CN110910411B - Shale crack automatic extraction method with size self-adaption function - Google Patents
Shale crack automatic extraction method with size self-adaption function Download PDFInfo
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Abstract
The embodiment of the invention discloses a size-adaptive shale fracture extraction method, which comprises the following steps: scanning electron microscope imaging and filtering pretreatment of a sample; determining a threshold value, carrying out threshold segmentation, dividing elements of an electron microscope image of the shale sample into four components, and eliminating a white edge (abnormal increase of gray scale caused by brightness enhancement at the periphery of a pore) by designing morphological operation; extracting fracture morphological features to distinguish fractures from pores; the method has the advantages that the method can accurately distinguish organic cracks and inorganic cracks and extract equivalent three-dimensional parameters, is favorable for accurately evaluating the pore structure and storage and transportation capacity of a reservoir of the shale sample, has high universality, can be used for researching shale samples of different types and different electron microscope scanning imaging qualities, and is short in measurement period, simple in processing process, low in cost and high in calculation efficiency and precision.
Description
Technical Field
The embodiment of the invention relates to the technical field of petroleum and natural gas geological exploration, in particular to a size-adaptive shale crack automatic extraction method.
Background
Shale gas has a huge reserve as a clean unconventional energy source. The extraction of the shale pore structure characteristics is the basis for carrying out accurate reserve evaluation and pore network migration capability calculation and is also the key point of shale gas exploration and development research. The shale has complex content of components, a pore structure develops, and the main components are organic matters, inorganic matters and pyrite. Organic pores and organic cracks develop in the organic matters; inorganic pores and inorganic cracks develop in the inorganic substance.
The shale fracture components form a basic framework for shale artificial fracturing fracture development, are also main channels for shale gas migration, and the analysis of a shale fracture network is the key point of shale gas exploration and development research. In a complex pore network structure, the cracks and Kong Bansheng cannot be effectively distinguished by means of a traditional method. Organic components and inorganic components exist in the fracture system, adsorption and migration capacities of the organic components and the inorganic components have great difference, and the organic and inorganic components are distinguished, so that the method is a great difficulty in shale gas storage evaluation and accurate recoverable quantity evaluation.
The method for obtaining the pore structure of the porous medium mainly comprises a fluid injection method and a direct observation method. The fluid injection method is a pore structure characterization method established according to a Kelvin equation by means of low-temperature nitrogen adsorption, low-temperature carbon dioxide adsorption, mercury injection and the like, and is widely applied at present. The direct observation method is an observation method developed based on a microscopic technology represented by a scanning electron microscope, and can directly obtain visual and intuitive information of the shale section. Based on a reasonable image processing technology, the information contained in the image can be effectively quantitatively extracted.
However, the existing automatic shale fracture extraction method has the following defects:
(1) The shale fracture components are very complex, and compared with Kong Bansheng, the existing automatic extraction method for shale fractures cannot distinguish the fracture components, effective information data is not sufficiently extracted, and the reservoir pore structure and storage and transportation capacity of a shale sample cannot be accurately evaluated;
(2) The existing shale crack automatic extraction method has low applicability, cannot analyze and research shale samples of different types and different electron microscope scanning imaging qualities, and is not beneficial to research;
(3) The existing automatic extraction method for the shale cracks is complex in operation, low in calculation efficiency and precision, long in measurement period and capable of wasting a large amount of manpower and material resources.
