.

[0034] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the metamorphic rock porosity analysis and evaluation method disclosed above are implemented.

[0035] It can be seen that the present application provides a metamorphic rock surface area analysis and evaluation method, including screening rock cuttings samples of metamorphic rocks, preprocessing the rock cuttings samples to obtain the preprocessed rock cuttings samples, scanning the preprocessed rock cuttings samples to obtain rock cuttings sample images; identifying and detecting the edges of minerals and background colors in the rock cuttings sample images and performing rock cutting gap image segmentation to obtain rock cutting particles, and determining the total particle area of ​​the rock cutting particles; using a threshold segmentation method to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles, using a maximum likelihood method to identify cracks in the rock cutting particles to calculate the crack area in the rock cutting particles, and using the pore area and the crack area to calculate the pore and crack area; calculating the surface area of ​​the metamorphic rock based on the total particle area and the pore and crack area, obtaining an imaging logging fracture interpretation result corresponding to the surface area, and analyzing and evaluating the imaging logging fracture interpretation result and the surface area to obtain an analysis and evaluation result. The present application can effectively focus on the research target range by identifying and detecting the edges of minerals and background colors in the rock chip sample image and segmenting the rock chip gap image, and solves the problem of the grayscale value of the base interfering with the identification result during the pore identification process of the rock chip particle sample. The threshold segmentation method and the maximum likelihood method are used to perform threshold truncation and crack identification on the rock chip particles respectively, which can solve the time-consuming problem of the existing crack identification relying on optical microscope to identify cast thin sections, and the difficulty of identifying large pores and cracks from scanning electron microscope photos. The surface ratio of metamorphic rocks is calculated based on the total area of ​​particles and the area of ​​pores and cracks, and the surface ratio and the corresponding imaging logging fracture interpretation results are analyzed and evaluated to obtain analysis and evaluation results, which can improve the accuracy and efficiency of the surface ratio analysis and evaluation of metamorphic rocks, reduce the complexity of the surface ratio analysis and evaluation of metamorphic rocks, thereby reducing manpower consumption and improving the efficiency and reliability of petrological research and geological engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0037] Figure 1 A flow chart of a metamorphic rock porosity analysis and evaluation method disclosed in this application;

[0038] Figure 2 A diagram of a pre-processed rock cutting sample disclosed in the present application;

[0039] Figure 3 A scanned rock cuttings sample diagram disclosed in the present application;

[0040] Figure 4 This is a hole and seam identification effect diagram disclosed in this application;

[0041] Figure 5 A flow chart of a metamorphic rock porosity analysis and evaluation method disclosed in this application;

[0042] Figure 6 This is a diagram showing the effect of the rock cutting particles after segmentation disclosed in the present application;

[0043] Figure 7 A graph showing the surface porosity statistics of rock particles of a single sample disclosed in the present application;

[0044] Figure 8 A rendering of a rock cutting sample disclosed in this application;

[0045] Fig. 9 This is an electronic scanning result diagram of a rock cutting sample disclosed in this application;

[0046] Fig.10 A background elimination display diagram based on rock debris segmentation disclosed in this application;

[0047] Fig.11 This is a rock cutting sample hole and crack identification effect diagram disclosed in this application;

[0048] Fig.12 A comparison diagram of a surface ratio calculation result and an imaging logging interpretation result disclosed in the present application;

[0049] Fig.13 This is a schematic diagram of the structure of a metamorphic rock porosity analysis and evaluation device disclosed in this application;

[0050] Fig.14 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] At present, the main methods used are grayscale threshold truncation and edge detection to extract porosity pixels from rock scanning electron microscope photos to achieve pore identification and pore ratio calculation. The current method for crack identification is mainly based on projecting different colors of light sources on the core casting thin sections to make the difference between the crack color and the matrix color more obvious, thereby realizing the calculation of the crack ratio. This is time-consuming and the thin section sample preparation is more prone to damage and distortion. The main limitations of the existing technology include: (1) When using rock cuttings for sample preparation, due to the different sizes of particles, there is a large amount of space between particles, which can interfere with the identification of pores; (2) The existing crack extraction method is based on an optical microscope, and its quantitative calculation is difficult and time-consuming. At the same time, the casting thin sections used as samples are more easily damaged and distorted, resulting in a large amount of time and manpower consumption, and low efficiency when processing a large number of samples. As can be seen from the above, how to improve the accuracy and efficiency of metamorphic rock surface ratio analysis and evaluation and reduce the complexity of metamorphic rock surface ratio analysis and evaluation are problems to be solved in this field.

