A method, device, equipment and medium for analyzing and evaluating metamorphic rock surface porosity

Abstract

The application discloses a metamorphic rock surface porosity analysis and evaluation method and device, equipment and medium, relates to the technical field of oil logging reservoir evaluation, and comprises the following steps: pretreating a rock sample, scanning the pretreated rock sample, and obtaining a rock sample image; identifying and detecting the mineral and background color edge in the rock sample image and segmenting the rock gap image to obtain rock particles and determine the total particle area; adopting a threshold segmentation method to perform threshold truncation on the rock particles, calculate the pore area in the rock particles, adopt a maximum likelihood method to identify the cracks in the rock particles, calculate the crack area in the rock particles, and calculate the pore and crack areas by using the pore area and the crack area; calculating the surface porosity based on the total particle area and the pore and crack areas, analyzing and evaluating the imaging logging crack interpretation result and the surface porosity, and obtaining an analysis and evaluation result. The application can improve the accuracy and efficiency of metamorphic rock surface porosity analysis and evaluation and reduce the complexity of metamorphic rock surface porosity analysis and evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of petroleum logging reservoir evaluation, and particularly relates to a metamorphic rock surface porosity analysis and evaluation method, device, equipment and medium. BACKGROUND

[0002] At present, the porosity pixels of rock scanning electron microscope photos are extracted by using the way of gray scale threshold truncation and edge detection to realize pore recognition and pore ratio calculation. The current method for identifying fractures mainly includes the following steps: different colors of light sources are projected on the core casting thin section to make the color difference between the fractures and the matrix more obvious, so that the fracture ratio is calculated. The method is time-consuming and the thin section sample is more likely to be damaged and distorted. The main limitations of the prior art include: (1) when using a debris sample, the particle size is not uniform, and there are a large number of particle spacing spaces that can interfere with the identification of pores; (2) the existing fracture extraction method is based on an optical microscope, which is difficult to quantify and time-consuming. At the same time, the casting thin section as a sample is more likely to be damaged and distorted, thereby causing a large amount of time and manpower to be consumed, and the efficiency is low when a large number of samples are processed.

[0003] From the above, how to improve the accuracy and efficiency of metamorphic rock surface porosity analysis and evaluation, and reduce the complexity of metamorphic rock surface porosity analysis and evaluation is a problem to be solved in the field. SUMMARY

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

[0005] In a first aspect, the present application discloses a metamorphic rock surface porosity analysis and evaluation method, comprising:

[0006] Screening a debris sample of metamorphic rock, pretreating the debris sample to obtain a pretreated debris sample, and scanning the pretreated debris sample to obtain a debris sample image;

[0007] Identifying and detecting the mineral and background color edge in the debris sample image and segmenting the debris gap image to obtain a debris particle, and determining the total area of the debris particle;

[0008] Threshold truncation of the debris particle is performed by using a threshold segmentation method to calculate the pore area in the debris particle, and the cracks in the debris particle are identified by using a maximum likelihood method to calculate the crack area in the debris particle, and the pore and crack areas are calculated by using the pore area and the crack area;

[0009] Calculate the surface porosity of the metamorphic rock based on the total area of the particles and the area of the pores and cracks, obtain an imaging logging crack interpretation result corresponding to the surface porosity, analyze and evaluate the imaging logging crack interpretation result and the surface porosity to obtain an analysis and evaluation result.

[0010] Optionally, the rock debris sample of the metamorphic rock is screened, and the rock debris sample is pretreated to obtain the pretreated rock debris sample, including:

[0011] An initial rock debris sample of the metamorphic rock is obtained, and the initial rock debris sample is filtered and screened to obtain the filtered and screened rock debris sample.

[0012] The rock debris sample is cut by a rock cutting machine, and is profiled and polished to obtain the pretreated rock debris sample.

[0013] Optionally, the pretreated rock debris sample is scanned to obtain a rock debris sample image, including:

[0014] The pretreated rock debris sample is preliminarily scanned by a scanning electron microscope and by setting scanning parameters to screen a scanning area, and the pretreated rock debris sample is high-resolution scanned according to the scanning area to obtain a rock debris sample image.

[0015] Optionally, the mineral and background color edges in the rock debris sample image are identified and detected, and the rock debris gap image is segmented to obtain rock debris particles, including:

[0016] An edge detection algorithm is used to identify and detect the mineral and background color edges in the rock debris sample image, and pixels in the rock debris sample image are spliced to obtain pixel edge lines.

