CN113808190B - Shale layer information quantitative extraction method based on electric imaging logging image - Google Patents
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Abstract
The invention discloses a shale layer information quantitative extraction method based on an electric imaging logging image, which comprises the following steps of: step 1: acquiring an electric imaging image, and carrying out bilateral filtering on the electric imaging image; step 2: converting the filtered image into an r-channel diagram, and processing the r-channel diagram to obtain a boundary contour diagram of the tattooing layer; step 3: changing a straight line equation of Hough transformation into a sinusoidal equation to form improved Hough transformation, and extracting a sinusoidal layer boundary in a layer boundary contour map by utilizing the improved Hough transformation to obtain a layer interface map; step 4: marking and counting sinusoidal lines in the boundary map of the tattoos by a connected domain marking method, obtaining sinusoidal connected domain parameters by a method of minimum circumscribed rectangular frames by the sinusoidal lines, and quantitatively representing information of the tattoos by adopting the sinusoidal connected domain parameters; the extraction method can extract thickness and yield information of the tattoo on the FMI image finely and quantitatively, and provides technical basis for the evaluation of the tattoo on a macroscopic scale.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a shale layer information quantitative extraction method based on an electric imaging logging image.
Background
Shale layers are used as one of the basic geological conditions required by the formation of shale gas dessert segments, but the layers are complex in type and strong in heterogeneity, and the formation and distribution of deep shale dessert segments are controlled to a certain extent. Therefore, the fine evaluation of the type and the characteristics of the tattoo is an important basis for the evaluation of shale gas reservoirs. At present, the classification of the tattoo type can be carried out on a microscopic scale through an SEM image, but the shale shows the development of the tattoo, the heterogeneity is strong, the lithology change is complex on a macroscopic scale, the physical property and the gas content difference of different tattoo types are large, and great challenges are brought to the analysis of the heterogeneity of the shale gas reservoir on the macroscopic scale.
At present, shale layers are mainly identified and characterized by means of core observation, sheet identification and the like. A large number of geologists engage in and perfect the identification of the type of the tattoo on a microscopic scale and the extraction of the parameters related to the tattoo. Because the resolution of the conventional logging curve is difficult to reach the level of identifying the tattoo, the related research of identifying the tattoo is lacking on a macroscopic scale. Because of the high resolution of the electrical imaging log, evaluation of the tattoo by the electrical imaging log has continued to occur. But are limited to, qualitatively identifying the layers, extracting the texture complexity of the different layers of the electrical imaging image, and characterizing the extent of development of the layers. Lack quantification and integrality that the tattoo information was drawed, very few deposit environment behind the reflection tattoo, and traditional mode of discernment tattoo adopts man-machine interaction's mode, and complex operation and subjective error have brought very big difficulty for shale tattoo's discernment.
For example, patent application No. 202010855005, a method for extracting parameters of sand layers of shale oil and gas reservoirs, comprising the following steps: s1: filling the electric imaging logging image by 360 degrees; s2: identifying a boundary of the tattooing layer; s3: extracting and storing outline boundaries; s4: classifying and identifying a target object; s5: extracting parameters of a land-phase shale gas sand stratum; compared with a man-machine interaction type tattoo identification method, the method can automatically identify the sandy tattoo, can accurately identify the tattoo, achieves continuous identification of the whole well section, and solves the problems of large man-machine interaction tattoo workload and experience. Although this method is reasonable for the extraction of the layer information, the following disadvantages still exist: a filling operation is required to be carried out on the blank bands of the electric imaging image, and whether the filling part can represent the original geological information is controversial; the outline boundary extraction is directly adopted, and the noise and the non-layer boundary of the image can generate larger error on information extraction; although the positions of the layers and the development degree of the layers are extracted, the related information such as the thickness, the inclination angle, the tendency, the trend and the like of the layers is not related.
