Method and device for determining content of organic maceral in rock sample

Abstract

The invention provides a method and a device for determining the content of organic macerals in a rock sample. The method for determining the content of the organic macerals in the rock sample comprises the following steps: collecting a plurality of images of the rock sample through a microscope; wherein the plurality of images are acquired by the microscope for multiple times; splicing the plurality of images to generate a spliced image; and determining the organic maceral content of the rock sample according to the spliced image. According to the method and the device for determining the content of the organic maceral in the rock sample, provided by the invention, manual full-vision field observation and photographing are not needed, and offline image processing can be carried out only by obtaining one or a plurality of large images, so that the time of occupying a microscope can be saved, and the efficiency can be greatly improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil and gas exploration, and particularly relates to a method and device for determining the content of organic maceral in a rock sample. BACKGROUND

[0002] Organic petrology is to study the solid dispersed organic matter visible in sedimentary rock, that is, to determine the type of organic matter and objectively evaluate the source rock by identifying and counting different macerals (Li Xianqing et al., 1996). In the prior art, the sample is ground and made into a light sheet, and the maceral is identified by using reflected light and fluorescence, and the number of maceral is quantitatively counted by using a counter. The point net distribution of maceral quantitative counting follows the formula rule S = 0.5dmax: when the particle diameter (d) of the sample is 1mm, the point distance and line distance (S) are both 0.5mm. The effective counting points of each sample need to be greater than 800, and the volume fraction of each maceral and mineral is expressed according to the percentage of the counting points in the total effective points. Therefore, the counter has a large amount of work under this mode, and there is a certain deficiency in expressing the volume fraction of the maceral according to the percentage of the effective points, especially for the maceral with large organic particles but small quantity, the percentage content of which will be less. SUMMARY

[0003] An object of the present application is to provide a method and device for determining the content of organic maceral in a rock sample, which can save the time of occupying the microscope and improve the efficiency by obtaining one or more large images for offline image processing without manual full-view observation and photographing.

[0004] Another object of the present application is to provide a device for determining the content of organic maceral in a rock sample. Still another object of the present application is to provide an electronic device including a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method for determining the content of organic maceral in a rock sample when executing the computer program. Still another object of the present application is to provide a readable medium storing a computer program, the computer program being executed by a processor to implement the steps of the method for determining the content of organic maceral in a rock sample.

[0005] To solve the technical problems in the background art, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for determining the content of organic maceral in a rock sample, comprising:

[0007] acquiring a plurality of images of the rock sample by using a microscope; wherein the plurality of images are obtained by the microscope multiple times;

[0008] stitching the plurality of images to generate a stitched image;

[0009] determining an organic maceral content of the rock sample according to the stitched image.

[0010] In some embodiments of the present application, the acquiring the plurality of images of the rock sample by the microscope comprises:

[0011] making a polished section of the rock sample;

[0012] acquiring the plurality of images of the polished section by the microscope at a preset magnification.

[0013] In some embodiments of the present application, the acquiring the plurality of images of the rock sample by the microscope further comprises:

[0014] acquiring the plurality of images by the microscope at a fixed brightness and a fixed exposure time.

[0015] In some embodiments of the present application, the determining the organic maceral content of the rock sample according to the stitched image comprises:

[0016] determining macerals in the stitched image according to gray scale and / or color;

[0017] determining areas corresponding to different macerals;

[0018] determining the organic maceral content of the rock sample according to the areas corresponding to the different macerals.

[0019] In some embodiments of the present application, the stitching the plurality of images to generate a stitched image comprises:

[0020] stitching the plurality of images in a rectangular manner with a preset moving step to generate the stitched image.

[0021] In some embodiments of the present application, the acquiring the plurality of images of the polished section by the microscope at a preset magnification comprises:

[0022] selecting a feature point in an initial image of the plurality of images;

[0023] acquiring the plurality of images of the polished section by the microscope in a preset direction and at the preset magnification successively from the feature point.

[0024] In some embodiments of the present application, the preset magnification is 50 or 20.

[0025] In a second aspect, the present application provides a device for determining an organic maceral content in a rock sample, the device comprising:

[0026] a plurality of image acquisition modules, configured to acquire a plurality of images of the rock sample by using a microscope, wherein the plurality of images are acquired by the microscope multiple times;

[0027] a stitched image generation module, configured to stitch the plurality of images to generate a stitched image;

[0028] an organic maceral content determination module, configured to determine an organic maceral content of the rock sample according to the stitched image.

[0029] In some embodiments of the present application, the plurality of image acquisition modules comprise:

[0030] a photomicrograph making unit, configured to make a photomicrograph of the rock sample;

[0031] a plurality of image acquisition first units, configured to acquire a plurality of images of the photomicrograph by using the microscope at a preset magnification.

[0032] In some embodiments of the present application, the plurality of image acquisition modules further comprise:

[0033] a plurality of image acquisition second units, configured to acquire the plurality of images by using the microscope at a fixed brightness and a fixed exposure time.

[0034] In some embodiments of the present application, the organic maceral content determination module comprises:

[0035] a maceral determination unit, configured to determine macerals in the stitched image according to grayscale and / or color;

[0036] an area determination unit, configured to determine areas corresponding to different macerals;

[0037] an organic maceral content determination unit, configured to determine the organic maceral content of the rock sample according to the areas corresponding to the different macerals.

[0038] In some embodiments of the present application, the stitched image generation module comprises:

[0039] a stitched image generation unit, configured to stitch the plurality of images in a rectangular manner at a preset movement step to generate the stitched image.