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

[0018] Optionally, the mineral and background color edges in the rock debris sample image are identified and detected by using an edge detection algorithm, and pixels in the rock debris sample image are spliced to obtain pixel edge lines, including:

[0019] A Gaussian blur processing is performed on the rock debris sample image by using an edge detection algorithm, a gradient of the processed rock debris sample image is calculated by using a Sobel operator, and a local maximum value of each pixel point in the processed rock debris sample image along a gradient direction is retained to identify and detect the mineral and background color edges, and the pixels in the rock debris sample image are spliced to obtain pixel edge lines.

[0020] Optionally, the threshold segmentation method is used to perform threshold truncation on the detritus particles to calculate the pore area in the detritus particles, and the maximum likelihood method is used to identify the cracks in the detritus particles to calculate the crack area in the detritus particles, including:

[0021] The threshold segmentation method is used to perform threshold truncation on the detritus particles by using a fixed gray value as a threshold to calculate the pore area in the detritus particles.

[0022] The maximum likelihood method is used to calculate the crack probability in the detritus particles by using a preset prior sample, and the cracks in the detritus particles are identified based on the crack probability to calculate the crack area.

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

[0024] The built-in function of the subprocess toolkit in the preset python code is obtained.

[0025] The built-in function is used to realize multi-threaded parallel acquisition of detritus sample images and the flow of surface porosity analysis and evaluation.

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

[0027] The sample processing module is used to screen the detritus sample of the metamorphic rock, pretreat the detritus sample to obtain the pretreated detritus sample, and scan the pretreated detritus sample to obtain a detritus sample image.

[0028] The identification and segmentation module is used to identify and detect the mineral and background color edge in the detritus sample image and segment the detritus gap image to obtain the detritus particles and determine the total particle area of the detritus particles.

[0029] The pore and crack calculation module is used to perform threshold truncation on the detritus particles by using the threshold segmentation method to calculate the pore area in the detritus particles, identify the cracks in the detritus particles by using the maximum likelihood method to calculate the crack area in the detritus particles, and calculate the pore and crack area by using the pore area and the crack area.

[0030] The analysis and evaluation module is used to calculate the surface porosity of the metamorphic rock based on the total particle area and the pore and crack area, obtain the imaging logging crack interpretation result corresponding to the surface porosity, analyze and evaluate the imaging logging crack interpretation result and the surface porosity to obtain the analysis and evaluation result.

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

[0032] a memory for storing the computer program;

[0033] a processor for executing the computer program to implement the metamorphic rock surface porosity analysis and evaluation method.

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

[0035] It can be seen that the present application provides a metamorphic rock surface porosity analysis and evaluation method, which includes screening a rock debris sample of metamorphic rock, pretreating the rock debris sample to obtain a pretreated rock debris sample, and scanning the pretreated rock debris sample to obtain a rock debris sample image; identifying and detecting mineral and background color edges in the rock debris sample image and segmenting a rock debris gap image to obtain rock debris particles, and determining a total particle area of the rock debris particles; using a threshold segmentation method to perform threshold truncation on the rock debris particles to calculate a pore area in the rock debris particles, using a maximum likelihood method to identify cracks in the rock debris particles to calculate a crack area in the rock debris particles, and using the pore area and the crack area to calculate pore and crack areas; calculating a surface porosity of the metamorphic rock based on the total particle area and the pore and crack areas, obtaining an imaging logging crack interpretation result corresponding to the surface porosity, and analyzing and evaluating the imaging logging crack interpretation result and the surface porosity to obtain an analysis and evaluation result. The present application can effectively focus on the target range by identifying and detecting mineral and background color edges in the rock debris sample image and segmenting a rock debris gap image, solves the problem of interference of the gray value of the substrate on the identification result in the process of identifying pores in the rock debris particle sample, uses the threshold segmentation method and the maximum likelihood method to perform threshold truncation and crack identification on the rock debris particles, respectively, solves the time-consuming problem of identifying cracks by relying on an optical microscope to identify a cast thin section and the problem of identifying large pores and cracks from a scanning electron microscope photo, calculates the surface porosity of the metamorphic rock based on the total particle area and the pore and crack areas, analyzes and evaluates the surface porosity and the corresponding imaging logging crack interpretation result to obtain the analysis and evaluation result, improves the accuracy and efficiency of the metamorphic rock surface porosity analysis and evaluation, reduces the complexity of the metamorphic rock surface porosity analysis and evaluation, thereby reducing the consumption of manpower and improving the efficiency and reliability of petrology research and geological engineering applications. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on the provided drawings.

