CN113450371A - Reservoir microfracture identification method and device and storage medium - Google Patents
Reservoir microfracture identification method and device and storage medium Download PDFInfo
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Abstract
The invention discloses a reservoir microfracture identification method, equipment and a storage medium, wherein the method comprises the following steps: acquiring a CT image of a reservoir, determining an optimal gray segmentation threshold according to pixel points in the gray level of the CT image of the reservoir, and performing binarization processing on the CT image according to the optimal gray segmentation threshold to obtain a binarization image containing micropores and microcracks; and calculating development characteristic parameters of the micropores and the microcracks according to the binary image, and extracting the microcracks in the binary image according to the development characteristic parameters of the micropores and the microcracks. The method solves the technical problem that the microcracks in unconventional oil and gas reservoirs cannot be effectively identified and extracted in the prior art.
Description
Technical Field
The invention relates to the technical field of oil exploration, in particular to a reservoir microcrack identification method, equipment and a storage medium.
Background
Reservoir fractures can be mainly divided into open fractures and micro fractures, wherein the micro fractures are main channels of oil-gas seepage and are important bridges and ties for effectively connecting micro pores and macro fractures, and the analysis and research work of the micro fractures is very important in the field of resource exploration of compact oil, compact gas, shale oil, shale gas and the like.
The development of a large number of natural fractures is very beneficial to the exploration and development of unconventional oil and gas in the later period, and the development condition of the microcracks is quantitatively represented by reservoir analysis, so that the method has a key guiding significance for selecting favorable fracturing horizons and intervals of the unconventional oil and gas. At present, the research on the cracks in the reservoir mostly stays in qualitative observation and analysis, and the micro cracks are generally researched together with micro pores when being analyzed based on micro electron microscope images and CT images, so that the micro cracks and the micro pores are difficult to be effectively separated.
Therefore, there is currently no effective means to quantitatively identify and extract microfractures in unconventional hydrocarbon reservoirs.
Disclosure of Invention
The invention aims to overcome the technical defects, provides a reservoir microfracture identification method, equipment and a storage medium, and solves the technical problem that microfractures in unconventional oil and gas reservoirs cannot be effectively identified and extracted in the prior art.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a reservoir microfracture identification method, which comprises the following steps:
acquiring a CT image of a reservoir, determining an optimal gray segmentation threshold value according to pixel points in each gray level of the CT image of the reservoir, and performing binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarization image containing micropores and microcracks;
and calculating development characteristic parameters of the micropores and the microcracks according to the binary image, and extracting the microcracks in the binary image according to the development characteristic parameters of the micropores and the microcracks.
Preferably, in the reservoir microfracture identification method, the reservoir microfracture identification method is implemented by the aid of a computer
The acquiring a CT image of a reservoir, determining an optimal gray segmentation threshold value according to pixel points in each gray level of the CT image of the reservoir, and performing binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarization image containing micropores and microcracks specifically comprises:
acquiring a CT image of a reservoir, and preprocessing the CT image;
the method comprises the steps of obtaining the number of pixel points of each gray level in a preprocessed CT image, calculating an optimal gray level segmentation threshold value according to the number of the pixel points of each gray level in the CT image, and carrying out binarization processing on the CT image according to the optimal gray level segmentation threshold value to obtain a binarization image containing micropores and microcracks.
Preferably, in the reservoir microcrack identifying method, the acquiring the number of pixel points of each gray level in the preprocessed CT image, calculating an optimal gray level segmentation threshold according to the number of pixel points of each gray level in the CT image, and performing binarization processing on the CT image according to the optimal gray level segmentation threshold to obtain a binarized image containing microvoids and microcracks specifically includes:
acquiring the number of pixel points of each gray level in the preprocessed CT image, and calculating the gray level distribution probability of each gray level;
determining an average gray scale calculation formula and a variance calculation formula of a high gray scale target and a low gray scale target according to the gray scale distribution probability of each gray scale; wherein the high gray target and the low gray target are determined according to a gray segmentation threshold;
determining an intra-class variance calculation formula, an inter-class variance calculation formula and a total variance calculation formula according to the average gray scale calculation formula and the variance calculation formula of the high gray scale target and the low gray scale target;
determining an optimal gray segmentation threshold according to the intra-class variance calculation formula, the inter-class variance calculation formula and the total variance calculation formula;
and carrying out binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarization image containing micropores and microcracks.