[0053] See also Figure 1 As shown, the embodiment of the present invention discloses a method for analyzing and evaluating the porosity of metamorphic rocks, which may specifically include:

[0054] Step S11: screening rock chip samples of metamorphic rocks, preprocessing the rock chip samples to obtain the preprocessed rock chip samples, and scanning the preprocessed rock chip samples to obtain rock chip sample images.

[0055] In this embodiment, an initial rock chip sample of metamorphic rock is obtained, the initial rock chip sample is filtered and screened to obtain the filtered and screened rock chip sample, the rock chip sample is cut by a rock cutter, and is polished and grinded to obtain the pre-processed rock chip sample, a scanning electron microscope is used to perform a preliminary scan on the pre-processed rock chip sample with set scanning parameters to screen a scanning area, and a high-resolution scan is performed on the pre-processed rock chip sample according to the scanning area to obtain a rock chip sample image.

[0056] Specifically, the initial rock cuttings samples of metamorphic rocks are selected through multiple filter screens to select the appropriate filtered and screened rock cuttings samples to ensure that the samples can represent the characteristics of the entire rock body, and then the large rocks are cut into small pieces suitable for the scanning electron microscope using a rock cutter, followed by polishing and grinding to ensure that the sample surface is flat and smooth for high-quality electron microscope scanning. Since the rock is non-conductive, carbon powder is sprayed on the surface to make it conductive to prevent the electron beam from accumulating on the sample surface and affecting the image quality. The pre-treated rock cuttings samples are as follows: Figure 2 Then, the pre-treated rock cuttings sample is mounted on the SEM (scanning electron microscope) sample stage and placed in the sample chamber of the SEM. Appropriate scanning parameters such as acceleration voltage, working distance, scanning rate, etc. are set to perform a preliminary scan, observe the sample surface, select a suitable scanning area, and then perform a high-resolution scan to obtain a rock cuttings sample image of the rock pore structure. The scanned rock cuttings sample image is shown in Figure 3 shown.

[0057] Step S12: identifying and detecting the minerals and background color edges in the rock cuttings sample image and performing rock cuttings gap image segmentation to obtain rock cuttings particles and determine the total particle area of ​​the rock cuttings particles.

[0058] Step S13: using the threshold segmentation method to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles, using the maximum likelihood method to identify the cracks in the rock cutting particles to calculate the crack area in the rock cutting particles, and using the pore area and the crack area to calculate the pore and crack areas.

[0059] In this embodiment, a threshold segmentation method is adopted and a fixed gray value is used as a threshold to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles, a preset priori sample is used and a maximum likelihood method is used to calculate the probability of cracks in the rock cutting particles, cracks in the rock cutting particles are identified based on the crack probability to calculate the crack area, and the pore area and the crack area are calculated using the pore area and the crack area.

[0060] Since there are many cracks and pore structures in the rock debris particles, the specific proportion of these structures cannot be directly seen from the photos. Therefore, this application adopts the classification and identification of pores and cracks, and the result superposition method is used to carry out pore and fracture identification. For pores, this paper adopts the threshold segmentation method to analyze the distribution state of the grayscale of the scanning electron microscope image, and uses a threshold of about 30 to perform threshold truncation on the particles that propose the influence of the base, extract its pore characteristics, and calculate the pore area.

