A method for evaluating the development degree of deep coal-rock cleats based on the end-face images of full-diameter cores

Abstract

The present invention discloses a method for evaluating the development degree of deep coal-rock cleats based on the full-diameter core end-face image. First, the full-diameter core end-face image is obtained, and the illumination balance processing of the core end-face image is carried out, including image brightness calculation and brightness equalization. Then, the threshold of the core end-face image is set, and the grayscale image is converted into a binary image according to the threshold. The proportion of coal-rock cleat pixels is calculated by using the binary image, and the cleat porosity of the coal-rock end-face and the fractal dimension of the coal-rock end-face are further calculated. Finally, an intersection diagram of the fractal dimension and the cleat porosity is established. According to the intersection relationship between the fractal dimension and the cleat porosity, the dividing lines of relatively developed, developed, and extremely developed are found, and then the development degree of the cleats on the coal-rock core end-face is classified and evaluated. The original data required by this method is the coal-rock core end-face image, and it is applicable to the analysis and calculation of coal-rock surface images in most cases, significantly improving the work efficiency and saving time and economic costs.

Description

Technical Field

[0001] The present invention relates to the technical field of coalbed methane exploration and development, and in particular to a method for evaluating the development degree of deep coal-rock cleats based on the end-face images of full-diameter cores. Background Art

[0002] The cleats of deep coal-rock are mainly fractures generated by the changes in the structure and texture of coal substances during the coalification process, also known as endogenous fractures. Cleats are mainly divided into face cleats and end cleats. Face cleats generally have better continuity and are approximately parallel to the bedding plane, while end cleats generally have poorer continuity and are approximately perpendicular to the bedding plane. The development degree of cleats will affect the storage and seepage capacity of coal-rock, etc. At present, the evaluation of the development degree of cleats can only be carried out qualitatively with reference to relevant national standards, and the evaluation results are mainly divided into several development degree categories such as relatively developed, developed, and extremely developed.

[0003] The methods commonly used to evaluate the development degree of coal-rock cleats are mainly CT scanning method and core surface observation method. The CT scanning method forms a grayscale image after CT scanning, and then further identifies, judges, and describes the development of cleats. This method can accurately reconstruct the three-dimensional model of the core, but the CT scanning has a high economic cost and only shows good results when scanning plug samples or small-diameter cores, while the scanning effect for full-diameter cores is relatively poor. In addition, it is difficult for plug samples or small-diameter cores to comprehensively and accurately represent the overall development degree of deep coal-rock cleats. In contrast, the core surface observation method has a lower cost, is easier to obtain, and can be carried out based on full-diameter cores, with better representativeness. This method can be evaluated by manual identification and statistics or by machine identification and calculation of cleat porosity. However, for the manual identification and evaluation method, different describers may select different observation and description positions and measurement points, resulting in differences in the measured and recorded data. Even due to the discontinuity and scattered distribution of end cleats, they cannot be statistically analyzed, and the workload is high, the efficiency is low, and the time cost is high. Although the machine identification and evaluation method saves a certain amount of time and improves a certain efficiency, it is easy to ignore factors such as the complexity of cleats, resulting in the situation where those with high cleat porosity but low actual development degree are misjudged as having a high development degree, making the evaluation effect worse. Therefore, there is still a lack of a simple and efficient method in the prior art that can accurately evaluate the development degree of deep coal-rock cleats. Summary of the Invention

[0004] In view of the above deficiencies in the existing evaluation methods for the development degree of coal-rock cleats, the present invention provides an efficient evaluation method for the development degree of deep coal-rock cleats based on the end-face images of full-diameter cores. This method realizes the accurate evaluation of the development degree of deep coal-rock cleats by using machine recognition of the end-face images of full-diameter deep coal-rock cores and adopting a method combining cleat porosity and fractal dimension. At the same time, it also significantly improves work efficiency and saves time and economic costs.

[0005] The evaluation method for the development degree of deep coal-rock cleats based on the end-face images of full-diameter cores provided by the present invention is specifically as follows:

[0006] S1. Obtain the end-face image of the full-diameter core and perform illumination balance processing on the core end-face image, including image brightness calculation and brightness equalization. Take a photo of the core end-face at the core sampling site, which is the core end-face image.

[0007] The method for calculating image brightness is as follows:

[0008] Convert the input color coal-rock image into a grayscale image; take the maximum value of the three components in the color image as the grayscale value of the grayscale image, and the pixel point brightness calculation formula is:

[0009] Gray(x,y) = max{R(x,y),G(x,y),B(x,y)}

[0010] In the formula, Gray(x,y) is the grayscale value of the pixel point at the (x,y) position in the grayscale image, and R(x,y), G(x,y), B(x,y) are the red, green, and blue components of the pixel point at the (x,y) position in the RGB image respectively.

