CN107133630B - Method for judging carbonate rock pore type based on scanned image - Google Patents

Method for judging carbonate rock pore type based on scanned image Download PDF

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CN107133630B
CN107133630B CN201610112618.6A CN201610112618A CN107133630B CN 107133630 B CN107133630 B CN 107133630B CN 201610112618 A CN201610112618 A CN 201610112618A CN 107133630 B CN107133630 B CN 107133630B
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廉培庆
高文彬
高慧梅
汤翔
谭学群
王付勇
张俊法
李宜强
高敏
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China Petroleum and Chemical Corp
Sinopec Exploration and Production Research Institute
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Abstract

The invention discloses a method for judging carbonate rock pore types by scanning images. The method comprises the following steps: the method comprises the steps of preprocessing a rock core scanning image of the carbonate rock, wherein the preprocessing comprises color-to-gray conversion and signal-to-noise ratio improvement, segmenting the preprocessed image to achieve the effect of distinguishing a pore region from a matrix region, extracting pore parameters through processing of key steps such as morphological processing, framework refining, Euclidean distance maps and the like on the basis of separating the pore region, identifying dissolved pores and cracks, calculating a pore classification coefficient after obtaining enough pore parameters, further judging the type of the rock core pores, and providing understanding for subsequent carbonate rock reservoir development. In addition, the method can also obtain important information such as the distribution condition, the development degree, the shape characteristics and the like of the carbonate rock core pores in batches, and provides basic parameters for hydrocarbon migration and oil-gas seepage rule research.

Description

Method for judging carbonate rock pore type based on scanned image
Technical Field
The invention relates to the technical field of geological exploration, in particular to a method for judging carbonate rock pore types based on a scanning image.
Background
Carbonate is affected by a plurality of factors such as lithofacies paleogeography, sedimentary structure evolution or later diagenesis and the like, so that the carbonate has more complex reservoir space and oil-gas seepage migration rule, and the risk and difficulty of carbonate reservoir development are higher. Therefore, the method is particularly important for correctly selecting the development mode and the development scheme of the carbonate rock and judging the type of the carbonate rock reservoir.
The traditional mode for judging the type of the carbonate reservoir emphasizes the deposition cause and hydrodynamic energy, and generally takes a rock mineral particle skeleton as a main classification basis. This makes the study of the laws of oil and gas migration difficult to apply in this classification method. In one common carbonate pore type classification approach, the key concept of "texture selectivity" divides carbonate pores into texture selective pores (including intergranular, intragranular, intergranular, and cast pores, etc.), non-texture selective pores (including fissures, channels, pores, and pores, etc.), and intermediate texture selective pores (breccia, biological down-the-hole, and shrinkage-porosity). This method is widely used in oil and gas exploration and development (Chrequte PW, Pray LC. geological nomenclature and classification of location in research carbonates. AAPGbulletin, 1970, Vol.54, No. 2, 207-. The classification method considering the relationship between the rock texture and the pore space mainly divides the carbonate pore space into inter-granular pores (including inter-granular pores and inter-granular pores) and dissolution pores, wherein the inter-granular pores are divided into coarse inter-granular pores, medium-fine inter-granular pores and powder-mud micro-crystalline pores according to the sizes of the particles, and the dissolution pores are divided into isolated dissolution pores (including mold pores, intra-granular pores and intra-fossil pores) and contact dissolution pores (cracks, cracks of corrosion enlargement, dissolution pores, and pebble pores) according to the connectivity (Lucia FJ. petrophysical parameter expressed from carbonate rocks: a field classification of pore space, Journal of Petroleum Technology, 1983, 35 Vol. No. 3, 626-.
The constant-speed mercury-pressing experiment ensures that the mercury-feeding process is carried out under quasi-static state by feeding mercury into the throat and the pores of the rock sample at extremely low constant speed, and the method can separate the throat from the pores and realize accurate measurement of the quantity and the size of the throat and the pores (Lisan, Sunwei, Wang power and the like, application of a constant-speed mercury-pressing technology in reservoir pore structure research, a broken block oil-gas field, 2013, 20 Vol No. 4, 485 plus 487). In recent years, with the wide application of nuclear magnetic resonance in core analysis, the description of pore distribution and pore structure characteristic parameters through the nuclear magnetic resonance T2 spectrum distribution of the core becomes a new method for core physical property analysis. By comparing the mercury intrusion curve with the distribution phase of the T2 spectrum, the conversion coefficient between the mercury intrusion curve and the T2 spectrum can be obtained (Wangsheng, the rock pore structure characteristics are analyzed by nuclear magnetic resonance, Xinjiang petroleum geology, 2009, 30(6): 768-. In addition, patent "a method and device for obtaining characteristic parameters of carbonate rock core holes" (CN 103325118A) provides a method for obtaining geological parameters of holes on full-diameter core scale by using drilled core images, and the method performs characteristic extraction and macroscopic and microscopic analysis on the holes on the surface of the carbonate rock core, and can provide good technical support for reservoir distribution condition prediction.