[0037] Figure 1 A metamorphic rock surface porosity analysis and evaluation method flowchart disclosed by the present application;

[0038] Figure 2 A pretreated rock debris sample graph disclosed by the present application;

[0039] Figure 3 A scanned rock debris sample graph disclosed by the present application;

[0040] Figure 4 A pore recognition effect graph disclosed by the present application;

[0041] Figure 5 A metamorphic rock surface porosity analysis and evaluation method flowchart disclosed by the present application;

[0042] Figure 6 A segmented rock debris particle effect display graph disclosed by the present application;

[0043] Figure 7 A single sample rock particle surface porosity statistical result graph disclosed by the present application;

[0044] Figure 8 A rock debris sample production effect graph disclosed by the present application;

[0045] Figure 9 A rock debris sample electronic scanning result graph disclosed by the present application;

[0046] Figure 10 A background elimination display graph based on rock debris segmentation disclosed by the present application;

[0047] Figure 11 A rock debris sample pore recognition effect graph disclosed by the present application;

[0048] Figure 12 A surface porosity calculation result and imaging logging interpretation result comparison graph disclosed by the present application;

[0049] Figure 13 A metamorphic rock surface porosity analysis and evaluation device structure schematic diagram disclosed by the present application;

[0050] Figure 14 An electronic device structure graph provided by the present application. DETAILED DESCRIPTION

[0051] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0052] At present, mainly by using the gray scale threshold truncation and edge detection method, the porosity pixels of the rock scanning electron microscope photos are extracted to realize the pore recognition and the pore ratio calculation. The current method for identifying the cracks mainly is based on projecting different color light sources on the core casting thin section to make the crack color and the matrix color more different, so as to realize the calculation of the crack ratio, which is time-consuming and the thin section sample is more likely to be damaged and distorted. The main limitations of the prior art include: (1) when using the debris sample, due to the different particle sizes, there are a large number of particle spacing spaces, which can interfere with the identification of the pores; (2) the existing crack extraction method is based on an optical microscope, which is difficult to quantitatively calculate and time-consuming, and the casting thin section as a sample is more likely to be damaged and distorted, thereby causing a large amount of time and manpower to be consumed, and the efficiency is low when a large number of samples are processed. As can be seen from the above, how to improve the accuracy and efficiency of the metamorphic rock surface porosity analysis and evaluation and reduce the complexity of the metamorphic rock surface porosity analysis and evaluation is a problem to be solved in the field.

[0053] Referring to Figure 1 The embodiment of the present application discloses a metamorphic rock surface porosity analysis and evaluation method, which can specifically include:

[0054] Step S11: screening the debris sample of the metamorphic rock, pretreating the debris sample to obtain the pretreated debris sample, and scanning the pretreated debris sample to obtain a debris sample image.

[0055] In this embodiment, an initial debris sample of the metamorphic rock is obtained, the initial debris sample is filtered and screened to obtain the filtered and screened debris sample, the debris sample is cut by a rock cutting machine, and is subjected to profile polishing and polishing to obtain the pretreated debris sample, the pretreated debris sample is preliminarily scanned by a scanning electron microscope and setting scanning parameters to screen a scanning area, and the pretreated debris sample is scanned at a high resolution according to the scanning area to obtain a debris sample image.

[0056] Specifically, in the initial rock debris sample of metamorphic rock, the appropriate filtered and screened rock debris sample is selected through multiple filtering screens to ensure that the sample can represent the characteristics of the whole rock body, and then the large rock is cut into small pieces suitable for being placed in the scanning electron microscope using a rock cutting machine, and then the sample is cut and polished to ensure that the sample surface is smooth and flat for high-quality electron microscope scanning. Since the rock is non-conductive, a carbon powder spraying method is used to make the surface conductive to prevent the electron beam from accumulating on the sample surface and affecting the image quality. The pretreated rock debris sample is as shown in Figure 2 Then the pretreated rock debris sample is installed on the SEM (scanning electron microscope) sample table and placed in the sample chamber of the scanning electron microscope. The appropriate scanning parameters such as acceleration voltage, working distance, scanning rate, etc. are set, and the sample surface is observed by preliminary scanning. Then, the appropriate scanning area is selected, and high-resolution scanning is performed to obtain the rock debris sample image of the rock pore structure. The scanned rock debris sample image is as shown in Figure 3

[0057] Step S12: The mineral and background color edges in the rock debris sample image are identified and detected, and the rock debris gap image is segmented to obtain the rock debris particles, and the total area of the rock debris particles is determined.

