Method and device for determining shale component distribution

Abstract

The invention provides a shale component distribution determination method and device.The method comprises the steps that a gray level image of a shale sample is obtained, sample components of the shale sample comprise pores, organic matter and inorganic minerals, and the gray level image is used for describing the gray level value of each pixel; according to the gray value of each pixel in the gray image, determining a first gray interval for distinguishing inorganic minerals according to a probability weighted discrete variance maximum principle; according to a gray value in a combined gray interval in the gray image, a second gray interval corresponding to the organic matters and a third gray interval corresponding to the pores are determined according to a probability weighted discrete variance maximum principle, and the combined gray interval refers to a gray interval except the first gray interval in the preset gray interval; and determining the distribution condition of each sample component in the shale sample according to the gray interval to which the gray value of each pixel in the gray image belongs.

Description

Technical Field

[0001] The present application relates to the technical field of shale oil and gas reservoir evaluation, and in particular to a method and device for determining shale component distribution. Background Art

[0002] The evaluation of shale oil and gas reservoirs is an important basis for the development and utilization of shale oil and gas resources. Argon ion polishing scanning electron microscopy observation is a common method for evaluating organic-rich shale reservoirs, which can intuitively see the micro-nano pores in the shale. Through the scanning electron microscopy observation of argon ion polished shale, the grayscale frequency distribution of the shale image obtained by the scanning electron microscope is statistically analyzed by using image processing software such as Photoshop and ImageJ, and the different components of the shale are distinguished by manually adjusting the grayscale interval value. However, the manually set grayscale interval value is not standardized. When the same researcher identifies the components of different samples, it is impossible to unify the standard based on the grayscale interval value set by the naked eye, so that the identification results of different shale samples have a large deviation, resulting in low reliability of the comparison between different samples; and the manually set grayscale interval value is not fast. Due to the limited field of view of the scanning electron microscope, it is usually necessary to perform a large number of identifications on many field of view images of a sample to make the reservoir analysis results more reliable in statistics, but the manual identification speed is slow, and it is difficult to quickly evaluate the shale reservoir. Summary of the invention

[0003] In view of this, the purpose of the present application is to at least provide a method and device for determining the distribution of shale components, by determining the grayscale value of each pixel in the grayscale image of the shale sample, using the probability weighted discrete variance maximum principle to first determine the first grayscale interval corresponding to the inorganic mineral component of the shale sample, and then through the combined grayscale intervals other than the first grayscale interval, combined with the probability weighted discrete variance maximum principle to determine the second grayscale interval corresponding to the organic matter of the shale sample and the third grayscale interval corresponding to the pores of the shale sample, so that by determining that each pixel in the grayscale image belongs to one of the first grayscale interval, the second grayscale interval and the third grayscale interval, the components of the shale sample are classified, which solves the technical problems of low accuracy and efficiency in determining shale components in the prior art, and enables the shale component determination to achieve standardized and automated technical effects.

[0004] This application mainly includes the following aspects:

[0005] In a first aspect, an embodiment of the present application provides a method for determining the distribution of shale components, the method comprising: obtaining a grayscale image of a shale sample, the sample components of the shale sample comprising pores, organic matter and inorganic minerals, the grayscale image being used to describe the grayscale value of each pixel; determining a first grayscale interval for distinguishing the inorganic minerals according to the grayscale value of each pixel in the grayscale image in accordance with the maximum probability weighted discrete variance principle; determining a second grayscale interval corresponding to the organic matter and a third grayscale interval corresponding to the pores according to the maximum probability weighted discrete variance principle according to the grayscale value in the grayscale image located in a combined grayscale interval, the combined grayscale interval refers to a grayscale interval in a preset grayscale interval except the first grayscale interval; determining each sample component in the shale sample according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs.

[0006] Optionally, the first grayscale interval for distinguishing the inorganic minerals is determined in the following manner: based on the grayscale value of each pixel in the grayscale image, the first grayscale distribution of the grayscale image is determined by the number of pixels corresponding to each first grayscale value in the preset grayscale interval and the total number of pixels of the grayscale image; through the first grayscale distribution, a first grayscale interval value is determined according to the maximum probability weighted discrete variance principle, and the first grayscale interval value is used to distinguish the inorganic minerals from other sample components except the inorganic minerals; in the preset grayscale interval, the first grayscale interval corresponding to the inorganic mineral is divided by the first grayscale interval value.

[0007] Optionally, the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are determined in the following manner: the number of pixels of each second grayscale value in the grayscale image that are located in the combined grayscale interval is counted; the second grayscale distribution is determined based on the ratio of the number of pixels corresponding to each second grayscale value to the total number of pixels belonging to the combined grayscale interval; through the second grayscale distribution, a second grayscale interval value is determined according to the maximum probability weighted discrete variance principle, and the second grayscale interval value is used to distinguish the pores from the organic matter; in the combined grayscale interval, the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are divided by the second grayscale interval value.

[0008] Optionally, the maximum value of the third grayscale interval corresponding to the pores is smaller than the minimum value of the second grayscale interval corresponding to the organic matter, and the maximum value of the second grayscale interval corresponding to the organic matter is smaller than the minimum value of the first grayscale interval corresponding to the inorganic minerals.