In the pore type classification method introduced above, the classification method considering the texture mainly depends on visual identification and empirical judgment, and a standard system for quantitative evaluation of the carbonate pore type cannot be established. The mercury intrusion method mainly depends on mercury intrusion saturation to judge the pore size distribution, and can not visually observe the distribution characteristics of various pore types. The nuclear magnetic method has low imaging speed and high price, and is difficult to be used for batch core scanning. The image processing related technology is mainly used for quantitatively calculating parameters of pores and holes in the carbonate rock research process, and the problem of crack identification is not involved.
Disclosure of Invention
Aiming at the problems, the invention provides a method for quantitatively characterizing carbonate rock core pores and judging the type of the carbonate rock pores on the basis of a CT scanning image. The method quantitatively represents the development condition of the pore type (hole, hole and crack) of the whole rock core by utilizing the pore parameters, gets rid of the traditional mode that the pore type of the carbonate rock is judged by depending on the experience of researchers in the prior art, and provides technical support for batch judgment of the pore type of the rock core of the reservoir.
A method for judging the type of carbonate rock pores based on a scanned image comprises the following steps:
s100, preprocessing a rock core scanning image of the carbonate rock, wherein the preprocessing comprises color-to-gray conversion and signal-to-noise ratio improvement;
s200, segmenting the image processed in the step S100 to achieve the effect of distinguishing a pore region and a matrix region in the image;
s300, extracting pore parameters from the pore region separated in the step S200, and identifying cracks and pores in the pore region;
s400, calculating a pore classification coefficient according to the pore parameters obtained in the step S300, and judging the pore type of the rock core according to the pore classification coefficient, wherein the pore type comprises a fracture type, a pore-dissolving type and a matrix type;
wherein the pore classification coefficient is a parameter for evaluating the relative size between fracture-type pores and pore-dissolving type pores in the pore region, and the value TC thereof1Is composed of
Figure BDA0000931519190000031
Wherein ELc is the cross-section crack communication coefficient; el (electro luminescence)LThe crack growth coefficient of the longitudinal section is taken as the crack growth coefficient; PC (personal computer)rThe relative development degree of cracks and holes;
Figure BDA0000931519190000032
is the porosity of the rock sample; and is
Figure BDA0000931519190000033
Wherein FL is the maximum fracture length in the cross-section; CL is the core length; EfL is the sum of the lengths of the cracks in the longitudinal section whose effective length is greater than a specified length; CR is the diameter of the core; fpThe area of the crack accounts for the percentage of the pore area; hpThe area of the pores is the percentage of the area of the pores.
According to an embodiment of the present invention, in the step S100, the signal-to-noise ratio of the image may be improved by median filtering, adaptive median filtering, gaussian filtering, low-pass filtering, high-pass filtering, or wiener filtering.
According to an embodiment of the present invention, in the step S200, the image may be segmented by using a porosity constraint algorithm based on normal distribution, where the algorithm includes the following steps:
1) counting the gray level probability density curves of different CT sections of the same core, fitting the gray level probability density curves by utilizing a positive-Tailored distribution curve to obtain a mean value mu and a variance sigma in the normal distribution curve,
Figure BDA0000931519190000034
in the formula, a is a peak value extension coefficient, b is a base line offset, mu is a normal distribution mean value, and sigma is a normal distribution variance;
2) within a given variance distance [ lambda ]min，λmax]Let λ be λ ═ λ0，λ0Is the initial variance distance;
3) respectively calculating image segmentation threshold values mu corresponding to different CT sections under the variance distancei-λσiAnd i is 1,2, … … n, n is the number of the scanned images, then the areas of black areas in the binary images are respectively calculated, and the surface porosity SP under different cross sections is countedi；
4) Calculating the average value SP of the surface porosity under different sectionsave；
5) Average value SP of cross section porosityaveWhether the absolute value of the difference from the porosity is less than a predetermined condition parameter epsilon:
if SPavePhi < epsilon, the condition is discontinued, and
if SPaveIf phi is less, then lambda is equal to lambdamaxAnd ending;
if SPaveIf phi is greater, then lambda is equal to lambdaminAnd ending;
if SPavePhi | ≧ epsilon, then
Figure BDA0000931519190000041
) Repeating the steps 3) to 5) until the condition is met and the operation is stopped;
6) based on the determined constant coefficient lambda, obtaining the segmentation threshold values of different images, wherein the size of the threshold value is mui-λσiAnd i is 1,2, … … n, n is the number of the scanning images, and finally, the pore region and the matrix region are distinguished according to the CT images at different section positions of the same test core.
According to an embodiment of the present invention, in the step S200, the image may be segmented by using a seed growing algorithm with the substrate region as a growing point.
According to an embodiment of the present invention, in the step S200, an edge extraction algorithm of the pore region may be adopted to segment the image.
According to an embodiment of the present invention, in the step S300, the process of extracting the pore parameter includes the following steps:
1) performing morphological opening and closing operation processing on the separated pore area, and selectively interrupting or connecting pores;
2) performing skeleton refining treatment on the pore region subjected to morphological treatment to obtain a topological structure of the pore region;
3) a pore parameter for the pore region is calculated based on the topology of the pore region.