[0040] In some embodiments of the present application, the plurality of image acquisition first units comprise:

[0041] a feature point selection unit, configured to select a feature point in an initial image in the plurality of images;

[0042] A plurality of image acquisition units are arranged in sequence, and are used to sequentially acquire a plurality of images of the light sheet from the feature points through the microscope in a preset direction and according to the preset multiple.

[0043] In some embodiments of the present application, the preset multiple is 50 or 20.

[0044] In a third aspect, the present application provides a computer program product, comprising computer programs / instructions, which, when executed by a processor, implement the steps of the method for determining the content of organic maceral in a rock sample.

[0045] In a fourth aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for determining the content of organic maceral in a rock sample when executing the program.

[0046] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for determining the content of organic maceral in a rock sample.

[0047] As can be seen from the above description, the present application provides a method and device for determining the content of organic maceral in a rock sample, and the corresponding method for determining the content of organic maceral in a rock sample comprises the following steps: first, acquiring a plurality of images of the rock sample by using a microscope; wherein the plurality of images are obtained by multiple acquisitions of the microscope; then, splicing the plurality of images to generate a spliced image; and finally, determining the content of organic maceral in the rock sample according to the spliced image.

[0048] The corresponding device for determining the content of organic maceral in a rock sample comprises: a plurality of image acquisition modules, which are used to acquire a plurality of images of the rock sample by using a microscope; wherein the plurality of images are obtained by multiple acquisitions of the microscope; a spliced image generation module, which is used to splice the plurality of images to generate a spliced image; and an organic maceral content determination module, which is used to determine the content of organic maceral in the rock sample according to the spliced image.

[0049] In summary, the present application provides a method and device for determining the content of organic maceral in a rock sample, which selects organic maceral with similar optical characteristics and performs relevant quantitative calculation, thereby improving the degree of automation, saving time and improving test efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of a method for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0052] Figure 2 A flowchart of step 100 of a method for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0053] Figure 3 Another flowchart of step 100 of a method for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0054] Figure 4 A flowchart of step 300 of a method for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0055] Figure 5 A flowchart of step 200 of a method for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0056] Figure 6 A flowchart of step 102 of a method for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0057] Figure 7 A flowchart of a method for determining the content of organic maceral in a rock sample in a specific embodiment of the present application;

[0058] Figure 8 An image splicing schematic diagram in a specific embodiment of the present application;

[0059] Figure 9 A schematic diagram of splicing a large image of a sample and selecting vitrinite maceral in a specific embodiment of the present application;

[0060] Figure 10 Another schematic diagram of splicing a large image of a sample and selecting vitrinite maceral in a specific embodiment of the present application;

[0061] Figure 11 A block diagram of a device for determining the content of organic maceral in a rock sample in an embodiment of the present application;

[0062] Figure 12 Structure diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0064] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover not exclusive inclusion, for example, a process, method, system, product or device containing a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0066] The acquisition, storage, use, processing and the like of data in the technical solutions of the present application comply with the relevant provisions of laws and regulations.

[0067] The application discloses a device for simultaneously measuring the maceral content and vitrinite reflectivity of coal, and belongs to the field of coal analysis. The device comprises a device integration and a measuring system. The device integration comprises an imaging device, an image acquisition device, a carrier table and an image scanning acquisition control system. The carrier table and the image acquisition device are arranged on the imaging device. The image acquisition device and the carrier table are in linkage operation. The image acquisition device and the carrier table are controlled by the image scanning acquisition control system. The measuring system comprises a maceral content and vitrinite reflectivity measuring device which is used for image calling, measuring point arrangement, component identification, linkage, vitrinite reflectivity measurement, result statistics and output of the images collected by the image scanning acquisition control system. The imaging device is a reflected polarized microscope. The image acquisition device is a digital microscope. The carrier table is an XYZ three-axis electric platform. The maceral content and vitrinite reflectivity measuring device comprises an image calling module which is used for calling out the images collected by the image scanning acquisition control system in batches, calling out the images in batches on a screen or calling out the images after splicing; a measuring point arrangement module which is used for adding identification marks to the images called out by the image calling module at measuring positions; a component identification module which is used for allowing an operator to identify the images called out by the image calling module according to a coal rock component classification scheme; a storage module a which is used for recording and storing the identification results of the component identification module into a database; a linkage module a which is used for linking the images called out by the image calling module with the identification results recorded by the storage module; a vitrinite reflectivity measuring module which is used for measuring the gray scale of the measuring points on the images of the standard sample or the sample to be measured identified as the vitrinite group by the component identification module, establishing a gray scale-vitrinite reflectivity working curve of the standard sample and a correction curve of the gray scale at different distances from the center of the image, and then automatically converting the gray scale of the measuring points of the sample to be measured into the vitrinite reflectivity according to the gray scale-vitrinite reflectivity working curve and the correction curve; a storage module b which is used for recording and storing the measuring results of the vitrinite reflectivity measuring module into the database; a linkage module b which is used for linking the images called out by the image calling module with the measuring point positions marked by the measuring point arrangement module and the measuring results recorded by the storage module b; and a result statistics and output module which is used for statistically outputting the measuring results recorded by the storage module a and the storage module b.

[0068] The corresponding method comprises obtaining a measuring report of the component content and the vitrinite reflectivity of the coal. The detection identification specifically comprises the following steps.