[0009] Optionally, the grayscale interval value is determined in the following manner: based on the grayscale distribution corresponding to the target grayscale interval, the weighted average value of the target grayscale interval is calculated, and the target grayscale interval is the preset grayscale interval or the combined grayscale interval; for each grayscale value in the target grayscale interval, the target grayscale interval is divided into a left interval and a right interval according to the grayscale value, and according to the grayscale distribution corresponding to the target grayscale interval, the probability value and the average grayscale corresponding to the left interval and the right interval are calculated, respectively, the grayscale distribution corresponding to the preset grayscale interval is the first grayscale distribution, and the grayscale distribution corresponding to the combined grayscale interval is the second grayscale distribution; for each grayscale value in the target grayscale interval, the probability weighted discrete variance of the grayscale value is calculated based on the weighted average value and the probability value and the average grayscale corresponding to the grayscale value in the left interval and the right interval respectively; the grayscale value corresponding to the largest probability weighted discrete variance is taken as the grayscale interval value.

[0010] Optionally, obtaining a grayscale image of a shale sample includes: processing the shale sample according to preset processing conditions, and obtaining an initial image of the processed shale sample through a scanning electron microscope, wherein the preset processing conditions are used to increase the smoothness and conductivity of the sample; determining the grayscale value of each pixel in the grayscale image based on the initial grayscale value of each pixel of the initial image and other pixels of the pixel within a preset sliding window.

[0011] Optionally, the grayscale value of each pixel in the grayscale image is determined based on the initial grayscale values ​​of each pixel of the initial image and other pixels of the pixel in a preset sliding window, including: for each pixel of the initial image, placing the pixel at a preset position of the preset sliding window, and determining other pixels corresponding to other positions in the preset sliding window except the preset position; for each pixel of the initial image, sorting the initial pixel values ​​corresponding to the pixel and other pixels, respectively, and using the sorted middle value as the grayscale value of the pixel in the grayscale image.

[0012] In a second aspect, an embodiment of the present application further provides a device for determining the distribution of shale components, the device comprising: an acquisition module, used to acquire a grayscale image of a shale sample, the sample components of the shale sample comprising pores, organic matter and inorganic minerals, the grayscale image being used to describe the grayscale value of each pixel; a first distinguishing module, used to determine a first grayscale interval for distinguishing the inorganic minerals according to the grayscale value of each pixel in the grayscale image and the maximum probability weighted discrete variance principle; a second distinguishing module, used to determine a second grayscale interval corresponding to the organic matter and a third grayscale interval corresponding to the pores according to the maximum probability weighted discrete variance principle based on the grayscale value in the combined grayscale interval in the grayscale image, the combined grayscale interval referring to the grayscale interval in the preset grayscale interval except the first grayscale interval; a determination module, used to determine the distribution of each sample component in the shale sample according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the method for determining the shale component distribution described in the first aspect or any possible implementation manner of the first aspect.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining the shale component distribution described in the first aspect or any possible implementation scheme of the first aspect are executed.

[0015] A method and device for determining shale component distribution provided in an embodiment of the present application include: obtaining a grayscale image of a shale sample, wherein the sample components of the shale sample include pores, organic matter and inorganic minerals, and the grayscale image is used to describe the grayscale value of each pixel; based on the grayscale value of each pixel in the grayscale image, determining a first grayscale interval for distinguishing the inorganic minerals according to the probability weighted discrete variance maximum principle; based on the grayscale value in the grayscale image located in a combined grayscale interval, determining a second grayscale interval corresponding to the organic matter and a third grayscale interval corresponding to the pores according to the probability weighted discrete variance maximum principle, wherein the combined grayscale interval refers to a grayscale interval other than the first grayscale interval in a preset grayscale interval; and determining the distribution of each sample component in the shale sample according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs. By determining the grayscale value of each pixel in the grayscale image of the shale sample, the first grayscale interval corresponding to the inorganic mineral component of the shale sample is first determined in combination with the probability weighted discrete variance maximum principle, and then the second grayscale interval corresponding to the organic matter of the shale sample and the third grayscale interval corresponding to the pores of the shale sample are determined through the combined grayscale intervals other than the first grayscale interval and the probability weighted discrete variance maximum principle is again combined. Thus, by determining that each pixel in the grayscale image belongs to one of the first grayscale interval, the second grayscale interval and the third grayscale interval, the components of the shale sample are classified, which solves the technical problem of low accuracy and efficiency in determining the shale components in the prior art, and achieves the technical effect of improving the accuracy and efficiency of determining the shale components.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a method for determining shale component distribution provided in an embodiment of the present application is shown.

[0019] Figure 2 A schematic diagram of an initial image provided by an embodiment of the present application is shown.

[0020] Figure 3 A schematic diagram showing a left interval and a right interval divided by a first grayscale interval value provided in an embodiment of the present application.

[0021] Figure 4 A schematic diagram showing the distribution of inorganic minerals provided in the embodiments of the present application.

[0022] Figure 5 A schematic diagram showing the distribution of organic matter and pores provided in an embodiment of the present application is shown.

[0023] Figure 6 A functional module diagram of a device for determining shale component distribution provided in an embodiment of the present application is shown.

[0024] Figure 7 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art, under the guidance of the content of the present application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0026] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0027] In the prior art, shale oil and gas reservoir evaluation is an important basis for the development and utilization of shale oil and gas resources. Argon ion polishing scanning electron microscopy observation is a common means of evaluating organic-rich shale reservoirs. This method can intuitively see the micro-nano pores in the shale. Through scanning electron microscopy observation of argon ion polished shale, researchers can qualitatively determine the cause, type, content and other information of the pores in the shale. With the development of computer technology, in recent years, researchers have used Photoshop, ImageJ and other image processing software to identify and extract pores in shale scanning electron microscope images, thereby realizing quantitative evaluation of shale reservoirs. When identifying and extracting pores, grayscale threshold segmentation is usually used. Researchers manually adjust the size of the grayscale threshold to separate the pores from other elements in the image, thereby conducting a more in-depth study of the pore part. However, there are some problems with manually adjusting the grayscale threshold: 1. Manual adjustment is not standardized. When the same researcher conducts pore identification on different samples, it is impossible to unify the standard based on visual judgment alone, which makes the pore identification results of different shale samples have large deviations, resulting in low reliability in the comparison between different samples; 2. Manual adjustment is not fast. Since the field of view of the scanning electron microscope is relatively limited, it is usually necessary to perform a large number of identifications on many field of view images of a sample to make the reservoir analysis results more reliable in statistics, but the speed of manual identification is slow, making it difficult to quickly evaluate shale reservoirs.