According to an embodiment of the present invention, the pore parameter includes at least one of an area, a convex area, a specific surface, an effective length, a meandering length, an equivalent width, a maximum inscribed circle radius, and an equivalent diameter of the pore region.
According to an embodiment of the present invention, the step S300 includes the following small steps:
1) the characteristic vector is established by preliminarily screening circular or elliptical dissolving hole pores and long-strip crack pores in the CT scanning image, and a training sample is provided for the subsequent training of a support vector machine;
2) and carrying out sample classification training by adopting a support vector machine, and further automatically completing classification and identification of cracks and dissolving holes in the crack area.
In accordance with an embodiment of the present invention EfL is the sum of the fracture lengths in longitudinal section having an effective length at least greater than 5 mm.
Further, if TC1>10, the carbonate rock pores are of a crack type; if 1<TC1<10, the carbonate rock pores are of a porous type; if TC1<1, carbonate rock pores are matrix type.
One or more embodiments of the present invention may have the following advantages over the prior art:
the method utilizes the concept of the pore classification coefficient to judge the pore type of the carbonate reservoir, gets rid of the traditional mode that the carbonate reservoir type is qualitatively judged by depending on the experience of professionals in the prior art, makes scientific quantitative evaluation on the development degree of reservoir cracks and dissolved pores, and provides powerful technical support for batch judgment of the pore type of the reservoir core. In addition, in the process of obtaining the pore classification coefficient, the invention can also obtain important information such as the distribution condition, the development degree, the shape characteristics and the like of the carbonate rock core pores in batches, and provides basic parameters for the research of hydrocarbon migration and oil-gas seepage rules.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for determining carbonate pore type in an embodiment of the present invention;
FIG. 2 schematically illustrates a CT scan of a core to obtain a screenshot in an embodiment of the invention;
FIGS. 3(a) and 3(b) are front and back views schematically illustrating the preprocessing of CT scan images according to an embodiment of the present invention;
FIG. 4 is a diagram schematically illustrating the working principle of the embodiment of the present invention for improving the signal-to-noise ratio of an image by using an adaptive median filtering method;
FIG. 5 is a diagram schematically illustrating fitting of CT scan gray scale probability density by normal distribution curve according to an embodiment of the present invention;
FIGS. 6(a) -6 (c) schematically illustrate the separation of the pore region and the matrix region by the porosity constraint algorithm in an embodiment of the present invention;
FIG. 7 is a diagram illustrating an exemplary growth process of a growing point in a seed growth algorithm according to an embodiment of the present invention;
FIGS. 8(a) and 8(b) schematically illustrate a pore region and a matrix region separated by a seed growth algorithm in an embodiment of the present invention;
FIGS. 9(a) and 9(b) schematically illustrate a pore region and a matrix region separated by a pore region edge extraction algorithm in an embodiment of the present invention;
FIGS. 10(a) and 10(b) show exemplary morphological images, skeleton images and azimuthal rose plots obtained in an embodiment of the invention;
fig. 11 exemplarily shows a morphological image and a euclidean distance map obtained in an embodiment of the present invention;
FIGS. 12(a) and 12(b) are diagrams illustrating classification test results of SVM training under a combination of two dimensionless parameter groups according to an embodiment of the present invention;
fig. 13 exemplarily shows reference images of cores of different pore types corresponding to pore classification coefficients of different sizes.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be noted that the exemplary embodiments and descriptions thereof in the present invention are only for explaining the present invention and do not limit the present invention.
The method for judging the carbonate rock pore type based on the scanned image mainly comprises the following four steps.
S100, preprocessing including color-to-gray conversion and signal-to-noise ratio improvement is carried out on the rock core scanning image of the carbonate rock.
And (3) carrying out CT image scanning on the rock core in a scanning mode shown in the attached figure 2, and then preprocessing the scanned image. The preprocessing includes converting the color image into a grayscale image and improving the signal-to-noise ratio of the image. Specifically, a CT scanning image is opened by using Photoshop software, a core region is outlined by using a magnetic lasso tool, and the core region is exported to a new blank document to be stored as a universal format file (as shown in FIG. 3). And converting the preprocessed image into a gray image, and carrying out effective noise reduction processing on the gray image so as to provide a basis for subsequent image segmentation. Specific embodiments may be as follows: and (3) importing the preprocessed CT image under the path by utilizing an imread function in Matlab software, converting the imported 8-bit unsigned RGB image into a gray image, and changing the three-dimensional data volume into two-dimensional data volume. In this example, the adaptive median filtering method is preferably adopted to perform noise reduction processing on the image. The working principle of the self-adaptive median filtering is that selective optimization is carried out on the basis of the median filtering, and the central pixel point of a filtering neighborhood is judged: if the judged pixel point is a neighborhood extreme value, performing noise reduction processing on the statistic ordering of median filtering; otherwise, the determined pixel is skipped, and the domain determination of the next pixel is performed (as shown in fig. 4). Of course, the signal-to-noise ratio of the image may also be improved by using a median filter, a gaussian filter, a low-pass filter, a high-pass filter, or a wiener filter, for example, which is not limited in the present invention.
S200, the image processed in the step S100 is segmented, so that the effect of distinguishing the pore region from the matrix region in the image is achieved.