[0061] For cracks, when there are large color differences between the minerals inside the cuttings, the image is segmented twice to reduce the interference of the mineral interface. In order to improve the accuracy of crack identification, this paper uses 10 pictures with artificially marked cracks and holes as prior samples, and segmented the sample pictures with a standard scale of 50*50 pixels to achieve sample enhancement and greatly increase the number of samples. Then, the maximum likelihood algorithm is used to establish the likelihood formula:

[0062] ;

[0063] Among them, the pixel intensity of the crack area follows a parameter: The probability distribution of , the pixel intensity in the non-crack area follows a parameter The probability distribution of X is the observed image data, C and B represent the pixel sets in the crack area and non-crack area in the image respectively. The artificially marked segmentation samples are iteratively trained, and the fitting of crack and hole parameters is completed through 20 steps of iterative training to find the parameters and The value of makes the likelihood function Maximum, the formula is as follows:

[0064] ;

[0065] Then, using the same size 50*50 frame, the cracks and pores of the unknown image are identified by moving the frame without overlapping. At the same time, since some subtle structural cracks are very small on the high-brightness mineral surface, it is very difficult to identify them. In order to fully characterize them, this paper carried out local difference recognition based on the maximum likelihood algorithm, using the 10*10 convolution kernel method to calculate the difference of all pixel points relative to the average value within the 10*10 range of the point, and extract the difference pixel points as the characterization points of subtle cracks.

[0066] The final hole and seam recognition effect is as follows: Figure 4 As shown, it can be seen that when there is an obvious boundary value, the threshold automatically set by OTSU (maximum between-class variance method) will cause fine cracks to be shielded, and the simple maximum likelihood crack extraction is not capable of identifying larger pores. The method in this paper can more fully extract the comprehensive proportion of cracks and pores.

[0067] Step S14: Calculate the surface area of ​​the metamorphic rock based on the total area of ​​the particles and the pore and fracture areas, obtain the imaging logging fracture interpretation result corresponding to the surface area, analyze and evaluate the imaging logging fracture interpretation result and the surface area to obtain an analysis and evaluation result.

[0068] In addition, the application also includes: obtaining the built-in function of the subprocess toolkit in the preset python code; using the built-in function to realize the process of multi-threaded parallel acquisition of rock cuttings sample images and face rate analysis and evaluation. That is, finally using the subprocess toolkit in the python code, multi-threaded parallelism is enabled, and 20 cores of the computer CPU (Central Processing Unit) are called at the same time, realizing parallel calculation of 20 images, greatly improving calculation efficiency.

[0069] In this embodiment, rock cuttings samples of metamorphic rocks are screened, the rock cuttings samples are preprocessed to obtain the preprocessed rock cuttings samples, and the preprocessed rock cuttings samples are scanned to obtain rock cuttings sample images; the mineral and background color edges in the rock cuttings sample images are identified and detected, and the rock cuttings gap image is segmented to obtain rock cutting particles, and the total particle area of ​​the rock cutting particles is determined; the rock cutting particles are threshold-cut using a threshold segmentation method to calculate the pore area in the rock cutting particles, the cracks in the rock cutting particles are identified using a maximum likelihood method to calculate the crack area in the rock cutting particles, and the pore and crack areas are calculated using the pore area and the crack area; the surface porosity of the metamorphic rock is calculated based on the total particle area and the pore and crack areas, and an imaging logging fracture interpretation result corresponding to the surface porosity is obtained, and the imaging logging fracture interpretation result and the surface porosity are analyzed and evaluated to obtain an analysis and evaluation result. The present application can effectively focus on the research target range by identifying and detecting the edges of minerals and background colors in the rock chip sample image and segmenting the rock chip gap image, and solves the problem of the grayscale value of the base interfering with the identification result during the pore identification process of the rock chip particle sample. The threshold segmentation method and the maximum likelihood method are used to perform threshold truncation and crack identification on the rock chip particles respectively, which can solve the time-consuming problem of the existing crack identification relying on optical microscope to identify cast thin sections, and the difficulty of identifying large pores and cracks from scanning electron microscope photos. The surface ratio of metamorphic rocks is calculated based on the total area of ​​particles and the area of ​​pores and cracks, and the surface ratio and the corresponding imaging logging fracture interpretation results are analyzed and evaluated to obtain analysis and evaluation results, which can improve the accuracy and efficiency of the surface ratio analysis and evaluation of metamorphic rocks, reduce the complexity of the surface ratio analysis and evaluation of metamorphic rocks, thereby reducing manpower consumption and improving the efficiency and reliability of petrological research and geological engineering applications.