[0028] Based on this, the embodiment of the present application provides a method and device for determining the distribution of shale components, by determining the grayscale value of each pixel in the grayscale image of the shale sample, combining the probability weighted discrete variance maximum principle to first determine the first grayscale interval corresponding to the inorganic mineral component of the shale sample, and then through the combined grayscale intervals other than the first grayscale interval, combined with the probability weighted discrete variance maximum principle to determine the second grayscale interval corresponding to the organic matter of the shale sample and the third grayscale interval corresponding to the pores of the shale sample, so that by determining that each pixel in the grayscale image belongs to one of the first grayscale interval, the second grayscale interval and the third grayscale interval, the components of the shale sample are classified, which solves the technical problem of low accuracy and efficiency of shale component determination in the prior art, and achieves the technical effect of improving the accuracy and efficiency of shale component determination, which is as follows:

[0029] See also Figure 1 , Figure 1 A flow chart of a method for determining shale component distribution provided in an embodiment of the present application. Figure 1 As shown, the method for determining shale component distribution provided in the embodiment of the present application includes the following steps:

[0030] S101: Acquire a grayscale image of a shale sample.

[0031] The sample components of the shale sample include pores, organic matter and inorganic minerals, that is, the distribution of the three components of pores, organic matter and inorganic minerals in the shale sample needs to be identified. The grayscale image is used to describe the grayscale value of each pixel, that is, each pixel in the grayscale image of the shale sample corresponds to a grayscale value, and the grayscale value is a value in the preset grayscale interval [0, 255].

[0032] The method of obtaining a grayscale image of a shale sample includes: processing the shale sample according to preset processing conditions, and obtaining an initial image of the processed shale sample through a scanning electron microscope, wherein the preset processing conditions are used to increase the smoothness and conductivity of the sample; and determining the grayscale value of each pixel in the grayscale image based on the initial grayscale value of each pixel of the initial image and the initial grayscale value of other pixels of the pixel in a preset sliding window.

[0033] The preset processing conditions include polishing, carbon spraying or gold spraying on the shale sample. Polishing is used to increase the smoothness of the sample, and carbon spraying or gold spraying is used to increase the conductivity of the shale sample. Exemplarily, the shale sample is a small piece with a side length of 1 cm, which is pasted on a nail table, first ground and polished with a mesh of not less than 2000, and then argon ion polishing is performed to increase the smoothness of the sample, which is more convenient for scanning electron microscopy (SEM) observation, and then the polished shale sample is carbon sprayed or gold sprayed to facilitate the flow of electrons in the scanning electron microscope on the shale sample. Shale samples are theoretically non-conductive, and therefore it is necessary to spray carbon or gold on the sample to achieve electron flow.

[0034] Exemplarily, the scanning electron microscope parameters are set to the secondary electron (SE2) mode and the shale sample is observed and the initial image of the shale sample is taken at a low voltage (about 1.5 kilovolts) and a high magnification (5,000 to 100,000 times). Among them, the secondary electron mode can obtain a more even grayscale distribution compared to the backscattered electron (BSE), which is more suitable for identifying different components. Backscattered electrons may cause the peaks of the frequency distribution of the grayscale values ​​corresponding to organic matter and inorganic minerals to be close, or even overlap, resulting in the inability to accurately distinguish between organic matter and inorganic minerals.

[0035] Furthermore, the grayscale value of each pixel in the grayscale image is calculated through the initial grayscale values ​​of each pixel in the initial image and other pixels in the preset sliding window, thereby obtaining the grayscale image corresponding to the shale sample.

[0036] Specifically, the grayscale value of each pixel in the grayscale image is determined based on the initial grayscale values ​​of each pixel of the initial image and other pixels of the pixel in a preset sliding window, including: for each pixel of the initial image, the pixel is placed at a preset position of the preset sliding window, and other pixels corresponding to other positions in the preset sliding window except the preset position are determined; for each pixel of the initial image, the initial pixel values ​​corresponding to the pixel and other pixels are sorted, and the sorted middle value is used as the grayscale value of the pixel in the grayscale image.

[0037] Furthermore, for each pixel of each initial image, the pixel is placed on the middle pixel of a preset sliding window, and the grayscale values ​​of the pixel and other pixels in the preset sliding window except the middle pixel are sorted, which can be arranged in ascending order or descending order, and the middle value of the sorted grayscale values ​​is used as the grayscale value of the pixel in the grayscale image, or the middle value replaces the initial grayscale value of the pixel in the initial image, so that the initial grayscale value of each pixel is replaced to obtain a grayscale image.