For example, patent application number 201911121469X, shale layer heterogeneity data processing method based on imaging logging image texture, which uses imaging logging images to obtain vertical rock structure images, and continuously and efficiently acquires rock image samples at different resolutions in the vertical direction. And adopting a tamura texture characteristic algorithm, and automatically extracting the roughness, contrast and direction degree of the imaging logging image by using a computer. Based on the extracted parameter results of a large number of images, each characteristic parameter is quantized by adopting an analytic hierarchy process, the occupied weight is analyzed, and a shale layer heterogeneity quantitative characterization model is constructed. The method can rapidly, efficiently and finely quantitatively characterize the heterogeneous characteristics of the shale layers, obtains the heterogeneous indexes of the layers, and provides scientific guidance for selecting the shale oil gas high-quality exploration target intervals on the profile. A processing method for shale grain layer texture parameters is provided, but Tamura texture parameters are extracted by calculating the whole image matrix, and the texture of the whole image is reflected, so that the related characteristics of a single grain layer cannot be expressed. The extracted information is texture parameters, and can not represent the thickness, development condition, number of layers, related dip angles, trends, trend and other yield information of the layers.
Disclosure of Invention
The invention provides a shale layer information quantitative extraction method based on an electric imaging logging image aiming at the problems existing in the prior art.
The technical scheme adopted by the invention is as follows:
a shale layer information quantitative extraction method based on an electric imaging logging image comprises the following steps:
step 1: acquiring an electric imaging image, and carrying out bilateral filtering on the electric imaging image;
step 2: converting the filtered image into an r-channel diagram, and processing the r-channel diagram to obtain a boundary contour diagram of the tattooing layer;
step 3: changing a straight line equation of Hough transformation into a sinusoidal equation to form improved Hough transformation, and extracting a sinusoidal layer boundary in a layer boundary contour map by utilizing the improved Hough transformation to obtain a layer interface map;
step 4: and marking and counting sine lines in the boundary map of the tattoos by a connected domain marking method, obtaining sine-type connected region parameters by the sine lines through a minimum circumscribed rectangular frame method, and quantitatively representing information parameters of the tattoos by adopting the sine-type connected region parameters.
Further, the bilateral filtering process in the step 1 is as follows:
wherein: i is an input image, BF is a bilateral filtered image, p is a central coordinate of a filter kernel window, q is a non-central coordinate of the filter kernel window, S is a spatial domain, W p As a result of the normalization factor,for spatial domain weight coefficients, +.>Is of value rangeA weight coefficient; i q Is the pixel value corresponding to the q coordinate.
Further, the processing in the step 2 includes global threshold segmentation and lateral edge extraction;
the transverse edge extraction is carried out by adopting an edge function in MATLAB, and the function calling mode is as follows:
I＝edge(g,′Prewitt′,′horizontal′)
wherein: i is a boundary contour map of a layer, g is an image after global threshold segmentation, prewitt is an edge detection operator, and horizontal is a Prewitt operator and is detected in the horizontal direction.
Further, the improved Hough transformation in the step 3 changes a linear equation into a sinusoidal equation through the dotted line duality of an image space and a parameter space; the sinusoid is:
y 0 ＝y-Asin(ωx-β)
wherein: x and y are coordinates of pixel points in the corresponding image space, A is amplitude of a sine line, omega is angular velocity, beta is initial phase, and y 0 Is the baseline position of the sinusoid.
Further, in the step 4, the sine lines in the boundary map of the tattoos are marked and counted by a connected domain marking method, specifically, a bwlabel function in MATLAB is adopted, and the call grammar is as follows:
[L,num]＝bwlabel(BW,n)
wherein BW is an input image, n is 4 or 8, num is the number of connected domains, and L is an output image matrix.
Further, in the step 4, the process of obtaining the sine position parameter by the method of performing the minimum circumscribed rectangular frame on the sine curve is as follows:
after the sinusoids are marked by the connected domain, carrying out minimum circumscribed rectangular frames on each sinusoid through a regionoprops function in MATLAB, obtaining three parameters of the top left vertex of the rectangular frame, the length of the rectangular frame and the width of the rectangular frame, and calling grammar as follows:
img 1 ＝regionprops(img,′boundingbox′)
wherein img is 1 For storing the coordinates and moment of the top left vertex of the rectangular frameThe two-dimensional matrix of three parameters of the shape frame length and the rectangle frame width, img is a marked sine curve graph, and boundingbox is a rectangle frame parameter command for extracting a connected region.
