CN109447944B - Lithofacies identification method and system for carbonate rock - Google Patents
Lithofacies identification method and system for carbonate rock Download PDFInfo
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Abstract
The invention provides a lithofacies identification method and system for carbonate rocks. The lithofacies identification method of the carbonate rock comprises the following steps: acquiring a color image of the carbonate rock; obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock; obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock; determining black pixels in the binary image according to the pixel values of the binary image; obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image; the lithofacies of the carbonate rock can be identified according to the rock structure number, the lithofacies of the carbonate rock can be accurately identified, and the identification working efficiency is improved.
Description
Technical Field
The invention relates to the field of geological exploration, in particular to a lithofacies identification method and system for carbonate rocks.
Background
The lithofacies logging identification method of carbonate rocks mainly comprises two main methods: the method is based on the conventional logging data identification method, and comprises a qualitative cross plot identification method and a quantitative mathematical statistics method such as a neural network, a support vector machine, a fuzzy theory and the like. And secondly, new logging technical data, such as electrical imaging logging for identifying lithofacies, electrical imaging plate method and the like, are utilized.
The method utilizes conventional logging data to identify the lithofacies, and has the biggest problems that the conventional logging data is low in resolution, thin layers and interbed layers cannot be identified, human factors are large, and the identification work efficiency is low.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a lithofacies identification method and a lithofacies identification system for carbonate rocks, so that the lithofacies of the carbonate rocks can be accurately identified, and the identification working efficiency is improved.
In order to achieve the above object, an embodiment of the present invention provides a method for identifying a lithofacies of a carbonate rock, including:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and identifying the lithofacies of the carbonate rock according to the rock formation number.
The embodiment of the invention also provides a lithofacies recognition system for carbonate rocks, which comprises:
the acquisition unit is used for acquiring a color image of the carbonate rock;
the gray level image unit is used for obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
the binary image unit is used for obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
a black pixel determination unit for determining a black pixel in the binary image from the pixel value of the binary image;
the rock structure number unit is used for obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and the lithofacies unit is used for identifying the lithofacies of the carbonate rock according to the rock structure number.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the following steps are implemented:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and identifying the lithofacies of the carbonate rock according to the rock formation number.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and identifying the lithofacies of the carbonate rock according to the rock formation number.
According to the carbonate rock lithofacies identification method and system, the color image of the carbonate rock is obtained, the gray image of the carbonate rock is obtained according to the color image of the carbonate rock, the binary image of the carbonate rock is obtained according to the gray image of the carbonate rock, the black pixels in the binary image are determined according to the pixel values of the binary image, the rock tectonic number is obtained according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image, and the lithofacies of the carbonate rock is identified according to the rock tectonic number.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for identifying lithofacies of carbonate rock in an embodiment of the present invention;
FIG. 2 is a schematic representation of a gray scale image of carbonate rock in an embodiment of the present invention;
FIG. 3 is a schematic representation of a binary image of carbonate rock in an embodiment of the invention;
FIG. 4a is a gray scale image of Mx13 wells at 4605m-4608m in an embodiment of the present invention;
FIG. 4b is a binary image of Mx13 wells at 4605m-4608m in an embodiment of the present invention;
FIG. 5a is a grayscale image of Mx13 wells at 4592m-4595m in an embodiment of the present invention;
FIG. 5b is a binary image of Mx13 wells at 4592m-4595m in an embodiment of the present invention;
FIG. 6A is a schematic illustration of a mudstone cloud in an embodiment of the invention;
FIG. 6B is a schematic diagram of a gray scale image of a mudstone cloud according to an embodiment of the present invention;
FIG. 6C is a schematic diagram of a binary image of a mudstone cloud according to an embodiment of the present invention;
FIG. 7A is a schematic illustration of a granular cloud in an embodiment of the present invention;
FIG. 7B is a schematic representation of a grayscale image of a granular cloud in an embodiment of the present invention;
FIG. 7C is a schematic representation of a binary image of a granular cloud in an embodiment of the present invention;
FIG. 8 is a schematic diagram of the lithofacies identification results of Mx13 wells at 4575m-4625m in an embodiment of the present invention;
fig. 9 is a block diagram showing a structure of a lithofacies recognition system for carbonate rocks in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In view of the fact that the prior art cannot accurately identify the lithofacies of the carbonate rocks and is low in working efficiency, the embodiment of the invention provides the method for identifying the lithofacies of the carbonate rocks so as to accurately identify the lithofacies of the carbonate rocks and improve the identification working efficiency. The present invention will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for identifying lithofacies of carbonate rock in an embodiment of the invention. FIG. 2 is a schematic diagram of a gray scale image of carbonate rock in an embodiment of the invention. FIG. 3 is a schematic diagram of a binary image of carbonate rock in an embodiment of the invention. As shown in fig. 1 to 3, the lithofacies identification method of carbonate rock may include:
s101: and acquiring a color image of the carbonate rock.