The pore region in the image is analyzed until the effect is achieved that the pore region is completely distinguished from the matrix region. In the present invention, three different image segmentation algorithms can be employed to distinguish pore and matrix regions in an image:
i) a porosity constraint algorithm based on normal distribution;
ii) a seed growth algorithm with the substrate area as a growth point;
iii) a pore region edge extraction algorithm.
These three methods may be carried out either individually or in combination.
The porosity constraint algorithm based on normal distribution is an algorithm capable of performing threshold value constraint in batches aiming at multiple CT scanning images of the same test core. The core of the algorithm is that the gray value at the position of a fixed distance lambda sigma from the gray peak value mu is searched as the threshold value of image segmentation, so that the determination of the constant coefficient lambda (usually, the numerical range of lambda is between 0 and 3) becomes the key of the algorithm, and the specific steps for determining the constant coefficient lambda are as follows:
1) and (4) counting gray level probability density curves of different CT sections of the same core, fitting the gray level probability density curves by using a positive-power distribution curve, and solving a mean value mu and a variance sigma in the normal distribution curve.
Figure BDA0000931519190000071
Where a is the peak extension coefficient, b is the baseline offset, μ is the normal distribution mean, and σ is the normal distribution variance.
Fig. 5 shows an exemplary fitting curve, from which it can be seen that the normal distribution f (x) has good adaptability to CT scan images. In different scanning images, the peak value mu and the variance sigma obtained based on normal distribution curve fitting are different, and in order to fully consider the influence of factors such as contrast, brightness and the like caused by the difference of the background environment of each image during shooting, the threshold value of each image is the gray value at the same position relative to the peak value mu to be used as the threshold value of image segmentation, so that the pore space and the matrix area are distinguished.
2) Within a given variance distance [ lambda ]min，λmax]Let λ be the initial variance distance λ0. Here, λ0The value range of (A) is between 0 and 3.
3) Respectively calculating image segmentation threshold values mu corresponding to different CT sections under the variance distancei-λσi(i is 1,2, … … n, n is the number of scanned images), then the areas of black areas in the binary images are respectively calculated, and the surface porosity SP under different cross sections is countedi。
4) Calculating the average value SP of the surface porosity under different sectionsave。
5) Average value SP of cross section porosityaveWhether the absolute value of the difference from the porosity is less than a predetermined condition parameter epsilon:
if SPavePhi < epsilon, the condition is discontinued, and
if SPaveIf phi is less, then lambda is equal to lambdamaxAnd ending;
if SPaveIf phi is greater, then lambda is equal to lambdaminAnd ending;
if SPavePhi | ≧ epsilon, then
Figure BDA0000931519190000081
And repeatedly executing the steps 3) to 5) until the condition is met and the operation is stopped.
The constant coefficient lambda capable of being matched with the porosity of the core can be obtained through the steps, so that the segmentation threshold values of different images are obtained, and the size of the threshold value is specifically mui-λσi(i is 1,2, … … n, n is scanning patternImage number), and finally, a pore region and a matrix region can be efficiently distinguished according to CT images at different section positions of the same test core.
FIG. 6 schematically shows a pore region separated from a matrix region by a porosity constraint algorithm. Wherein, fig. 6(a) is a pore region and a matrix region separated based on a fracture type CT scanning image; FIG. 6(b) is a diagram of a pore region and a matrix region separated based on a hole type CT scan image; fig. 6(c) is a diagram of the separated pore region and matrix region based on the matrix type CT scan image.
Of course, another segmentation method may be used: and (3) a seed growing algorithm taking the substrate area as a growing point. The seed growth algorithm with the substrate region as the growth point is to use the growth point defined in advance in a man-machine interaction mode as the initial growth point, and grow according to the requirement of a similar criterion, and the growth process of the preselected growth point can be seen in fig. 7 (the seed growth similar criterion is set to be 1 in fig. 7, and the seed growth is searched in 8 neighborhoods). In the embodiment, the seed growing point is set on a developing ubiquitous matrix pixel, the similar growing threshold value is set to be 40 (the highest value is not more than 50), then the pore region separation is carried out on the gray level image, the black region (namely 0 region) in the obtained binary image is a pore region, and the white region is a rock skeleton region.
FIG. 8 schematically shows a pore region and a matrix region separated by a seed growth algorithm. Wherein, fig. 8(a) is a pore region and a matrix region separated based on a fracture type CT scanning image; fig. 8(b) is a diagram of the separated pore region and matrix region based on the hole type CT scan image.
And the pore region edge extraction algorithm may be used herein to verify the segmentation effect of other segmentation algorithms, such as the two algorithms described above. For example, in the present embodiment, Canny operator with better noise immunity and higher recognition accuracy can be called using an edge function built in Matlab, and the aperture region edge is extracted by preferably setting the parameter calling gray scale range to [40, 70], setting the calling variance to 1.25.
FIG. 9 schematically shows a pore region separated from a matrix region by a pore region edge extraction algorithm. Wherein, fig. 9(a) is a pore region and a matrix region separated based on a fracture type CT scanning image; fig. 9(b) is a diagram of the separated pore region and matrix region based on the hole type CT scan image.