[0070] See also Figure 5 As shown, the embodiment of the present invention discloses a method for analyzing and evaluating the porosity of metamorphic rocks, which may specifically include:

[0071] Step S21: screening rock chip samples of metamorphic rocks, preprocessing the rock chip samples to obtain the preprocessed rock chip samples, and scanning the preprocessed rock chip samples to obtain rock chip sample images.

[0072] Step S22: using an edge detection algorithm to identify and detect the edges of minerals and background colors in the rock cuttings sample image, and splicing the pixels in the rock cuttings sample image to obtain pixel edge lines, decomposing and filling the rock cuttings sample image and segmenting the rock cuttings gap image according to the pixel edge lines to obtain rock cuttings particles, and determining the total particle area of ​​the rock cuttings particles.

[0073] In this embodiment, an edge detection algorithm is used to perform Gaussian blur processing on the rock cuttings sample image, and the gradient of the processed rock cuttings sample image is calculated using the Sobel operator, and the local maximum value of each pixel point in the processed rock cuttings sample image along the gradient direction is retained to identify and detect the edges of minerals and background colors, and the pixels in the rock cuttings sample image are spliced ​​to obtain pixel edge lines. The rock cuttings sample image is decomposed and filled according to the pixel edge lines, and the rock cuttings gap image is segmented to obtain rock cuttings particles, and the total particle area of ​​the rock cuttings particles is determined.

[0074] Specifically, in order to eliminate the interference of rock cuttings spacing on the pore and fracture identification results, it is necessary to first use image segmentation technology to separate the rock cuttings particles in the sample, and then identify the pores and fractures based on the particles, thereby avoiding any pore or fracture information extraction from the rock cuttings spacing.

[0075] In order to achieve the segmentation of rock cuttings particles, this application uses Canny (edge ​​detection algorithm) to detect the edges of minerals and background colors in the image, so as to highlight the morphology of rock cuttings particles. The Canny algorithm is mainly divided into 6 steps: First, the image is Gaussian blurred to reduce the noise in the image. Then the Sobel operator is used to calculate the gradient of the image to find the edges in the image. Then, in the gradient image, for each pixel point, only the local maximum along the gradient direction is retained to refine the edge. Then, according to the set two thresholds (high threshold and low threshold), the pixels in the image are divided into three categories: strong edge, weak edge and non-edge. Then, by connecting the strong edge pixels with the surrounding weak edge pixels, the complete edge lines are finally obtained. Finally, the different contours are decomposed and filled to form different rock cuttings particles. The effect of the segmented rock cuttings particles is shown as follows Figure 6 As shown, the colored part is the area of ​​the identified rock debris particles, and the white transparent part is the gap between the rock particles that have been removed.

[0076] Step S23: using the threshold segmentation method to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles, using the maximum likelihood method to identify the cracks in the rock cutting particles to calculate the crack area in the rock cutting particles, and using the pore area and the crack area to calculate the pore and crack areas.

[0077] Step S24: Calculate the surface area of ​​the metamorphic rock based on the total area of ​​the particles and the pore and fracture areas, obtain the imaging logging fracture interpretation result corresponding to the surface area, analyze and evaluate the imaging logging fracture interpretation result and the surface area to obtain an analysis and evaluation result.

[0078] In this embodiment, a mask method is used to screen specific rock cuttings in the mineral scanning results, and finally the surface porosity statistics of rock particles of a single sample are formed as follows: Figure 7 As shown in the figure, the total area of ​​each particle in each sample and the proportion of pores and fractures (face ratio) are introduced in detail. At the same time, the depth position of the sample is recorded and the position is marked in the logging results, thereby realizing the information calibration of the logging results and improving the interpretation results of the target well in the current well section.

[0079] For example, in the case study of a specific metamorphic well, considering that the main storage and flow space of metamorphic rocks is mainly composed of cracks and dissolution pores between particles, the research team collected rock cuttings samples from the well section for detailed analysis. To ensure the clear display of rock particles in the rock cuttings, precise sample preparation was first carried out. The sample display effect after preparation can be seen in Figure 1. Figure 8 Subsequently, the samples were scanned in detail using electronic scanning technology, and the scan results revealed the rock fragments grain structure filled with tiny cracks and pores in the samples, such as Fig. 9 shown.