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

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

[0060] Since there are many cracks and pore structures in the rock debris particles, it is not possible to directly see the specific proportion of these structures from the photo. Therefore, the application adopts the pore and crack classification identification and result superposition method to carry out the pore and crack identification. For pores, the threshold segmentation method is used to analyze the distribution state of the scanning electron microscope image gray value, and a threshold value of about 30 is used to perform threshold truncation on the particles affected by the substrate to extract the pore characteristics and calculate the pore area.

[0061] ​For cracks, secondary image segmentation is used during use for those with large color differences in the internal minerals of the debris to reduce the interference of mineral interfaces. In order to improve the accuracy of crack identification, this paper uses 10 artificially marked crack and hole pictures as prior samples, and uses 50*50 pixel standard scale to segment the sample pictures, realizes sample enhancement, greatly improves the number of pictures, and then uses maximum likelihood algorithm to establish the likelihood formula:

[0062] ;

[0063] Among them, the pixel intensity of the crack area follows a probability distribution with a parameter The pixel intensity of the non-crack area follows a probability distribution with a parameter X is the observed image data, and C and B represent the pixel set of the crack area and the non-crack area in the image, respectively. Through 20-step iterative training, the parameters of cracks and holes are fitted, and the values of parameters and are found, so that the likelihood function is maximized, and the formula is as follows:

[0064] ;

[0065] Then, using the same size of 50*50 picture frame, the cracks and pores of unknown pictures are identified by moving the frame body without overlapping. At the same time, due to the small size of some fine structural cracks on the surface of high-brightness minerals, it is difficult to identify. In order to fully depict, this paper carries out local difference recognition based on maximum likelihood algorithm, uses 10*10 convolution kernel method to calculate the difference of all pixel points relative to the average value within the range of 10*10, and extracts the difference pixel points as fine crack representation points.

[0066] The final crack and pore recognition effect is shown in Figure 4 It can be seen that OTSU (maximum between-class variance method) will cause fine cracks to be shielded when there is a clear boundary value, and the recognition ability of simple maximum likelihood crack extraction for larger pores is insufficient. The method of this paper can more fully extract the comprehensive proportion of cracks and pores.

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

[0068] ​​In addition, the application also comprises: acquiring a built-in function of a subprocess toolkit in a preset python code; and implementing a multi-thread parallel flow of acquiring a cutting sample image and a face porosity analysis and evaluation by using the built-in function. That is, finally, by using the subprocess toolkit in the python code, multiple threads are started in parallel, and 20 cores of a computer CPU (Central Processing Unit) are called at the same time, so that parallel calculation of 20 images is realized, and the calculation efficiency is greatly improved.

[0069] In the embodiment, the cutting sample of the metamorphic rock is screened, the cutting sample is pretreated to obtain a pretreated cutting sample, and the pretreated cutting sample is scanned to obtain a cutting sample image. The mineral and background color edges in the cutting sample image are identified and detected, and the cutting gap image is segmented to obtain cutting particles. The total area of the cutting particles is determined. The threshold segmentation method is used to cut off the thresholds of the cutting particles to calculate the pore area in the cutting particles. The maximum likelihood method is used to identify the cracks in the cutting particles to calculate the crack area in the cutting particles. The pore and crack areas are calculated by using the pore area and the crack area. The face porosity of the metamorphic rock is calculated based on the total area of the cutting particles and the pore and crack areas. An imaging logging crack interpretation result corresponding to the face porosity is acquired. The imaging logging crack interpretation result and the face porosity are analyzed and evaluated to obtain an analysis and evaluation result. By identifying and detecting the mineral and background color edges in the cutting sample image and segmenting the cutting gap image, the research target range can be effectively focused. The interference problem of the gray value of the substrate on the identification result in the process of identifying the pores of the cutting particle sample is solved. The threshold segmentation method and the maximum likelihood method are used to cut off the thresholds of the cutting particles and identify the cracks, respectively. The time-consuming problem of relying on an optical microscope to identify the cast thin section and the difficulty problem of identifying large pores and cracks from a scanning electron microscope photo are solved. The face porosity of the metamorphic rock is calculated based on the total area of the cutting particles and the pore and crack areas. The face porosity and the corresponding imaging logging crack interpretation result are analyzed and evaluated to obtain the analysis and evaluation result. The accuracy and efficiency of the metamorphic rock face porosity analysis and evaluation are improved, the complexity of the metamorphic rock face porosity analysis and evaluation is reduced, the human consumption is reduced, and the efficiency and reliability of the petrology research and the geological engineering application are improved.