S300, extracting pore parameters from the pore region separated in the step S200, and identifying cracks and pores in the pore region.
Pore parameters are extracted based on the pore region image separated in the step S200, so that a basis is provided for establishing a dimensionless parameter group for subsequent information such as pore type distribution, development degree and the like, and further carrying out quantitative characterization. The method mainly comprises two stages of regional characteristic extraction and crack and pore dissolution identification. Wherein:
the (first) region feature extraction (namely, pore parameter extraction) mainly comprises three small steps of morphological processing, skeleton thinning processing and image feature calculation.
1) Morphological treatment: and performing morphological opening and closing operation processing on the divided pore area, selectively interrupting or connecting pores, completing morphological correction and smoothing processing on the divided pore area, and removing invalid noise points. In the present invention, it is preferable to correct the pore region by a combination of an open-close operation and a closed-open operation. The specific implementation method is that a Strel function is used in Matlab to establish a square structural element, a square side length pixel is set to be 2, and an imopen function and an imoclose function are respectively used to perform morphological operation on a binary image, namely, firstly, an open operation is used to perform morphological processing on a divided binary image, and then, a close operation is used to perform processing, so that a pore area in the image is selectively connected or disconnected, and the effect of morphological smoothing processing of the pore area is achieved.
2) Framework refining treatment: stripping important topological structures such as a central axis skeleton, an end point, an internal hole and the like of the divided pore area. In this embodiment, the specific implementation process is as follows: in a 3 × 3 neighborhood, the foreground color (mostly black) is set to 1 and the background color (mostly white) is set to 0, and selective deletion is performed according to the following rules:
① 2 NZ (P) is less than or equal to 6, NZ (P) represents 8 pixel points (P) around P pointOn the upper part、PLower part、PLeft side of、PRight side、PLeft lower part、PUpper left of、PUpper right part、PLower right) The number of (1) s;
②Z0(P)＝1，Z0(P) representing the number of different points of adjacent pixels in the pixel points around P;
③Pon the upper part×PLeft side of×P Right side0 or Z0(P)≠1；
④POn the upper part×PLeft side of×P Lower part0 or Z0(PLeft side of)≠1。
Meanwhile, when the above rules are satisfied, the center pixel point P changes from black to white, i.e., the foreground color is deleted.
The shape characteristics of the skeleton can be reflected emphatically by removing the redundant edge pixels of the connected domain and stripping the skeleton structure in the connected domain. Fig. 10 exemplarily shows a morphological image and a skeleton image obtained from a CT scan image. Wherein, fig. 10(a) is a morphological image and a skeleton image obtained based on a slit-type CT scan image; fig. 10(b) is a morphological image and a skeleton image obtained based on a hole-type CT scan image.
3) Calculating image characteristics: mainly the calculation and extraction of the pore parameters. Two types of parameters of the area and the length of the pore area can be obtained through the step, and the parameters can comprise more than 10 parameters of the area, the convex area, the specific surface, the effective length, the tortuosity length, the equivalent width, the maximum inscribed circle radius, the equivalent diameter, the eccentricity, the azimuth and the like. The specific calculation method for these parameters is as follows:
(1) area of void region and convex area
The calculation of the regional parameters requires that the pore region be labeled first. Specifically, a bwleabel function can be used for marking serial numbers of the pore regions, and then a regionally propeps function is used for calling an Area operator Area to obtain the Area of the pore pixels of the regions according to the marked serial numbers; the convex area can be calculated by calling a convex area operator ConvexArea by using a regionprops function, and the obtained area and the convex area are both the image pixel area.
(2) Direction of pore region
The direction of the pore region needs to be obtained by means of the topological structure of the pore region obtained by a skeleton refinement algorithm, namely, the direction is judged on the basis of the topological structure of the pore region. The specific implementation method comprises the following steps: and performing bwlabel labeling on the pore region, calling a thicken operator in a bwmorphh function to perform erosion processing on the single pore region, and acquiring the direction spread of the pore region by using an organization operator in a regionprops function after acquiring an eroded framework. FIG. 10 is an exemplary azimuthal rose plot showing the directions of the characterized pore regions obtained based on the skeleton image topology. Wherein FIG. 10(a) is an azimuthal rose of the fracture type; FIG. 10(b) is an azimuthal rose of the hole pattern.
(3) Effective length and tortuosity length
The effective length of the pore is used for evaluating the linear distance of the pore area, and is calculated by the length and width of a rectangle covered by the pore area, and the specific calculation method is as follows:
Figure BDA0000931519190000101
where L, W is the length and width of the rectangle circumscribing the region and θ is the aperture azimuth.
In specific implementation, the length and width of the rectangle in the above formula can be obtained by using a Bounding Box operator in a regionprops function, and then by combining with a known direction parameter (aperture azimuth angle), the length and width of the rectangle can be obtained by using a Givens matrix in the above formula as coordinate rotation.
The tortuosity length can be calculated based on the axial skeleton in the pore region obtained by a skeleton refinement algorithm according to the following formula:
Figure BDA0000931519190000102
in the formula, xi、yiRespectively representing coordinates in the X direction and the Y direction in the middle shaft skeleton, and i representing pixel points at different positions on the middle shaft skeleton.
