WO2019192625A1 - 一种细粒沉积岩纹层结构的表征方法 - Google Patents

一种细粒沉积岩纹层结构的表征方法 Download PDF

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WO2019192625A1
WO2019192625A1 PCT/CN2019/084171 CN2019084171W WO2019192625A1 WO 2019192625 A1 WO2019192625 A1 WO 2019192625A1 CN 2019084171 W CN2019084171 W CN 2019084171W WO 2019192625 A1 WO2019192625 A1 WO 2019192625A1
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bright
layer
dark
image
continuity
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PCT/CN2019/084171
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French (fr)
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王冠民
熊周海
李明鹏
何凌霄
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中国石油大学(华东)
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Priority to US16/729,260 priority Critical patent/US10643321B1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/24—Earth materials
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G06T5/30—Erosion or dilatation, e.g. thinning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/952—Inspecting the exterior surface of cylindrical bodies or wires
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/954—Inspecting the inner surface of hollow bodies, e.g. bores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30242—Counting objects in image
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B23/00—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
    • G09B23/40—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for geology

Definitions

  • the present application belongs to the field of unconventional shale oil and gas exploration technology, and in particular to a method for characterizing fine grain sedimentary rock stratum structure.
  • the present application provides a characterization method capable of characterizing fine-grained sedimentary rock stratum structure in view of the problems of quantitative analysis of the stratigraphic structure existing in the development of existing fine-grained sedimentary rocks.
  • the present application provides a method for depositing a fine grained rock stratum structure, the specific steps of which are:
  • the second threshold is set, and the position of the trough is found according to two adjacent peaks. If the elevation difference between the peak and the adjacent trough is greater than the second threshold, the trough is considered to be located, and the position of the accurate peak and trough is obtained, that is, the effective peak and the effective a trough; wherein the number of effective peaks is a number of bright layers, and the number of effective troughs is a number of dark layers;
  • the bright layer or the dark layer take the pixel points of each row in the image as the unit, count the corresponding number of breakpoints, and reciprocate the average number of breakpoints.
  • the size of the reciprocal character is used to characterize the continuity of the bright layer or the continuity of the dark layer.
  • the number of bright layers, the number of dark layers, the average width of the bright layer, the average width of the dark layer, the average width of the bright and dark layers, the continuity of the bright layer, and the continuity of the dark layer , Bright and dark layer continuity, bright layer width variance, dark layer width variance and bright and dark layer average width variance can be written into Excel.
  • the mean filtering means that the mean value of the neighboring pixel points represents the target pixel point, thereby removing the target pixel point, that is, the local pixel, and obtaining the effective pixel; the expansion operation expands the effective pixel to eliminate the whole of the image after the mean filtering. ⁇ ; Binarization means that the gray value of the pixel on the image is set to 0 or 255, and only a clear black-and-white image is presented.
  • stripe_map represents the gradation simulation map
  • bw represents the binary image
  • the size of the striated simulation map is obtained by bw
  • threshold2 is the second threshold
  • the position of the trough is found according to the adjacent two peaks, and the elevation difference between the peak and the adjacent trough
  • the positions of the peaks and troughs that is, the effective peaks and the effective troughs, can be accurately obtained
  • the number of effective peaks is the number of bright stripes
  • the number of effective troughs is the number of dark layers, thereby determining The number of bright dark layers.
  • the bandwidth calculation manner of the bright layer and the dark layer is set, for example, the value of the boundary of the bright dark layer is set to be 2/5 of the peak, or 1/2, etc., so that each bright layer and The width of the dark layer.
  • step S6 if each pixel adjacent to each line in the image has the same color, it is recorded as a breakpoint.
  • the process of determining the continuity of the luster layer is: counting the number of dark break points of each row of pixels of each luster layer, and further determining the average number of breakpoints according to the total number of rows of each luster layer. That is, the first average number, and finally the average number of dark break points of the image, that is, the second average number, is calculated according to the number of bright layer layers, and the reciprocal of the second average number is the continuity of the image bright layer.
  • the process of determining the continuity of the dark layer is: counting the number of bright breakpoints of each row of pixels of each dark layer, and further determining the average number of breakpoints according to the total number of rows of each dark layer. That is, the third average number, and finally the average number of bright break points of the image, that is, the fourth average number, is calculated according to the number of dark layers, and the reciprocal of the fourth average is the continuity of the image dark layer.
  • step S6 the continuity of the image bright layer and the continuity of the image dark layer are averaged, that is, the continuity of the bright layer of the image.
  • This application uses image analysis method to quantitatively characterize the structural characteristics of fine-grained sedimentary rock stratum.
  • the process is convenient and fast, and it can accurately characterize the development characteristics of fine-grained sedimentary stratigraphic layers, and satisfactorily characterize the development characteristics of fine-grained sedimentary strata.
  • the requirements have fundamentally solved the problem of more time-consuming and low accuracy in manual statistics, and provided technical support for the exploration and development of shale oil and gas.
  • This application calculates the continuity of the stratum by counting the number of “breakpoints” corresponding to the bright and dark layers, and calculates the corresponding reciprocal, which not only conforms to the meaning of sedimentary petrology continuity, but also evaluates the stratigraphic layer.
  • the continuity provides a reliable numerical basis for the influence of other factors such as brittleness, fracture toughness, and fractureability of the rock.
  • FIG. 1 is a flow chart showing a method for characterizing a fine-grained sedimentary rock stratum structure according to the present application
  • Example 2 is a microscopic image of a fine-grained sedimentary rock sheet before image processing in Example 1 of the present application;
  • FIG. 3 is a result diagram of performing mean filtering, expansion calculation, and binarization processing on FIG. 2;
  • FIG. 4 is a waveform diagram obtained by accumulating bright pixel points of each row in FIG. 3;
  • FIG. 5 is a schematic diagram of determining a texture layer by a first threshold and a second threshold in Embodiment 1;
  • Embodiment 6 is a determination diagram of a layer bandwidth in Embodiment 1;
  • Figure 7 is a simulation diagram of an image layer in Embodiment 1;
  • Embodiment 9 is a microscopic image of a fine-grained sedimentary rock sheet before image processing in Embodiment 2 of the present application;
  • FIG. 10 is a view showing a result of performing mean filtering, expansion calculation, and binarization processing on FIG. 9; FIG.
  • FIG. 11 is a schematic diagram of determining a texture layer by a first threshold and a second threshold in Embodiment 2;
  • Figure 12 is a simulation diagram of an image pattern layer in the second embodiment.
  • fine-grained sedimentary rocks described in this application generally have sedimentary rocks with a particle size of less than or equal to 0.01 mm in China, while foreign research objects generally have a particle size of less than or equal to 0.0039 mm; In terms of particle size, the characterization methods described herein are all applicable.