Disclosure of Invention
Therefore, the shale fracture automatic extraction method with size self-adaption provided by the embodiment of the invention has the advantages that the pore structure parameters of the shale scanning electron microscope image are extracted by adopting a digital image processing mode, organic fractures and inorganic fractures can be accurately distinguished, equivalent three-dimensional parameters are extracted, the reservoir pore structure and storage and transportation capacity of the shale sample can be accurately evaluated, meanwhile, the method is high in universality, shale samples of different types and different electron microscope scanning imaging qualities can be researched, the research is facilitated, in addition, the method is short in measurement period, simple in processing process, low in cost, high in calculation efficiency and precision, manpower and material resources can be greatly saved, and the problems in the prior art can be effectively solved.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions: a size-adaptive shale fracture automatic extraction method comprises the following steps:
s100, processing a sample, scanning and imaging, and preprocessing an electron microscope image;
s200, threshold segmentation, namely dividing elements of the shale sample electron microscope image into four components, and eliminating 'white edges';
s300, connecting the hole seams, extracting the length and the width of the hole seams, and distinguishing cracks and holes according to the length and the width of the hole seams;
s400, extracting the component content and size information of the fracture, performing data processing, and obtaining each characterization parameter of the shale pore structure.
Further, in step S100, the method specifically includes:
s101, selecting a sample, and performing sample cutting, surface grinding and polishing treatment to obtain a sample slice with a size required by the observation of a scanning electron microscope;
s102, obtaining a series of scanning electron microscope images of the shale sample with a representative volume scale through scanning electron microscope observation;
and S103, preprocessing the image, cutting off the black edge of the image, and filtering the electron microscope image.
Further, in step S103, the method specifically includes:
extracting four vertex angles of a rectangular region with the image gray value not being 0, reserving the region in the rectangle, and cutting out the region outside the rectangle;
the method comprises the following steps of firstly utilizing mean filtering to eliminate the non-smoothness of the shape scanning of the cut image, and then utilizing median filtering to eliminate the salt and pepper noise of the image, and specifically comprises the following steps:
and (3) carrying out mean value filtering on the trimmed image: generating a 3 × 3 (or 5 × 5) template, calculating an average value of 8 (or 24) pixels (excluding the target point itself) around the target point, replacing the pixel value of the target point with the average value, and then sequentially calculating the average value of all the pixels of the image to obtain a filtered image;
median filtering the pruned image: and generating a 3 × 3 (or 5 × 5) template, solving a median value of 8 (or 24) pixels (excluding the target point) around the target point, replacing the pixel value of the target point with the median value, and then solving the median values of all pixel points of the image in sequence to obtain the filtered image.
Further, in step S200, the method specifically includes:
s201, selecting a representative image in the cut image, namely an image containing more pores, organic matters, inorganic matters and pyrite;
s202, respectively adjusting and distinguishing three threshold values of the four components by using a binarization method, namely a pore threshold value, a segmentation threshold value of organic matters and inorganic matters and a segmentation threshold value of inorganic matters and pyrite;
and S203, after the three thresholds are obtained, carrying out threshold segmentation on the scanning electron microscope images of the samples, and dividing the elements of each image into four components, namely holes, organic matters (including holes), inorganic matters (including holes) and pyrite.
Further, in step S200, the specific steps of performing "white edge" elimination are:
sequentially marking each communicated pyrite region in the sample image, counting the area of each block, and extracting the boundary coordinate, the barycentric coordinate, the long axis length, the short axis length and the equivalent radius of each pyrite block;
calculating the ratio of the long axis to the short axis of each pyrite block;
counting the average gray value of 9 pixel points in the 3 multiplied by 3 area size near the center of gravity point;
if the ratio of the long axis to the short axis of the pyrite block is larger than 10 or the average gray value of 9 pixel points near the gravity center is smaller than the pyrite threshold, determining that the pyrite block is actually the 'white edge' interference at the periphery of the hole;
finding out a minimum rectangle containing the block according to the boundary coordinates, and giving out coordinates of four vertexes of the rectangle;
expanding the equivalent radius size outwards from the extracted minimum rectangle to obtain a new rectangle as the neighborhood of the hole;
when a certain outwards expanded neighborhood edge exceeds the boundary, inwards shrinking according to the step length of 3 pixels until the shrinking is stopped when the first requirement that the boundary range is not exceeded, and the shrinking processes of all edges are mutually independent;
counting the neighborhoods of the communicating blocks, and respectively calculating the content of organic matters and inorganic matters in each neighborhood, particularly the content of the organic matters and the inorganic matters in the region contacted with the pyrite region;
judging the real composition of the white edge according to the relative content of the organic matter component and the inorganic matter component in the neighborhood, if the organic matter content is high, considering the white edge as the organic matter, and filling the white edge area as the gray value of the organic matter; if the inorganic matter content is high, the real component of the "white edge" is considered to be inorganic matter, and the "white edge" region is filled with the gray value of the inorganic matter.