Parameters such as tortuosity and the like can be further acquired according to the effective length and the tortuosity, so that effective judgment can be made on the direction distribution and the rule degree of the pore area.
(4) Equivalent width and equivalent diameter
The equivalent width is determined by simultaneous calculation according to the pore area and the tortuosity length, and the specific calculation method is as follows:
equivalent width-pore area/tortuosity length
The value of the width in the connected pore region that varies with position can be uniformized by using the equivalent width.
Further, on the basis of the pore area, the irregular pore area can be simulated into an equal-area circle, the diameter of the circle is called as an equivalent diameter, and the specific calculation method is as follows:
Figure BDA0000931519190000111
the size of the net area of the irregular pores can be resolved by using the equivalent diameter of the irregular-shaped pore region.
(5) Maximum radius of inscribed circle
The maximum inscribed circle radius is an important parameter in pore evaluation, and inscribed circle radius values at different positions in a pore region can be obtained through an Euclidean distance graph, and then the maximum value is taken as the maximum inscribed circle radius. In specific implementation, a bwdist function can be used for converting a Euclidean distance map of a background color region of a binary image, and then the maximum value of the Euclidean distance map is selected as the maximum inscribed circle radius. Fig. 11 exemplarily shows a morphological image and a euclidean distance map obtained from a CT scan image.
(6) Specific surface area
The specific surface area of the pore region is a very important parameter of the pore region, and can be obtained by calculating the total circumference of the pore region by the following specific calculation method:
SV＝4/π·BA
in the formula, BAIs the length of the middle boundary within a unit area of the image, SVIs the surface area per unit volume.
In specific implementation, Sobel operator edge extraction can be firstly carried out on the binary image after morphological processing, the length converted by edge pixels after edge extraction is subjected to statistical addition operation, and then the length is multiplied by 2/pi, and the numerical value is the specific surface of all pore areas in the image.
And (II) identifying the cracks and the dissolved holes mainly comprises two small steps of establishing a feature vector and carrying out classification identification training by using an SVM (support vector machine).
1) And establishing a feature vector. The step is to establish a feature vector by screening a circular or elliptical pore-dissolving pore and a long-strip crack-type pore (namely, a pore with typical features) so as to provide a training sample for subsequent training of a support vector machine.
Firstly, extracting characteristic parameters of a rock core image, and selecting parameters of a strip-shaped crack and a similar round hole with typical characteristics in a CT scanning image after image segmentation and morphological processing as characteristic vectors. For example, table 1 shows the preliminary classification result of the pore types of 166H cores after region characteristic parameter extraction, and each pore connected domain in the table determines the pore type through preliminary discrimination, where type 1 is a fracture type, type 2 is a matrix type, and type 3 is a solvent pass type. After the regional characteristic parameter extraction, the pore type preliminary calibration and other steps are carried out on the cores with the rest numbers by the same method, the characteristic parameters of the strip-shaped crack type and the similar-to-circular hole and hole type connected domains with typical characteristics are respectively selected from a large number of hole connected domains to establish a standard characteristic vector, and the standard characteristic vector is selected as shown in table 2. And finally, calculating a feature vector consisting of the dimensionless variables by using the screened standard feature parameters, wherein the dimensionless parameter group calculation method specifically comprises the following steps:
(1) aspect ratio and aspect ratio
The length-width ratio and the aspect ratio are important parameters for evaluating the development degree of the strip-shaped morphology of the pore space, the difference is that the parameters used in the calculation are different, the length-width ratio adopts the calculation parameter as the tortuosity length, and the specific calculation method comprises the following steps:
length-width ratio as meandering length divided by equivalent width
The aspect ratio is corresponded to, the calculation parameter becomes the effective length, the specific calculation method is:
aspect ratio-effective length ÷ equivalent width
(2) Tortuosity
The tortuosity length is mainly used for evaluating the bending degree of a middle shaft skeleton in a pore space, and the related parameters mainly comprise the tortuosity length and the effective length, and the specific calculation method comprises the following steps:
tortuosity is devillicated length ÷ effective length
The numerical variation of the tortuosity is 1 or more, and the larger the numerical value is, the larger the degree of bending is reflected.
(3) Degree of centrifugation
The centrifugation degree is an important parameter for evaluating the shape characteristics of the pore space, the calculation method is to approximate the pore space into an ellipse, the length of the long axis a and the length of the short axis b of the approximate ellipse are respectively calculated, and the centrifugation degree is specifically calculated as follows:
Figure BDA0000931519190000121
the numerical range of the centrifugation degree is between 0 and 1, the closer the numerical value is to 1, the flatter the pore space shape is; conversely, the closer the value is to 0, the closer the pore space shape is to a circle. Generally, the calculation result can be obtained by calling an Eccentricity operator in a function of a compiling function regionprops in Matlab in a specific implementation process.