S102: and obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock.
S103: and obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock.
S104: and determining black pixels in the binary image according to the pixel values of the binary image.
S105: and obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image.
S106: and identifying the lithofacies of the carbonate rock according to the rock formation number.
The execution subject of the lithofacies identification method of carbonate rock shown in fig. 1 may be a computer. As can be seen from the process shown in fig. 1, the method for identifying the lithofacies of the carbonate rock according to the embodiment of the present invention first obtains the color image of the carbonate rock, obtains the gray level image of the carbonate rock according to the color image of the carbonate rock, obtains the binary image of the carbonate rock according to the gray level image of the carbonate rock, determines the black pixels in the binary image according to the pixel values of the binary image, obtains the rock formation number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image, and identifies the lithofacies of the carbonate rock according to the rock formation number, so that the lithofacies of the carbonate rock can be accurately identified, and the identification work efficiency is improved.
In an embodiment, S102 specifically includes: obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock by the following formula:
Gray＝R*0.299+G*0.587+B*0.114；
wherein Gray is a Gray value, R is a red value, G is a green value, and B is a blue value.
In an embodiment, S103 specifically includes: judging whether the gray value in each pixel of the gray image is greater than a first preset value or not; when the gray value of each pixel of the gray image is greater than a first preset value, adjusting the gray value to be 0, and setting the pixel value to be 0; when the gray value in each pixel of the gray image is less than or equal to the first preset value, the gray value is adjusted to 255 and the pixel value is 1.
The gray scale value of the gray scale image is generally divided into 256 levels of 0 to 255, wherein 0 is darkest (completely black), 255 is brightest (completely white), and the middle is transitional gray. The first preset value is 100.
In an embodiment, S104 specifically includes: and judging whether the pixel value of the pixel in the binary image is 0 or not. When the pixel value is 0, the pixel is a black pixel, representing a low resistivity region, such as a layer interface (as shown in fig. 6C), or a via, crack, etc. (as shown in fig. 7C). When the pixel value is 1, the pixel is a white pixel, representing a high resistivity region, such as a rock skeleton.
In one embodiment, the number of rock formations is obtained by the following formula:
wherein ARFNFMIFor number of rock formations, xpixavgIs the average value of the horizontal number of black pixels in the binary image, ypixavgThe average value of the longitudinal number of the black pixels in the binary image is shown.
In an embodiment, S106 specifically includes: judging whether the rock formation number is greater than a second preset value or not; when the rock formation number is larger than a second preset value, the lithofacies of the carbonate rock are argillite cloud lithofacies; and when the rock formation number is less than or equal to a second preset value, the lithofacies of the carbonate rock are granular cloud lithofacies.
Wherein the second preset value is 1. The mudstone cloud phase has the layered characteristics and comprises mudstone cloud, argillaceous mudstone cloud, mud powder mudstone cloud and the like; the granular dolomite phase has blocky characteristics and comprises granular dolomite, fine-grained dolomite, powder-grained dolomite, residual granular dolomite and the like, and mainly comprises coarse-grained dolomite.