  • the present application discloses a method for characterizing a fine-grained sedimentary rock stratum structure, the specific steps of which are:
  • the resulting information about the texture can be made more accurate.
  • the final result can be made to have a good comparability.
  • Mean filtering divides the neighboring pixel points to represent the target pixel points, thereby removing the target pixel points, ie, local ⁇ , to obtain effective pixels; the expansion operation expands the effective pixels to eliminate the overall ⁇ in the average filtered image;
  • the processing means that the gray value of the pixel on the image is set to 0 or 255, and only a clear black and white image is presented;
  • Counting the bright pixel points of each line of the image a waveform diagram can be obtained, and according to the peak, that is, the high pixel value of the bright pixel, the first threshold is set, and whether the image is developed according to the first threshold is determined;
  • the second threshold is set, and the position of the trough is found according to two adjacent peaks. If the elevation difference between the peak and the adjacent trough is greater than the second threshold, the trough is considered to be located, and the position of the accurate peak and trough is obtained, that is, the effective peak and the effective a trough; wherein the number of effective peaks is a number of bright layers, and the number of effective troughs is a number of dark layers;
  • each pixel of each line in the image has the same color, it is recorded as a breakpoint; in the bright layer or the dark layer, the pixel number of each row in the image is used as a unit, and the corresponding number of breakpoints is counted. And reciprocal the average number of breakpoints, the magnitude of the reciprocal character is used to characterize the continuity of the bright layer or the continuity of the dark layer;
  • the number of bright layers, the number of dark layers, the average width of the bright layer, the average width of the dark layer, the average width of the bright and dark layers, the continuity of the bright layer, and the continuity of the dark layer , Bright and dark layer continuity, bright layer width variance, dark layer width variance and bright and dark layer average width variance can be written into Excel.
  • the above method of the present application is based on fine-grained sedimentary rock flakes, and the image analysis method is used to quantitatively characterize the number of strata, the width of the stratum, the width of the stratigraphic layer, and the continuity of the stratum.
  • the width difference of the texture layer is represented by a mathematical variance
  • the continuity of the texture layer is characterized by a method of counting the number of breakpoints. It can accurately characterize the structural characteristics of fine-grained sedimentary rock stratum, and it has better effect than the prior art, which satisfies the requirements of systematically characterizing the development characteristics of fine-grained sedimentary stratigraphic layers, and provides technical support for the exploration and development of shale oil and gas. .
  • the horizontal pixel point accumulated value, threshold1 is the peak threshold value, that is, the first threshold value. If the v1 value exceeds the first threshold value and is determined to have a peak, it is judged to have a striate layer. If the return v1 is empty, that is, there is no peak, it is judged as none. Texture layer.
  • the present application may select some initial peaks, and after setting the second threshold, the effective peaks may be further filtered on the basis of the initial peak, and the effective valleys may be simultaneously determined. .
  • the average width of the bright layer In order to facilitate statistics to determine the average width of the bright layer, the average width of the dark layer, the average width of the bright and dark layer, and the average width of the bright and dark layer, it is necessary to set the bandwidth of the bright layer and the dark layer;
  • the bandwidth of the layer and the bright layer When the bandwidth of the layer and the bright layer is present, since there are bright and dark transition bands between the bright layer and the dark layer, referring to Fig. 6, it is preferable to set the value of the bright dark layer boundary to be 2/5 of the peak.
  • other selection ratios can also be considered according to the law of the change between the bright and dark layers under specific geological conditions, for example, it can be set to 1/2, 3/5, and the like.
  • the widths of the various bright and dark layers can be obtained.
  • the average width and the average width variance it is a common sense of mathematics, and will not be repeated here.
  • step S6 the process of determining the continuity of the bright layer is: counting the number of dark break points of each row of pixels of each bright layer, and further obtaining the total number of lines of each bright layer The average number of breakpoints, that is, the first average number, finally calculates the average number of dark breakpoints of the image according to the number of bright stripes, that is, the second average, and the reciprocal of the second average is the continuity of the bright layer of the image.
  • the process of determining the continuity of the dark layer is: counting the number of bright breakpoints of each row of pixels of each dark layer, and further calculating the average number of breakpoints according to the total number of rows of each dark layer, that is, the third average, and finally Calculate the average number of bright breakpoints of the image according to the number of dark layers, that is, the fourth average, and the reciprocal of the fourth average is the continuity of the image dark layer.
  • the continuity of the image bright layer and the continuity of the image dark layer are averaged, that is, the continuity of the bright and dark layer of the image.
  • it is also possible to normalize the continuity that is, the finer-grained sedimentary rock whose degree of continuity is closer to 1, the better the continuity of the grain layer, and vice versa, the closer the continuity is to 0, the grain layer The worse the continuity.
  • Example 1 Taking the fine-grained sedimentary rock at 3296.44m in the NY1 well as the research object in the Dongying Sag of Jiyang Depression, the stratigraphic structure was characterized by the above method. With continued reference to Figure 1, the specific steps are:
  • the pixel selected by the specified size is 1944 ⁇ 2592, that is, the specified size is 2592 pixels long and 1944 pixels wide. In order to process the uniform feature parameter values.
  • the same 1944*2592 pixel standard is adopted for fine-grained sedimentary rocks collected from different batches or different locations, so that the obtained data are based on the same pixel value, so that the obtained results have good comparability.
  • the bright pixel of each row of the statistical image is set according to a peak, that is, a high pixel value, and a first threshold is set, and whether the image is developed or not is determined according to the first threshold.
  • the threshold 1 is the peak threshold, that is, the first threshold. If the v1 value exceeds the peak threshold and is determined to have a peak, it is determined to be a striate layer. If the return v1 is empty, that is, there is no peak, it is determined to be a smectic layer.
  • FIG. 3 and FIG. 4 wherein the number of horizontal pixels in FIG. 3 is 2592 and the number of vertical pixels is 1944; the number of bright pixels in each row of the graph 3 (total 1944 lines) will be in FIG.
  • the longitudinal 1944 pixel points are taken as the abscissa of FIG. 4, and the counted number of bright pixel points is taken as the ordinate of FIG. 4, thereby obtaining the waveform diagram of FIG.
  • two relatively large bright pixel dot areas appear at the A area and the B area of FIG. 3, and the number of bright pixel points is relatively large, so that the corresponding feedback can be correspondingly shown in FIG. 4, and the peaks thereof are relatively high.
  • the first threshold is used for preliminary determination of a peak, and may be selected according to an actual situation or requirement, for example, may refer to the obtained waveform diagram; or if the desired processing result is more refined, the first threshold may be selected. A small value; if the desired result is coarser, the first threshold can be too large.
  • the first threshold is selected as 500.
  • the peaks above 500 are selected initial peaks, and the layer is determined to be embossed, less than 500. It is judged to be no grain layer.