(4) Form factor
The shape factor is used primarily to describe the smoothness and regularity of the pore edges, and its magnitude is used to characterize the degree to which the pore shape is uniformly regular. The shape factor can be used for quantitatively characterizing the form of the pore, so that the shape factor is an important parameter for subsequently judging the types of cracks and dissolved pores. The form factor is calculated as follows:
shape factor 4 pi area/perimeter2
(5) Solidity degree
The porosity of the zone can be further determined according to the sizes of the zone area and the convex area. The specific calculation method of the area pore solidity is as follows:
solidity is area/area of convexity
The solidity can reflect the degree of shape regularity and the development condition of internal holes, and provides important parameters for subsequent crack and pore dissolution identification.
After the above non-dimensional parameter group is calculated, a standard feature vector composed of non-dimensional parameters can be generated, which provides a basis for subsequent sample learning of an SVM (support vector machine), and the non-dimensional standard feature vector can be specifically shown in table 3.
Table 1166H core different connected domain characteristic parameters and pore classification results
Figure BDA0000931519190000131
TABLE 2 fracture-type and pore-type connected domain eigenvectors
Figure BDA0000931519190000132
Figure BDA0000931519190000141
TABLE 3 dimensionless feature vectors
Figure BDA0000931519190000142
Figure BDA0000931519190000151
2) And (4) SVM (support vector machine) classification recognition training. In the step, the pore types are classified and screened automatically by using a support vector machine, so that great convenience is provided for subsequent pore segmentation, extraction and type judgment. In the embodiment of the invention, the crack and the pore dissolving type are preferably trained respectively by adopting a mode of combining two parameters, and the classification effect is evaluated in sequence. Specifically, the classification methods of the shape factor & the centrifugation degree and the solidity & the centrifugation degree are respectively optimized, and the specific classification effect can be seen in fig. 12.
Fig. 12 exemplarily shows classification test results of SVM training under two dimensionless parameter group combinations, wherein fig. 12(a) is a graph of classification training effects of SVM on both of a keyhole-type pore and a fracture-type pore under the parameter combination of a shape factor & centrifugation degree, and fig. 12(b) is a graph of classification training effects of SVM on a keyhole-type pore and a fracture-type pore under the parameter combination of a solidity & centrifugation degree, it can be known from fig. 12 that "+" represents a standard feature vector of a keyhole-type pore, and "+" represents a standard feature vector of a fracture-type pore, and type boundary lines under both of the shape factor & centrifugation degree or the solidity & centrifugation degree have good classification effects.
S400, calculating a pore classification coefficient according to the pore parameters obtained in the step S300, and judging the pore type of the rock core according to the pore classification coefficient, wherein the pore type comprises a fracture type, a pore-dissolving type and a matrix type.
It should be noted that the present invention focuses on the classification between fracture-type pores, erosion-type pores, and pore matrix-type carbonate rocks, and no further subdivision evaluation is made on the erosion-type pore types. Here, the term "erosion hole" is used as a typical erosion type pore type.
On the basis of obtaining the pore parameters, judging the single pore type is improved to the judgment of the core pore type by calculating the pore classification coefficient. In the invention, the classification coefficient of the carbonate reservoir pores is a parameter for evaluating the relative size between the crack type pores and the pore solution type pores in the pore region, and the value TC thereof1Comprises the following steps:
Figure BDA0000931519190000161
wherein ELc is the cross-section crack communication coefficient; el (electro luminescence)LFor slittingA face crack growth coefficient; PC (personal computer)rThe relative development degree of cracks and holes;
Figure BDA0000931519190000162
is the porosity of the rock sample; and is
Figure BDA0000931519190000163
Wherein FL is the maximum fracture length in the cross-section; CL is the core length; EfL is the sum of the lengths of the cracks in the longitudinal section whose effective length is greater than a specified length; CR is the diameter of the core; fpThe area of the crack accounts for the percentage of the pore area; hpThe area of the pores is the percentage of the area of the pores.
As the number of parameters in the calculation process is large, the first-closing parameters can be exported by writing a function TypePoreExport file during specific implementation, and the exported parameters are directly used for calculating the pore classification coefficients. In the present embodiment, EfL is set to the total length of the fracture having an effective length of more than 5mm in the longitudinal section, and the classification coefficient of porosity TC calculated according to the above formula1：
If TC1>10, the carbonate rock pores are of a crack type;
if 1<TC1<10, the carbonate rock pores are of a porous type;
if TC1<1, carbonate rock pores are matrix type.
Fig. 13 exemplarily shows reference images of cores of different pore types corresponding to pore classification coefficients of different sizes.
The above description is only an embodiment of the present invention, and the protection scope of the present invention is not limited thereto, and any person skilled in the art should modify or replace the present invention within the technical specification of the present invention.