[0069] (1) placing the sample to be measured and the standard sample under the objective lens of the imaging device respectively, moving the sample, automatically adjusting the focal length according to the set program to perform scanning, and collecting images through the image acquisition device;

[0070] (2) The image collected in step (1) is transmitted to a device for measuring the content of microscopic components and vitrinite reflectance for detection. Step (2) specifically includes the following steps: 1. First, start the image calling module to call out the images of the sample to be tested and the standard product one by one, multiple images per screen, or after splicing; 2. Start the measurement point arrangement module to automatically mark the identification mark at the center of the image of the sample to be tested and the standard product obtained in step 1 as the measurement point, and then the operator confirms and fine-tunes the position to meet the measurement requirements;

[0071] 3. Start the component identification module to identify the image retrieved in step 1 through the center point of step 2. The operator specifies the group to which the image belongs according to the coal and rock component classification scheme;

[0072] 4. The storage module a records and counts the identification results obtained in step 3 and stores them in the database. At the same time, the link module a is started to link the image called by the image calling module with the identification results recorded by the storage module;

[0073] 5. Restart the vitrinite reflectance determination module, carry out grayscale measurement on the image of the standard substance and the tested sample identified as vitrinite in step 4 in step 2, and establish the grayscale value grayscale-reflectivity working curve of the standard substance and the correction curve of grayscale at different distances from the image center, then automatically convert the grayscale value of the tested sample tested point into reflectivity according to the grayscale-reflectivity working curve and the correction curve;

[0074] 6. Start the storage module b to record and count the measurement results obtained in step 5 and store them in the database. At the same time, start the link module b to link the image called by the image calling module with the measurement point positions marked by the measurement point arrangement module and the measurement results recorded by the storage module b.

[0075] 7. Start the result statistics and output module to automatically count the measurement results recorded by the storage module a and the storage module b in steps 4 and 6, generate a reflectance measurement result report of the sample to be measured, and print it out.

[0076] The above method still requires manual browsing and single-shot shooting during use, which is also the most labor-intensive step and takes a long time.

[0077] It is not difficult to find from the above steps that manual full-field observation of samples is the most labor-intensive step and takes a long time.

[0078] Example 1:

[0079] Based on the above reasons, the embodiment of the present invention provides a specific implementation method of a method for determining the content of organic microscopic components in a rock sample, see Figure 1 , specifically including the following contents:

[0080] Step 100: acquiring a plurality of images of the rock sample by a microscope; wherein the plurality of images are acquired by the microscope for multiple times;

[0081] Step 200: stitching the plurality of images to generate a stitched image;

[0082] Step 300: determining the content of organic maceral of the rock sample according to the stitched image.

[0083] From the above description, it can be known that the embodiment of the present application provides a method for determining the content of organic maceral in a rock sample, which comprises the following steps: firstly, acquiring a plurality of images of the rock sample by a microscope; wherein the plurality of images are acquired by the microscope for multiple times; then, stitching the plurality of images to generate a stitched image; and finally, determining the content of organic maceral of the rock sample according to the stitched image.

[0084] In summary, the present application provides a method for determining the content of organic maceral in a rock sample, which uses image stitching to acquire the overall appearance of the sample, and then selects the maceral and calculates the percentage content. That is, the sample surface is scanned under a 20x or 50x objective lens and the field of view is stitched to obtain a large image with high resolution. The specific maceral in the image is selected according to its microscopic characteristics, the area percentage of the maceral in the image is determined, and the sum of the percentages of all organic matter is calculated to obtain the content of organic maceral in the sample. Finally, the area percentage of each maceral is divided by the total content of organic maceral to obtain the percentage content of each maceral in the sample. The whole process does not require manual full-view observation and photography, and only one or several large images are needed for offline image processing, which can save the time of using the microscope and greatly improve the efficiency.

[0085] Embodiment two:

[0086] For step 100, first, a thin section is cut from the rock sample. For organic matter analysis, the thickness of the thin section is generally controlled between 20 to 30 microns. In addition, considering that organic matter may be sensitive to some chemical reagents, the preparation process should avoid using chemicals that may damage organic matter as much as possible. Then, a microscope is selected, and different microscopes can be selected for different rocks:

[0087] Fluorescence microscope: suitable for observing rock thin sections containing organic matter. Organic matter will emit fluorescence under the irradiation of excitation light of a specific wavelength. By observing the intensity and color of these fluorescent light, the type and maturity of organic matter can be preliminarily judged.

[0088] Reflective light microscope: also used for observing organic matter in rocks, especially for observing high-maturity organic matter (such as graphite).

[0089] When using a fluorescence microscope, specific wavelengths of excitation light are used, and appropriate viewing light is selected through filters. Information such as the fluorescence properties, color, morphology, and relationship to surrounding minerals of the organic matter is recorded. By observing the fluorescence color and intensity of the organic matter, its thermal maturity is estimated. Generally, the higher the maturity, the more the fluorescence color shifts towards yellow or red.

[0090] For step 200, image stitching is the process of combining multiple images into one or more large images. When performing image stitching, the goal is to minimize discontinuities and distortions at the seams while maintaining image quality.

[0091] Preferably, step 200 includes the following:

[0092] 1. Image Acquisition

[0093] Shooting angle and position: Ensure that the multiple pictures taken have enough overlap. It is generally recommended to have 20-30% overlap between each image.

[0094] Exposure and white balance: Try to keep the exposure and white balance consistent across all images to reduce color and brightness inconsistencies when stitching.

[0095] 2. Image Preprocessing

[0096] Correcting distortions: Use image processing software to correct lens distortions and perspective distortions, especially when using wide-angle lenses.