One of the specific embodiments of the present invention is as follows:
the practical application of the invention is elaborated by taking the Mx13 well of Longwanggao group in Miao district of Miao in Sichuan basin as an example:
1. basic geological conditions and regional parameter conditions:
the Longwanomi group stratum in the Mirabilitum Moxi region in Sichuan basin is a set of marine carbonate rock stratum, and the lithology is mainly dolomite and a small amount of argillaceous dolomite. The method is characterized in that an electrical imaging logging data is measured by using a Schlumberger formation resistivity scanning logging imaging instrument (FMI, Formation MicroScanner Image), the coverage rate of the measured data is over 75%, and the application condition is met.
2. The implementation steps are as follows:
step 1: and acquiring a color image of the carbonate rock, and acquiring a gray image of the carbonate rock according to the color image of the carbonate rock.
Step 2: judging whether the gray value in each pixel of the gray image is greater than 100; when the gray value in each pixel of the gray image is more than 100, adjusting the gray value to 0; when the gradation value in each pixel of the gradation image is less than or equal to 100, the gradation value is adjusted to 255. FIG. 4a is a gray scale image of Mx13 wells at 4605m-4608m in an embodiment of the present invention. FIG. 4b is a binary image of Mx13 wells at 4605m-4608m in an embodiment of the present invention. FIG. 5a is a grayscale image of Mx13 wells at 4592m-4595m in an embodiment of the present invention. FIG. 5b is a binary image of Mx13 wells at 4592m-4595m in an embodiment of the present invention. As shown in fig. 4a to 5b, the digital image processing function of matlab software may be used to convert the gray image of the carbonate rock into a binary image of the carbonate rock, and specifically, the rgb2gray function may be used to convert the gray image into a binary image.
And step 3: and judging whether the pixel value of the pixel in the binary image is 0 or not. When the pixel value is 0, the pixel is a black pixel, and the black pixel in the binary image can be determined.
And 4, step 4: and obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image.
FIG. 6A is a schematic illustration of a mudstone cloud in an embodiment of the invention. Fig. 6B is a schematic diagram of a gray scale image of the mudstone cloud in the embodiment of the invention. Fig. 6C is a schematic diagram of a binary image of a mudstone cloud in an embodiment of the invention. As shown in fig. 6A to 6C, the mudstone facies has a thin interbedded feature with an average value of the transverse number of black pixels of 87 and an average value of the longitudinal number of 27 on the binary image, and has a rock formation number of 3.119 greater than 1. Fig. 7A is a schematic illustration of a granular cloud in an embodiment of the invention. Fig. 7B is a schematic grayscale image of the granular cloud rock according to an embodiment of the present invention. Fig. 7C is a schematic diagram of a binary image of a granular cloud in an embodiment of the present invention. As shown in fig. 7A to 7C, the particle cloud rock phase is a block-like feature whose average value of the horizontal number of black pixels on the binary image is 20, average value of the vertical number is 34, and whose rock formation number is less than 1 is 0.5893.
And 5: judging whether the rock structure number is more than 1; when the number of rock structures is more than 1, the lithofacies of the carbonate rock is a marbled rock facies; when the rock formation number is less than or equal to 1, the lithofacies of the carbonate rock are granular cloud lithofacies.
FIG. 8 is a schematic diagram of the lithofacies recognition results of an Mx13 well at 4575m-4625m in an embodiment of the invention. As shown in fig. 8, the 1 st trace in fig. 8 is a depth trace, the 2 nd trace to the 4 th trace are conventional logging curve traces, wherein the 2 nd trace is a natural gamma GR and a caliper curve CAL, the 3 rd trace is a neutron, density and sound wave trace, the 4 th trace is a deep resistivity RT and a flushing zone resistivity RXO, the 5 th trace is a core lithofacies histogram, the 6 th trace is a description of a core lithofacies, that is, the 5 th trace is a description, the 7 th trace is a rock formation number, the numerical value range is from 0 to 8, the middle vertical line is 1, more than 1 is a mudstone cloud lithofacies, and less than or equal to 1 is a granular cloud lithofacies. Lane 8 is an explanation of lithofacies types.