[0070] Referring to Figure 5 The embodiment of the application discloses a metamorphic rock face porosity analysis and evaluation method, which can specifically comprise:

[0071] Step S21: screening a cutting sample of a metamorphic rock, pretreating the cutting sample to obtain a pretreated cutting sample, and scanning the pretreated cutting sample to obtain a cutting sample image.

[0072] Step S22: The edge detection algorithm is used to identify and detect the mineral and background color edges in the cutting sample image, and the pixels in the cutting sample image are spliced to obtain pixel edge lines. The cutting sample image is decomposed and filled according to the pixel edge lines, and the cutting gap image is segmented to obtain cutting particles, and the total area of the cutting particles is determined.

[0073] In this embodiment, the edge detection algorithm is used for Gaussian blur processing of the cutting sample image, the Sobel operator is used to calculate the gradient of the processed cutting sample image, and the local maximum value of each pixel point in the processed cutting sample image along the gradient direction is retained to identify and detect the mineral and background color edges. The pixels in the cutting sample image are spliced to obtain pixel edge lines. The cutting sample image is decomposed and filled according to the pixel edge lines, and the cutting gap image is segmented to obtain cutting particles, and the total area of the cutting particles is determined.

[0074] Specifically, in order to eliminate the interference of cutting interval space on the identification results of pores and cracks, it is necessary to first use image segmentation technology to distinguish the cutting particles in the sample, and then identify the pores and cracks based on the particles, so as to avoid any extraction of pore or crack information for the cutting interval space.

[0075] In order to realize the segmentation of cutting particles, the Canny (edge detection algorithm) is used to detect the edges of minerals and background colors in the image to highlight the morphology of cutting particles. The Canny algorithm mainly includes six steps: first, Gaussian blur is performed on the image to reduce 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, only the local maximum value along the gradient direction is retained for each pixel point to refine the edges. Subsequently, according to the set high threshold and low threshold, the pixels in the image are divided into three categories: strong edge, weak edge and non-edge. Then, the strong edge pixels and the surrounding weak edge pixels are connected to obtain complete edge lines. Finally, different contours are decomposed and filled to form different cutting particle areas. The segmented cutting particle effect is shown in Figure 6

[0076] Step S23: The threshold segmentation method is used to cut off the cutting particles to calculate the pore area in the cutting particles. The maximum likelihood method is used to identify the cracks in the cutting particles to calculate the crack area in the cutting particles. The pore area and the crack area are used to calculate the pore and crack area.​

[0077] Step S24: calculating the surface porosity of the metamorphic rock based on the total area of the particles and the pore and crack area, obtaining the imaging logging crack interpretation result corresponding to the surface porosity, analyzing and evaluating the imaging logging crack interpretation result and the surface porosity to obtain an analysis and evaluation result.

[0078] In this embodiment, the mask method is used to screen specific cuttings in the mineral scanning result, and finally the statistical result of the surface porosity of the rock particles of a single sample is formed as shown in Figure 7 The total area of each particle in each sample and the proportion of pores and cracks (surface porosity) are introduced in detail, and the depth position of the sample is also recorded and marked in the logging result, so as to realize the information calibration of the logging result and perfect the interpretation result of the target well in the current well section.