Further, in step S300, the manner of the hole-seam connection is to perform directional expansion operation, open the crack blocked by the small impurities or noise, and remove the impurities in the crack direction, which specifically includes:
traversing and extracting the direction, length and width of a single pore by using a regionprops method, performing size expansion according to a rectangle along the pore direction, wherein the epitaxial length at two ends along the pore direction is 1/4 of the length of the crack, and the width of the epitaxial length is the width of the crack, so as to obtain a directional expansion area of the pore;
searching for the hole seams in the directional expansion area, and if the included angle between the distribution direction of the searched hole seams and the distribution direction of the original hole seams is within +/-30 degrees, determining that the searched hole seams are the same hole seams;
after finding the same aperture, the smallest rectangle containing the two block segments is extracted, and the two blocks are closed in the smallest rectangle with the radius of 1/4 of the length of the large aperture, so that the two blocks are fused.
Further, in step S300, the step of distinguishing the cracks from the holes is:
performing closed operation on the extracted hole seam to fill the small concave block, and then performing open operation to eliminate the protruding burrs;
extracting a minimum rectangle where the hole seam is located, and extracting the length of a diagonal line of the rectangle as the length of the hole seam;
extracting the pixel area of the hole, and dividing the pixel area by the length of the hole to obtain the equivalent width of the hole;
dividing the length by the width by the power of 1.1 to obtain a judgment proportion parameter, and when the parameter is less than a preset proportion number, considering the hole seam as a hole; and when the parameter is larger than the preset proportional number, judging that the hole gap is a crack, and respectively marking the judgment result.
Further, according to the marking results of the holes and the cracks, extracting the minimum rectangle containing each block, extracting internal component information, and dividing the cracks into organic cracks and inorganic cracks, specifically:
extracting a minimum rectangular area according to the extracted boundary information;
expanding the equivalent radius size outwards from the extracted minimum rectangle to obtain a new rectangle as the neighborhood of the hole;
when a certain outwards expanded neighborhood edge exceeds the boundary, inwards shrinking according to the step length of 3 pixels until the shrinking is stopped when the first requirement that the boundary range is not exceeded, and the shrinking processes of all edges are mutually independent;
counting the neighborhoods of the communicating blocks, and respectively calculating the content of effective organic matters and inorganic matters in the neighborhoods, particularly the content of the organic matters and the inorganic matters in the areas contacted with the cracks;
judging the component attribute of the crack according to the relative content of the organic matter component and the inorganic matter component in the neighborhood, and if the organic matter content is high, considering the crack as an organic crack; if the inorganic matter content is high, the crack is considered to be an inorganic crack.
Further, in step S400, the fracture component content and size information includes: crack content, organic crack content, inorganic crack content, average organic crack length and width, average inorganic crack length and width, and average total crack length and width.
Further, in step S400, performing data processing on the extraction result, specifically, equivalently converting the two-dimensional scanning electron microscope extraction parameter into a three-dimensional parameter, including:
establishing a probability distribution function when the random section of the three-dimensional hole is projected to a two-dimensional plane:
wherein R is a two-dimensional aperture, R is a three-dimensional aperture, and P (R) represents the probability that the three-dimensional aperture with the aperture R is subjected to two-dimensional random cross-section projection to obtain the aperture with the aperture R;
average of probability distribution of two-dimensional random cross-sectional projection:
and (4) statistically dividing the obtained aperture size related value by pi/4, and then carrying out deep calculation and solving to complete the dimension-increasing conversion.