To sum up, the carbonatite facies identification method of the embodiment of the invention firstly obtains the color image of the carbonatite, obtains the gray level image of the carbonatite according to the color image of the carbonatite, obtains the binary image of the carbonatite according to the gray level image of the carbonatite, determines the black pixels in the binary image according to the pixel values of the binary image, obtains the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image, and finally identifies the facies of the carbonatite according to the rock structure number, so that the facies of the carbonatite can be accurately identified, and the identification work efficiency is improved.
Based on the same inventive concept, the embodiment of the invention also provides a lithofacies recognition system for carbonate rocks, and as the problem solving principle of the system is similar to that of the lithofacies recognition method for carbonate rocks, the implementation of the system can refer to the implementation of the method, and repeated parts are not described again.
Fig. 9 is a block diagram showing a structure of a lithofacies recognition system for carbonate rocks in an embodiment of the present invention. As shown in fig. 9, the lithofacies recognition system for carbonate rocks includes:
the acquisition unit is used for acquiring a color image of the carbonate rock;
the gray level image unit is used for obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
the binary image unit is used for obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
a black pixel determination unit for determining a black pixel in the binary image from the pixel value of the binary image;
the rock structure number unit is used for obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and the lithofacies unit is used for identifying the lithofacies of the carbonate rock according to the rock structure number.
In one embodiment, the binary image unit specifically includes:
the judging subunit is used for judging whether the gray value in each pixel of the gray image is greater than a first preset value or not;
a first adjusting subunit, configured to adjust the grayscale value to 0;
and the second adjusting subunit is used for adjusting the gray value to 255.
In one embodiment, the number of rock formations is obtained by the following formula:
wherein ARFNFMIFor number of rock formations, xpixavgIs the average value of the horizontal number of black pixels in the binary image, ypixavgThe average value of the longitudinal number of the black pixels in the binary image is shown.
In one embodiment, the lithofacies unit is specifically configured to:
judging whether the rock formation number is greater than a second preset value or not;
when the rock formation number is larger than a second preset value, the lithofacies of the carbonate rock are argillite cloud lithofacies;
and when the rock formation number is less than or equal to a second preset value, the lithofacies of the carbonate rock are granular cloud lithofacies.
To sum up, the facies identification system of the carbonate rocks in the embodiment of the invention firstly obtains the color image of the carbonate rocks, obtains the gray level image of the carbonate rocks according to the color image of the carbonate rocks, obtains the binary image of the carbonate rocks according to the gray level image of the carbonate rocks, determines the black pixels in the binary image according to the pixel values of the binary image, obtains the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image, and finally identifies the facies of the carbonate rocks according to the rock structure number, so that the facies of the carbonate rocks can be accurately identified, and the identification work efficiency is improved.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the following steps are implemented:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and identifying the lithofacies of the carbonate rock according to the rock formation number.
To sum up, the computer device of the embodiment of the invention firstly obtains the color image of the carbonate rock, obtains the gray level image of the carbonate rock according to the color image of the carbonate rock, obtains the binary image of the carbonate rock according to the gray level image of the carbonate rock, determines the black pixels in the binary image according to the pixel values of the binary image, obtains the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image, and finally identifies the lithofacies of the carbonate rock according to the rock structure number, so that the lithofacies of the carbonate rock can be accurately identified, and the identification work efficiency is improved.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
and identifying the lithofacies of the carbonate rock according to the rock formation number.
To sum up, the computer-readable storage medium of the embodiment of the present invention first obtains a color image of carbonate rock, obtains a gray level image of the carbonate rock according to the color image of the carbonate rock, obtains a binary image of the carbonate rock according to the gray level image of the carbonate rock, determines a black pixel in the binary image according to a pixel value of the binary image, obtains a rock formation number according to an average value of longitudinal numbers of the black pixel in the binary image and an average value of transverse numbers of the black pixel in the binary image, and finally identifies a lithofacies of the carbonate rock according to the rock formation number, so that the lithofacies of the carbonate rock can be accurately identified, and the identification work efficiency is improved.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A lithofacies identification method for carbonate rocks, comprising:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
identifying lithofacies of the carbonate rock according to the rock formation number;
the rock formation number is obtained by the following formula:
wherein ARFNFMIFor number of rock formations, xpixavgIs the average value of the horizontal number of black pixels in the binary image, ypixavgThe average value of the longitudinal number of the black pixels in the binary image is obtained;
identifying facies of carbonate rock from the number of rock formations comprises:
judging whether the rock structure number is larger than a second preset value or not;
when the rock formation number is larger than the second preset value, the lithofacies of the carbonate rock are argillite lithofacies;
and when the rock formation number is less than or equal to the second preset value, the lithofacies of the carbonate rock are granular cloud lithofacies.