[0079] For example, in the research case of a specific metamorphic rock well, considering that the main storage and flow space of metamorphic rock is mainly composed of cracks and intergranular dissolution pores, the research team collected the cutting samples in the well section for detailed analysis. In order to ensure the clear display of rock particles in the cutting, the sample preparation work was first carried out, and the display effect of the prepared sample is shown in Figure 8 Subsequently, the sample was scanned in detail by using electronic scanning technology, and the scanning result revealed the cutting particle structure full of small cracks and pores in the sample, as shown in Figure 9

[0080] Further, the pore and crack analysis in this embodiment is carried out by using the pore and crack identification algorithm removed in this paper. The initial step of the algorithm is to segment the cutting particles from the background to eliminate the interference of the background on the pore and crack analysis, and to ensure that the calculation is only performed on the color marked area. The background elimination based on cutting segmentation is shown in Figure 10 Continuing this process, the surface area and pore and crack area of multiple cutting particles at the same depth are quantitatively identified, and the cutting sample pore and crack identification effect is shown in Figure 11 The total pore and crack area of each sample and the total area of the cutting particles are calculated by using the method of summing up the cutting particle groups based on the sample number. Subsequently, the formula: total area of a single sample = total area of sample particle pores and cracks / sample particle total area is applied to calculate the surface porosity of a single sample, i.e. the ratio of the pore and crack area to the total area of the sample.

[0081] The ultimate step of this research involves comparing and analyzing the calculated surface porosity with the imaging logging crack interpretation result at the corresponding depth, and the comparison of the surface porosity calculation result and the imaging logging interpretation result is shown in Figure 12 ​The results show that in the well section above 3900m, due to the dense fractures, the calculated surface porosity shows high values, which is consistent with the high fracture density shown by the imaging logging results. In the depth below 3900m, with the decrease of the fracture density shown by the imaging logging, the calculated surface porosity also decreases significantly, and is basically consistent with the local fluctuation of the imaging logging results, which proves the effectiveness and application potential of the method in accurately identifying and quantifying the characteristics of metamorphic rock reservoirs.

[0082] The innovations and advantages of the present application are as follows: 1. Efficient particle gap elimination method: advanced image segmentation + edge erosion technology, effectively identifying the outline of the particle, eliminating the interference of the base between the particles on the identification process of the pores and fractures in the particles; 2. Precise surface porosity identification algorithm: for pores, a simple threshold cutting method is used to extract due to the low gray level of the pores, and for fractures, a maximum likelihood method is used in combination with local blur + contrast enhancement to enhance the performance characteristics of the fractures in different color minerals. Finally, the pore and fracture areas are accurately extracted from the particle scanning electron microscope images of the metamorphic rock, so as to obtain the surface porosity; 3. High-speed parallel technology: the method calls CPU computing resources in a multi-parallel way to realize parallel calculation of 40 samples, and it only takes 30 minutes to complete the automatic analysis and evaluation of the surface porosity of 12600+ particles of 580 samples, which greatly liberates human resources, improves the evaluation speed, and improves the interpretation accuracy.

[0083] In the embodiment, a rock debris sample of metamorphic rock is screened, the rock debris sample is pretreated to obtain a pretreated rock debris sample, the pretreated rock debris sample is scanned to obtain a rock debris sample image; mineral and background color edges in the rock debris sample image are recognized and detected and rock debris gap image segmentation is performed to obtain rock debris particles, and a total particle area of the rock debris particles is determined; a threshold segmentation method is used to perform threshold truncation on the rock debris particles to calculate a pore area in the rock debris particles, a maximum likelihood method is used to recognize cracks in the rock debris particles to calculate a crack area in the rock debris particles, and the pore area and the crack area are used to calculate a pore and crack area; the total particle area and the pore and crack area are used to calculate a surface pore rate of the metamorphic rock, an imaging logging crack interpretation result corresponding to the surface pore rate is obtained, and the imaging logging crack interpretation result and the surface pore rate are analyzed and evaluated to obtain an analysis and evaluation result. Through the recognition and detection of the mineral and the background color edges in the rock debris sample image and the rock debris gap image segmentation, the target range can be effectively focused, the interference of the gray value of the substrate on the recognition result in the process of recognizing the pores of the rock debris particle sample is solved, the threshold segmentation method and the maximum likelihood method are used to perform threshold truncation and crack recognition on the rock debris particles respectively, the time-consuming problem of relying on an optical microscope to recognize the cast body thin section and the difficulty problem of recognizing large pores and cracks from a scanning electron microscope photo are solved, the total particle area and the pore and crack area are used to calculate the surface pore rate of the metamorphic rock, the surface pore rate and the corresponding imaging logging crack interpretation result are analyzed and evaluated to obtain the analysis and evaluation result, the accuracy and the efficiency of the metamorphic rock surface pore rate analysis and evaluation are improved, the complexity of the metamorphic rock surface pore rate analysis and evaluation is reduced, and thus the human consumption is reduced and the efficiency and the reliability of the petrology research and the geological engineering application are improved.