The embodiment of the invention has the following advantages:
(1) According to the invention, the pore structure parameters of the shale scanning electron microscope image are extracted by adopting a digital image processing method, so that organic cracks and inorganic cracks can be accurately distinguished, equivalent three-dimensional parameters are extracted, and the reservoir pore structure and storage and transportation capacity of a shale sample can be accurately evaluated;
(2) The method has higher universality, can be used for researching shale samples of different types and different electron microscope scanning imaging qualities, and is beneficial to research;
(3) The method has the advantages of short measurement period, simple processing process, lower cost, higher calculation efficiency and precision and capability of greatly saving manpower and material resources.
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 or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary and that other implementation drawings may be derived from the provided drawings by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
FIG. 1 is a schematic diagram of the overall flow structure of the present invention;
FIG. 2 is a flow chart of the algorithm of the present invention;
FIG. 3 is a schematic diagram of the organic fracture extraction results of the shale samples according to the present invention;
FIG. 4 is a schematic diagram of the result of extracting inorganic fractures from a shale sample according to the present invention;
FIG. 5 is a schematic view of the directional expansion of the present invention for a slot joint;
FIG. 6 is a schematic view of the directional expansion and fusion of the hole-to-seam joint of the present invention;
FIG. 7 is a schematic diagram of the minimum quadrilateral extraction of the aperture of the present invention;
FIG. 8 is a diagram illustrating neighborhood extraction corresponding to the aperture of the present invention.
Detailed Description
The present invention is described in terms of specific embodiments, and other advantages and benefits of the present invention will become apparent to those skilled in the art from the following disclosure. 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.
As shown in fig. 1 and fig. 2, the invention provides an automatic extraction method of a size-adaptive shale fracture, which comprises the following steps:
and S100, processing a sample, scanning and imaging, and preprocessing an electron microscope image.
In step S100, the method specifically includes:
and S101, selecting a sample, and performing sample cutting, surface grinding and polishing treatment to obtain a sample slice with the size required by the observation of a scanning electron microscope.
And S102, observing through a scanning electron microscope to obtain a series of scanning electron microscope images of the shale sample with a representative volume scale.
Cutting the obtained shale core into blocks of 1cm multiplied by 0.5cm, selecting a surface of 1cm multiplied by 0.5cm as an observation surface, grinding, and putting into argon ion polishing equipment for polishing. After polishing, the sample is placed in an observation bin of a scanning electron microscope device for scanning electron microscope observation, and a (REV) scanning electron microscope image reaching a representative size is obtained, wherein the size of the image in the embodiment is 398.5 μm × 398.5 μm.
And S103, preprocessing the image, cutting off the black edge of the image, and filtering the electron microscope image.
In step S103, the method specifically includes:
extracting four vertex angles of a rectangular region with the image gray value not being 0, reserving the region in the rectangle, and cutting out the region outside the rectangle; the method comprises the following steps of firstly utilizing mean filtering to eliminate the non-smoothness of the shape scanning of the cut image, and then utilizing median filtering to eliminate the salt and pepper noise of the image, and specifically comprises the following steps:
and (3) carrying out mean value filtering on the trimmed image: and generating a 3 × 3 (or 5 × 5) template, calculating an average value of 8 (or 24) pixels (excluding the target point itself) around the target point, replacing the pixel value of the target point with the average value, and then sequentially calculating the average value of all pixel points of the image to obtain the filtered image.
Median filtering the removed image: and generating a 3 × 3 (or 5 × 5) template, solving a median value of 8 (or 24) pixels (excluding the target point) around the target point, replacing the pixel value of the target point with the median value, and then solving the median values of all pixel points of the image in sequence to obtain the filtered image.
And S200, performing threshold segmentation, namely dividing elements of the shale sample electron microscope image into four components, and eliminating white edges.
In step S200, the method specifically includes:
step S201, selecting a representative image in the cut image, namely an image containing more pores, organic matters, inorganic matters and pyrite.
Step S202, three threshold values for distinguishing the four components, namely a pore threshold value, a partition threshold value of organic matters and inorganic matters and a partition threshold value of inorganic matters and pyrite, are respectively adjusted by a binarization method.