Further, in the step 4, the process of calculating the information parameters of the layer according to the sine position parameters is as follows:
s1: calculating the coordinate position W of the tattoo interface in the vertical direction:
wherein: a and b are respectively the upper left vertex ordinate and the rectangular frame width of the minimum circumscribed rectangle of n positive chord lines in the boundary map of the tattooing layer;
s2: calculating the variable W of the thickness of the deposited pattern layer 1 ：
W 1 ＝diff(W)
S3: calculating the actual grain thickness W 2 ：
Wherein: m is the image length, and Dep is the actual depth of the stratum section corresponding to the image;
s4: calculating the inclination angle H of the tattooing layer:
wherein: n is the width of the image, pi is the circumference ratio;
s5: calculating the tattoo tendency T:
wherein: y is 2 Is the transverse coordinate value of the trough of the sinusoidal curve;
s6: calculating the trend U of the tattoos:
U＝|T±90°|
the tattooing information comprises tattooing thickness, tattooing inclination angle, tattooing tendency and tattooing trend.
The invention has the beneficial effects that:
(1) The extraction method can extract thickness and yield information of the tattoo on the FMI image finely and quantitatively, and provides technical basis for the evaluation of the tattoo on a macroscopic scale;
(2) According to the invention, through bilateral filtering, noise reduction can be realized, and edge information can be reserved, so that the extraction of a subsequent tattoo interface is more accurate;
(3) According to the invention, the image is converted into the r-channel image, and the threshold segmentation and the transverse edge extraction are carried out, so that the non-tattoo boundary contour generated by the common methods of direct image graying, threshold segmentation and edge extraction can be reduced;
(4) The invention adopts the improved Hough transformation to extract the boundary contour map of the tattoo, so that the deposition characteristics of the tattoo interface are reserved while the tattoo interface is completely extracted;
(4) The invention can effectively pick up parameters such as thickness, inclination angle, tendency, trend and the like of the tattoo interface through the connected domain mark.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is an effect diagram of the electrical imaging log (a) and the bilateral filtered (b) in embodiment 1 of the present invention.
Fig. 3 is an r-channel diagram (a), a threshold segmentation diagram (b) and a lateral edge extraction diagram (c) in embodiment 1 of the present invention.
Fig. 4 is a schematic plan view showing the image development of a borehole wall according to embodiment 1 of the present invention.
Fig. 5 is a diagram of an improved Hough transform extraction layer interface in example 1 of the present invention.
Fig. 6 is a layer interface diagram (a) and a layer interface diagram (b) circumscribing a minimum rectangular frame in example 1 of the present invention.
FIG. 7 is a flow chart of the extraction of parameters of thickness, inclination, trend of the layers according to the present invention.
Detailed Description
The invention will be further described with reference to the drawings and specific examples.
As shown in fig. 1, a shale layer information quantitative extraction method based on an electric imaging logging image comprises the following steps:
step 1: acquiring an electric imaging image, and carrying out bilateral filtering on the electric imaging image; the bilateral filtering process is as follows:
wherein: i is an input image, BF is a bilateral filtered image, p is a central coordinate of a filter kernel window, q is a non-central coordinate of the filter kernel window, S is a spatial domain, W p As a result of the normalization factor,for spatial domain weight coefficients, +.>Is a value domain weight coefficient; i q Is the pixel value corresponding to the q coordinate.
When an edge condition occurs in the window,the weight becomes large so that the edge information remains. When the pixel values in the window are smoothed, the weight becomes smaller, +.>So that the region is noise-reduced. The electrographic image and the bilateral filtered image are shown in fig. 2.