[0080] Furthermore, this embodiment uses the pore identification algorithm eliminated in this paper to perform pore analysis. The initial step of the algorithm is to segment rock debris particles from the background to eliminate the interference of the background on the pore analysis and ensure that the calculation is performed only on the color-marked area. The background elimination based on rock debris segmentation is shown as follows: Fig.10 Continuing this process, the surface area and pore area of ​​multiple rock cuttings particles at the same depth were quantitatively identified. The pore identification effect of rock cutting samples is shown in Fig.11 As shown in the figure, the total pore area and the total area of ​​rock cuttings of each sample are calculated by summing the rock cuttings grouped according to the sample number. Then, the porosity of a single sample is calculated by applying the formula: total area of ​​a single sample = total pore area of ​​sample particles / total area of ​​sample particles, i.e., the ratio of the pore area to the total area of ​​the sample.

[0081] The final step of this study involves comparing the calculated face ratio with the imaging logging fracture interpretation results at the corresponding depth. Fig.12As shown in the figure. The results show that in the well section above 3900m, due to the dense fractures, the surface porosity calculated by the algorithm continues to show high values, which is consistent with the high fracture density of the imaging logging results. At depths below 3900m, as the fracture density shown by the imaging logging decreases, the surface porosity calculated by the algorithm also decreases significantly, and is basically consistent with the local fluctuations of the imaging logging results, proving the effectiveness and application potential of this method in accurately identifying and quantifying the characteristics of metamorphic reservoirs.

[0082] The innovations and advantages of this application are as follows: 1. Efficient particle gap elimination method: advanced image segmentation + edge erosion technology, effectively identify the outline of rock debris particles, and eliminate the interference of the base between particles on the identification process of pores and cracks within the particles; 2. Accurate face rate recognition algorithm: For holes, due to the low grayscale of holes, a simple threshold truncation method is used for extraction. For cracks, the maximum likelihood method is used in combination with local blurring + contrast enhancement to enhance the performance characteristics of cracks in minerals of different colors. Finally, the quantitative and accurate extraction of pore and crack areas from the scanning electron microscope images of rock debris particles of metamorphic rocks is achieved, thereby obtaining face rates; 3. High-speed parallel technology: This method calls CPU computing resources in a multi-parallel manner to achieve parallel calculation of 40 samples. It only takes 30 minutes to complete the automatic analysis and evaluation of the face rates of 12,600+ particles of 580 samples, which greatly liberates human resources, improves evaluation speed, and improves interpretation accuracy.

[0083] In this embodiment, rock cuttings samples of metamorphic rocks are screened, the rock cuttings samples are preprocessed to obtain the preprocessed rock cuttings samples, and the preprocessed rock cuttings samples are scanned to obtain rock cuttings sample images; the mineral and background color edges in the rock cuttings sample images are identified and detected, and the rock cuttings gap image is segmented to obtain rock cutting particles, and the total particle area of ​​the rock cutting particles is determined; the rock cutting particles are threshold-cut using a threshold segmentation method to calculate the pore area in the rock cutting particles, the cracks in the rock cutting particles are identified using a maximum likelihood method to calculate the crack area in the rock cutting particles, and the pore and crack areas are calculated using the pore area and the crack area; the surface porosity of the metamorphic rock is calculated based on the total particle area and the pore and crack areas, and an imaging logging fracture interpretation result corresponding to the surface porosity is obtained, and the imaging logging fracture interpretation result and the surface porosity are analyzed and evaluated to obtain an analysis and evaluation result. The present application can effectively focus on the research target range by identifying and detecting the edges of minerals and background colors in the rock chip sample image and segmenting the rock chip gap image, and solves the problem of the grayscale value of the base interfering with the identification result during the pore identification process of the rock chip particle sample. The threshold segmentation method and the maximum likelihood method are used to perform threshold truncation and crack identification on the rock chip particles respectively, which can solve the time-consuming problem of the existing crack identification relying on optical microscope to identify cast thin sections, and the difficulty of identifying large pores and cracks from scanning electron microscope photos. The surface ratio of metamorphic rocks is calculated based on the total area of ​​particles and the area of ​​pores and cracks, and the surface ratio and the corresponding imaging logging fracture interpretation results are analyzed and evaluated to obtain analysis and evaluation results, which can improve the accuracy and efficiency of the surface ratio analysis and evaluation of metamorphic rocks, reduce the complexity of the surface ratio analysis and evaluation of metamorphic rocks, thereby reducing manpower consumption and improving the efficiency and reliability of petrological research and geological engineering applications.