Claims (10)

1. A method for judging the type of carbonate rock pores based on a scanned image comprises the following steps:
s100, preprocessing a rock core scanning image of the carbonate rock, wherein the preprocessing comprises color-to-gray conversion and signal-to-noise ratio improvement;
s200, segmenting the image processed in the step S100 to achieve the effect of distinguishing a pore region and a matrix region in the image;
s300, extracting pore parameters from the pore region separated in the step S200, and identifying cracks and pores in the pore region;
s400, calculating a pore classification coefficient according to the pore parameters obtained in the step S300, and judging the pore type of the rock core according to the pore classification coefficient, wherein the pore type comprises a crack type, a pore-dissolving type and a matrix type;
wherein the pore classification coefficient is a parameter for evaluating the relative size between fracture-type pores and pore-dissolving type pores in the pore region, and the value TC thereof1Is composed of
Figure FDA0002231430420000011
Wherein ELc is the cross-section crack communication coefficient; el (electro luminescence)LThe crack growth coefficient of the longitudinal section is taken as the crack growth coefficient; PC (personal computer)rThe relative development degree of cracks and holes; phi is the rock sample porosity; and is
Figure FDA0002231430420000012
Wherein FL is the maximum fracture length in the cross-section; CL is the core length; EfL is the sum of the lengths of the cracks in the longitudinal section whose effective length is greater than a specified length; CR is the diameter of the core; fpThe area of the crack accounts for the percentage of the pore area; hpThe area of the pores is the percentage of the area of the pores.
2. The method for determining carbonate rock pore type based on scanned images according to claim 1, wherein:
in step S100, the signal-to-noise ratio of the image is improved by median filtering, adaptive median filtering, gaussian filtering, low-pass filtering, high-pass filtering, or wiener filtering.
3. The method for judging the type of the carbonate rock pore space based on the scanned image according to claim 1, wherein in the step S200, the image is segmented by a porosity constraint algorithm based on normal distribution, and the algorithm comprises the following steps:
1) counting the gray level probability density curves of different CT sections of the same core, fitting the gray level probability density curves by utilizing a positive-Tailored distribution curve to obtain a mean value mu and a variance sigma in the normal distribution curve,
Figure FDA0002231430420000021
in the formula, a is a peak value extension coefficient, b is a base line offset, mu is a normal distribution mean value, and sigma is a normal distribution variance;
2) within a given variance distance [ lambda ]min,λmax]Let λ be λ ═ λ0，λ0Is the initial variance distance;
3) respectively calculating image segmentation threshold values mu corresponding to different CT sections under the variance distancei-λσiAnd i is 1,2, … … n, n is the number of the scanned images, then the areas of black areas in the binary images are respectively calculated, and the surface porosity SP under different cross sections is countediWherein, muiIs the normal distribution mean, σ, of the gray level probability density curve of the ith scan imageiIs the normal distribution variance of the gray level probability density curve of the ith scanning image;
4) calculating the average value SP of the surface porosity under different sectionsave；
5) Average value SP of cross section porosityaveWhether the absolute value of the difference from the porosity is less than a predetermined condition parameter epsilon:
if SPavePhi < epsilon, the condition is discontinued, and
if SPaveIf phi is less, then lambda is equal to lambdamaxAnd ending;
if SPaveIf phi is greater, then lambda is equal to lambdaminAnd ending;
if SPavePhi | ≧ epsilon, then
Figure FDA0002231430420000022
Repeating the steps 3) to 5) until the condition is met and stopping;
6) based on the determined constant coefficient lambda, obtaining the segmentation threshold values of different images, wherein the size of the threshold value is mui-λσiAnd i is 1,2, … … n, n is the number of the scanning images, and finally, the pore region and the matrix region are distinguished according to the CT images at different section positions of the same test core.
4. The method for determining carbonate rock pore type based on scanned images according to claim 1, wherein:
in the step S200, the image is segmented by using a seed growth algorithm with the substrate region as a growth point.
5. The method for determining carbonate rock pore type based on scanned images according to claim 1, wherein:
in step S200, an edge extraction algorithm for a pore region is used to segment the image.
6. The method for determining carbonate rock pore type based on scanned image according to claim 1, wherein the step S300 of extracting pore parameters comprises the following steps:
1) performing morphological opening and closing operation processing on the separated pore area, and selectively interrupting or connecting pores;
2) performing skeleton refining treatment on the pore region subjected to morphological treatment to obtain a topological structure of the pore region;
3) a pore parameter for the pore region is calculated based on the topology of the pore region.
7. The method for determining carbonate rock pore type based on scanned images according to claim 1, wherein:
the pore parameters include at least one of area, convex area, specific surface, effective length, tortuosity length, equivalent width, maximum inscribed circle radius, and equivalent diameter of the pore region.
8. The method for determining carbonate rock pore type based on scanned image according to claim 1, characterized in that said step S300 comprises the following small steps:
1) the characteristic vector is established by preliminarily screening circular or elliptical dissolving hole pores and long-strip crack pores in the CT scanning image, and a training sample is provided for the subsequent training of a support vector machine;
2) and carrying out sample classification training by adopting a support vector machine, and further automatically completing classification and identification of cracks and dissolving holes in the crack area.
9. The method for determining carbonate rock pore type based on scanned images according to claim 1, wherein:
EfL is the sum of the lengths of the slits in the longitudinal section having an effective length at least greater than 5 mm.
10. The method for determining carbonate pore type based on scanned images of claim 9, wherein:
if TC1>10, the carbonate rock pores are of a crack type;
if 1<TC1<10, the carbonate rock pores are of a porous type;
if TC1<1, carbonate rock pores are matrix type.
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