[0097] Adjusting exposure and color: If there are differences in exposure or color between images, they can be adjusted using software to match.

[0098] 3. Feature Matching

[0099] Feature point detection: Detect feature points in each image. These feature points should be distinct features that are easy to match between images, such as corner points, edges, or specific textures.

[0100] Feature point matching: Find matching points between feature points of different images. Specifically, key points and their feature vectors are identified by finding extreme points in the image and calculating their direction gradient histograms. These key points remain invariant to rotation, scale, and brightness changes, and even have some stability to viewpoint changes and affine transformations. The specific matching process is as follows: Calculate a 128-dimensional feature descriptor for each key point. Compare the descriptors of key points in different images using Euclidean distance or other similarity measures to find the best matching points. Usually, a nearest neighbor ratio test is used to exclude false matches.

[0101] 4. Image Alignment and Transformation

[0102] Compute transformation matrix: Based on the matched feature points, compute one or more transformation matrices that define the geometric transformation (such as rotation, scaling, and translation) between the images. Specifically, the transformation matrix can be computed by the following methods:

[0103] Based on the matched feature points, the transformation matrix can be computed by the following methods. A 3x3 matrix is used to describe the plane-to-plane mapping in 3D space. When the shooting angles of two images are different, or there is a perspective deformation between them, a homography matrix can be used to describe the transformation between them.

[0104] Given at least 4 pairs of matching points in a pair of images, the homography matrix between them can be calculated. This is because each pair of matching points can provide 2 equations, while the homography matrix has 8 unknowns (the ninth number is set to 1). By solving this system of 8 equations, the homography matrix can be found.

[0105] A linear transformation from two-dimensional coordinates to two-dimensional coordinates, plus a translation, can achieve the effects of rotation, scaling, translation, and tilting. The affine transformation matrix is a 3x3 matrix, but its third row is fixed as \[0,0,1\]. Given at least 3 pairs of matching points in a pair of images, the affine transformation matrix between them can be calculated. Each pair of matching points provides 2 equations, a total of 6 equations are needed to solve 6 unknowns.

[0106] Apply transformation: Align the images using the transformation matrix to ensure they are properly arranged in a common reference frame.

[0107] 5. Image fusion

[0108] Seam selection: intelligently select the position of the seam to avoid important visual elements and reduce the visibility of the stitching.

[0109] Fusion and smoothing: Use various image fusion techniques (such as multi-band fusion, Laplacian pyramid fusion) to smooth the transition in the overlapping area to reduce the visibility of the seam.

[0110] 6. Final adjustment

[0111] Crop: Remove irregular parts of the edge of the stitched image to get a neat border.

[0112] Detail optimization: sharpen, color correct, and brightness adjust the final image to improve visual effects.

[0113] In some embodiments of the present application, referring to Figure 2 , step 100 comprises:

[0114] Step 101: Make a light sheet of the rock sample;

[0115] Specifically, the rock sample is ground into a thin section (thin section area 10mmX10mm), specifically, the thin section is made according to the sampling—gluing—flattening (coarse grinding—fine grinding—precision grinding)—polishing steps, and the polished surface of the thin section should be free of stains, needle-shaped scratches, cloth patterns, clear boundaries between components, and scratches.

[0116] Step 102: Collecting multiple images of the thin section by the microscope at a preset magnification.

[0117] In some embodiments of the present application, referring to Figure 3 , step 100 further comprises:

[0118] Step 103: Collecting the multiple images by the microscope at a fixed brightness and a fixed exposure time.

[0119] When using a microscope to take pictures of rock thin sections, correct brightness and exposure settings are crucial for obtaining high-quality images. These settings affect the light and dark, contrast, and detail clarity of the image.

[0120] Adjusting the brightness of the microscope, all microscopes are equipped with adjustable light sources such as LEDs or halogen lamps. By adjusting the intensity of the light source, the brightness level when observing or photographing the sample can be directly affected. For some special applications (such as fluorescence microscopy), adjusting the light source may also involve changing the wavelength or using specific filters.

[0121] Aperture adjustment, all microscopes are equipped with adjustable condenser apertures and / or field apertures. By adjusting the size of these apertures, the amount of light passing through the sample and the contrast of the image can be affected. Reducing the aperture will increase the contrast but reduce the brightness, and vice versa. In addition, using appropriate objectives, different objectives have different optical properties, including light flux. High magnification objectives usually require more light.

[0122] Microscope exposure settings:

[0123] Exposure time: refers to the length of time the microscope's photosensitive element (such as a CCD or CMOS sensor) is exposed to light. The longer the exposure time, the brighter the image. However, too long an exposure can cause overexposure and motion blur.

[0124] ISO sensitivity: the higher the ISO value, the more sensitive the microscope is to light, and the brighter the image will be. However, high ISO can also bring more noise, affecting image quality.

[0125] Aperture: in microscope image acquisition, the aperture setting of the microscope is usually fixed, as the light is controlled by the objective and condenser system of the microscope. However, in some special photography systems, adjusting the size of the aperture can affect exposure and depth of field.

[0126] Use exposure compensation: If the microscope allows, exposure levels can be fine-tuned by exposure compensation, without directly changing the exposure time or ISO.

[0127] In some embodiments of the present application, referring to Figure 4 , step 300 comprises:

[0128] Step 301 : Determine the microscopic components in the stitched image according to the gray scale and / or color;

[0129] Preferably, before step 301, it further comprises: removing outliers in the gray scale values;

[0130] Specifically, remove noise or repair damaged parts in the image, here provide the following 3 specific methods to remove outliers:

[0131] 1. Replace the value of each pixel point with the median of all pixel values in its neighborhood. This way can well preserve the edge information of the image, while removing outliers.