Preferably, in the reservoir microfracture identification method, the average gray scale calculation formula of the high gray scale target is as follows:
the variance calculation formula of the high gray level target is as follows:
wherein, mu1Average gray scale of high gray scale object, PiIs the probability of gray distribution with gray level i, k is the threshold of gray division, P1Representing the probability of the gray distribution of a high gray object, p (k) and μ (k) being the cumulative occurrence probability and the average gray level of gray levels from 0 to k-1, respectively,
variance of high gray level target;
the average gray scale calculation formula of the low gray scale target is as follows:
the variance calculation formula of the high gray level target is as follows:
wherein, mu2Average gray of low gray object, PiIs the probability of gray distribution with gray level i, k is the threshold of gray division, P2Representing the probability of the gray distribution of a low gray object, p (k) and μ (k) being the cumulative probability of occurrence and the average gray level of gray levels from 0 to k-1, respectively, μ being the average gray level of the entire CT image,
is the variance of the low gray object.
Preferably, in the reservoir microfracture identification method, the intra-class variance calculation formula is as follows:
the inter-class variance calculation formula is as follows:
the total variance calculation formula is as follows:
wherein the content of the first and second substances,
is the variance within the class of the data,
is the between-class variance, σ2Is the total variance.
Preferably, in the reservoir microfracture identification method, the calculation formula of the optimal grayscale segmentation threshold is as follows:
K*＝Arg max0≤k≤L-1η(k)，
wherein, K*Representing the optimal gray scale division threshold.
Preferably, in the method for identifying reservoir microfractures, the developmental characteristic parameters at least include a shape factor and a roundness.
Preferably, in the reservoir microcrack identification method, the calculating a development characteristic parameter of a microvia and a microcrack according to the binarized image, and extracting a microcrack in the binarized image according to the development characteristic parameter of the microvia and the microcrack further include:
and carrying out quantitative characterization analysis on the legal instrument characteristics of the microcracks according to the microcracks in the extracted binary image.
In a second aspect, the present invention also provides a reservoir microfracture identification device, comprising: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the reservoir microfracture identification method as described above.
In a third aspect, the present invention also provides a computer readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the steps in the reservoir microfracture identification method as described above.
Compared with the prior art, the reservoir microcrack identification method, the reservoir microcrack identification device and the storage medium provided by the invention have the advantages that the grayscale segmentation threshold value is determined according to the number of the pixel points of each grayscale in the CT image by acquiring the CT image of the reservoir, so that microporosities and microcracks can be separated from background mineral information according to the grayscale segmentation threshold value, and then the microcracks are independently extracted according to the development characteristic parameters of the microporosities and the microcracks, such as the morphological characteristics of the microcracks and the microporosities, so that the microcracks in the binary image are extracted, the microcracks in an unconventional oil and gas reservoir are effectively identified and extracted, and the development conditions of the microcracks can be conveniently analyzed by workers.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of a reservoir microfracture identification method provided by the present invention;
FIG. 2 is a schematic view of an embodiment of a core plug sample of the present disclosure;
FIG. 3 is a schematic representation of a preferred embodiment of a CT image of a reservoir of the present invention;
FIG. 4 is a flowchart of an embodiment of step S100 shown in FIG. 1;
FIG. 5 is a schematic diagram illustrating an embodiment of denoising a CT image according to a straight filtering method in the present invention;
FIG. 6 is a schematic diagram of a CT image after denoising processing according to a preferred embodiment of the present invention;
FIG. 7 is a flowchart of one embodiment of step S120 of FIG. 1;
FIG. 8 is a schematic view of a preferred embodiment of the present invention for microcrack identification;
FIG. 9 is a distribution plot of one embodiment of the microcrack opening evaluation of the present invention;
FIG. 10 is a schematic view of a preferred embodiment of a reservoir microfracture identification apparatus of the present invention;
FIG. 11 is a schematic diagram of an operating environment of a preferred embodiment of the reservoir microfracture identification process of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a reservoir microfracture identification method provided by an embodiment of the present invention includes the following steps:
s100, obtaining a CT image of a reservoir, determining an optimal gray segmentation threshold value according to pixel points in the gray level of the CT image of the reservoir, and performing binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarization image containing micropores and microcracks;
s200, calculating development characteristic parameters of the micro-pores and the micro-cracks according to the binary image, and extracting the micro-cracks in the binary image according to the development characteristic parameters of the micro-pores and the micro-cracks.