[0084] Referring to Figure 13 As shown in the drawings, the embodiment of the present application discloses a metamorphic rock surface pore rate analysis and evaluation device, which can specifically include:

[0085] The sample processing module 11 is configured to screen a rock debris sample of metamorphic rock, pretreat the rock debris sample to obtain a pretreated rock debris sample, and scan the pretreated rock debris sample to obtain a rock debris sample image.

[0086] The recognition and segmentation module 12 is configured to recognize and detect mineral and background color edges in the rock debris sample image and perform rock debris gap image segmentation to obtain rock debris particles and determine a total particle area of the rock debris particles.

[0087] The pore and crack calculation module 13 is configured to perform threshold segmentation on the rock debris particles to calculate pore areas in the rock debris particles, identify cracks in the rock debris particles by using a maximum likelihood method to calculate crack areas in the rock debris particles, and calculate pore and crack areas by using the pore areas and the crack areas.

[0088] The analysis and evaluation module 14 is configured to calculate a surface porosity of the metamorphic rock based on the total particle area and the pore and crack areas, obtain an imaging logging crack interpretation result corresponding to the surface porosity, and analyze and evaluate the imaging logging crack interpretation result and the surface porosity to obtain an analysis and evaluation result.

[0089] In the embodiment, a rock debris sample of the metamorphic rock is screened, the rock debris sample is pretreated to obtain a pretreated rock debris sample, and the pretreated rock debris sample is scanned to obtain a rock debris sample image. The mineral and background color edges in the rock debris sample image are identified and detected, and a rock debris gap image is segmented to obtain rock debris particles, and a total particle area of the rock debris particles is determined. The rock debris particles are threshold segmented by using a threshold segmentation method to calculate pore areas in the rock debris particles, cracks in the rock debris particles are identified by using a maximum likelihood method to calculate crack areas in the rock debris particles, and pore and crack areas are calculated by using the pore areas and the crack areas. The surface porosity of the metamorphic rock is calculated based on the total particle area and the pore and crack areas, an imaging logging crack interpretation result corresponding to the surface porosity is obtained, and the imaging logging crack interpretation result and the surface porosity are analyzed and evaluated to obtain an analysis and evaluation result. By identifying and detecting the mineral and background color edges in the rock debris sample image and segmenting the rock debris gap image, the research target range can be effectively focused, the problem of interference of the gray value of the substrate on the identification result in the process of identifying pores in the rock debris particle sample is solved, the rock debris particles are threshold segmented and cracks are identified by using the threshold segmentation method and the maximum likelihood method, respectively, the time-consuming problem of identifying cracks by relying on an optical microscope to identify a cast thin section and the difficulty problem of identifying large pores and cracks by using a scanning electron microscope are solved, the surface porosity of the metamorphic rock is calculated based on the total particle area and the pore and crack areas, the surface porosity and the corresponding imaging logging crack interpretation result are analyzed and evaluated to obtain an analysis and evaluation result, the accuracy and efficiency of the analysis and evaluation of the surface porosity of the metamorphic rock are improved, the complexity of the analysis and evaluation of the surface porosity of the metamorphic rock is reduced, and thus the human consumption is reduced and the efficiency and reliability of the petrology research and the geological engineering application are improved.

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

[0091] A filtering and screening module, configured to obtain an initial rock chip sample of the metamorphic rock, and filter and screen the initial rock chip sample to obtain the filtered and screened rock chip 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, configured to identify and detect edges of minerals and background colors in the rock cuttings sample image using an edge detection algorithm, and to splice 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] A 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 is used to perform 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 crack area calculation module is configured to calculate a crack probability in the detritus particle by using a preset prior sample and a maximum likelihood method, identify cracks in the detritus particle based on the crack probability, and calculate a crack area.

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

[0104] The built-in function acquisition module is configured to acquire a built-in function of a subprocess toolkit in a preset python code.

[0105] The multi-thread parallel module is configured to implement a multi-thread parallel process of acquiring a detritus sample image and a surface porosity analysis and evaluation by using the built-in function.