Step 2: converting the filtered image into an r-channel diagram, and processing the r-channel diagram to obtain a boundary contour diagram of the tattooing layer; the processing comprises global threshold segmentation and lateral edge extraction;
the transverse edge extraction is carried out by adopting an edge function in MATLAB, and the function calling mode is as follows:
I＝edge(g,′Prewitt′,′horizontal′)
wherein: i is a boundary contour map of a layer, g is an image after global threshold segmentation, prewitt is an edge detection operator, and horizontal is a Prewitt operator and is detected in the horizontal direction. The r-channel map (a), the threshold segmentation map (b), and the lateral edge extraction map (c) are shown in fig. 3.
Step 3: changing a straight line equation of Hough transformation into a sinusoidal equation to form improved Hough transformation, and extracting a sinusoidal layer boundary in a layer boundary contour map by utilizing the improved Hough transformation to obtain a layer interface map;
the boundary of the tattoo is tangent to the shaft at a certain inclination angle, and according to the shaft plane development diagram (fig. 4), the tangent development of the tattoo interface and the shaft is a single-period sinusoidal curve, so that the boundary contour of the tattoo in the electric imaging image is a single-period sine curve, and the Hough transformation linear equation is improved for accurately extracting the tattoo interface.
The improvement process is as follows:
the improved Hough transformation changes a linear equation into a sinusoidal equation through the dotted line duality of an image space and a parameter space; because the sinusoid in the electrographic image is single period, the sinusoid is:
y 0 ＝y-Asin(ωx-β)
wherein: x and y are coordinates of pixel points in the corresponding image space, A is amplitude of a sine line, omega is angular velocity, beta is initial phase, and y 0 Is the baseline position of the sinusoid.
Since the sinusoid is monocycle, i.e. ω is known, by cycling a and β through corresponding ranges, each cycle results in one y 0 . Then A, beta and y 0 A three-dimensional array is constructed. And adding 1 to the three-dimensional group number to obtain a Hough transformation curved surface of the parameter space of the point (x, y). And then mapped to an image space, the improved Hough transformation can be realized to extract the sinusoids in the electrical imaging image. The improved Hough transform extraction layer interface diagram is shown in FIG. 5.
Step 4: and marking and counting sine lines in the boundary map of the tattoos by a connected domain marking method, obtaining sine-type connected region parameters by the sine lines through a minimum circumscribed rectangular frame method, and quantitatively representing information parameters of the tattoos by adopting the sine-type connected region parameters.
The principle of the connected domain labeling method is that in a binary image, adjacent pixels of a white pixel (target) are white, and then the white pixels are connected. The general connected neighborhood mode is 4 neighborhood connected or 8 neighborhood connected.
Marking and counting sine lines in a boundary map of a tattooing layer by a connected domain marking method, specifically adopting a bwlabel function in MATLAB, and calling grammar as follows:
[L,num]＝bwlabel(BW,n)
wherein BW is an input image, n is 4 or 8, the connected region mode is 4 neighborhood or 8 neighborhood, num is the number of connected regions, and L is an output image matrix. The background in L is marked 0, the pixels in the first connected region are marked 1, and the pixels in the second connected region are marked 2 …, as per the counting of connected regions, until all connected regions are marked. The layer interface diagram (a) and the layer interface diagram (b) circumscribing the minimum rectangular box are shown in fig. 6.
The process of obtaining sine position parameters by the method of carrying out minimum circumscribed rectangular frames on the sine curves is as follows:
after the sinusoids are marked by the connected domain, carrying out minimum circumscribed rectangular frames on each sinusoid through a regionoprops function in MATLAB, obtaining three parameters of the top left vertex of the rectangular frame, the length of the rectangular frame and the width of the rectangular frame, and calling grammar as follows:
img 1 ＝regionprops(img,′boundingbox′)
wherein img is 1 The method is characterized in that the method comprises the steps of storing a two-dimensional matrix with three parameters of the top left vertex coordinates of a rectangular frame, the length of the rectangular frame and the width of the rectangular frame, wherein img is a marked sinusoidal curve graph, and boundingbox is a rectangular frame parameter command for extracting a connected region.