[0038] The preset sliding window is set as a 3×3 pixel window, that is, a pixel window of three rows and three columns is constructed by setting three pixels in one row and three pixels in one column. For each pixel of each initial image, if the pixel is located at the edge of the initial image, the number of other pixels determined when the pixel is located in the middle position of the preset sliding window is less than 8. For example, if the pixel is located at the four corners of the initial image, the total number of the pixel and the other pixels is determined to be four, and the determined intermediate value can be one of the second grayscale value and the third grayscale value after sorting. If the pixel is located at a position other than the four corners on the four sides of the initial image, the total number of the pixel and the other pixels is determined to be six, and the determined intermediate value can be one of the third grayscale value and the fourth grayscale value after sorting. If the pixel is not located at the four sides of the initial image, the total number of the pixel and the other pixels is determined to be nine, and the determined intermediate value can be the fifth grayscale value after sorting.

[0039] For example, see Figure 2 , Figure 2 Schematic diagram of the initial image provided in the embodiment of the present application. Figure 2As shown, the initial image provided by the embodiment of the present application is a 5×5 image, with five rows from R1 to R5, and five columns from C1 to C5, respectively. The preset sliding window A0 can be understood as a nine-square grid of pixels, and the middle pixel of the preset sliding window A0 is used as the preset position. Then, for the pixel corresponding to the R1 row and C1 column of the initial image, the pixel is placed at the position where the middle pixel of the preset sliding window A0 is located, and other pixels other than the pixel are determined in the preset sliding window as the pixel corresponding to the R1 row and C2 column, the pixel corresponding to the R2 row and C1 column, and the pixel corresponding to the R2 row and C2 column. Then, the pixels corresponding to the R1 row and C1 column, the pixels corresponding to the R1 row and C2 column, the pixels corresponding to the R2 row and C1 column, and the pixels corresponding to the R2 row and C2 column are sorted according to their respective grayscale values, and the second grayscale value or the third grayscale value after the grayscale value sorting is used as the middle value, and the middle value is used as the grayscale value of the pixel corresponding to the R1 row and C2 column in the grayscale image. For the pixel corresponding to the R3 row and C5 column of the initial image, the pixel is placed at the position of the middle pixel of the preset sliding window A0, and the pixels other than the pixel are determined in the preset sliding window as the pixel corresponding to the R2 row and C4 column, the pixel corresponding to the R2 row and C5 column, the pixel corresponding to the R3 row and C4 column, the pixel corresponding to the R4 row and C4 column, and the pixel corresponding to the R4 row and C5 column. Then, the pixels corresponding to the R2 row and C4 column, the pixel corresponding to the R2 row and C5 column, the pixel corresponding to the R3 row and C4 column, the pixel corresponding to the R3 row and C5 column, the pixel corresponding to the R4 row and C4 column, and the pixel corresponding to the R4 row and C5 column are sorted according to their respective grayscale values, and the third grayscale value or the fourth grayscale value after the grayscale values ​​are sorted is used as the intermediate value, and the intermediate value is used as the grayscale value of the pixel corresponding to the R3 row and C5 column in the grayscale image.

[0040] S102: determining a first grayscale interval for distinguishing the inorganic minerals according to the grayscale value of each pixel in the grayscale image and the maximum probability weighted discrete variance principle;

[0041] The first grayscale interval for distinguishing the inorganic minerals is determined in the following manner: based on the grayscale value of each pixel in the grayscale image, the first grayscale distribution of the grayscale image is determined by the number of pixels corresponding to each first grayscale value in the preset grayscale interval and the total number of pixels in the grayscale image; through the first grayscale distribution, a first grayscale interval value is determined according to the maximum probability weighted discrete variance principle, and the first grayscale interval value is used to distinguish the inorganic minerals from other sample components except the inorganic minerals; in the preset grayscale interval, the first grayscale interval corresponding to the inorganic mineral is divided by the first grayscale interval value.

[0042] That is to say, each grayscale value in the preset grayscale interval is taken as the first grayscale value. According to the grayscale value of each pixel in the grayscale image, the number of pixels corresponding to each first grayscale value in the preset grayscale interval is counted, so as to obtain the frequency distribution of the grayscale image in the preset grayscale interval. Furthermore, according to the frequency distribution of the preset grayscale interval, the frequency distribution of the grayscale image in the preset grayscale interval is obtained by the ratio of the number of pixels corresponding to each first grayscale value in the preset grayscale interval to the total number of pixels of the grayscale image, and the frequency distribution is taken as the first grayscale distribution of the grayscale image, that is, the first grayscale distribution is relative to the complete preset grayscale interval.

[0043] The grayscale interval value is determined in the following manner: according to the grayscale distribution corresponding to the target grayscale interval, a weighted average value of the target grayscale interval is calculated, and the target grayscale interval is the preset grayscale interval or the combined grayscale interval; for each grayscale value in the target grayscale interval, the target grayscale interval is divided into a left interval and a right interval according to the grayscale value, and according to the grayscale distribution corresponding to the target grayscale interval, the probability value and the average grayscale corresponding to the left interval and the right interval are calculated, respectively, the grayscale distribution corresponding to the preset grayscale interval is the first grayscale distribution, and the grayscale distribution corresponding to the combined grayscale interval is the second grayscale distribution; for each grayscale value in the target grayscale interval, the probability weighted discrete variance of the grayscale value is calculated according to the weighted average value and the probability value and the average grayscale corresponding to the grayscale value in the left interval and the right interval respectively; the grayscale value corresponding to the largest probability weighted discrete variance is taken as the grayscale interval value.