[0084] See also Fig.13 As shown, the embodiment of the present invention discloses a metamorphic rock porosity analysis and evaluation device, which may specifically include:

[0085] The sample processing module 11 is used to screen rock chip samples of metamorphic rocks, pre-process the rock chip samples to obtain the pre-processed rock chip samples, and scan the pre-processed rock chip samples to obtain rock chip sample images;

[0086] The identification and segmentation module 12 is used to identify and detect the mineral and background color edges in the rock cuttings sample image and segment the rock cuttings gap image to obtain rock cuttings particles and determine the total particle area of ​​the rock cuttings particles;

[0087] The pore and crack calculation module 13 is used to perform threshold truncation on the rock cutting particles by using a threshold segmentation method to calculate the pore area in the rock cutting particles, identify the cracks in the rock cutting particles by using a maximum likelihood method to calculate the crack area in the rock cutting particles, and calculate the pore and crack area by using the pore area and the crack area;

[0088] The analysis and evaluation module 14 is used to calculate the surface area of ​​the metamorphic rock based on the total area of ​​the particles and the pore and fracture areas, obtain the imaging logging fracture interpretation results corresponding to the surface area, analyze and evaluate the imaging logging fracture interpretation results and the surface area to obtain the analysis and evaluation results.

[0089] In this embodiment, rock cuttings samples of metamorphic rocks are screened, the rock cuttings samples are preprocessed to obtain the preprocessed rock cuttings samples, and the preprocessed rock cuttings samples are scanned to obtain rock cuttings sample images; the mineral and background color edges in the rock cuttings sample images are identified and detected, and the rock cuttings gap image is segmented to obtain rock cutting particles, and the total particle area of ​​the rock cutting particles is determined; the rock cutting particles are threshold-cut using a threshold segmentation method to calculate the pore area in the rock cutting particles, the cracks in the rock cutting particles are identified using a maximum likelihood method to calculate the crack area in the rock cutting particles, and the pore and crack areas are calculated using the pore area and the crack area; the surface porosity of the metamorphic rock is calculated based on the total particle area and the pore and crack areas, and an imaging logging fracture interpretation result corresponding to the surface porosity is obtained, and the imaging logging fracture interpretation result and the surface porosity are analyzed and evaluated to obtain an analysis and evaluation result. The present application can effectively focus on the research target range by identifying and detecting the edges of minerals and background colors in the rock chip sample image and segmenting the rock chip gap image, and solves the problem of the grayscale value of the base interfering with the identification result during the pore identification process of the rock chip particle sample. The threshold segmentation method and the maximum likelihood method are used to perform threshold truncation and crack identification on the rock chip particles respectively, which can solve the time-consuming problem of the existing crack identification relying on optical microscope to identify cast thin sections, and the difficulty of identifying large pores and cracks from scanning electron microscope photos. The surface ratio of metamorphic rocks is calculated based on the total area of ​​particles and the area of ​​pores and cracks, and the surface ratio and the corresponding imaging logging fracture interpretation results are analyzed and evaluated to obtain analysis and evaluation results, which can improve the accuracy and efficiency of the surface ratio analysis and evaluation of metamorphic rocks, reduce the complexity of the surface ratio analysis and evaluation of metamorphic rocks, thereby reducing manpower consumption and improving the efficiency and reliability of petrological research and geological engineering applications.

[0090] In some specific embodiments, the sample processing module 11 may specifically include:

[0091] A filtering and screening module, used for obtaining an initial rock cutting sample of the metamorphic rock, filtering and screening the initial rock cutting sample to obtain the filtered and screened rock cutting sample;

[0092] The polishing and grinding module is used to cut the rock cutting sample using a rock cutting machine, and perform polishing and grinding processes to obtain the pre-processed rock cutting sample.