[0011] The method for image brightness equalization: Take the brightness of the local area with small brightness difference as the background brightness, calculate the background brightness value, and then subtract the background brightness value from the brightness value of the image to obtain the brightness difference image, which is the image after brightness equalization.

[0012] After brightness equalization, recalculate the relevant parameters of the new image to obtain the RGB image after brightness equalization.

[0013] S2. Set the threshold for the core end-face image; the method is as follows:

[0014] Automatically obtain the initial segmentation threshold T0 based on the maximum between-class variance method, then take at least three values greater than T0 and at least three values less than T0 around T0, and calculate the cleat pixel area corresponding to each value. Take the threshold as the abscissa and the cleat pixel area as the ordinate to establish a curve graph between the cleat pixel area and the threshold, and then fit the starting segment and the ending segment of the curve. Take the intersection point of the two fitted straight lines as the threshold T.

[0015] In subsequent steps, for the sake of simplifying the calculation, the threshold can also be further simplified to take the integer closest to the threshold T.

[0016] S3. Binary segmentation of the core end face image;

[0017] Convert the grayscale image into a binary image according to the threshold determined in step S2. Determine all pixels with a grayscale greater than or equal to the threshold T as the coal-rock skeleton and set their grayscale value to 255 (white); conversely, judge them as coal-rock cleats and set their grayscale value to 0 (black).

[0018] S4. Calculation of the cleat porosity of the coal-rock end face;

[0019] Calculate the proportion of cleat pixels in the coal-rock using the binary image. The formula is as follows:

[0020]

[0021] In the formula, S is the proportion of pixel points where the cleats are located within the coal-rock end face, A is the number of pixels occupied by the coal-rock cleats, and B is the total number of pixel points on the coal-rock end face;

[0022] Convert the scale of the proportion of cleat pixels in the coal-rock into cleat porosity. The formula is as follows:

[0023]

[0024] In the formula, X is the cleat porosity calculated by the scale, S is the proportion of cleat pixels before scaling, d and c are the maximum and minimum values of the cleat porosity in the study area respectively, and b and a are the maximum and minimum values of the proportion of cleat pixels in the target coal-rock respectively;

[0025] S5. Calculation of the fractal dimension of the coal-rock end face. The formula is as follows:

[0026]

[0027] In the formula, ε is the length of one side of the small cube, and N(ε) is the number obtained by covering the measured cleats with this small cube.

[0028] S6. Evaluate the development degree of the cleats on the coal-rock core end face;

[0029] Through steps S1 - S5, complete the processing of all deep coal-rock core end face images, the identification of cleats, and the calculation of cleat porosity and fractal dimension; then, with the fractal dimension as the abscissa and the cleat porosity as the ordinate, establish a crossplot of the fractal dimension and the cleat porosity; based on the crossplot relationship between the fractal dimension and the cleat porosity, find the dividing lines for relatively developed, developed, and extremely developed, and further classify and evaluate the development degree of the cleats on the coal-rock core end face.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] (1) The method of the present invention can evaluate the cleat results on the coal-rock surface more accurately. By comparing the method of the present invention with the conventional method, the average accuracy rate evaluated by this method is 85.03%, while the average accuracy rate evaluated by the conventional method is 67.86%. This method significantly improves the accuracy of evaluating the development degree of deep coal-rock end-face cleats.

[0032] (2) Wide application range. The original data required for the method of the present invention is the image of the coal-rock core end-face, and it is applicable to the analysis and calculation of coal-rock surface images in most cases, especially the full-diameter core end-face image. Regardless of how the shooting direction and angle of the acquired image change, the illumination can be made uniform. It overcomes the defect that the existing CT scanning method has poor scanning effect on the full-diameter core.

[0033] (3) The method of the present invention rationally utilizes machine recognition of the full-diameter core end-face image of deep coal-rock, improves the efficiency of description and evaluation, and saves a large amount of time cost and economic cost of manual recognition and evaluation.

[0034] Other advantages, objectives and features of the present invention will be partially reflected by the following description, and partially will be understood by those skilled in the art through the research and practice of the present invention. Description of the Drawings

[0035] Figure 1 It is a contrast diagram of the brightness balance of the core image. (a) is the coal-rock end-face image before brightness balance, and (b) is the coal-rock end-face image after brightness balance.