[0044] That is to say, the weighted average value corresponding to the preset grayscale interval is calculated through the first grayscale distribution corresponding to the preset grayscale interval. Moreover, for each first grayscale value S1 in the preset grayscale interval, the preset grayscale interval is divided into a closed interval from 0 to S1 and a closed interval from S1+1 to 255 through the first grayscale value S1, that is, the closed interval from 0 to S1 is the left interval, and the closed interval from S1+1 to 255 is the right interval. The probability value of the grayscale value of the pixel falling into the left interval and the probability value of the grayscale value of the pixel falling into the right interval are determined through the first grayscale distribution corresponding to the preset grayscale interval, and the average grayscale corresponding to the left interval and the right interval are calculated, so as to calculate the probability weighted discrete variance of the first grayscale value S1.

[0045] Exemplarily, the probability weighted discrete variance of the first gray value is calculated by the following formula:

[0046]

[0047] In formulas (1) to (6), L T refers to the weighted average value corresponding to the preset grayscale interval; i refers to the first pixel value in the preset grayscale interval; p iIt refers to the ratio of the number of pixels with gray value i in the gray image to the total number of pixels in the gray image; S1 refers to a first gray value in a preset gray interval; p A (S1) refers to the probability value that the grayscale value of the pixel falls into the left interval [0, S1] corresponding to S1; Refers to the p corresponding to each gray value in the left interval [0, S1] i The sum of L A (S1) refers to the average grayscale of the left interval [0, S1]; p B (S1) refers to the probability value that the grayscale value of the pixel falls into the right interval [S1+1, 255] corresponding to S1; S1+1 refers to the next first grayscale value of a first grayscale value S1 in the preset grayscale interval; Refers to the p corresponding to each gray value in the right interval [S1+1, 255] i The sum of L B (S1) refers to the average grayscale of the right interval [S1+1, 255]; Refers to the probability-weighted discrete variance of the first gray value S1.

[0048] Then, the probability weighted discrete variance corresponding to each first gray value in the preset gray interval [0, 255] is calculated according to formulas (1) to (6), the first gray value S1 corresponding to the largest probability weighted discrete variance is used as the first gray interval value, and the preset gray interval [0, 255] is divided into a left interval [0, S1] and a right interval [S1+1, 255] by the first gray interval value. The right interval [S1+1, 255] is used as the first gray interval corresponding to inorganic minerals, and the left interval [0, S1] is used as the combined gray interval in the preset gray interval except the first gray interval. The combined gray interval can be understood as the gray interval corresponding to pores and organic matter.

[0049] For example, see Figure 3 , Figure 3 This is a schematic diagram of dividing the left interval and the right interval into the first grayscale interval value provided in the embodiment of the present application. Figure 3 As shown, the horizontal axis is the first grayscale value of the preset grayscale interval, and the value range is [0, 255]. The vertical axis on the left side of the figure is the number of pixels corresponding to the first grayscale value, that is, the number of pixels of each first grayscale value in the grayscale image. The vertical axis on the right side of the figure is used to indicate the probability weighted discrete variance of the first grayscale value. The orange curve in the figure is used to indicate the curve obtained by data fitting of the probability weighted discrete variance of the first grayscale value, and the first grayscale value S corresponding to the maximum probability weighted discrete variance is obtained. 1,max Divide the left interval and the right interval, S 1,maxThe value is 101. The main peak 1 is used to represent the first gray value corresponding to the maximum number of pixels in the left interval [0, 101], which can also be understood as the average gray value L in the left interval A (S1), L A (S1) takes a value of 74.06. The main peak 2 is used to represent the first grayscale value corresponding to the maximum number of pixels in the right interval [102, 255], which can also be understood as the average grayscale L in the right interval. B (S1), L B (S1) takes a value of 126.41, and the first gray value S corresponding to the largest probability weighted discrete variance 1,max It can also be approximated as the middle value between the main peak 1 of the left interval and the main peak 2 of the right interval, through S 1,max The divided left interval [0, 101] is used to indicate the combined gray interval corresponding to pores and organic matter, and the right interval [102, 255] is used to indicate the first gray interval corresponding to inorganic minerals.

[0050] For example, see Figure 4 , Figure 4 This is a schematic diagram of the distribution of inorganic minerals provided in the embodiments of this application. Figure 4 As shown, the distribution of inorganic minerals in the shale sample, as well as the distribution of pores and organic matter, is determined by the first grayscale interval value. For each pixel in the grayscale image, when the grayscale value of the pixel is in the right interval [S1+1, 255], the grayscale value of the pixel is set to 0, and when the grayscale value of the pixel is in the left interval [0, S1], the grayscale value of the pixel is set to 255, thereby modifying the grayscale image to a black and white image that reflects the distribution of pores and organic matter in the shale sample. For each pixel in the grayscale image, when the grayscale value of the pixel is in the right interval [S1+1, 255], the grayscale value of the pixel is set to 255, and when the grayscale value of the pixel is in the left interval [0, S1], the grayscale value of the pixel is set to 0, thereby modifying the grayscale image to a black and white image that reflects the distribution of inorganic minerals in the shale sample.

[0051] S103: According to the grayscale values ​​in the grayscale image located in the combined grayscale interval, the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are determined according to the probability weighted discrete variance maximum principle.

[0052] The combined grayscale interval refers to the grayscale interval other than the first grayscale interval in the preset grayscale interval.

[0053] The second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are determined in the following manner: the number of pixels of each second grayscale value in the grayscale image that are located in the combined grayscale interval is counted; the second grayscale distribution is determined based on the ratio of the number of pixels corresponding to each second grayscale value to the total number of pixels belonging to the combined grayscale interval; through the second grayscale distribution, a second grayscale interval value is determined according to the maximum probability weighted discrete variance principle, and the second grayscale interval value is used to distinguish the pores from the organic matter; in the combined grayscale interval, the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are divided by the second grayscale interval value.