[0132] 2. Smooth the image, which can effectively reduce image noise. Specifically, assign a weight to each pixel point, which is determined by the distance between the point and other points in the neighborhood (the closer the distance, the greater the weight), and then calculate the weighted average to update the value of each pixel point.

[0133] 3. Remove outliers by two functions: one is a spatial neighbor function, and the other is a pixel difference function. These two functions together determine how the final value of each pixel in the image is calculated by the weighted average of the pixel values in its neighborhood.

[0134] Spatial weight (spatial neighbor function): This weight is based on the spatial distance between pixels, the closer the distance to the center pixel, the greater the impact on the center pixel.

[0135] Intensity weight (pixel difference function): This weight is based on the difference between pixel values (i.e. intensity), pixels with similar values have greater impact on each other, which helps to protect the edges, because the intensity of pixels near the edges changes greatly.

[0136] Specifically, the output gray scale value can be obtained by the following formula:

[0137]

[0138] Where: I(x) is the pixel value of the original image at position (x). S is the pixel neighborhood centered at x. r f is the weight function based on the difference in pixel intensity, usually a Gaussian function. s g is the weight function based on spatial distance, also usually a Gaussian function. Wp is a normalization factor, ensuring that the sum of the weights is 1.

[0139] Step 302: determining areas corresponding to different micro-components;

[0140] Step 303: determining the content of organic micro-components of the rock sample according to the areas corresponding to the different micro-components.

[0141] For step 302 and step 303, different micro-components of different gray scales or colors are selected, and the selected areas are quantitatively counted to calculate the area percentage of each micro-component in the field of view.

[0142] In some embodiments of the present application, referring to Figure 5 , step 200 comprises:

[0143] Step 201: rectangularly splicing the plurality of images with a preset moving step to generate the spliced image.

[0144] The moving step (X-axis step 1000 um, Y-axis step 1000 um) is set, and the scanning mode (rectangular splicing) and the number of fields of view are set, and splicing is performed according to the set step. After the splicing of one large field of view is completed, the position is positioned to the last field of view position, and step 201 is repeated to splice the next field of view. It should be noted that the intensity of the incident light of the microscope is kept unchanged during the whole splicing process, and all conditions are kept consistent.

[0145] In some embodiments of the present application, referring to Figure 6 , step 102 comprises:

[0146] Step 1021: selecting a feature point in an initial image in the plurality of images;

[0147] The feature point can be a corner position in the initial image (the first image to be spliced in the splicing process), or a position with special geological meaning or special texture in the initial image.

[0148] Step 1022: starting from the feature point, sequentially collecting a plurality of images of the light sheet through the microscope according to a preset direction and a preset multiple.

[0149] In some embodiments of the present application, the preset multiple is 50 or 20.

[0150] As can be seen from the above description, the embodiment of the present application provides a method for determining the content of organic micro-components in a rock sample, comprising: first, collecting a plurality of images of the rock sample through a microscope; wherein the plurality of images are obtained by the microscope collecting multiple times; then, splicing the plurality of images to generate a spliced image; and finally, determining the content of organic micro-components of the rock sample according to the spliced image.

[0151] In summary, the present application provides a method for determining the content of organic maceral in a rock sample, which uses image stitching to collect the overall appearance of the sample, and then selects maceral and calculates the percentage content. That is, under a 20x or 50x objective, the sample surface is scanned and the field of view is stitched to obtain a large image with high resolution. The specific maceral in the picture is selected according to its microscopic characteristics, the area percentage of the maceral in the picture is determined, and the sum of the percentages of all organic matter is calculated to obtain the organic maceral content of the sample. Finally, the area percentage of each maceral is divided by the total organic maceral content to obtain the percentage content of each maceral in the sample. The whole process does not require manual full-view observation and photography, only one or several large images are needed for offline image processing, which can save microscope time and greatly improve efficiency.

[0152] Example Three:

[0153] To further illustrate the scheme, in one specific embodiment, the present application also provides a specific embodiment of a method for determining the content of organic maceral in a rock sample, which is described in detail in Figure 7 , which comprises the following steps.

[0154] S1: Grind the rock sample into a whole rock section with an area of 10mm*10mm.

[0155] Specifically, the rock sample is ground into a section (section area 10mmX10mm), and specifically, the section is made according to the steps of sampling-gelatinization-grinding plane (coarse grinding-fine grinding-fine grinding)-polishing, the polished surface of the section should be free of stains, needle-shaped scratches, cloth patterns, clear boundaries between components, and scratches.

[0156] S2: Place the section under a microscope and observe it with a 50x lens, set the shooting parameters, and select a characteristic point in the corner of the sample as the starting point for image acquisition.

[0157] Specifically, the sample starting point is set, and under the 50x lens, the sample is observed from one corner, and a characteristic point observed is selected as the starting point. The starting point is selected according to the principle of high recognition, which can be organic matter or mineral. At the same time, the brightness and exposure time of the image are controlled to ensure the best effect of the collected picture.

[0158] S3: Set the moving step length and select the number of fields of view for stitching.

[0159] Set the moving step length (X-axis step length 1000um, Y-axis step length 1000um), select the scanning style (rectangular stitching), select the number of fields of view, and stitch according to the set step length.