In this embodiment, the reservoir microfracture identification method is suitable for reservoir microfracture identification, and may be used in reservoir microfracture identification equipment, such as a computer, a notebook, a palm computer, and the like for identifying reservoir microfractures.
According to the embodiment of the invention, the CT image of the reservoir is obtained, the gray segmentation threshold is determined according to the number of the pixel points of each gray level in the CT image, so that the micro-pores and the micro-cracks can be separated from the background mineral information according to the gray segmentation threshold, and then the micro-cracks are extracted independently according to the development characteristic parameters of the micro-pores and the micro-cracks, such as the morphological characteristics of the micro-cracks and the micro-pores, so that the micro-cracks in the binary image are extracted, the micro-cracks in an unconventional oil and gas reservoir are effectively identified and extracted, and the development condition of the micro-cracks can be conveniently analyzed by a worker.
In a preferred embodiment, in step S100, since the micro-fracture development scale is significantly lower than the macro-fracture, the observation of the micro-fracture is usually performed on a core sample with a smaller sample. Therefore, before acquiring a CT image of a reservoir, firstly, a drill core sample or a field fresh reservoir sample is acquired, the specification of the reservoir sample needs to be at least larger than that of a plunger sample to be acquired, and the acquisition of the nano CT image is based on analysis of the small plunger sample. In one embodiment, the format of the small plug sample is shown in FIG. 2 as a regular cylinder with a base diameter of 25mm and a height of 50 mm. Because tight reservoirs develop microscopic pores and fractures, rock samples are often fractured using conventional drilling machines, and therefore drilling of plunger samples is performed using a diamond wire cutter to ensure complete drilling and smooth boundaries of small plunger samples.
When the plunger sample is obtained, projection imaging is carried out through X-rays with different incidence angles, and then the graphs with different layers can be separated in two-dimensional imaging. After the incidence of the X-rays at different angles, a series of two-dimensional projection images of the sample are obtained, and the gray values of the images represent the absorption coefficients of different positions of the sample, which have direct relation with the material structure at the positions. For the observation of a small plug sample of reservoir rock, the gray value of micro pores and cracks is close to 255, and the background of an image view field except the micro pores and the micro cracks presents background areas with different gray levels due to various mineral types and large content change in the rock, as shown in fig. 3. Once the CT image is obtained, step S100 can be performed. Specifically, referring to fig. 4, the step S100 specifically includes:
s110, acquiring a CT image of a reservoir, and preprocessing the CT image;
s120, obtaining the number of pixel points of each gray level in the preprocessed CT image, calculating an optimal gray level segmentation threshold value according to the number of the pixel points of each gray level in the CT image, and performing binarization processing on the CT image according to the optimal gray level segmentation threshold value to obtain a binarization image containing micro-pores and micro-cracks.
In this embodiment, before performing image analysis, the image needs to be preprocessed to facilitate obtaining of the pixel points, specifically, step S110 specifically includes:
and acquiring a CT image of the reservoir, and performing noise reduction on the CT image by adopting a median filtering method.
Specifically, due to the influence of factors such as uneven radiation diffraction, impurities, bright shadows and the like, the quality of the acquired CT image is reduced, and the difficulty in feature identification is increased. Therefore, it is necessary to reduce or eliminate these disturbances (or noises) and to preprocess the image. The method for filtering noise mainly comprises a median filtering algorithm and a mean filtering algorithm. The mean filtering often filters noise and blurs the edges of the cracks, so that the median filtering method is preferably used for carrying out noise reduction on the CT image, and the edges of the cracks can be protected while the noise is removed.
On the image, the pixels to be processed are given a template comprising its surrounding neighboring pixels. The method replaces the original pixel value with the average value of all pixels in the template. Arranging all pixel values in the filter range from small to large, selecting a median value of the sequencing sequence as a new pixel value at the center of the filter, moving the filter to the next position, and repeating the operation of sequencing and taking the median value until all pixel points of the image are corresponded by the center of the filter, as shown in fig. 5. By the noise reduction process, many noise points in the CT image can be effectively reduced, as shown in fig. 6.