[0054] Among them, the maximum value of the third grayscale interval corresponding to the pores is smaller than the minimum value of the second grayscale interval corresponding to the organic matter, and the maximum value of the second grayscale interval corresponding to the organic matter is smaller than the minimum value of the first grayscale interval corresponding to the inorganic minerals.

[0055] Among them, because the pore is a concave space, it is difficult for the secondary electron (SE2) mode to capture the flow of electrons at a certain depth. Therefore, even if the conductivity of the shale sample is increased by spraying gold or carbon, the scanning electron microscope cannot illuminate the image of the electrons flowing into the pores. Furthermore, the part corresponding to the pore should be black, and the smaller the gray value, the darker the color, so the maximum value of the third gray interval corresponding to the pore should be less than the minimum value of the second gray interval corresponding to the organic matter, and less than the minimum value of the first gray interval corresponding to the inorganic mineral.

[0056] Specifically, for each second grayscale value in the combined grayscale interval, the number of pixels of the second grayscale value is determined in the grayscale image, and the ratio of the number of pixels corresponding to the second grayscale value to the total number of pixels in the grayscale image belonging to the combined grayscale interval is taken, so that the ratio corresponding to each second grayscale value is used as the second grayscale distribution of the grayscale image in the combined grayscale interval.

[0057] That is, the first grayscale interval in the preset grayscale interval is deleted, the first grayscale interval is not considered, and the sum of the number of pixels of each second grayscale value of the combined grayscale interval in the grayscale image is taken as the total number of pixels belonging to the combined grayscale interval in the grayscale image.

[0058] Specifically, the second grayscale interval value is determined in the following manner: based on the second grayscale distribution corresponding to the combined grayscale interval, a weighted average value of the combined grayscale interval is calculated; for each second grayscale value in the combined grayscale interval, the combined grayscale interval is divided into a left interval and a right interval according to the second grayscale value, and according to the second grayscale distribution corresponding to the combined grayscale interval, the probability value and the average grayscale corresponding to the left interval and the right interval are calculated respectively; for each second grayscale value in the combined grayscale interval, based on the weighted average value of the combined grayscale interval and the probability value and the average grayscale corresponding to the second grayscale value in the left interval and the right interval respectively, the probability weighted discrete variance of the second grayscale value is calculated; and the second grayscale value corresponding to the largest probability weighted discrete variance is taken as the second grayscale interval value.

[0059] That is to say, first, the weighted average value of the combined grayscale interval is calculated according to the second grayscale distribution corresponding to the combined grayscale interval, and for each second grayscale value S2 in the combined grayscale interval, the combined grayscale interval is divided into a closed interval from 0 to S2 and a closed interval from S2 to S1 through the second grayscale value S2, that is, the closed interval from 0 to S2 is the left interval, and the closed interval from S2 to S1 is the right interval. The probability value of the grayscale value of the pixel falling into the left interval and the probability value of the grayscale value of the pixel falling into the right interval are determined through the second grayscale distribution corresponding to the combined grayscale interval, and the average grayscale corresponding to the left interval and the right interval are calculated, so as to calculate the probability weighted discrete variance of the second grayscale value S2.

[0060] Exemplarily, the probability weighted discrete variance of the second gray value is calculated by the following formula:

[0061]

[0062] In formulas (7) to (12), L T ′ refers to the weighted average value corresponding to the combined grayscale interval; j refers to the second pixel value in the combined grayscale interval; p j It refers to the ratio of the number of pixels with grayscale value j in the grayscale image to the total number of pixels in the grayscale image belonging to the combined grayscale interval; S2 refers to a second grayscale value in the combined grayscale interval; p A (S2) refers to the probability value that the grayscale value of the pixel falls into the left interval [0, S2] corresponding to S2; Refers to the p corresponding to each gray value in the left interval [0, S2] j The sum of L A (S2) refers to the average grayscale of the left interval [0, S2]; p B (S2) refers to the probability value that the grayscale value of the pixel falls into the right interval [S2+1, S1] corresponding to S2; S2+1 refers to the next second grayscale value of a second grayscale value S2 in the combined grayscale interval; Refers to the p corresponding to each gray value in the right interval [S2+1, S1] i The sum of L B (S2) refers to the average grayscale of the right interval [S2+1, S1]; Refers to the probability-weighted discrete variance of the second grayscale value S2.

[0063] Furthermore, the probability weighted discrete variance corresponding to each second gray value in the combined gray interval [0, S1] is calculated according to formulas (7) to (12), the second gray value S2 corresponding to the largest probability weighted discrete variance is used as the second gray interval value, and the combined gray interval [0, S1] is divided into a left interval [0, S2] and a right interval [S2+1, S1] by the second gray interval value, the right interval [S2+1, S1] is used as the second gray interval corresponding to organic matter, and the left interval [0, S2] is used as the third gray interval corresponding to pores.

[0064] For example, see Figure 5 , Figure 5 This is a schematic diagram of the distribution of organic matter and pores provided in the embodiment of this application. Figure 5 As shown, the distribution of organic matter and pores can be distinguished by the second grayscale interval value under the common distribution of pores and organic matter. For each pixel in the grayscale image, when the grayscale value of the pixel is in the interval [S2+1, S1], the grayscale value of the pixel is set to 0, and when the grayscale value of the pixel is not in the interval [S2+1, S1], the grayscale value of the pixel is set to 255, thereby modifying the grayscale image to a black and white image that reflects the distribution of organic matter in the shale sample. For each pixel in the grayscale image, when the grayscale value of the pixel is in the interval [0, S2], the grayscale value of the pixel is set to 255, and when the grayscale value of the pixel is not in the interval [0, S2], the grayscale value of the pixel is set to 0, thereby modifying the grayscale image to a black and white image that reflects the distribution of pores in the shale sample.