Adjusting and extracting the pore threshold, taking the gray critical values with different sizes as the threshold, and judging the quality of the gray value according to two standards: firstly, whether the extracted holes are consistent with those seen by naked eyes or not; and secondly, whether the obtained face porosity is close to the measured porosity or not. The best gray value is selected as the final division threshold, and the final value of the pore threshold in this embodiment is 70.
When the organic matter and inorganic matter partition threshold is selected, the grayscale critical values with different sizes larger than the pore threshold are taken for extraction test, the grayscale value of the extracted organic matter region closest to the result recognized by naked eyes is the basic threshold for distinguishing organic matter from inorganic matter, and in this embodiment, the final value of the basic threshold is 135.
When the inorganic substance and pyrite segmentation threshold is selected, the grayscale critical values with different sizes larger than the basic threshold are taken for extraction test, and the grayscale value with the closest result of the extracted pyrite region and the result identified by naked eyes is used as the pyrite threshold for distinguishing the inorganic substance and the pyrite, and the final value of the pyrite threshold in the embodiment is 205.
The main components in the shale sample in this embodiment are organic pores, inorganic pores, organic cracks, inorganic cracks, organic matter, inorganic matter, pyrite and other clay minerals, wherein the gray values of the organic pores, the inorganic pores, the organic cracks and the inorganic cracks are the smallest (black) and cannot be distinguished by a single gray value, the gray value of the organic matter is larger (black gray), the gray value of the inorganic matter is larger (gray), and the gray value of the pyrite is the largest (gray or white).
And S203, after the three threshold values are obtained, carrying out threshold segmentation on the scanning electron microscope images of the samples, and dividing elements of each image into four components, namely holes, organic matters (including holes), inorganic matters (including holes) and pyrite.
In step S200, the specific steps of performing "white edge" removal are:
and sequentially marking each communicated pyrite region in the sample image, counting the area of each block, and extracting the boundary coordinate, the barycentric coordinate, the long axis length, the short axis length and the equivalent radius of each pyrite block.
Calculating the ratio of the long axis length to the short axis length of each pyrite block; and counting the average gray value of 9 pixel points in the 3 multiplied by 3 area size near the center of gravity point.
And if the ratio of the long axis to the short axis of the pyrite block is larger than 10 or the average gray value of 9 pixel points near the gravity center is smaller than the pyrite threshold, determining that the pyrite block is substantially the white edge interference of the hole seam periphery.
Finding out a minimum rectangle containing the block according to the boundary coordinates, and giving out coordinates of four vertexes of the rectangle; and expanding the equivalent radius size outwards from the extracted minimum rectangle to obtain a new rectangle as the neighborhood of the hole.
When a certain outward expansion neighborhood edge meets the exceeding boundary, the contraction is inwards performed according to the step length of 3 pixels until the first time that the contraction does not exceed the boundary range is met, and the contraction processes of all edges are independent.
Counting the neighborhoods of the communicating blocks, and respectively calculating the content of organic matters and inorganic matters in each neighborhood, particularly the content of the organic matters and the inorganic matters in the region contacted with the pyrite region; judging the real composition of the white edge according to the relative content of the organic matter component and the inorganic matter component in the neighborhood, if the organic matter content is high, considering the white edge as the organic matter, and filling the white edge area as the gray value of the organic matter; if the inorganic matter content is high, the real component of the white edge is considered to be inorganic matter, and the white edge area is filled with the gray value of the inorganic matter.
And step S300, connecting the holes, extracting the length and the width of the holes, and distinguishing the cracks from the holes according to the length and the width of the holes.
In step S300, the manner of hole-seam connection is to perform directional expansion operation, open a crack blocked by a small piece of impurity or noise, and remove impurities in the crack direction, specifically including:
traversing and extracting the direction, length and width of a single pore by using a regionprops method, performing size expansion according to a rectangle along the pore direction, wherein the epitaxial length at two ends along the pore direction is 1/4 of the length of the crack, and the width of the epitaxial length is the width of the crack, so as to obtain a directional expansion area of the pore.