[0160] S4: After splicing the corresponding field of view, the position of the last field of view is calibrated, the initial conditions are maintained, and the next field of view splicing is performed; if only one picture is spliced, the next step is directly performed.

[0161] After one large field of view splicing is completed, the last field of view position is located, step S3 is repeated, and the next field of view splicing is performed. The intensity of the incident light of the microscope is kept unchanged throughout the splicing, and all conditions are kept consistent.

[0162] S5: Microcomponent percentage content statistics, different gray or color microcomponents are selected, and the quantitative area statistics of the selected regions are performed.

[0163] Microcomponent statistics, different gray or color microcomponents are selected, and the quantitative area statistics of the selected regions are performed, and the area percentage of each microcomponent in the field of view can be calculated.

[0164] Specific embodiment 1: take one rock sample, select a resin light sheet with an area of 10mmX10mm, and perform slicing according to the slicing requirements of the industry standard "Whole rock light sheet microcomponent identification and statistics method" for rock samples. The organic petrology characteristics of the test sample are observed under the polarizing microscope (reflected light), and the sample is mainly vitrinite and fusinite.

[0165] Vitrinite and fusinite are two microcomponents used to describe the maturity of organic matter in organic petrology, and are the manifestations of kerogen at different maturity stages.

[0166] Vitrinite is a kerogen component evolved from plant cell wall material, which shows characteristic reflected light characteristics under a microscope. Vitrinite is mainly derived from lignin, cellulose and other cell wall components of higher plants, and is formed through a complex evolution process of biological degradation, chemical and physical action during deposition. Vitrinite reflectance is an important parameter for judging the maturity of organic matter. The higher the reflectance, the higher the maturity of organic matter, and the higher the corresponding thermal evolution level.

[0167] Fusinite is another main organic component in coal, which is characterized by high reflectivity under a microscope. It is mainly formed by plant debris in an oxidizing environment through fire, microbial action or chemical oxidation. Compared with vitrinite, the formation of fusinite is more affected by external conditions, such as plant debris burned by fire, which will form high reflectivity fusinite. The presence of fusinite is also important for evaluating the maturity and thermal evolution history of coal. Coal with high fusinite content usually indicates that the formation environment of coal has undergone strong oxidation or arid conditions. With the increase of the maturity of organic matter, the reflectivity of vitrinite will gradually increase. The change of the ratio of fusinite and vitrinite also reflects the change of the deposition environment and the maturity of organic matter.

[0168] According to steps S1 to S5, select the feature points as starting points, and splice according to the set step and route (see Figure 8 , Figure 1, starting feature point; 2, resin light sheet; 3, view a splicing end point; 4, splicing route), to obtain Figure 9 a (view a), save to local storage. Calculate the microscopic component area content in the large graph, and the software selects the microscopic component (vitrinite). Select the interface as shown in Figure 9 b (view b). After manual screening, the vitrinite area is calculated to be about 35.24%. The same step is used to select the fusinite component, and its area is about 5.75%. The organic microscopic component content of the sample is 40.99%, vitrinite accounts for 85.9%, and fusinite accounts for 14.1%.

[0169] Specific embodiment 2: Another rock sample is taken, and the sample preparation method is the same as that of embodiment 1. Under the polarizing microscope, the organic petrology characteristics of the sample are observed through the fluorescence light path. The sample is mainly composed of sapropel algal body.

[0170] Sapropel refers to sediment or rock rich in organic matter, which mainly comes from the remains of aquatic organisms, especially algae. Sapropel is formed under anoxic or low-oxygen conditions, which slows down the decomposition rate of organic matter, allowing a large amount of organic matter to be preserved. They usually appear in deep water environments, the bottom of lakes or oceans, especially in areas with high productivity, stable water bodies, and anoxic bottom layers. The characteristics and formation conditions of sapropel are as follows:

[0171] High organic matter content: The organic microscopic component content in sapropel is very high, and these organic substances mainly come from the remains of dead microorganisms such as algae, plankton and other aquatic organisms.

[0172] Anoxic conditions: The formation of sapropel usually requires anoxic or low-oxygen conditions, which can inhibit the rate of microbial decomposition of organic matter, allowing organic matter to accumulate.

[0173] High productivity: High productivity means that a large amount of biomass is generated on the surface of the water body and eventually deposited at the bottom, providing the material basis for the formation of sapropel.

[0174] Water body stability: The stratification stability of the water body is also a key factor in the formation of sapropel, which prevents oxygen exchange between the deep and surface layers of the water body, further exacerbating the anoxic conditions at the bottom.

[0175] Sapropel can be used as an indicator of paleoenvironmental conditions, especially with respect to paleoclimatic change, sea level change, and changes in ecosystem productivity. Because of their high organic maceral content, sapropels can become important source rocks for oil and gas after appropriate thermal evolution in the geological history. Analysis of sapropel layers can reveal patterns of paleoclimatic change, particularly when studying global or regional warm and cold periods.

[0176] The algae bodies show bright yellow fluorescence under blue-violet light excitation, and can be selected according to this feature. The photographing and splicing processes are the same as in Example 1, and a (view a) of Figure 10 is obtained. The area content of macerals in the large image is calculated, and the software selects the maceral (algae body), and the interface is shown in b (view b) of Figure 10 After manual screening, the area content of the algae body maceral is about 2.88%, and the vitrinite is about 0.08%. The organic maceral content of the sample is 2.96%, the proportion of the algae body is 97.3%, and the proportion of the vitrinite is 2.7%.