[0065] S104: Determine the distribution of each sample component in the shale sample according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs.

[0066] Furthermore, for each pixel in the grayscale image, it is determined whether the grayscale value of the pixel belongs to one of the first grayscale interval, the second grayscale interval and the third grayscale interval, and the shale component corresponding to the grayscale interval to which the pixel belongs is used as the shale component corresponding to the pixel.

[0067] For example, if the grayscale value of a pixel belongs to the first grayscale interval, the shale component of the pixel is considered to be an inorganic mineral. Furthermore, each pixel can be displayed and distinguished by the display parameters corresponding to different shale components, and the display parameters can be color, grayscale, transparency, etc. For example, in the initial image, the pixels corresponding to inorganic minerals can be marked as a first color, the pixels corresponding to organic matter can be marked as a second color, and the pixels corresponding to pores can be marked as a third color, so as to distinguish different shale components by different colors.

[0068] Exemplarily, in order to focus on the pores, the grayscale values ​​of the pixels corresponding to the pores in the initial image can be set to a first value, and the grayscale values ​​of the shale components other than the pores can be set to a second value, that is, the grayscale values ​​of the pixels corresponding to the inorganic minerals are set to the second value, and the grayscale values ​​of the pixels corresponding to the organic matter are set to the second value, so as to achieve binarization processing of the initial image, so as to count the number of pixels corresponding to the pores and the number of pixels of the shale components other than the pores, so as to determine the distribution area of ​​the pores, which is convenient for technicians to use and analyze.

[0069] Exemplarily, the complete shale can be divided into multiple shale samples, and each shale sample is processed by the shale component distribution determination method of the present application to analyze the shale components of the complete shale, the distribution area of ​​pores, etc., so as to simplify the manual processing process of each shale sample.

[0070] Furthermore, the present application aims to solve the problem of standardized and rapid identification of pores in argon ion polished scanning electron microscope images of organic-rich shales. Based on the grayscale spectrum characteristics of scanning electron microscope images of organic-rich shales, the present invention calculates the grayscale cutoff value of pores through grayscale spectrum, proposes a two-level maximum probability weighted discrete variance segmentation process, realizes automatic identification of pores in argon ion polished scanning electron microscope images of organic-rich shales, and realizes standard and rapid pore identification and extraction to meet the needs of shale oil and gas reservoir evaluation.

[0071] Based on the same application concept, the embodiments of the present application also provide a device for determining the shale component distribution corresponding to the method for determining the shale component distribution provided in the above embodiments. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the method for determining the shale component distribution in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0072] like Figure 6 As shown, Figure 6A functional module diagram of a device for determining shale component distribution provided in an embodiment of the present application. The device 10 for determining shale component distribution includes: an acquisition module 101, used to acquire a grayscale image of a shale sample, wherein the sample components of the shale sample include pores, organic matter and inorganic minerals, and the grayscale image is used to describe the grayscale value of each pixel; a first distinguishing module 102, used to determine the first grayscale interval for distinguishing the inorganic minerals according to the grayscale value of each pixel in the grayscale image and the maximum probability weighted discrete variance principle; a second distinguishing module 103, used to determine the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores according to the maximum probability weighted discrete variance principle based on the grayscale value in the combined grayscale interval in the grayscale image, wherein the combined grayscale interval refers to the grayscale interval in the preset grayscale interval except the first grayscale interval; a determining module 104, used to determine the distribution of each sample component in the shale sample according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs.

[0073] Based on the same application concept, see Figure 7 As shown, it is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device 20 includes: a processor 201, a memory 202 and a bus 203, the memory 202 stores machine-readable instructions executable by the processor 201, when the electronic device 20 is running, the processor 201 and the memory 202 communicate through the bus 203, and the machine-readable instructions are executed by the processor 201 when running to perform the steps of the method for determining the shale component distribution as described in any of the above embodiments.

[0074] Specifically, when the machine-readable instructions are executed by the processor 201, the following processing can be performed: a grayscale image of a shale sample is obtained, the sample components of the shale sample include pores, organic matter and inorganic minerals, and the grayscale image is used to describe the grayscale value of each pixel; based on the grayscale value of each pixel in the grayscale image, a first grayscale interval for distinguishing the inorganic minerals is determined according to the maximum probability weighted discrete variance principle; based on the grayscale value in the grayscale image located in the combined grayscale interval, a second grayscale interval corresponding to the organic matter and a third grayscale interval corresponding to the pores are determined according to the maximum probability weighted discrete variance principle, and the combined grayscale interval refers to a grayscale interval in the preset grayscale interval except the first grayscale interval; based on the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs, the distribution of each sample component in the shale sample is determined.

[0075] Based on the same application concept, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining the shale component distribution provided in the above embodiment are executed.

[0076] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, the above-mentioned method for determining the distribution of shale components can be executed. By determining the grayscale value of each pixel in the grayscale image of the shale sample, the first grayscale interval corresponding to the inorganic mineral component of the shale sample is first determined in combination with the probability weighted discrete variance maximum principle, and then the second grayscale interval corresponding to the organic matter of the shale sample and the third grayscale interval corresponding to the pores of the shale sample are determined by combining the grayscale intervals other than the first grayscale interval and combining the probability weighted discrete variance maximum principle again. Thus, by determining that each pixel in the grayscale image belongs to one of the first grayscale interval, the second grayscale interval and the third grayscale interval, the components of the shale sample are classified, which solves the technical problem of low accuracy and efficiency in determining the shale components in the prior art, and achieves the technical effect of improving the accuracy and efficiency of determining the shale components.