  • Bw represents a binary image
  • the size of the simulated image is obtained by bw
  • threshold2 is the second threshold
  • the position of the valley is found according to the adjacent two peaks, and if the elevation difference between the peak and the adjacent valley is greater than threshold2, the valley is considered to exist. This can accurately obtain the position of the peaks and troughs.
  • the peak obtained at this time is the effective peak, and the trough is the effective trough.
  • the effective number of peaks is the number of bright layers, and the number of effective troughs is the number of dark layers, thus determining the brightness and darkness. See Figure 5 for the number of texels and the results for the determined bright crepe layers.
  • the bandwidth of the dark layer and the bandwidth of the bright layer are set.
  • the value of the boundary of the bright and dark layer is set to be 2/5 of the peak.
  • the average width of the bright layer, the average width of the dark layer, the average width of the bright dark layer, and the average width variance of the bright dark layer are determined. See Figure 7 for the simulated stratum of rock flakes.
  • the position of the trough is found by the two adjacent initial peaks. If the elevation difference between the initial peak and the trough is greater than the second threshold, the trough is selected as the effective trough; if the elevation difference between the initial peak and the trough is less than or equal to the second threshold, then the initial peak and the corresponding trough are simultaneously removed, thereby obtaining further The filtered peak, the effective peak, can finally determine the effective trough.
  • the second threshold is used to determine the trough and further to screen the initial peaks, ultimately resulting in an effective peak and an effective trough.
  • the second threshold may be appropriately adjusted according to the development of the layer of the specific sheet, and the common value is between 200 and 300, for example, but not limited to 200, 220, 240, 250, 260, 280, 300, etc., the implementation
  • the second threshold value selected in the example is 300, and the number of effective peaks obtained is the number of bright stripes, and the effective trough number is the number of dark stripes. As shown in FIG. 5, a total of 14 effective peaks and 15 effective troughs are obtained.
  • the bandwidth of the highlight layer and the dark layer can be set according to the effective peak and the effective valley.
  • Select point B of Fig. 5 as the description object.
  • the value of the boundary of the bright dark layer is set to be 2/5 of the peak, then the corresponding bright layer occupies 2/5, and the dark layer occupies 3/5.
  • the width of each bright layer and the width of each dark layer corresponding to the number of pixels, thereby obtaining the simulated pattern of the rock sheet, wherein the width of the bright layer and the dark layer are summed can be obtained. It is 1944 pixels (obviously, the larger the width, the more pixels are occupied), corresponding to the abscissa of Fig. 5, and the sizes of Fig. 7 and Fig. 3 are equal.
  • the average width of the bright layer, the average width of the dark layer, the average width of the bright dark layer, and the average width of the bright dark layer can be further obtained from the width of each of the bright layer and the width of each of the dark layers.
  • each pixel of each row in the image has the same color, it is recorded as a breakpoint, as shown in FIG. 8; the number of dark breakpoints of each row of pixels of each bright layer is counted, and then according to each The total number of lines of the bright layer is further determined by the average number of break points. Finally, the average number of dark break points of the image is calculated according to the number of bright lines, and the reciprocal of the average number of dark break points of the image bright layer is the continuity of the bright layer of the image. Count the number of bright breakpoints of each row of pixels in each dark layer, and then further calculate the average number of breakpoints according to the total number of rows of each dark layer.
  • the embodiment has 14 bright layers and 15 dark layers, which can be divided into 14 bright layer regions and 15 dark layers in the longitudinal direction.
  • each of the texel regions has a plurality of pixel rows, and all of the texel regions correspond to 1944 pixel rows.
  • the number of bright breakpoints of each row of each dark layer region is counted; for these bright layer regions, the dark breakpoint of each row of each bright layer region is counted. The number.
  • FIG. 8 The statistical method for the number of breakpoints is exemplified in FIG. 8. It is worth noting that only a statistical example of 10 pixels per line is provided in FIG. 8, which is actually 2592 pixels for each line of the present application; FIG. 8 is only an example. A bright layer area and a dark layer area are provided. In FIG. 8, for each row of the bright layer region, adjacent dark pixels appear as a dark breakpoint. Therefore, the number of dark breakpoints in each row of the bright layer region in the example is 3, 1, 0, and 1, respectively. For each row of the dark layer region, similarly, the adjacent bright pixel points appear as a bright breakpoint. Therefore, the number of bright breakpoints in each row in the example is 2, 3, 0, and 3, respectively.
  • the average number of breakpoints of the bright grain layer can be obtained by averaging, and the reciprocal is the continuity of the bright grain layer; similarly, the darkness can be obtained.
  • the average number of breakpoints and continuity of the layer are averaged to obtain a continuous dark layer continuity.
  • the number of bright layers, the number of dark layers, the average width of the bright layer, the average width of the dark layer, the average width of the bright and dark layers, the continuity of the bright layer, and the continuity of the dark layer , Bright and dark layer continuity, bright layer width variance, dark layer width variance and bright and dark layer average width variance can be written into Excel. See Table 1 for the results of the characterization.
  • Output type value Output type value Bright layer 14 Dark layer 15 Bright layer continuity 0.2877 Dark layer continuity 0.3844 Bright layer average width 0.0508mm Dark layer average width 0.0425mm
  • the width unit given in Table 1 is mm instead of the number of pixel points; this is because the actual length of the photo of the sheet corresponding to 1944*2592 pixels in this embodiment is 1350 ⁇ m*1800 ⁇ m, therefore, After the corresponding pixel point, it is converted again to represent the actual length in mm.
  • Example 2 The fine-grained sedimentary rock at 3451.85 m in the NY1 well was taken as the research object in the Dongying Sag of Jiyang Depression, and the stratigraphic structure was characterized by the above method of the present application. With continued reference to Figure 1, the specific steps are:
  • the pixel selected by the specified size is 1944 ⁇ 2592, that is, the specified size is 2592 pixels long and 1944 pixels wide.
  • Embodiment 1 and Embodiment 2 have better comparison between the two embodiments because the same pixels are selected.
  • bw represents a binary image
  • the size of the simulated image is obtained by bw
  • threshold2 is the second threshold
  • the position of the valley is found according to the adjacent two peaks, and if the elevation difference between the peak and the adjacent valley is greater than threshold2, the valley is considered to exist.
  • the positions of the peaks and troughs can be accurately obtained, wherein the number of effective peaks is the number of bright stripes, and the number of effective troughs is the number of dark layers, thereby determining the number of bright and dark layers, and determining the bright and dark layers.
  • the bandwidth of the dark layer and the bandwidth of the bright layer according to the peaks and troughs. When setting the bandwidth, refer to Figure 6.