In a preferred embodiment, since the micro cracks and pores have gray values substantially identical (about 255), and the color in the CT image is black, which is different from the color of the mineral mechanism in the background, the identification and extraction of the micro cracks and pores in the CT image can be realized by setting the threshold, so that the embodiment of the present invention realizes the setting of the gray segmentation threshold through step S200, and then the extraction of the micro pores and the micro cracks can be performed according to the gray segmentation threshold. Specifically, referring to fig. 7, the step S120 specifically includes:
s121, acquiring the number of pixel points of each gray level in the preprocessed CT image, and calculating the gray level distribution probability of each gray level;
s122, determining an average gray scale calculation formula and a variance calculation formula of a high gray scale target and a low gray scale target according to the gray scale distribution probability of each gray scale; wherein, the high gray target and the low gray target are determined according to the optimal gray segmentation threshold;
s123, determining an intra-class variance calculation formula, an inter-class variance calculation formula and a total variance calculation formula according to the average gray scale calculation formula and the variance calculation formula of the high gray scale target and the low gray scale target;
s124, determining an optimal gray segmentation threshold according to the intra-class variance calculation formula, the inter-class variance calculation formula and the total variance calculation formula;
and S125, performing binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarized image containing micropores and microcracks.
In this embodiment, let the CT image f (x, y) have L-level gray, and the number of pixels with gray level i be niIf the total number of pixels is N, the probability of gray distribution of each gray level is Pi＝ni/N，Pi≥0，
Assume that a target R is divided into low gradations by a gradation division threshold k1(i.e., micro-pores and micro-cracks) and high-gray background R2(i.e. background mineral information), the probability, the average gray level and the variance value of the two types of targets can be further obtained, and specifically, the average gray level calculation formula of the high-gray level target is as follows:
the variance calculation formula of the high gray level target is as follows:
wherein, mu1Average gray scale of high gray scale object, PiIs the probability of gray distribution with gray level i, k is the threshold of gray division, P1Representing the probability of the gray distribution of a high gray object, p (k) and μ (k) being the cumulative occurrence probability and the average gray level of gray levels from 0 to k-1, respectively,
is the variance of the high gray object.
The average gray scale calculation formula of the low gray scale target is as follows:
the variance calculation formula of the high gray level target is as follows:
wherein, mu2Average gray of low gray object, PiIs the probability of gray distribution with gray level i, k is the threshold of gray division, P2Representing the probability of the gray distribution of a low gray object, p (k) and μ (k) being the cumulative probability of occurrence and the average gray level of gray levels from 0 to k-1, respectively, μ being the average gray level of the entire CT image,
is the variance of the low gray object.
In a further embodiment, after obtaining the average gray scale calculation formula and the variance calculation formula of the high gray scale target and the low gray scale target, an intra-class variance calculation formula, an inter-class variance calculation formula, and a total variance calculation formula can be obtained, specifically, the intra-class variance calculation formula is:
the inter-class variance calculation formula is as follows:
the total variance calculation formula is as follows:
wherein the content of the first and second substances,
is the variance within the class of the data,
is the between-class variance, σ2Is the total variance.
Further, in order to obtain the optimal gray-scale division threshold, it is necessary to maximize the inter-class variance, and therefore, the optimal gray-scale division threshold is calculated by the following formula:
K*＝Arg max0≤k≤L-1η(k)，
wherein, K*Representing the optimal gray scale division threshold.
Calculating the optimal gray segmentation threshold K according to the formula*Then, the background mineral information and the micro pore gaps can be effectively segmented, and the binarization image information of the micro pore gaps and the micro cracks can be obtained.
In a preferred embodiment, in step S200, the developmental characteristic parameters include at least a shape factor and a circularity. Specifically, in view of the fact that the gray levels of the micro-pores and the micro-cracks are basically consistent, the micro-crack information is difficult to effectively and independently extract only based on the optimal gray level segmentation threshold segmentation, the morphological characteristics of the micro-cracks and the micro-pores are obviously different, the micro-cracks are usually long, and parameters such as length-width ratios are obviously different from those of the micro-pores, so that the micro-cracks and the micro-pores can be effectively distinguished based on development characteristic parameters such as shape factors and roundness. The calculation formulas of the shape factor and the roundness are as follows:
form factor: f-4 pi S/L2，
Wherein S is the area of a communication region; l is its perimeter.
Roundness: p ∑ Σ R/N · R,
wherein, r1+r2+r3……+rnThe sum of the measurement radiuses of the roundness on the maximum projection surface of the curvature target of each corner of the hole crack is shown, R is the maximum inscribed circle radius in the contour of the target, and N is the number of the curvature radiuses of the measured corners.