And searching for the pore gaps in the directional expansion area, and if the included angle between the distribution direction of the searched pore gaps and the distribution direction of the original pore gaps is within +/-30 degrees, determining that the searched pore gaps are the same pore gap.
After finding the same aperture, the smallest rectangle containing the two block segments is extracted, and the two blocks are closed in the smallest rectangle with the radius of 1/4 of the length of the large aperture, so that the two blocks are fused. Fig. 5 is a schematic view of directional expansion of hole-seam connection in the present embodiment, and fig. 6 is a schematic view of directional expansion and fusion of hole-seam connection in the present embodiment.
In step S300, the step of distinguishing the cracks from the holes is:
and performing closed operation on the extracted hole seam to fill the small concave block, and then performing open operation to eliminate the protruding burr.
Extracting a smallest rectangle with a hole seam, and extracting the length of a diagonal line of the rectangle as the length of the hole seam; and extracting the pixel area of the aperture, and dividing the pixel area by the aperture length to obtain the equivalent width of the aperture.
Dividing the length by the width by the power of 1.1 to obtain a judgment proportion parameter, and when the parameter is less than a preset proportion number, considering the hole seam as a hole; and when the parameter is larger than the preset proportional number, judging that the hole gap is a crack, and respectively marking the judgment result.
According to the marking results of the holes and the cracks, extracting the minimum rectangle containing each block, extracting internal component information, and dividing the cracks into organic cracks and inorganic cracks, specifically comprising the following steps:
extracting a minimum rectangular area according to the extracted boundary information, and fig. 7 is a schematic diagram of extracting a minimum quadrangle of the pore in the embodiment; the extracted minimum rectangle is expanded outward by the equivalent radius size to obtain a new rectangle as a neighborhood of the hole, and fig. 8 is a neighborhood extraction diagram corresponding to the aperture in this embodiment.
When a certain outwards expanded neighborhood edge exceeds the boundary, the neighborhood edge shrinks inwards according to the step length of 3 pixels until the first time that the shrinkage does not exceed the boundary range is met, and the shrinking processes of all the edges are independent.
And counting the neighborhoods of the communicating blocks, and respectively calculating the content of effective organic matters and inorganic matters in the neighborhoods, particularly the content of the organic matters and the inorganic matters in the areas contacted with the cracks.
Judging the component attribute of the crack according to the relative content of the organic matter component and the inorganic matter component in the neighborhood, and if the organic matter content is high, considering the crack as an organic crack; if the inorganic matter content is high, the crack is considered to be an inorganic crack. Fig. 3 is a schematic diagram of an extraction result of an organic fracture of the shale sample in this embodiment, and fig. 4 is a schematic diagram of an extraction result of an inorganic fracture of the shale sample in this embodiment.
And S400, extracting the component content and size information of the fracture, performing data processing, and obtaining each characterization parameter of the shale pore structure.
In step S400, the fracture component content and size information includes: crack content, organic crack content, inorganic crack content, average organic crack length and width, average inorganic crack length and width, and average total crack length and width. Counting the crack area according to the organic cracks and the inorganic cracks, and dividing the crack area by the total area of the sample image to obtain the organic crack content, the inorganic crack content and the total crack content; and respectively counting the single cracks according to the organic cracks and the inorganic cracks, extracting the single length and width, and counting to obtain the average value of the length and the width of the organic cracks, the average value of the length and the width of the inorganic cracks and the average value of the total length and the width of the cracks.
In step S400, the data processing is performed on the extraction result, specifically, the equivalent conversion of the two-dimensional scanning electron microscope extraction parameters into three-dimensional parameters includes:
establishing a probability distribution function when the random section of the three-dimensional hole is projected to a two-dimensional plane:
wherein R is a two-dimensional aperture, R is a three-dimensional aperture, and P (R) represents the probability that the three-dimensional aperture with the aperture R is subjected to two-dimensional random cross-section projection to obtain the aperture with the aperture R;
average of probability distribution of two-dimensional random cross-sectional projection:
and (4) statistically dividing the obtained aperture size related value by pi/4, and then carrying out deep calculation solving to complete the dimension-increasing conversion.