  • each pixel of each row in the image has the same color, it is recorded as a breakpoint; the number of dark breakpoints of each row of pixels of each bright layer is counted, and then according to each bright layer The total number of lines is further determined by the average number of breakpoints. Finally, the average number of dark break points of the image is calculated according to the number of bright lines, and the reciprocal of the average number of dark break points of the image bright layer is the continuity of the bright layer of the image. Count the number of bright breakpoints of each row of pixels in each dark layer, and then further calculate the average number of breakpoints according to the total number of rows of each dark layer.
  • the number of bright layers, the number of dark layers, the average width of the bright layer, the average width of the dark layer, the average width of the bright and dark layers, the continuity of the bright layer, and the continuity of the dark layer , Bright and dark layer continuity, bright layer width variance, dark layer width variance and bright and dark layer average width variance can be written into Excel. See Table 2 for the results of the characterization.
  • Output type value Output type value Bright layer 8 Dark layer 9 Bright layer continuity 0.8631 Dark layer continuity 0.9464 Bright layer average width 0.0864mm Dark layer average width 0.0731mm Bright grain width variance 0.0459 Dark layer width variance 0.0569 Bright and dark layer average continuity 0.9048 Bright and dark layer average width variance 0.0514 Bright and dark layer average width 0.0798mm
  • Embodiment 2 The processing steps and principles of Embodiment 2 are substantially the same as those of Embodiment 1, wherein the description in Embodiment 1 is more detailed for ease of understanding, and related matters are not repeatedly described in Embodiment 2.
  • the width data given in Table 2 has the same principle as Table 1, and the actual length of the corresponding sheet photo is also 1350 ⁇ m*1800 ⁇ m.
  • the above characterization method of the present application can be programmed by means of MATLAB software, and the image analysis method is used to quantitatively characterize the number of layers of the fine-grained sedimentary rock, the width of the layer (ie, the thickness of the fingerprint layer), and the difference in the width of the layer. Sexuality and continuity of the layer.
  • the width difference of the texture layer is represented by a mathematical variance, and the continuity of the texture layer is characterized by a method of counting the number of breakpoints.

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Abstract

本申请涉及一种细粒沉积岩纹层结构的表征方法，包括：S1、图像预处理；S2、加载图像，并归一化到规定尺寸；S3、对图像进行均值滤波、膨胀运算和二值化处理；S4、判断是否发育纹层；S5、确定亮纹层和暗纹层的数量；S6、确定亮暗纹层的连续度；S7、根据统计和计算的结果，将表征参数写入Excel即可。本申请能够准确定表征细粒沉积岩纹层的结构特征，与现有技术相比，效果更好，满足了系统性刻画细粒沉积岩纹层发育特征的要求，为页岩油气的勘探开发提供了技术支持。

Description

一种细粒沉积岩纹层结构的表征方法 技术领域
本申请属于非常规页岩油气勘探技术领域，具体地说，涉及一种细粒沉积岩纹层结构的表征方法。
背景技术