[0177] From the above description, the specific embodiment of the present application provides a method for determining the content of organic macerals in a rock sample, which comprises: first, collecting multiple images of the rock sample through a microscope; wherein the multiple images are obtained by multiple times of collection of the microscope; then splicing the multiple images to generate a spliced image; and finally determining the content of organic macerals in the rock sample according to the spliced image.

[0178] The present application improves the image collection step in the statistics of macerals, specifically, the required pictures are collected by image splicing, that is, the sample surface is scanned under a 20x or 50x objective lens and the view field is spliced to obtain a large image with unchanged resolution, specific macerals in the picture are selected according to their microscopic characteristics, the area percentage of the macerals in the picture is determined, the sum of the percentages of all organic matters is calculated to obtain the content of organic macerals of the sample. Finally, the area percentage of each maceral is divided by the total content of organic macerals to obtain the percentage content of each maceral in the sample. It is not necessary to manually observe and photograph the entire view field, only one or several large images are needed to perform offline image processing, which can save the time of occupying the microscope and improve the efficiency.

[0179] In summary, compared with the existing method, the present application does not need manual observation and photographing of the entire view field, under the condition that the related parameters are reasonably set, the sample surface is scanned and the view field is spliced to obtain one or several large images with unchanged resolution, which can be processed offline, saving the time of occupying the microscope and improving the efficiency. Compared with the traditional point counting method, the calculation result of the present application is more accurate and more comparable.

[0180] Example Four:

[0181] Based on the same inventive concept, the application further provides a device for determining the content of organic maceral in a rock sample, which can be used to implement the method described in the above embodiments, as follows. Since the device for determining the content of organic maceral in a rock sample solves the problem by the same principle as the method for determining the content of organic maceral in a rock sample, the implementation of the device for determining the content of organic maceral in a rock sample can be referred to the implementation of the method for determining the content of organic maceral in a rock sample, and the repeated parts will not be described herein. The terms "unit" or "module" used below can be a combination of software and / or hardware that can implement a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware or a combination of software and hardware is also possible and contemplated.

[0182] The embodiments of the application provide a specific implementation of a device for determining the content of organic maceral in a rock sample, which can implement the method for determining the content of organic maceral in a rock sample, as follows. Figure 11 The device for determining the content of organic maceral in a rock sample comprises:

[0183] a plurality of image acquisition modules 10, configured to acquire a plurality of images of the rock sample by using a microscope; wherein the plurality of images are acquired by the microscope multiple times;

[0184] a spliced image generation module 20, configured to splice the plurality of images to generate a spliced image;

[0185] an organic maceral content determination module 30, configured to determine the content of organic maceral in the rock sample according to the spliced image.

[0186] In some embodiments of the application, the plurality of image acquisition modules comprise:

[0187] a light sheet making unit, configured to make a light sheet of the rock sample;

[0188] a plurality of image acquisition first units, configured to acquire a plurality of images of the light sheet by using the microscope at a preset magnification.

[0189] In some embodiments of the application, the plurality of image acquisition modules further comprise:

[0190] a plurality of image acquisition second units, configured to acquire the plurality of images by using the microscope at a fixed brightness and a fixed exposure time.

[0191] In some embodiments of the application, the organic maceral content determination module comprises:

[0192] a maceral determination unit, configured to determine maceral in the spliced image according to grayscale and / or color;

[0193] an area determination unit configured to determine areas corresponding to different micro-components;

[0194] an organic micro-component content determination unit configured to determine an organic micro-component content of the rock sample according to the areas corresponding to the different micro-components.

[0195] In some embodiments of the present application, the stitched image generation module comprises:

[0196] a stitched image generation unit configured to stitch the multiple images in a rectangular manner with a preset moving step to generate the stitched image.

[0197] In some embodiments of the present application, the multiple image acquisition first unit comprises:

[0198] a feature point selection unit configured to select a feature point in an initial image in the multiple images;

[0199] a multiple image sequential acquisition unit configured to sequentially acquire multiple images of the light sheet from the feature point in a preset direction and according to the preset multiple.

[0200] In some embodiments of the present application, the preset multiple is 50 or 20.

[0201] As can be seen from the above description, the embodiments of the present application provide a device for determining an organic micro-component content in a rock sample, which comprises: a multiple image acquisition module configured to acquire multiple images of a rock sample by using a microscope; wherein the multiple images are obtained by multiple acquisitions of the microscope; a stitched image generation module configured to stitch the multiple images to generate a stitched image; and an organic micro-component content determination module configured to determine an organic micro-component content of the rock sample according to the stitched image.

[0202] In summary, the present application provides a device for determining an organic micro-component content in a rock sample, which selects organic micro-components with similar optical characteristics and performs relevant quantitative calculations, thereby improving the degree of automation, saving time and improving test efficiency.

[0203] Embodiment five:

[0204] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method for determining an organic micro-component content in a rock sample in the above embodiments, as described in Figure 12 , the electronic device specifically comprises the following contents:

[0205] The processor 1201, the memory 1202, the communications interface 1203, and the bus 1204;

[0206] The processor 1201, the memory 1202, the communications interface 1203, and the bus 1204;

[0207] The processor 1201 is configured to invoke a computer program in the memory 1202, and the processor implements all steps in the method for determining the content of organic macerals in a rock sample in the embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:

[0208] Collecting a plurality of images of the rock sample by using a microscope, wherein the plurality of images are obtained by the microscope through multiple times of collection;

[0209] Splicing the plurality of images to generate a spliced image;

[0210] Determining the content of organic macerals in the rock sample according to the spliced image.