According to the method, the pore structure parameters of the shale scanning electron microscope image are extracted in a digital image processing mode, so that organic cracks and inorganic cracks can be accurately distinguished, equivalent three-dimensional parameters are extracted, the reservoir pore structure and storage and transportation capacity of the shale sample can be accurately evaluated, meanwhile, the method is high in universality, shale samples of different types and different electron microscope scanning imaging qualities can be researched, and research is facilitated.
Although the invention has been described in detail with respect to the general description and the specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (5)
1. A size-adaptive shale fracture automatic extraction method is characterized by comprising the following steps:
s100, sample processing, scanning imaging and image preprocessing;
s200, performing threshold segmentation, namely dividing elements of the shale sample electron microscope image into four components, and eliminating a white edge, wherein the white edge is a phenomenon that the pore edge gray value is abnormally increased due to the edge enhancement effect of a scanning electron microscope;
s300, connecting the hole seams, further extracting the length and the width of the hole seams, and distinguishing cracks and holes according to the length and the width;
the mode of aperture seam connection is for carrying out directional expansion operation, will be broken through by fritter impurity or noise crack of blocking, rejects the impurity in the crack direction, specifically includes:
traversing and extracting the direction, length and width of a single pore by using a region of MatLab, performing size expansion according to a rectangle along the pore direction, wherein the epitaxial lengths at two ends along the pore direction are 1/4 of the length of the crack, and the width of the crack is the width of the crack, so as to obtain a directional expansion area of the pore;
searching for the hole seams in the directional expansion area, and if the included angle between the distribution direction of the searched hole seams and the distribution direction of the original hole seams is within +/-30 degrees, determining that the searched hole seams are the same hole seams;
after finding the same aperture, extracting a minimum rectangle containing two block sections, and performing closed operation on the two blocks in the minimum rectangle, wherein the radius of the minimum rectangle is 1/4 of the length of the large aperture, so that the minimum rectangle is fused;
the steps of distinguishing the cracks and the holes are as follows:
performing closed operation on the extracted hole seam to fill the small concave block, and then performing open operation to eliminate the protruding burrs;
extracting a minimum rectangle where the hole seam is located, and extracting the length of a diagonal line of the rectangle as the length of the hole seam;
extracting the pixel area of the hole seam, and dividing the pixel area by the length of the hole seam to obtain the equivalent width of the hole seam;
dividing the length by the width to the power of 1.1 to obtain a judgment proportion parameter, and when the parameter is less than a preset proportion number, considering the hole seam as a hole; when the parameter is larger than the preset proportion number, judging that the hole seam is a crack, and respectively marking the judgment result;
according to the marking result of the holes and the cracks, extracting the minimum rectangle containing each block, extracting internal component information, and dividing the cracks into organic cracks and inorganic cracks, wherein the method specifically comprises the following steps:
extracting a minimum rectangular area according to the extracted boundary information;
expanding the equivalent radius size outwards from the extracted minimum rectangle to obtain a new rectangle as the neighborhood of the hole;
when a certain outwards expanded neighborhood edge exceeds the boundary, inwards shrinking according to the step length of 3 pixels until the shrinking is stopped when the first requirement that the boundary range is not exceeded, and the shrinking processes of all edges are mutually independent;
counting the neighborhoods of the communicating blocks, and respectively calculating the contents of effective organic matters and inorganic matters in the neighborhoods;
judging the component attribute of the crack according to the relative content of the organic matter component and the inorganic matter component in the neighborhood, and if the organic matter content is high, considering the crack as an organic crack; if the inorganic matter content is high, the crack is considered as an inorganic crack;
s400, extracting the content and size information of the fracture components and obtaining characterization parameters of the shale pore structure through morphological analysis;
fracture component content and size information includes: the crack content, the organic crack content, the inorganic crack content, the average value of the length and the width of the organic crack, the average value of the length and the width of the inorganic crack and the average value of the total length and the width of the crack;
and (3) carrying out data processing on the extraction result, specifically, equivalently converting the two-dimensional scanning electron microscope extraction parameters into three-dimensional parameters, wherein the data processing comprises the following steps:
establishing a probability distribution function when the random section of the three-dimensional hole is projected to a two-dimensional plane:
wherein R is a two-dimensional aperture, R is a three-dimensional aperture, and P (R) represents the probability of obtaining the aperture of the three-dimensional hole with the aperture of R through two-dimensional random cross-section projection;
average of probability distribution of two-dimensional random cross-sectional projection:
and (4) statistically dividing the obtained aperture size related value by pi/4, and then carrying out deep calculation solving to complete the dimension-increasing conversion.