By calculating the shape factor and the roundness, microcracks and microporosities can be distinguished according to the size of the shape factor and the roundness, so that microcracks can be effectively and independently extracted from a microcrack system, as shown in fig. 8.
In a preferred embodiment, after the step S200, the reservoir microfracture identification method further includes:
and carrying out quantitative characterization analysis on the legal instrument characteristics of the microcracks according to the microcracks in the extracted binary image.
In this embodiment, the opening, average area, linear density, and porosity of the microcracks are key quantitative parameters for quantitatively characterizing the microcrack development characteristics. More than 2 thousand two-dimensional slice images can be acquired based on the nano CT test, the operations of the steps S100 to S300 are carried out on each Image, batch processing can be carried out in the process, and the micro-crack extraction and characteristic information statistics of batch images can be realized by writing a macro program based on the software operations of Image J and Image pro plus. In a specific embodiment, the opening frequency distribution of the microcracks obtained by the CT image of the rock reservoir sample is shown in fig. 9, the opening of the microcracks is mainly concentrated at 0.3mm, and secondary peaks also appear at 0.5mm and 0.8mm openings, the opening of the microcracks of the rock sample is mostly less than <1.0mm, and only < 5% of the openings of the microcracks are greater than 1.0 mm.
Referring to fig. 10, based on the reservoir microfracture identification method, an embodiment of the present invention further provides a reservoir microfracture identification apparatus 300, which includes:
the first extraction module 310 is configured to obtain the number of pixel points of each gray level in the CT image, calculate an optimal gray level segmentation threshold according to the number of pixel points of each gray level in the CT image, and perform binarization processing on the CT image according to the optimal gray level segmentation threshold to obtain a binarized image including micro pores and micro cracks;
and a second extraction module 320, configured to calculate development characteristic parameters of the microvoids and the microcracks according to the binarized image, and extract the microcracks in the binarized image according to the development characteristic parameters of the microvoids and the microcracks.
Since the reservoir microfracture identification method has been described in detail above, it is not described herein again.
As shown in fig. 11, based on the reservoir microfracture identification method, the invention further provides a reservoir microfracture identification device, which may be a mobile terminal, a desktop computer, a notebook, a palm computer, a server and other computing devices. The reservoir microfracture identification device includes a processor 10, a memory 20, and a display 30. Fig. 10 shows only some of the components of the reservoir microfracture identification device, but it should be understood that not all of the shown components are required and that more or fewer components may be implemented instead.
The memory 20 may in some embodiments be an internal storage unit of the reservoir microfracture identification device, such as a hard disk or a memory of the reservoir microfracture identification device. The memory 20 may also be an external storage device of the reservoir microfracture identification device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the reservoir microfracture identification device. Further, the memory 20 may also include both internal and external storage units of the reservoir microfracture identification device. The memory 20 is used for storing application software installed in the reservoir microfracture identification device and various types of data, such as program codes for installing the reservoir microfracture identification device. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a reservoir microfracture identification program 40, and the reservoir microfracture identification program 40 is executable by the processor 10 to implement the reservoir microfracture identification method according to embodiments of the application.
The processor 10 may be, in some embodiments, a Central Processing Unit (CPU), microprocessor or other data Processing chip for executing program codes stored in the memory 20 or Processing data, such as executing the reservoir micro fracture identification method.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used to display information at the reservoir microfracture identification device and to display a user interface for visualization. The components 10-30 of the reservoir microfracture identification device communicate with each other over a system bus.
In an embodiment, the steps in the reservoir microfracture identification method described above are implemented when the processor 10 executes the reservoir microfracture identification program 40 in the memory 20.
In summary, according to the reservoir microcrack identification method, the reservoir microcrack identification device and the reservoir medium provided by the invention, the CT image of the reservoir is obtained, and the gray segmentation threshold is determined according to the number of the pixel points of each gray level in the CT image, so that the microporosity and the microcracks can be separated from the background mineral information according to the gray segmentation threshold, and then the microcracks are extracted independently according to the development characteristic parameters of the microporosity and the microcracks, such as the morphological characteristics of the microcracks and the microporosity, so that the microcracks in the binary image are extracted, the microcracks in the unconventional oil and gas reservoir are effectively identified and extracted, and the development condition of the microcracks can be conveniently analyzed by workers.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A reservoir microfracture identification method is characterized by comprising the following steps:
acquiring a CT image of a reservoir, determining an optimal gray segmentation threshold value according to pixel points in each gray level of the CT image of the reservoir, and performing binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarization image containing micropores and microcracks;
and calculating development characteristic parameters of the micropores and the microcracks according to the binary image, and extracting the microcracks in the binary image according to the development characteristic parameters of the micropores and the microcracks.