近年来，随着页岩气在美国获得重大突破，细粒沉积岩逐渐受到人们的高度重视。在国内，自2014年四川威201气井得到突破后，这也标志着我国对非常规油气资源的勘探开发取得了新的进展。但是，从近两年钻探的牛页1井、梁页1井、利页1井、樊页1井以及渤页平1井等十几口页岩气井发现，在湖相细粒沉积岩中的页岩油气开发均未获得明显的突破。由此说明我国在陆相页岩气勘探开发理论技术上还存在一系列亟待解决的难题，尤其是细粒沉积岩的可压裂性是其中一个至关重要的因素，纹层结构的定量化表征又是评价可压裂性的基础。因此，研究细粒沉积岩纹层结构的表征方法对我国页岩油气的勘探开发具有十分重要的意义。
发明内容
本申请针对现有细粒沉积岩油气开发过程中存在的量化表征纹层结构困难等问题，提供一种能够表征细粒沉积岩纹层结构的表征方法。
为了达到上述目的，本申请提供了一种细粒沉积岩纹层结构的方法，其具体步骤为：
S1、图像预处理；
获取细粒沉积岩薄片镜下图像，对细粒沉积岩薄片镜下图像中的非纹层特征进行涂抹处理；
S2、加载图像，并把图像归一化到规定尺寸，如规定的像素；
S3、对图像依次进行均值滤波、膨胀运算和二值化处理；
S4、判断是否发育纹层；
统计图像每一行的亮像素点，根据波峰，即亮像素点高值情况，设定第一阈值，根据第一阈值判断图像是否发育纹层；
S5、确定亮纹层和暗纹层的数量；
设定第二阈值，根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于第二阈值时，认为存在波谷，获取准确的波峰和波谷的位置，即有效波峰和有效波谷；其中，有效波峰数为亮纹层数，有效波谷数为暗纹层数；
S6、确定亮纹层和暗纹层的连续度
在亮纹层或暗纹层内，以图像中每一行像素点为单元，统计相应的断点数，并对平均断点数进行求倒数，倒数的大小表征亮纹层连续度或暗纹层连续度的强弱；
S7、根据统计和计算的结果，将亮纹层数、暗纹层数、亮纹层平均宽度、暗纹层平均宽 度、亮暗纹层平均宽度、亮纹层连续度、暗纹层连续度、亮暗纹层连续度、亮纹层宽度方差、暗纹层宽度方差以及亮暗纹层平均宽度方差写入Excel即可。
优选的，步骤S3中，均值滤波将周围临近像素点均值代表目标像素点，从而去掉目标像素点，即局部瑕疵，得到有效像素；膨胀运算将有效像素进行扩充，消除均值滤波后图像中的整体瑕疵；二值化处理是指将图像上的像素点的灰度值设置为0或255，只呈现出明显的黑白图像。
优选的，所述步骤S4中，通过统计图像每一行的亮像素点，可以得到波形图；由函数[v1,l1]＝attain_peak(y1,threshold1)获取图像的波峰，其中，[v1,l1]代表波峰值，y1为横向像素点累加值，threshold1为波峰阈值，即第一阈值，如果v1值超过该波峰阈值被认定为有波峰，则判断为有纹层，如果返回v1为空，即没有波峰，则判断为无纹层。
优选的，步骤S5中，由函数[bright_stripe,dull_stripe,stripe_map]＝cal_index(y1,[v1,l1],bw,threshold2)确定波谷的位置，式中，bright_stripe代表亮纹层，dull_stripe代表暗纹层，stripe_map代表纹层模拟图，bw代表二值图像，由bw得到纹层模拟图的尺寸，threshold2为第二阈值；根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于theshold2时，认为存在波谷，由此可以准确地得到波峰和波谷的位置，即有效波峰和有效波谷；其中有效波峰数即为亮纹层数，有效波谷数量即为暗纹层数，从而确定亮暗纹层的数量。
优选地，设定亮纹层和暗纹层的带宽计算方式，比如设定亮暗纹层界限的值为波峰的2/5，或者1/2等等，那么就可以得到各个亮纹层和暗纹层的宽度。
优选的，步骤S6中，根据二值化处理结果，若图像中每一行相邻的像素点同色，记为一个断点。
优选的，步骤S6中，确定亮纹层连续度的过程为：统计每个亮纹层的每行像素点的暗断点数，再根据每个亮纹层的总行数进一步求出平均断点数，即第一平均数，最后根据亮纹层数计算出图像的平均暗断点数，即第二平均数，第二平均数的倒数即为图像亮纹层的连续度。
优选的，步骤S6中，确定暗纹层连续度的过程为：统计每个暗纹层的每行像素点的亮断点数，再根据每个暗纹层的总行数进一步求出平均断点数，即第三平均数，最后根据暗纹层数计算出图像的平均亮断点数，即第四平均数，第四平均数的倒数即为图像暗纹层的连续度。
优选的，步骤S6中，对图像亮纹层的连续度和图像暗纹层的连续度求平均，即为图像亮暗纹层的连续度。
与现有技术相比，本申请的有益效果在于：
(1)本申请利用图像分析方法定量表征细粒沉积岩纹层的结构特征，过程处理方便、快捷，能够准确刻画细粒沉积岩纹层发育特征，满足系统性地刻画细粒沉积岩纹层发育特征的要求，从根本上解决了人工统计上耗时多、准确度低的难题，为页岩油气的勘探开发提供技术支持。
(2)本申请基于沉积岩石学的相关理论为指导，分别定量描述了细粒沉积岩亮纹层、暗纹层的各自特征及亮暗纹层综合特征，并以数学方差为指标，量化了不同纹层厚度差异性。
(3)本申请通过统计亮纹层、暗纹层对应的“断点数”，并计算出相应的倒数来表征纹层的连续度，不仅符合沉积岩石学连续度的含义，还为评价纹层连续度对其他因素(如岩石的脆性、断裂韧性、可压裂性等)的影响提供了可靠的数值基础。
附图说明
图1为本申请所述细粒沉积岩纹层结构的表征方法的流程图；
图2为本申请实施例1中图像处理前的细粒沉积岩薄片镜下图像；
图3为对图2进行均值滤波、膨胀运算以及二值化处理后的结果图；
图4为由图3中每一行的亮像素点累积得到的波形图；
图5为实施例1中由第一阈值和第二阈值确定纹层的示意图；
图6为实施例1中一种纹层带宽的确定图；
图7为实施例1中图像纹层的模拟图；
图8为实施例1中纹层连续度计算示例图；
图9为本申请实施例2中图像处理前的细粒沉积岩薄片镜下图像；
图10为对图9进行均值滤波、膨胀运算以及二值化处理后的结果图；
图11为实施例2中由第一阈值和第二阈值确定纹层的示意图；
图12为实施例2中图像纹层的模拟图。
具体实施方式
下面，通过示例性的实施方式对本申请进行具体描述。然而应当理解，在没有进一步叙述的情况下，一个实施方式中的元件、结构和特征也可以有益地结合到其他实施方式中。
值得注意的是，本申请所述的细粒沉积岩，在中国国内的研究对象一般为粒径小于或等于0.01mm的沉积岩，而国外的研究对象一般为粒径小于或等于0.0039mm；至少对于这些粒径来讲，本申请所述的表征方法都是适用的。
参见图1，本申请揭示了一种细粒沉积岩纹层结构的表征方法，其具体步骤为：
S1、图像预处理；
获取细粒沉积岩薄片镜下图像，对细粒沉积岩薄片镜下图像中的非纹层特征进行涂抹处理；其中，非纹层特征主要包括生物碎屑和裂纹等特征；
通过对非纹层特征的涂抹，可以使得得到的关于纹层的信息更加准确。
S2、加载图像，并把图像归一化到规定尺寸，如规定或统一的像素；
尤其是对于同时分析多个不同的图像时，通过归一化到一个统一规定的像素大小，可以使得最终得到的结果具有比较好的可比性。
S3、对图像依次进行均值滤波、膨胀运算和二值化处理；其具体步骤为：
均值滤波将周围临近像素点均值代表目标像素点，从而去掉目标像素点，即局部瑕疵，得到有效像素；膨胀运算将所述的有效像素进行扩充，消除均值滤波后图像中的整体瑕疵；二值化处理是指将图像上的像素点的灰度值设置为0或255，只呈现出明显的黑白图像；