2. The method for automatically extracting shale fractures with size self-adaptation according to claim 1 is characterized in that in step S100, the method specifically comprises:
s101, selecting a sample, processing the sample, and observing under a scanning electron microscope to obtain a series of images;
and S102, preprocessing the image, cutting off the black edge of the image, and filtering the electron microscope image.
3. The method for automatically extracting shale fractures with size self-adaptation according to claim 2, characterized in that in step S103, the method specifically comprises:
extracting four vertex angles of a rectangular region with a gray value of the image being not 0, reserving the region in the rectangle, and removing the region outside the rectangle;
and eliminating the non-smoothness of the appearance scanning of the cut image by mean filtering, and then eliminating the salt and pepper noise of the image by median filtering.
4. The method for automatically extracting shale fractures with size self-adaptation according to claim 1, wherein in step S200, the method specifically comprises:
s201, selecting a representative image in the cut image, namely an image containing more pores, organic matters, inorganic matters and pyrite;
s202, respectively adjusting and distinguishing three threshold values of the four components by using a binarization method, namely a pore threshold value, a segmentation threshold value of organic matters and inorganic matters and a segmentation threshold value of inorganic matters and pyrite;
and S203, after the three thresholds are obtained, performing threshold segmentation on the scanning electron microscope images of the samples, and dividing elements of each image into four components, namely holes, organic matters containing the holes, inorganic matters containing the holes and pyrite.
5. The method for automatically extracting shale fractures with size self-adaptation according to claim 1, wherein in step S200, the specific steps of "white edge" elimination are as follows:
sequentially marking each communicated pyrite region in the sample image, counting the area of each block, and extracting the boundary coordinate, the barycentric coordinate, the long axis length, the short axis length and the equivalent radius of each pyrite block;
calculating the ratio of the long axis to the short axis of each pyrite block;
counting the average gray value of 9 pixel points in the 3 multiplied by 3 area size near the center of gravity point;
if the ratio of the long axis to the short axis of the pyrite block is larger than 10 or the average gray value of 9 pixel points near the gravity center is smaller than the pyrite threshold, determining that the pyrite block is actually the 'white edge' interference at the periphery of the hole;
finding out the minimum rectangle containing the block according to the boundary coordinates, and giving out coordinates of four vertexes of the rectangle;
expanding the equivalent radius size outwards from the extracted minimum rectangle to obtain a new rectangle as the neighborhood of the hole;
when a certain outwards expanded neighborhood edge exceeds the boundary, inwards shrinking according to the step length of 3 pixels until the shrinking is stopped when the first requirement that the boundary range is not exceeded, and the shrinking processes of all edges are mutually independent;
counting the neighborhoods of the communicating blocks, and respectively calculating the content of organic matters and inorganic matters in the neighborhoods;
judging the real composition of the white edge according to the relative content of the organic matter component and the inorganic matter component in the neighborhood, if the content of the organic matter is high, considering that the white edge is the organic matter, and filling the white edge area as the gray value of the organic matter; if the inorganic matter content is high, the real component of the white edge is considered to be inorganic matter, and the white edge area is filled with the gray value of the inorganic matter.
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