[0036] Figure 2 It is a diagram for threshold selection.

[0037] Figure 3 It is a contrast diagram of the core binary image.

[0038] Figure 4 It is a diagram for calculating the fractal dimension of the image.

[0039] Figure 5 It is a diagram of the evaluation result of the development degree of the coal-rock end-face cleats by the method of the present invention.

[0040] Figure 6 It is a diagram of the evaluation result of the development degree of the coal-rock end-face cleats by Method 1.

[0041] Figure 7 It is a diagram of the evaluation result of the development degree of the coal-rock end-face cleats by Method 2.

[0042] Figure 8 It is a contrast diagram of the coal-rock end-face before and after uniform illumination. Detailed Embodiment

[0043] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0044] As Figure 1-8 shown, the method for evaluating the development degree of deep coal-rock cleats based on the full-diameter core end-face image provided by the present invention specifically includes the following steps:

[0045] (1) Illumination balance of the core end-face image

[0046] Image brightness equalization is an important image processing technology aimed at solving the problem of uneven brightness in images and improving the quality and visibility of images. In an image with uneven brightness, the brightness distribution of the image will directly affect the extraction of cleat features, resulting in differences in the cleat features extracted from different regions of the image. Through this technology, the cleats in the image can be made clearer and easier to distinguish, improving the accuracy and efficiency of image processing and analysis. Therefore, when extracting the cleat features of the core end-face, appropriate preprocessing techniques are needed to balance the brightness distribution of the image. This mainly includes image brightness calculation and brightness equalization processing.

[0047] Image brightness calculation: For the input coal-rock color image, it needs to be converted into a grayscale image. The grayscale processing is the process of converting a color image into a black-and-white image. In the RGB color model, each pixel is represented by three components: red (R), green (G), and blue (B). The maximum value of the three components in the color image is used as the grayscale value of the grayscale image. The size of the grayscale value determines the brightness of the pixel, thus affecting the brightness of the entire image. The pixel brightness calculation formula is:

[0048] Gray(x,y)=max{R(x,y),G(x,y),B(x,y)}

[0049] where Gray(x,y) is the grayscale value of the pixel at the (x,y) position in the grayscale image, and R(x,y), G(x,y), and B(x,y) are the red, green, and blue components of the pixel at the (x,y) position in the RGB image, respectively.

[0050] Brightness equalization processing: The specific calculation formula is

[0051] I(x,y)=f(x,y)–0.5*bg(x,y)

[0052] Wherein, I(x,y) is the image brightness value after brightness equalization, f(x,y) is the image brightness value before brightness equalization, and bg(x,y) is the background brightness value. In practical applications, the background brightness ratio needs to be reasonably adjusted to avoid over-equalization. In this embodiment, the ratios are both 0.5. After brightness equalization, relevant parameters of the new image are recalculated to obtain the RGB image after brightness equalization, and the results are as Figure 1 shown. In the figure, (a) is the image of the coal-rock end face before brightness equalization, and (b) is the image of the coal-rock end face after brightness equalization.

[0053] (2) Threshold setting for the core end face image

[0054] Based on the maximum inter-class variance method, the initial segmentation threshold T0 is automatically obtained. In this embodiment, T0 = 40.8. Then, within the range of adding and subtracting 30 before and after this threshold, with a step size of 10, multiple numerical points (10, 20, 30, 40, 50, 60, 70, 80) are set, and the fracture pixel area corresponding to each numerical point is calculated; as Figure 2 shown, a curve graph between the fracture pixel area and the threshold is established, and then the starting segment and the ending segment are fitted, and the intersection point of the two fitted lines is used as the threshold T, T = 47.6.

[0055] (3) Binary segmentation of the core end face image

[0056] Image binary segmentation is an important image processing technology. It sets the gray value of the pixel points of the image to 0 or 255, so that the image shows an obvious black and white effect. This technology can significantly reduce the data volume in the image and highlight the outline of the fractures, facilitating subsequent analysis and processing. The specific steps are as follows: Based on the threshold 47.6 determined in step (2), for the convenience of calculation, in this embodiment, the integer 50 closest to 47.6 is taken as the final threshold, that is, T = 50. The gray image is converted into a binary image. Usually, all pixels with gray levels greater than or equal to the threshold are determined to belong to the coal-rock skeleton, and their gray values are set to 255 (white); otherwise, they are judged as coal-rock fractures, and their gray values are set to 0 (black). The calculation formula is as follows:

[0057]

[0058] Wherein, B(x,y) is the pixel value of the binary image at the position (x,y), I(x,y) is the pixel value of the original gray image at the position (x,y), and T is the set threshold, T = 50.