S4、判断是否发育纹层；
统计图像每一行的亮像素点，可以得到波形图，根据波峰，即亮像素点高值情况，设定第一阈值，根据第一阈值判断图像是否发育纹层；
S5、确定亮纹层和暗纹层的数量；
设定第二阈值，根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于第二阈值时，认为存在波谷，获取准确的波峰和波谷的位置，即有效波峰和有效波谷；其中，有效波峰数为亮纹层数，有效波谷数为暗纹层数；
S6、确定亮纹层和暗纹层的连续度
根据二值化处理结果，若图像中每一行相邻的像素点同色，记为一个断点；在亮纹层或暗纹层内，以图像中每一行像素点为单元，统计相应的断点数，并对平均断点数进行求倒数，倒数的大小表征亮纹层连续度或暗纹层连续度的强弱；
S7、根据统计和计算的结果，将亮纹层数、暗纹层数、亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度、亮纹层连续度、暗纹层连续度、亮暗纹层连续度、亮纹层宽度方差、暗纹层宽度方差以及亮暗纹层平均宽度方差写入Excel即可。
本申请上述方法以细粒沉积岩薄片为基础，利用图像分析方法定量化表征了细粒沉积岩的纹层数量、纹层宽度、纹层宽度差异性以及纹层连续性。尤其是纹层的宽度差异性利用数学方差来表示，纹层的连续性通过统计断点数的方法来表征。可以准确表征细粒沉积岩纹层的结构特征，且与现有技术相比，效果更好，满足了系统性刻画细粒沉积岩纹层发育特征的要求，为页岩油气的勘探开发提供了技术支持。
作为上述方法的优选设计，步骤S4中，由函数[v1,l1]＝attain_peak(y1,threshold1)获取图像的波峰，其中，[v1,l1]代表波峰值，y1为所述二值化图像中横向像素点累加值，threshold1为波峰阈值，即第一阈值，如果v1值超过第一阈值被认定为有波峰，则判断为有纹层，如果返回v1为空，即没有波峰，则判断为无纹层。
作为上述方法的优选设计，步骤S5中，由函数[bright_stripe,dull_stripe,stripe_map]＝cal_index(y1,[v1,l1],bw,threshold2)确定波谷的位置，式中，bright_stripe代表亮纹层，dull_stripe代表暗纹层，stripe_map代表纹层模拟图，bw代表二值图像，由bw得到纹层模拟图的尺寸，threshold2为第二阈值；根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于threshold2时，认为存在波谷，由此可以准确地得到波峰和波谷的位置，即确定了有效波峰和有效波谷；其中有效波峰数即为亮纹层数，有效波谷数量即为暗纹层数，从而确定亮暗纹层的数量。
通过上述步骤可知，本申请在设定第一阈值后，可以选定一些初始波峰，在设定第二阈值后，可以在初始波峰地基础上进一步地筛选出有效波峰，并能同时确定有效波谷。
为了便于统计从而确定亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度以及亮暗纹层平均宽度方差，需要设定亮纹层和暗纹层的带宽；在设定暗纹层和亮纹层的带宽时，由于亮纹层与暗纹层存在亮暗过渡带，参见图6，因此可优选设定亮暗纹层界限的值为波峰的2/5处。但是其他的选取比例也是可以根据具体地质条件下亮暗纹层之间的递变规律而考虑的， 例如可以设定为1/2，3/5等等。
当设定好带宽的计算方式后，就可以得到各个亮纹层和暗纹层的宽度了。而对于平均宽度和平均宽度方差的计算方法，都属于数学常识，本处不再赘述。
作为上述方法的优选设计，步骤S6中，确定亮纹层连续度的过程为：统计每个亮纹层的每行像素点的暗断点数，再根据每个亮纹层的总行数进一步求出平均断点数，即第一平均数，最后根据亮纹层数计算出图像的平均暗断点数，即第二平均数，第二平均数的倒数即为图像亮纹层的连续度。确定暗纹层连续度的过程为：统计每个暗纹层的每行像素点的亮断点数，再根据每个暗纹层的总行数进一步求出平均断点数，即第三平均数，最后根据暗纹层数计算出图像的平均亮断点数，即第四平均数，第四平均数的倒数即为图像暗纹层的连续度。对图像亮纹层的连续度和图像暗纹层的连续度求平均，即为图像亮暗纹层的连续度。
一般地，断点数越多图像纹层的连续度越低，反之，连续度越高。此外，为了便于对比分析，还可以对连续度进行归一化处理，即连续度越接近于1的细粒沉积岩，其纹层连续性越好，反之，连续度越接近于0，其纹层连续性越差。
为了能更清楚地对上述方法进行说明，以下分别以不同的实施例做出进一步说明。
实施例1：以济阳坳陷东营凹陷，NY1井3296.44m处的细粒沉积岩为研究对象，通过本申请上述方法进行纹层结构表征。继续参见图1，其具体步骤为：
S1、选取本实施例中研究区中的细粒沉积岩图像，图像处理前的岩石薄片镜下图像参见图2，利用Photoshop对细粒沉积岩薄片镜下图像的生物碎屑和裂纹进行涂抹处理，将这些非纹层特征的因素去掉。
S2、加载图像，并把图像归一化到规定的尺寸，本实施例中，规定的尺寸所选取的像素为1944×2592，也就是说，规定的尺寸为长2592像素点，宽1944像素点，以便处理得到统一的特征参数值。
例如对来自不同批次或者不同地点采集的细粒沉积岩都采用同样的1944*2592像素的标准，可以使得得到的数据都是基于相同的像素值，从而使得得到的结果具有较好的可比性。
S3、对图像进行均值滤波，用周围临近像素点均值代表目标像素点，从而去掉目标像素点，得到有效像素；然后进行膨胀运算，将所述的有效像素进行扩充；最后进行二值化处理，将图像上的像素点的灰度值设置为0或255，只呈现出明显的黑白图像。处理结果参见图3。
S4、统计图像每一行的亮像素点，根据波峰，即亮像素点高值情况，设定第一阈值，根据第一阈值判断图像是否发育纹层。
由函数[v1,l1]＝attain_peak(y1,threshold1)获取图像的波峰，其中，[v1,l1]代表波峰值，y1为图3中横向像素点累加值，y1累加后的结果参见图4，threshold1为波峰阈值，即第一阈值，如果v1值超过该波峰阈值被认定为有波峰，则判断为有纹层，如果返回v1为空，即没有波峰，则判断为无纹层。
具体地，参考图3和图4，其中图3的横向像素点为2592个，纵向像素点为1944个；统计图3中每一行(共计1944行)的亮像素点的数量，将图3中纵向的1944个像素点作为 图4的横坐标，将统计出来的亮像素点的数量作为图4的纵坐标，从而得到图4的波形图。例如在图3的A区域处和B区域处出现了两个比较大的亮像素点区域，亮像素点数量比较多，那么则可以对应地反馈在图4中，其波峰比较高。
所述第一阈值用于初步判定波峰，在选取时可以根据实际的情况或者需求而选取，例如可以参考得到的波形图；或者如果希望得到的处理结果比较细化，那么第一阈值可以选取偏小的值；如果希望得到的结果比较粗放，那么第一阈值可以偏大。本实施例在选取时，主要是参考了形成的波形图，选择第一阈值为500，那么在图4中，波峰高于500的均为选取的初始波峰，判定为有纹层，低于500的，判定为无纹层。
可以通过调取MATLAB中的attain_peak函数，并由函数[v1,l1]＝attain_peak(y1,threshold1)获取图像的初始波峰，例如得到各个初始波峰的坐标值。