2. The reservoir microcrack identification method according to claim 1, wherein the acquiring a CT image of a reservoir, determining an optimal grayscale segmentation threshold according to pixel points in each grayscale of the CT image of the reservoir, and performing binarization processing on the CT image according to the optimal grayscale segmentation threshold to obtain a binarized image containing microcracks and microcracks specifically comprises:
acquiring a CT image of a reservoir, and preprocessing the CT image;
the method comprises the steps of obtaining the number of pixel points of each gray level in a preprocessed CT image, calculating an optimal gray level segmentation threshold value according to the number of the pixel points of each gray level in the CT image, and carrying out binarization processing on the CT image according to the optimal gray level segmentation threshold value to obtain a binarization image containing micropores and microcracks.
3. The reservoir microcrack identification method according to claim 2, wherein the obtaining of the number of pixel points of each gray level in the preprocessed CT image, calculating an optimal gray level segmentation threshold value according to the number of pixel points of each gray level in the CT image, and performing binarization processing on the CT image according to the optimal gray level segmentation threshold value to obtain a binarized image containing the microcracks and the microcracks specifically comprises:
acquiring the number of pixel points of each gray level in the preprocessed CT image, and calculating the gray level distribution probability of each gray level;
determining an average gray scale calculation formula and a variance calculation formula of a high gray scale target and a low gray scale target according to the gray scale distribution probability of each gray scale; wherein the high gray target and the low gray target are determined according to a gray segmentation threshold;
determining an intra-class variance calculation formula, an inter-class variance calculation formula and a total variance calculation formula according to the average gray scale calculation formula and the variance calculation formula of the high gray scale target and the low gray scale target;
determining an optimal gray segmentation threshold according to the intra-class variance calculation formula, the inter-class variance calculation formula and the total variance calculation formula;
and carrying out binarization processing on the CT image according to the optimal gray segmentation threshold value to obtain a binarization image containing micropores and microcracks.
4. The reservoir microfracture identification method of claim 3, wherein the average gray scale calculation formula of the high gray scale target is:
the variance calculation formula of the high gray level target is as follows:
wherein, mu1Average gray scale of high gray scale object, PiIs the probability of gray distribution with gray level i, k is the threshold of gray division, P1Representing the probability of the gray distribution of a high gray object, p (k) and μ (k) being the cumulative occurrence probability and the average gray level of gray levels from 0 to k-1, respectively,
variance of high gray level target;
the average gray scale calculation formula of the low gray scale target is as follows:
the variance calculation formula of the high gray level target is as follows:
wherein, mu2Average gray of low gray object, PiIs the probability of gray distribution with gray level i, k is the threshold of gray division, P2Representing the probability of the gray distribution of a low gray object, p (k) and μ (k) being the cumulative probability of occurrence and the average gray level of gray levels from 0 to k-1, respectively, μ being the average gray level of the entire CT image,
is the variance of the low gray object.
5. The reservoir microfracture identification method of claim 4, wherein the intra-class variance calculation formula is:
the inter-class variance calculation formula is as follows:
the total variance calculation formula is as follows:
wherein the content of the first and second substances,
is the variance within the class of the data,
is the between-class variance, σ2Is the total variance.
6. The reservoir microfracture identification method of claim 5, wherein the optimal gray scale segmentation threshold is calculated by the formula:
K*＝Arg max0≤k≤L-1η(k)，
wherein, K*Representing the optimal gray scale division threshold.
7. The method of identifying reservoir microfractures as claimed in claim 1 wherein the developmental characteristic parameters include at least a shape factor and roundness.
8. The reservoir microcrack identification method according to claim 1, wherein the calculating of the development characteristic parameters of the microvoids and the microcracks according to the binarized image, and the extracting of the microcracks in the binarized image according to the development characteristic parameters of the microvoids and the microcracks further comprises:
and carrying out quantitative characterization analysis on the development characteristics of the microcracks according to the extracted microcracks in the binary image.
9. A reservoir microfracture identification device, comprising: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, performs the steps in the method of reservoir microfracture identification as defined in any one of claims 1-8.
10. A computer readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of the method for reservoir microfracture identification as claimed in any one of claims 1-8.
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