[0077] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0080] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0081] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining shale component distribution, characterized in that: The method comprises: Acquire a grayscale image of a shale sample, wherein the sample components of the shale sample include pores, organic matter, and inorganic minerals, and the grayscale image is used to describe the grayscale value of each pixel; According to the gray value of each pixel in the gray image, a first gray interval for distinguishing the inorganic minerals is determined according to the maximum probability weighted discrete variance principle; According to the grayscale values ​​in the grayscale image located in the combined grayscale interval, the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are determined according to the maximum probability weighted discrete variance principle, wherein the combined grayscale interval refers to the grayscale interval in the preset grayscale interval except the first grayscale interval; The distribution of each sample component in the shale sample is determined according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs.

2. The method according to claim 1, characterized in that: The first grayscale interval for distinguishing the inorganic minerals is determined by: According to the grayscale value of each pixel in the grayscale image, the first grayscale distribution of the grayscale image is determined by the number of pixels corresponding to each first grayscale value in the preset grayscale interval and the total number of pixels in the grayscale image; Determine a first grayscale interval value through the first grayscale distribution according to the maximum probability weighted discrete variance principle, wherein the first grayscale interval value is used to distinguish the inorganic mineral from other sample components except the inorganic mineral; The first grayscale interval corresponding to the inorganic mineral is divided in the preset grayscale interval by the first grayscale interval value.

3. The method according to claim 1, characterized in that The second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores are determined in the following manner: Counting the number of pixels of each second grayscale value in the grayscale image that are located in the combined grayscale interval; Determining a second grayscale distribution according to a ratio of the number of pixels corresponding to each second grayscale value to the total number of pixels belonging to the combined grayscale interval; Determine a second grayscale interval value through the second grayscale distribution according to the maximum probability weighted discrete variance principle, wherein the second grayscale interval value is used to distinguish the pores from the organic matter; In the combined grayscale interval, a second grayscale interval corresponding to the organic matter and a third grayscale interval corresponding to the pores are divided according to the second grayscale interval value.

4. The method according to any one of claims 1 to 3, characterized in that: The maximum value of the third grayscale interval corresponding to the pores is smaller than the minimum value of the second grayscale interval corresponding to the organic matter, and the maximum value of the second grayscale interval corresponding to the organic matter is smaller than the minimum value of the first grayscale interval corresponding to the inorganic minerals.

5. The method according to claim 2 or 3, characterized in that: The grayscale interval value is determined by: Calculating a weighted average value of a target grayscale interval according to a grayscale distribution corresponding to the target grayscale interval, wherein the target grayscale interval is the preset grayscale interval or the combined grayscale interval; For each grayscale value in the target grayscale interval, the target grayscale interval is divided into a left interval and a right interval according to the grayscale value, and according to the grayscale distribution corresponding to the target grayscale interval, the probability value and the average grayscale corresponding to the left interval and the right interval are calculated, the grayscale distribution corresponding to the preset grayscale interval is the first grayscale distribution, and the grayscale distribution corresponding to the combined grayscale interval is the second grayscale distribution; For each grayscale value in the target grayscale interval, calculate the probability weighted discrete variance of the grayscale value according to the weighted average value and the probability value and average grayscale corresponding to the grayscale value in the left interval and the right interval respectively; The grayscale value corresponding to the maximum probability weighted discrete variance is taken as the grayscale interval value.

6. The method according to claim 1, characterized in that The step of obtaining a grayscale image of a shale sample comprises: The shale sample is processed according to preset processing conditions, and an initial image of the processed shale sample is obtained through a scanning electron microscope, wherein the preset processing conditions are used to increase the smoothness and conductivity of the sample; The grayscale value of each pixel in the grayscale image is determined according to the initial grayscale values ​​of each pixel of the initial image and other pixels of the pixel in a preset sliding window.

7. The method according to claim 6, characterized in that Determining the grayscale value of each pixel in the grayscale image based on each pixel of the initial image and the initial grayscale values ​​of other pixels of the pixel in a preset sliding window includes: For each pixel of the initial image, the pixel is placed at a preset position of a preset sliding window, and other pixels corresponding to other positions in the preset sliding window except the preset position are determined; For each pixel of the initial image, the initial pixel values ​​corresponding to the pixel and other pixels are sorted, and the sorted middle value is used as the grayscale value of the pixel in the grayscale image.

8. A device for determining shale component distribution, characterized in that: The device comprises: An acquisition module, used for acquiring a grayscale image of a shale sample, wherein the sample components of the shale sample include pores, organic matter and inorganic minerals, and the grayscale image is used for describing the grayscale value of each pixel; A first distinguishing module, used to determine a first grayscale interval for distinguishing the inorganic minerals according to the grayscale value of each pixel in the grayscale image and the maximum probability weighted discrete variance principle; A second differentiation module is used to determine the second grayscale interval corresponding to the organic matter and the third grayscale interval corresponding to the pores according to the grayscale value in the combined grayscale interval in the grayscale image and the maximum probability weighted discrete variance principle, wherein the combined grayscale interval refers to the grayscale interval in the preset grayscale interval except the first grayscale interval; The determination module is used to determine the distribution of each sample component in the shale sample according to the grayscale interval to which the grayscale value of each pixel in the grayscale image belongs.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the method for determining the shale component distribution as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining shale component distribution according to any one of claims 1 to 7 are executed.

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