The information parameter process of calculating the layer according to the sine position parameter is as follows: as shown in FIG. 7
The information parameters of the tattoo include thickness, inclination, trend and trend of the tattoo
When n sinusoids, namely n connected areas, exist in the layer interface diagram, the upper left vertex ordinate a and the rectangular frame width b of the minimum circumscribed rectangle of the n sinusoids can be obtained through a regionprons function. In defining the thickness of the layers, we establish the coordinates of the layers in the vertical direction by defining the coordinate position of the layer interface in the vertical direction as the longitudinal axis coordinates of the center point of each sinusoidal curve,
s1: calculating the coordinate position W of the tattoo interface in the vertical direction:
wherein: a and b are respectively the upper left vertex ordinate and the rectangular frame width of the minimum circumscribed rectangle of n positive chord lines in the boundary map of the tattooing layer; a is x 1 The variable W for storing the thickness of the tattoo is obtained by carrying out differential calculation on the vertical direction of the tattoo 1 。
S2: calculating the variable W of the thickness of the deposited pattern layer 1 ：
W 1 ＝diff(W)
In which W is 1 The variable holds the thickness of the layer (sum of the number of pixels). Obtaining the actual thickness W of the tattoo through the conversion of the number of pixels of the image and the actual depth corresponding to the image 2 。
S3: calculating the actual grain thickness W 2 ：
Wherein: m is the image length (sum of the number of pixels in the vertical direction), and Dep is the actual depth of the layer segment to which the image corresponds.
S4: calculating the inclination angle H of the tattooing layer:
wherein: n is the width of the image, pi is the circumference ratio;
s5: calculating the tattoo tendency T: the position of the trough tending to be sinusoidal
Wherein: y is 2 Is the transverse coordinate value of the trough of the sine curve.
S6: calculating the trend U of the tattoos: direction of trend is the vertical direction of trend
U＝|T±90°|
The tattooing information comprises tattooing thickness, tattooing inclination angle, tattooing tendency and tattooing trend.
The information on the thickness of the tattoos (inclination angle, trend) and the information on the shape of the tattoos (obtained by the above method) are shown in table 1.
TABLE 1 extraction parameter table of tattoo information
The invention establishes a quantitative extraction method of the tattoo information based on improved Hough transformation and connected domain marking, can finely and quantitatively extract the thickness and the birth state information of the tattoo on an FMI image, and provides a technical basis for the tattoo evaluation on the electric imaging (macro scale). According to the invention, after bilateral filtering is adopted, noise reduction can be realized, but edge information is reserved, so that the extraction of a subsequent tattoo interface is more accurate. The non-tattoo boundary contour generated by the common methods of direct image graying, threshold segmentation and edge extraction can be reduced by converting the image into an r-channel image and then performing threshold segmentation and lateral edge extraction. And extracting a tattooing interface from the tattooing boundary contour map by adopting improved Hough transformation, and preserving the deposition characteristics of the tattooing interface while completely extracting the tattooing interface. Parameters such as thickness, inclination, tendency, trend and the like of a tattoo interface can be effectively picked up through the connected domain mark.