S5、设定第二阈值，根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于第二阈值时，认为存在波谷，获取准确的波峰和波谷的位置，即得到有效波峰和有效波谷；其中，有效波峰数为亮纹层数，有效波谷数为暗纹层数。
由函数[bright_stripe,dull_stripe,stripe_map]＝cal_index(y1,[v1,l1],bw,threshold2)确定波谷的位置，式中，bright_stripe代表亮纹层，dull_stripe代表暗纹层，stripe_map代表纹层模拟图，bw代表二值图像，由bw得到模拟图的尺寸，threshold2为第二阈值；根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于threshold2时，认为存在波谷，由此可以准确地得到波峰和波谷的位置，此时得到的波峰为有效波峰，波谷为有效波谷，其中有效波峰数即为亮纹层数，有效波谷数量即为暗纹层数，从而确定亮暗纹层的数量，确定的亮暗纹层的结果参见图5。根据波峰和波谷设定暗纹层的带宽和亮纹层的带宽，在进行带宽设定时，参见图6，本实施例设定在亮暗纹层界限的值为波峰的2/5处，从而确定亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度以及亮暗纹层平均宽度方差。岩石薄片的模拟纹层参见图7。
具体地，设定第二阈值后，通过相邻的两个初始波峰找到波谷的位置。如果初始波峰与波谷的高程差大于第二阈值，选取该波谷为有效波谷；如果初始波峰与波谷的高程差小于或等于第二阈值，那么同时移除该初始波峰和相应的波谷，从而得到进一步筛选后的波峰，即有效波峰，并可以最终确定有效波谷。
所述第二阈值用于判断波谷和进一步地筛选初始波峰，最终得到有效波峰和有效波谷。所述第二阈值可根据具体薄片的纹层发育情况适当调节，常用值介于200～300，例如可以选取但不限制于200、220、240、250、260、280、300等等，本实施例选取的第二阈值为300，得到的有效波峰数即为亮纹层数，有效波谷数即为暗纹层数，如图5所示，一共得到了14个有效波峰和15个有效波谷。
可以通过调取MATLAB软件中的cal_index函数，并由[bright_stripe,dull_stripe,stripe_map]＝cal_index(y1,[v1,l1],bw,threshold2)来确定波谷的位置。并进一步地计算出有效波峰和有效波谷。
可以根据有效波峰和有效波谷设定亮纹层和暗纹层的带宽。选择图5的B点为说明对象，如图6所示，设置亮暗纹层界限的值为波峰的2/5，那么相应地亮纹层占据2/5，暗纹层占据3/5，从而可以得到各个亮纹层的宽度和各个暗纹层的宽度，所述宽度对应着像素点的个数，从而得到岩石薄片的模拟纹层图7，其中亮纹层和暗纹层的宽度总和为1944个像素点(显然宽度越大，占有的像素点越多)，与图5的横坐标是相对应的，而且图7与图3的大小是相等的。由各个亮纹层宽度和各个暗纹层的宽度可以进一步地得到亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度以及亮暗纹层平均宽度方差。
S6、根据二值化处理结果，若图像中每一行相邻的像素点同色，记为一个断点，参见图8；统计每个亮纹层的每行像素点的暗断点数，再根据每个亮纹层的总行数进一步求出平均断点数，最后根据亮纹层数计算出图像的平均暗断点数，图像亮纹层平均暗断点数的倒数即为图像亮纹层的连续度。统计每个暗纹层的每行像素点的亮断点数，再根据每个暗纹层的总行数进一步求出平均断点数，最后根据暗纹层数计算出图像的平均亮断点数，图像暗纹层平均亮断点数的倒数即为图像暗纹层的连续度。对图像亮纹层的连续度和图像暗纹层的连续度求平均，即为图像亮暗纹层的连续度。
具体地，由模拟纹层图7可知，本实施例具有14个亮纹层和15个暗纹层，可以据此把图3在纵向上分为14个亮纹层区域和15个暗纹层区域，对应到图3中，每个纹层区域都具有多个像素行，所有纹层区域对应1944个像素行。在图3中，对于这些暗纹层区域，统计每个暗纹层区域的每一行的亮断点的数；对于这些亮纹层区域，统计每个亮纹层区域的每一行的暗断点的数。
对于断点数的统计方法以图8为例，值得注意的是图8中仅提供了每一行10个像素点的统计例子，对于本申请每一行实际上为2592个像素点；图8也仅示例性地提供了一个亮纹层区域和一个暗纹层区域。在图8中，对于亮纹层区域的每一行，出现的相邻的暗像素统计为一个暗断点，因此，示例中的亮纹层区域各行暗断点数分别为3,1,0和1；对于暗纹层区域的每一行，同理地，出现的相邻的亮像素点统计为一个亮断点，因此，示例中的各行亮断点数分别为2,3,0,3。采用这种方法，将14个亮纹层区域的各行的暗断点数统计后，通过平均，可以得到亮纹层的平均断点数，其倒数即为亮纹层的连续度；同理可以得到暗纹层的平均断点数和连续度。将亮纹层连续度和暗纹层的连续度进行平均，得到亮暗纹层连续度。
S7、根据统计和计算的结果，将亮纹层数、暗纹层数、亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度、亮纹层连续度、暗纹层连续度、亮暗纹层连续度、亮纹层宽度方差、暗纹层宽度方差以及亮暗纹层平均宽度方差写入Excel即可。表征结果参见表1。
表1
输出类型 值 输出类型 值
亮纹层数 14 暗纹层数 15
亮纹层连续度 0.2877 暗纹层连续度 0.3844
亮纹层平均宽度 0.0508mm 暗纹层平均宽度 0.0425mm
亮纹层宽度方差 0.1205 暗纹层宽度方差 0.1380
亮暗纹层平均连续度 0.3360 亮暗纹层平均宽度方差 0.1292
亮暗纹层平均宽度 0.0467mm    
值得注意的是，表1中给出的宽度单位为mm，而非像素点数量；这是因为本实施例中1944*2592像素所对应的薄片照片的实际长度为1350μm*1800μm，因此，在得到相应的像素点后，再次转化为了以mm为单位的实际长度的表示方法。
实施例2：以济阳坳陷东营凹陷，NY1井3451.85m处的细粒沉积岩为研究对象，通过本申请上述方法进行纹层结构表征。继续参见图1，其具体步骤为：
S1、选取本实施例中研究区中的细粒沉积岩图像，图像处理前的岩石薄片镜下图像参见图9，利用Photoshop对细粒沉积岩薄片镜下图像的生物碎屑和裂纹进行涂抹处理，将这些非纹层特征的因素去掉。
S2、加载图像，并把图像归一化到规定的尺寸，本实施例中，规定的尺寸所选取的像素为1944×2592，也就是说，规定的尺寸为长2592像素点，宽1944像素点，以便处理得到统一的特征参数值。实施例1和实施例2由于选用相同的像素，因此这两个实施例之间具有较好的比较性。
S3、对图像进行均值滤波，用周围临近像素点均值代表目标像素点，从而去掉目标像素点，即局部瑕疵；然后进行膨胀运算，将有效像素进行扩充，消除均值滤波后图像中的整体瑕疵；最后进行二值化处理，将图像上的像素点的灰度值设置为0或255，只呈现出明显的黑白图像。处理结果参见图10。