[0106] Figure 14 A structural schematic diagram of an electronic device is provided in the embodiments of the present application. The electronic device 20 can 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 configured to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the related steps in the metamorphic rock surface porosity analysis and evaluation method performed by the electronic device disclosed in any of the foregoing embodiments.

[0107] In the embodiments, the power supply 23 is configured to provide a 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 external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solutions of the present application, which is not specifically limited herein. The input / output interface 25 is configured to acquire external input data or output data to the outside world, and the specific interface type can be selected according to specific application needs, which is not specifically limited herein.

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

[0109] The operating system 221 is used to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows, Unix, Linux, etc. The computer program 222 can further include computer programs for completing other specific work in addition to the computer program for completing the metamorphic rock surface porosity analysis and evaluation method performed by the electronic device 20 disclosed in any of the foregoing embodiments. The data 223 can include data transmitted by an external device received by the metamorphic rock surface porosity analysis and evaluation device, data collected by the self input / output interface 25, etc.

[0110] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0111] Further, the embodiments of the present application also disclose a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the metamorphic rock surface porosity analysis and evaluation method steps disclosed in any of the foregoing embodiments.

[0112] Finally, it needs to be explained that, in the present text, the relationship terms such as first and second 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 sequence between the entities or operations. Moreover, the terms “include”, “contain” or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement “including a…” does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0113] The metamorphic rock surface porosity analysis and evaluation method, device, equipment and storage medium provided by the present application are described in detail above, and the principles and implementation modes of the present application are described by applying specific examples. The above example is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description should not be understood as a limitation of the present application.

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 pre-processed rock chip samples, and scanning the pre-processed rock chip samples to obtain rock chip sample images; Identifying and detecting mineral and background color edges in the rock cuttings sample image and segmenting the rock cuttings gap image to obtain rock cutting particles and determine the total particle area of ​​the rock cutting particles; Performing threshold segmentation on the rock cutting particles to calculate the pore area in the rock cutting particles, identifying cracks in the rock cutting particles to calculate the crack area in the rock cutting particles using a maximum likelihood method, and calculating the pore and crack areas 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 metamorphic rock chip samples 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, and filtering and screening the initial rock chip sample to obtain the filtered and screened rock chip sample; The rock cutting sample is cut by a rock cutting machine, and then polished and ground to obtain the pre-processed 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 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 method for analyzing and evaluating the porosity of metamorphic rocks according to claim 1, wherein: 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 cutting particles includes: Using an edge detection algorithm to identify and detect 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; 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 method for analyzing and evaluating the porosity of metamorphic rocks according to claim 4, wherein: The method of using an edge detection algorithm to identify and detect 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 chip sample image, and the gradient of the processed rock chip sample image is calculated using the Sobel operator. The local maximum value of each pixel point in the processed rock chip sample image along the gradient direction is retained to identify the edges of the mineral and background colors, and the pixels in the rock chip 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 performing threshold segmentation on the rock cutting particles to calculate the pore area in the rock cutting particles, and identifying cracks in the rock cutting particles to calculate the crack area in the rock cutting particles by using the maximum likelihood method, 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 using a preset priori sample and 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 method for analyzing and evaluating the surface porosity of metamorphic rocks 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 face surface rate analysis and evaluation.

8. A metamorphic rock porosity analysis and evaluation device, characterized in that: include: a sample processing module, configured to screen rock chip samples of metamorphic rocks, pre-process the rock chip samples to obtain pre-processed rock chip samples, and scan the pre-processed rock chip samples to obtain rock chip sample images; an identification and segmentation module for identifying and detecting mineral and background color edges in the rock cuttings sample image and performing rock cuttings gap image segmentation to obtain rock cutting particles and determine the total particle area of ​​the rock cutting particles; a pore and crack calculation module, configured to perform threshold segmentation on the rock cutting particles to calculate the pore area in the rock cutting particles, identify cracks in the rock cutting particles using a maximum likelihood method to calculate the crack area in the rock cutting particles, and calculate the pore and crack areas using the pore area and the crack area; An analysis and evaluation module is used to calculate the porosity of the metamorphic rock based on the total area of ​​the particles and the pore and fracture areas, obtain an imaging logging fracture interpretation result corresponding to the porosity, and analyze and evaluate the imaging logging fracture interpretation result and the porosity to obtain an analysis and evaluation result.

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

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

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