[0093] In some specific embodiments, the sample processing module 11 may specifically include:

[0094] The scanning module is used to perform a preliminary scan on the pre-processed rock cuttings sample by using a scanning electron microscope and setting scanning parameters to screen a scanning area, and perform a high-resolution scan on the pre-processed rock cuttings sample according to the scanning area to obtain a rock cuttings sample image.

[0095] In some specific embodiments, the identification and segmentation module 12 may specifically include:

[0096] An identification, detection and splicing module is used to identify and detect the edges of minerals and background colors in the rock cuttings sample image using an edge detection algorithm, and to splice the pixels in the rock cuttings sample image to obtain pixel edge lines;

[0097] The filling and segmentation module is used to decompose and fill the rock cutting sample image and segment the rock cutting gap image according to the pixel edge lines to obtain rock cutting particles.

[0098] In some specific embodiments, the identification and segmentation module 12 may specifically include:

[0099] The pixel edge line determination module is used to perform Gaussian blur processing on the rock cuttings sample image using an edge detection algorithm, calculate the gradient of the processed rock cuttings sample image using a Sobel operator, and retain the local maximum value of each pixel point in the processed rock cuttings sample image along the gradient direction to identify and detect the edges of minerals and background colors, and splice the pixels in the rock cuttings sample image to obtain pixel edge lines.

[0100] In some specific embodiments, the pore and crack calculation module 13 may specifically include:

[0101] A threshold truncation module, for performing threshold truncation on the rock cutting particles by using a threshold segmentation method and a fixed gray value as a threshold, so as to calculate the pore area in the rock cutting particles;

[0102] The fracture area calculation module is used to calculate the fracture probability in the rock cutting particles by using a preset priori sample and adopting a maximum likelihood method, and to identify the fractures in the rock cutting particles based on the fracture probability to calculate the fracture area.

[0103] In some specific embodiments, the metamorphic rock porosity analysis and evaluation device may further include:

[0104] Built-in function acquisition module, used to obtain the built-in functions of the subprocess toolkit in the preset Python code;

[0105] The multi-thread parallel module is used to utilize the built-in function to realize the multi-thread parallel acquisition of rock cuttings sample images and the process of face rate analysis and evaluation.

[0106] Fig.14 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the metamorphic rock porosity analysis and evaluation method performed by the electronic device disclosed in any of the aforementioned embodiments.

[0107] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0108] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0109] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the metamorphic rock surface ratio analysis and evaluation method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to data transmitted from an external device received by the metamorphic rock surface ratio analysis and evaluation device, the data 223 can also include data collected by its own input and output interface 25.

[0110] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0111] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the metamorphic rock porosity analysis and evaluation method disclosed in any of the aforementioned embodiments are implemented.

[0112] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0113] The above is a detailed introduction to the metamorphic rock porosity analysis and evaluation method, device, equipment and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for analyzing and evaluating the porosity of metamorphic rocks, characterized in that: include: Screening rock chip samples of metamorphic rocks, pre-processing the rock chip samples to obtain the pre-processed rock chip samples, and scanning the pre-processed rock chip samples to obtain rock chip sample images; Identify and detect the mineral and background color edges in the rock cuttings sample image and perform rock cuttings gap image segmentation to obtain rock cuttings particles and determine the total particle area of ​​the rock cuttings particles; Performing threshold truncation on the rock cutting particles by using a threshold segmentation method to calculate the pore area in the rock cutting particles, identifying cracks in the rock cutting particles by using a maximum likelihood method to calculate the crack area in the rock cutting particles, and calculating the pore and crack areas by using the pore area and the crack area; The surface porosity of the metamorphic rock is calculated based on the total area of ​​the particles and the pore and fracture areas, an imaging logging fracture interpretation result corresponding to the surface porosity is obtained, and the imaging logging fracture interpretation result and the surface porosity are analyzed and evaluated to obtain an analysis and evaluation result.

2. The metamorphic rock porosity analysis and evaluation method according to claim 1, characterized in that: The screening of the rock chip samples of metamorphic rocks and pre-processing the rock chip samples to obtain the pre-processed rock chip samples include: Obtaining an initial rock chip sample of metamorphic rock, filtering and screening the initial rock chip sample to obtain the filtered and screened rock chip sample; The rock cutting machine is used to cut the rock cutting sample, and the rock cutting sample is polished and ground to obtain the pre-treated rock cutting sample.