[0059] Figure 3 The comparison between the results obtained when selecting multiple different thresholds (30, 40, 50, 60) and the original image is given. It can be seen that the effect is better when binaryzation is performed with the threshold T = 50.

[0060] (4) Calculate the cleat porosity of the coal-rock end face;

[0061] After binarizing the coal-rock end face image, calculate the proportion of cleat pixels using the binary image to obtain the number of pixels occupied by the cleats and the total number of pixels in the coal-rock end face image, and then the cleat ratio of the coal-rock end face can be obtained. The formula is as follows:

[0062]

[0063] In the formula, S is the proportion of pixel points where the cleats are located in the coal-rock end face, A is the number of pixels occupied by the cleats in the coal-rock end face, and B is the total number of pixel points in the coal-rock end face.

[0064] Then, convert the scale of the proportion of coal-rock cleat pixels to the cleat porosity according to the cleat porosity characteristics of the research area. The formula is as follows:

[0065]

[0066] In the formula, X is the cleat porosity calculated by the scale, s is the proportion of cleat pixels before the scale, d and c are the maximum and minimum cleat porosities in the research area respectively, and b and a are the maximum and minimum proportions of cleat pixels of the target coal-rock respectively.

[0067] (5) Fractal of the coal-rock end face;

[0068] Image fractal is a geometric shape that contains detailed structures at any small scale, that is, it can be described as a rough or fragmented geometric shape, which can be divided into multiple parts, and each part (at least approximately) is a reduced copy of the whole. The image fractal dimension is a quantitative description of the complexity and irregularity of the image. A higher fractal dimension usually indicates that the surface of the coal-rock end face image contains more detailed information and higher complexity, while a lower fractal dimension indicates that the surface of the coal-rock end face image is smoother, contains less detailed information, and lower complexity. The formula for calculating the fractal dimension is as follows;

[0069]

[0070] In the formula, ε is the length of one side of the small cube, N(ε) is the number obtained by covering the measured cleats with this small cube, and the dimension formula means determining the dimension of the cleats by covering the measured cleats with small cubes with side length ε. Figure 4 These are the calculation results of the two image fractal dimensions for this embodiment.

[0071] (6) Through the processing of the end-face images of all deep coal-rock cores, the identification of cleats, and the calculation of cleat porosity and fractal dimension in the above-mentioned 5 steps; then, taking the fractal dimension as the abscissa and the cleat porosity as the ordinate, an intersection diagram of the fractal dimension and the cleat porosity is established; based on the intersection relationship between the fractal dimension and the cleat porosity, the dividing lines for relatively developed, developed, and extremely developed are found, and then the development degree of the cleats on the end-face of the coal-rock core is classified and evaluated.

[0072] Figure 5 is the intersection diagram of the fractal dimension and the cleat porosity obtained in this embodiment. In the figure, the dotted line is used as the dividing line, and the areas are divided into relatively developed, developed, and extremely developed regions from the lower left to the upper right in sequence. The region where each coal-rock core sample is located represents its development degree. To verify the accuracy of the method of the present invention, the existing manual surface observation method is used to evaluate the development degree of the cleats of each coal-rock core, and the evaluation results are marked in Figure 5 as shown in the scatter points shown in Figure 5 Each point represents the development degree of the cleats of a coal-rock core. It can be seen from the figure that the evaluation results of the evaluation method of the present invention are roughly similar to those of the manual observation method, and there are only differences in the evaluation results of a small number of individual samples. For example, it is manually determined to be extremely developed, while the method of the present invention determines it to be developed. This is because the end-face characteristics of some cores are not particularly obvious, which can be identified manually, but the method of this patent may not be able to identify them well, resulting in differences in the evaluation results. It can be concluded that the average accuracy rate of the method of the present invention is 85.03%.

[0073] The method of the present invention is compared with other methods (Method 1 and Method 2). Method 1 refers to using only the cleat porosity to judge, and the results are shown in Figure 6 ; Method 2 is to use only the fractal dimension to judge, and the results are shown in Figure 7 . Method 1 and Method 2 are evaluated as relatively developed, developed, and extremely developed from left to right with the dotted line as the dividing line. It can be concluded that the average accuracy rate of the method of the present invention is 85.03%, while the average accuracy rate of Method 1 is 67.86%, and the average accuracy rate of Method 2 is 81.9%. The method of the present invention significantly improves the accuracy of the evaluation of the development degree of the cleats on the deep coal-rock end-face.