Claims (6)
1. The shale layer information quantitative extraction method based on the electric imaging logging image is characterized by comprising the following steps of:
step 1: acquiring an electric imaging image, and carrying out bilateral filtering on the electric imaging image;
step 2: converting the filtered image into an r-channel diagram, and processing the r-channel diagram to obtain a boundary contour diagram of the tattooing layer;
step 3: changing a straight line equation of Hough transformation into a sinusoidal equation to form improved Hough transformation, and extracting a sinusoidal layer boundary in a layer boundary contour map by utilizing the improved Hough transformation to obtain a layer interface map;
step 4: marking and counting sinusoidal curves in the tattooing interface diagram by a connected domain marking method, wherein the sinusoidal curves obtain sinusoidal connected domain parameters by a method of minimum circumscribed rectangular frames; the parameters of the minimum circumscribed rectangular frame comprise the top left vertex of the rectangular frame, the length of the rectangular frame and the width of the rectangular frame;
quantitatively representing information parameters of the tattoos by adopting sinusoidal connected region parameters; the information parameter process for quantitatively characterizing the tattooing layer by adopting the sinusoidal connected region parameters is as follows: defining the coordinate position of the layer interface in the vertical direction as the longitudinal axis coordinate of the central point of each sinusoidal curve to establish the coordinate of the layer in the vertical direction;
s1: calculating the coordinate position W of the tattoo interface in the vertical direction:
wherein: a and b are respectively the upper left vertex ordinate and the rectangular frame width of the minimum circumscribed rectangle of n sinusoids in the tattoo interface diagram;
s2: calculating the variable W of the thickness of the deposited pattern layer 1 ：
W 1 ＝diff(W)
diff is the differential calculation;
s3: calculating the actual thickness of the layerW 2 ：
Wherein: m is the image length, and Dep is the actual depth of the stratum section corresponding to the image;
s4: calculating the inclination angle H of the tattooing layer:
wherein: n is the width of the image, pi is the circumference ratio;
s5: calculating the tattoo tendency T:
wherein: y is 2 Is the transverse coordinate value of the trough of the sinusoidal curve;
s6: calculating the trend U of the tattoos:
U＝|T±90°|
the information parameters of the tattoo include tattoo thickness, tattoo inclination and tattoo trend.
2. The quantitative shale layer information extraction method based on the electric imaging logging image according to claim 1, wherein the bilateral filtering process in the step 1 is as follows:
wherein: i is an input image, BF is a bilateral filtered image, p is a central coordinate of a filter kernel window, q is a non-central coordinate of the filter kernel window, S is a spatial domain, W p As a result of the normalization factor,is weighted by the space domainCoefficient of->Is a value domain weight coefficient; i q Is the pixel value corresponding to the q coordinate.
3. The quantitative shale layer information extraction method based on the electric imaging logging image according to claim 1, wherein the processing in the step 2 comprises global threshold segmentation and lateral edge extraction;
the transverse edge extraction is carried out by adopting an edge function in MATLAB, and the function calling mode is as follows:
I＝edge(g，'Prewitt'，'horizontal′)
wherein: i is a boundary contour map of a layer, g is an image after global threshold segmentation, prewitt is an edge detection operator, and horizontal is a Prewitt operator and is detected in the horizontal direction.
4. The quantitative shale layer information extraction method based on the electric imaging logging image according to claim 1, wherein the improved Hough transformation in the step 3 changes a linear equation into a sinusoidal equation through the dotted line duality of an image space and a parameter space; the sinusoid is:
y 0 ＝y-Asin(ωx-β)
wherein: x and y are coordinates of pixel points in the corresponding image space, A is amplitude of a sine line, omega is angular velocity, beta is initial phase, and y 0 Is the baseline position of the sinusoid.
5. The quantitative shale layer information extraction method based on the electric imaging logging image according to claim 1, wherein in the step 4, the sinusoids in the layer interface diagram are marked and counted through a connected domain marking method, and specifically, a bwlabel function in MATLAB is adopted, and the grammar is called:
[L，num]＝bwlabel(BW，n)
wherein BW is an input image, n is 4 or 8, num is the number of connected domains, and L is an output image matrix.
6. The quantitative shale layer information extraction method based on the electric imaging logging image of claim 5, wherein in the step 4, the process of obtaining the sinusoidal connected region parameters through the minimum circumscribed rectangular frame method for the sinusoidal is as follows:
after the sinusoids are marked by the connected domain, carrying out minimum circumscribed rectangular frames on each sinusoid through a regionoprops function in MATLAB, obtaining three parameters of the top left vertex of the rectangular frame, the length of the rectangular frame and the width of the rectangular frame, and calling grammar as follows:
img 1 ＝regionprops(img，′boundingbox′)
wherein img is 1 The method is characterized in that the method comprises the steps of storing a two-dimensional matrix with three parameters of the top left vertex coordinates of a rectangular frame, the length of the rectangular frame and the width of the rectangular frame, wherein img is a marked sinusoidal curve graph, and boundingbox is a rectangular frame parameter command for extracting a connected region.
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