3. The metamorphic rock porosity analysis and evaluation method according to claim 1, characterized in that: Scanning the pre-processed rock cuttings sample to obtain a rock cuttings sample image includes: The pre-processed rock cuttings sample is preliminarily scanned by using a scanning electron microscope and setting scanning parameters to screen a scanning area, and a high-resolution scan is performed on the pre-processed rock cuttings sample according to the scanning area to obtain a rock cuttings sample image.

4. The metamorphic rock porosity analysis and evaluation method according to claim 1, characterized in that: The identifying and detecting the mineral and background color edges in the rock cuttings sample image and segmenting the rock cuttings gap image to obtain rock cuttings particles includes: Using an edge detection algorithm to identify and detect the edges of minerals and background colors in the rock cuttings sample image, and splicing the pixels in the rock cuttings sample image to obtain pixel edge lines; The rock cutting sample image is decomposed and filled and the rock cutting gap image is segmented according to the pixel edge lines to obtain rock cutting particles.

5. The metamorphic rock porosity analysis and evaluation method according to claim 4, characterized in that: The method of using an edge detection algorithm to identify and detect the edges of minerals and background colors in the rock cuttings sample image, and splicing pixels in the rock cuttings sample image to obtain pixel edge lines, includes: An edge detection algorithm is used to perform Gaussian blur processing on the rock cuttings sample image, and the gradient of the processed rock cuttings sample image is calculated using the Sobel operator. The local maximum value of each pixel point in the processed rock cuttings sample image along the gradient direction is retained to identify the edges of the detected minerals and background colors, and the pixels in the rock cuttings sample image are spliced ​​to obtain pixel edge lines.

6. The metamorphic rock porosity analysis and evaluation method according to claim 1, characterized in that: The method of using a threshold segmentation method to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles, and using a maximum likelihood method to identify cracks in the rock cutting particles to calculate the crack area in the rock cutting particles includes: A threshold segmentation method is adopted and a fixed gray value is used as a threshold to perform threshold truncation on the rock cutting particles to calculate the pore area in the rock cutting particles; The fracture probability in the rock cutting particles is calculated by using a preset priori sample and adopting a maximum likelihood method, and the fractures in the rock cutting particles are identified based on the fracture probability to calculate the fracture area.

7. The metamorphic rock porosity analysis and evaluation method according to any one of claims 1 to 6, characterized in that: Also includes: Get the built-in functions of the subprocess toolkit in the preset Python code; The built-in function is used to realize the process of multi-threaded parallel acquisition of rock cuttings sample images and surface surface rate analysis and evaluation.

8. A metamorphic rock porosity analysis and evaluation device, characterized in that: include: A sample processing module, used for screening rock chip samples of metamorphic rocks, preprocessing the rock chip samples to obtain the preprocessed rock chip samples, and scanning the preprocessed rock chip samples to obtain rock chip sample images; An identification and segmentation module, used for identifying and detecting the edges of minerals and background colors in the rock cuttings sample image and performing rock cuttings gap image segmentation to obtain rock cuttings particles and determine the total particle area of ​​the rock cuttings particles; a pore and crack calculation module, for performing threshold truncation on the rock cutting particles by using a threshold segmentation method to calculate the pore area in the rock cutting particles, identifying cracks in the rock cutting particles by using a maximum likelihood method to calculate the crack area in the rock cutting particles, and calculating the pore and crack areas by using the pore area and the crack area; The analysis and evaluation module is used to calculate the surface area of ​​the metamorphic rock based on the total area of ​​the particles and the pore and fracture areas, obtain the imaging logging fracture interpretation results corresponding to the surface area, analyze and evaluate the imaging logging fracture interpretation results and the surface area to obtain the analysis and evaluation results.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the metamorphic rock surface porosity analysis and evaluation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store computer programs; wherein, when the computer program is executed by a processor, the metamorphic rock surface fraction analysis and evaluation method as described in any one of claims 1 to 7 is implemented.

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