[0074] The original data required by the method of the present invention is the end-face image of the coal-rock core, and it is applicable to the analysis and calculation of the coal-rock surface image in most cases. Regardless of how the shooting direction and angle of the image are changed on-site, the illumination can be made uniform. For example, in the case shown in Figure 8 , it can be seen that after the illumination is made uniform, the problems of missing recognition or over-recognition before uniformity can be optimized, so that the calculated fractal dimension and cleat porosity are closer to the actual situation.

[0075] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the technical content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the development degree of deep coal-rock cleats based on the end-face images of full-diameter cores, characterized in that, It includes the following steps: S1. Obtain the full-diameter core end-face image and perform illumination balance processing on the core end-face image, including image brightness calculation and brightness equalization; S2. Set the threshold of the core end-face image; S3. Binary segmentation of the core end-face image; Convert the grayscale image into a binary image according to the threshold determined in step S2. Determine all pixels with a grayscale value greater than or equal to the threshold T as the coal-rock skeleton and set their grayscale value to 255; otherwise, judge them as coal-rock cleats and set their grayscale value to 0; S4. Calculate the cleat porosity of the coal-rock end-face; Calculate the proportion of cleat pixels in the coal-rock using the binary image. The formula is as follows: In the formula, S is the proportion of pixel points where the cleats are located within the coal-rock end-face, A is the number of pixels occupied by the coal-rock cleats, and B is the total number of pixel points on the coal-rock end-face; Convert the scale of the proportion of cleat pixels in the coal-rock into cleat porosity. The formula is as follows: In the formula, X is the cleat porosity calculated by the scale, S is the proportion of cleat pixels before the scale, d and c are the maximum and minimum values of the cleat porosity in the study area respectively, and b and a are the maximum and minimum values of the proportion of cleat pixels in the target coal-rock respectively; S5. Calculate the fractal dimension of the coal-rock end-face; S6. Evaluate the development degree of the cleats on the coal-rock core end-face; Complete the processing of all deep coal-rock core end-face images, the identification of cleats, and the calculation of cleat porosity and fractal dimension through steps S1 - S5; then, with the fractal dimension as the abscissa and the cleat porosity as the ordinate, establish a cross-plot of the fractal dimension and the cleat porosity; based on the cross-relationship between the fractal dimension and the cleat porosity, find the division lines for relatively developed, developed, and extremely developed, and further classify and evaluate the development degree of the cleats on the coal-rock core end-face.

2. The evaluation method for the development degree of deep coal-rock cleats based on the full-diameter core end-face image according to claim 1, characterized in that The method for setting the threshold of the core end-face image in step S2 is as follows: Automatically obtain the initial segmentation threshold T0 based on the maximum between-class variance method. Then, take at least three values greater than T0 and at least three values less than T0 around T0 respectively, and calculate the cleat pixel area corresponding to each value. Take the threshold as the abscissa and the cleat pixel area as the ordinate to establish a curve graph between the cleat pixel area and the threshold. Then, fit the starting segment and the ending segment of the curve, and take the intersection point of the two fitted straight lines as the threshold T.

3. The method for evaluating the development degree of deep coal-rock cleats based on the full-diameter core end-face image according to claim 1, wherein, In step S1, the method for calculating the image brightness is as follows: Convert the input coal-rock color image into a grayscale image; take the maximum value of the three components in the color image as the grayscale value of the grayscale image. The pixel point brightness calculation formula is: Gray(x, y) = max{R(x, y), G(x, y), B(x, y)} In the formula, Gray(x, y) is the grayscale value of the pixel point at the (x, y) position in the grayscale image, and R(x, y), G(x, y), B(x, y) are the red, green, and blue components of the pixel point at the (x, y) position in the RGB image respectively.

4. The method for evaluating the development degree of deep coal rock cleats based on the full-diameter core end-face image according to claim 3, characterized in that In step S1, the method for brightness equalization: Take the brightness of the local area with small brightness difference as the background brightness, calculate the obtained background brightness value, and then subtract the background brightness value from the brightness value of the image to obtain the brightness difference image, which is the image after brightness equalization.

5. The evaluation method for the development degree of deep coal-rock cleats based on the full-diameter core end-face image according to claim 1, characterized in that, In step S5, the formula for calculating the fractal dimension is as follows: Where ε is the length of one side of the small cube, and N(ε) is the number obtained by covering the measured fracture with this small cube.

6. The method for evaluating the development degree of deep coal-rock cleats based on the end-face image of full-diameter core as claimed in claim 1, wherein The core end face image is a photo of the core end face taken by a camera at the sampling site.

Images