[0211] In some embodiments of the present application, the collecting of the plurality of images of the rock sample by using the microscope comprises:

[0212] Making a light slice of the rock sample;

[0213] Collecting a plurality of images of the light slice by using the microscope at a preset magnification.

[0214] In some embodiments of the present application, the collecting of the plurality of images of the rock sample by using the microscope further comprises:

[0215] Collecting the plurality of images by using the microscope at a fixed brightness and a fixed exposure time.

[0216] In some embodiments of the present application, the determining of the content of organic macerals in the rock sample according to the spliced image comprises:

[0217] Determining macerals in the spliced image according to gray scale and / or color;

[0218] Determining areas corresponding to different macerals;

[0219] Determining the content of organic macerals in the rock sample according to the areas corresponding to the different macerals.

[0220] In some embodiments of the present application, the splicing the plurality of images to generate a spliced image comprises:

[0221] The plurality of images are rectangularly spliced with a preset moving step to generate the spliced image.

[0222] In some embodiments of the present application, the plurality of images of the light sheet are collected by the microscope with a preset magnification, comprising:

[0223] Selecting a feature point in an initial image in the plurality of images;

[0224] Collecting the plurality of images of the light sheet by the microscope in a preset direction and the preset magnification from the feature point.

[0225] In some embodiments of the present application, the preset magnification is 50 or 20.

[0226] Embodiment six:

[0227] The embodiments of the present application also provide a computer readable storage medium capable of realizing all steps of the method for determining the content of organic maceral in a rock sample in the above embodiments. The computer readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the method for determining the content of organic maceral in a rock sample in the above embodiments are realized. For example, when the processor executes the computer program, the following steps are realized:

[0228] Collecting a plurality of images of the rock sample by a microscope; wherein the plurality of images are obtained by the microscope multiple times;

[0229] Splicing the plurality of images to generate a spliced image;

[0230] Determining the content of organic maceral in the rock sample according to the spliced image.

[0231] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, for the hardware+program type embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0232] The above-described embodiments of the application have several aspects, no single one of which is solely responsible for the application's desirable attributes. Without limiting the scope of this application as to the particular embodiments described, some further embodiments of this application will be described.

[0233] While the application has been presented in the form of a method operational steps as in an embodiment or flow chart, more or fewer operational steps can be included based on conventional or non-creative labor. The order in which steps are listed in the embodiments is only one of many ways to execute the steps, and does not represent the only way to execute the steps. In actual implementation, the device or client product can execute the method in the order shown in the embodiments or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0234] For the convenience of description, the above device is described as various modules respectively described in function. Of course, in the implementation of the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0235] Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer readable program code, the same function can also be implemented by logically programming the method steps to make the controller in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0236] In one typical arrangement, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0237] Memory can include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory such as read only memory (ROM) or flash memory, etc. Memory is an example of computer-readable media.

[0238] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the description of the method embodiments. In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present specification. The illustrative description of the above terms in the present specification does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0239] The above only describes the embodiments of the embodiments of the present specification and does not limit the embodiments of the present specification. The embodiments of the present specification can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification shall be included in the scope of the claims of the embodiments of the present specification.

Claims

1. A method for determining the content of organic microscopic components in a rock sample, characterized in that: include: Collecting multiple images of the rock sample through a microscope; wherein the multiple images are obtained by collecting multiple images through the microscope; stitching the multiple images to generate a stitched image; The organic microscopic component content of the rock sample is determined based on the stitched image.

2. The method for determining the content of organic microscopic components in rock samples according to claim 1, characterized in that: The collecting of multiple images of the rock sample by a microscope comprises: making a light sheet of the rock sample; A plurality of images of the light sheet are collected by the microscope at a preset magnification.

3. The method for determining the content of organic microscopic components in rock samples according to claim 1, characterized in that: The collecting a plurality of images of the rock sample by a microscope further comprises: The plurality of images are collected by the microscope at a fixed brightness and a fixed exposure time.

4. The method for determining the content of organic microscopic components in a rock sample according to claim 1, characterized in that: Determining the organic microscopic component content of the rock sample according to the spliced ​​image includes: determining microscopic components in the stitched image based on grayscale and / or color; Determine the area corresponding to different microscopic components; The organic microscopic component content of the rock sample is determined based on the areas corresponding to the different microscopic components.

5. The method for determining the content of organic microscopic components in a rock sample according to any one of claims 1 to 4, characterized in that: The stitching of the multiple images to generate a stitched image includes: The multiple images are rectangularly stitched with a preset moving step length to generate the stitched image.

6. The method for determining the content of organic microscopic components in rock samples according to claim 2, characterized in that: Collecting a plurality of images of the light sheet at a preset magnification through the microscope includes: selecting feature points in an initial image among the plurality of images; Starting from the feature point, multiple images of the light sheet are sequentially collected through the microscope according to a preset direction and the preset magnification.

7. The method for determining the content of organic microscopic components in a rock sample according to claim 6, characterized in that: The preset multiple is 50 or 20.

8. A device for determining the content of organic microscopic components in a rock sample, characterized in that: include: Multiple image acquisition modules, used to acquire multiple images of the rock sample through a microscope; wherein the multiple images are acquired by the microscope multiple times; a stitching image generation module, configured to stitch the multiple images together to generate a stitching image; The organic microscopic component content determination module is used to determine the organic microscopic component content of the rock sample based on the spliced ​​image.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for determining the content of organic microscopic components in a rock sample as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining the content of organic microscopic components in a rock sample as described in any one of claims 1 to 7 are implemented.

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