2. The method for identifying the lithofacies of the carbonate rocks according to claim 1, wherein obtaining a binary image of the carbonate rocks according to the gray-scale image of the carbonate rocks specifically comprises:
judging whether the gray value in each pixel of the gray image is larger than a first preset value or not;
when the gray value of each pixel of the gray image is greater than the first preset value, adjusting the gray value to be 0;
and when the gray value of each pixel of the gray image is less than or equal to the first preset value, adjusting the gray value to 255.
3. A lithofacies identification system for carbonate rock, comprising:
the acquisition unit is used for acquiring a color image of the carbonate rock;
the gray level image unit is used for obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
the binary image unit is used for obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
a black pixel determination unit for determining a black pixel in the binary image from a pixel value of the binary image;
the rock structure number unit is used for obtaining a rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
the lithofacies unit is used for identifying the lithofacies of the carbonate rock according to the rock structure number;
the rock formation number is obtained by the following formula:
wherein ARFNFMIFor number of rock formations, xpixavgIs the average value of the horizontal number of black pixels in the binary image, ypixavgThe average value of the longitudinal number of the black pixels in the binary image is obtained;
the lithofacies unit is specifically configured to:
judging whether the rock structure number is larger than a second preset value or not;
when the rock formation number is larger than the second preset value, the lithofacies of the carbonate rock are argillite lithofacies;
and when the rock formation number is less than or equal to the second preset value, the lithofacies of the carbonate rock are granular cloud lithofacies.
4. The system for lithofacies recognition of carbonate rock of claim 3, wherein the binary image unit specifically comprises:
the judging subunit is used for judging whether the gray value in each pixel of the gray image is greater than a first preset value or not;
a first adjusting subunit, configured to adjust the grayscale value to 0;
and the second adjusting subunit is used for adjusting the gray value to 255.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
identifying lithofacies of the carbonate rock according to the rock formation number;
the rock formation number is obtained by the following formula:
wherein ARFNFMIFor number of rock formations, xpixavgIs the average value of the horizontal number of black pixels in the binary image, ypixavgThe average value of the longitudinal number of the black pixels in the binary image is obtained;
identifying facies of carbonate rock from the number of rock formations comprises:
judging whether the rock structure number is larger than a second preset value or not;
when the rock formation number is larger than the second preset value, the lithofacies of the carbonate rock are argillite lithofacies;
and when the rock formation number is less than or equal to the second preset value, the lithofacies of the carbonate rock are granular cloud lithofacies.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of:
acquiring a color image of the carbonate rock;
obtaining a gray level image of the carbonate rock according to the color image of the carbonate rock;
obtaining a binary image of the carbonate rock according to the gray level image of the carbonate rock;
determining black pixels in the binary image according to the pixel values of the binary image;
obtaining the rock structure number according to the average value of the longitudinal number of the black pixels in the binary image and the average value of the transverse number of the black pixels in the binary image;
identifying lithofacies of the carbonate rock according to the rock formation number;
the rock formation number is obtained by the following formula:
wherein ARFNFMIFor number of rock formations, xpixavgIs the average value of the horizontal number of black pixels in the binary image, ypixavgThe average value of the longitudinal number of the black pixels in the binary image is obtained;
identifying facies of carbonate rock from the number of rock formations comprises:
judging whether the rock structure number is larger than a second preset value or not;
when the rock formation number is larger than the second preset value, the lithofacies of the carbonate rock are argillite lithofacies;
and when the rock formation number is less than or equal to the second preset value, the lithofacies of the carbonate rock are granular cloud lithofacies.
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