S4、由函数[v1,l1]＝attain_peak(y1,threshold1)获取图像的波峰，其中，[v1,l1]代表波峰值，y1为横向像素点累加值，threshold1为波峰阈值，即第一阈值，如果v1值超过该波峰阈值被认定为有波峰，则判断为有纹层，如果返回v1为空，即没有波峰，则判断为无纹层。
S5、由函数[bright_stripe,dull_stripe,stripe_map]＝cal_index(y1,[v1,l1],bw,threshold2)确定波谷的位置，式中，bright_stripe代表亮纹层，dull_stripe代表暗纹层，stripe_map代表纹层模拟图，bw代表二值图像，由bw得到模拟图的尺寸，threshold2为第二阈值；根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于threshold2时，认为存在波谷，由此可以准确地得到波峰和波谷的位置，其中有效波峰数即为亮纹层数，有效波谷数量即为暗纹层数，从而确定亮暗纹层的数量，确定的亮暗纹层的结果参见图11。岩石薄片的模拟纹层参见图12。根据波峰和波谷设定暗纹层的带宽和亮纹层的带宽，在进行带宽设定时，参见图6，设定在亮暗纹层界限的值为波峰的2/5处，从而确定亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度以及亮暗纹层平均宽度方差。
S6、根据二值化处理结果，若图像中每一行相邻的像素点同色，记为一个断点；统计每个亮纹层的每行像素点的暗断点数，再根据每个亮纹层的总行数进一步求出平均断点数，最后根据亮纹层数计算出图像的平均暗断点数，图像亮纹层平均暗断点数的倒数即为图像亮纹层的连续度。统计每个暗纹层的每行像素点的亮断点数，再根据每个暗纹层的总行数进一步 求出平均断点数，最后根据暗纹层数计算出图像的平均亮断点数，图像暗纹层平均亮断点数的倒数即为图像暗纹层的连续度。对图像亮纹层的连续度和图像暗纹层的连续度求平均，即为图像亮暗纹层的连续度。
S7、根据统计和计算的结果，将亮纹层数、暗纹层数、亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度、亮纹层连续度、暗纹层连续度、亮暗纹层连续度、亮纹层宽度方差、暗纹层宽度方差以及亮暗纹层平均宽度方差写入Excel即可。表征结果参见表2。
表2
输出类型 值 输出类型 值
亮纹层数 8个 暗纹层数 9个
亮纹层连续度 0.8631 暗纹层连续度 0.9464
亮纹层平均宽度 0.0864mm 暗纹层平均宽度 0.0731mm
亮纹层宽度方差 0.0459 暗纹层宽度方差 0.0569
亮暗纹层平均连续度 0.9048 亮暗纹层平均宽度方差 0.0514
亮暗纹层平均宽度 0.0798mm    
实施例2的处理步骤和原理与实施例1基本相同，其中，实施例1中的描述更加详细以便于理解，相关的事项在实施例2中不再赘述。
此外，表2中给出的宽度数据其原理同表1，所对应的薄片照片的实际长度也为1350μm*1800μm
由上述实施例可知，本申请上述表征方法可以采用MATLAB软件编程为手段，利用图像分析方法定量化表征了细粒沉积岩的纹层数量、纹层宽度(也即指纹层厚度)、纹层宽度差异性以及纹层连续性。尤其是纹层的宽度差异性利用数学方差来表示，纹层的连续性通过统计断点数的方法来表征。能够系统刻画细粒沉积岩纹层发育特征，为进一步准确评价细粒沉积岩的可压裂性与开发潜力以及选择有效的压力作业方式和材料，以期能为我国页岩油气的勘探开发提供技术支持。
以上所举实施例仅用为方便举例说明本申请，并非对本申请保护范围的限制，在本申请所述技术方案范畴，所属技术领域的技术人员所作各种简单变形与修饰，均应包含在以上申请专利范围中。

Claims (13)

  1. 一种细粒沉积岩纹层结构的表征方法，其特征在于，其具体步骤为：
    S1、图像预处理；
    获取细粒沉积岩薄片镜下图像，对细粒沉积岩薄片镜下图像中的非纹层特征进行涂抹处理；
    S2、加载图像，并把图像归一化到规定尺寸；
    S3、对图像依次进行均值滤波、膨胀运算和二值化处理；
    S4、判断是否发育纹层；
    统计图像每一行的亮像素点，根据波峰，即亮像素点高值情况，设定第一阈值，根据第一阈值判断图像是否发育纹层；
    S5、确定亮纹层和暗纹层的数量；
    设定第二阈值，根据相邻两个波峰找到波谷的位置，若波峰和相邻波谷的高程差大于第二阈值时，认为存在波谷，获取准确的波峰和波谷的位置，即有效波峰和有效波谷；其中，有效波峰数为亮纹层数，有效波谷数为暗纹层数；
    S6、确定亮纹层和暗纹层的连续度
    在亮纹层或暗纹层内，以图像中每一行像素点为单元，统计相应的断点数，并对平均断点数进行求倒数，倒数的大小表征亮纹层连续度或暗纹层连续度的强弱；
    S7、根据统计和计算的结果，将亮纹层数、暗纹层数、亮纹层平均宽度、暗纹层平均宽度、亮暗纹层平均宽度、亮纹层连续度、暗纹层连续度、亮暗纹层连续度、亮纹层宽度方差、暗纹层宽度方差以及亮暗纹层平均宽度方差写入Excel即可。
  2. 根据权利要求1所述的表征方法，其特征在于，所述步骤S3中，均值滤波将周围临近像素点均值代表目标像素点，从而去掉目标像素点，得到有效像素；膨胀运算将有效像素进行扩充；二值化处理是指将图像上的像素点的灰度值设置为0或255，只呈现出明显的黑白图像。
  3. 根据权利要求2所述的表征方法，其特征在于，所述步骤S4中，由函数[v1,l1]＝attain_peak(y1,threshold1)获取图像的波峰，其中，[v1,l1]代表波峰值，y1为横向像素点累加值，threshold2为波峰阈值，即第一阈值，如果v1值超过该波峰阈值被认定为有波峰，则判断为有纹层，如果返回v1为空，即没有波峰，则判断为无纹层。
  4. 根据权利要求3所述的表征方法，其特征在于，所述步骤S5中，由函数[bright_stripe,dull_stripe,stripe_map]＝cal_index(y1,[v1,l1],bw,threshold2)确定波谷的位置，式中，bright_stripe代表亮纹层，dull_stripe代表暗纹层，stripe_map代表纹层模拟图，bw代表二值图像，由bw得到纹层模拟图的尺寸，theshold1为第二阈值；根据相邻两个波峰找到波 谷的位置，若有效波峰和相邻波谷的高程差大于threshold2时，认为存在波谷，由此可以准确地得到波峰和波谷的位置，即有效波峰和有效波谷；其中有效波峰数即为亮纹层数，有效波谷数量即为暗纹层数，从而确定亮暗纹层的数量。
  5. 根据权利要求1或4所述的表征方法，其特征在于，所述步骤S6中，根据二值化处理结果，若图像中每一行相邻的像素点同色，记为一个断点。
  6. 根据权利要求5所述的表征方法，其特征在于，所述步骤S6中，确定亮纹层连续度的过程为：统计每个亮纹层的每行像素点的暗断点数，再根据每个亮纹层的总行数进一步求出平均断点数，最后根据亮纹层数计算出图像的平均暗断点数，平均暗断点数的倒数即为图像亮纹层的连续度。
  7. 根据权利要求6所述的表征方法，其特征在于，所述步骤S6中，确定暗纹层连续度的过程为：统计每个暗纹层的每行像素点的亮断点数，再根据每个暗纹层的总行数进一步求出平均断点数，最后根据暗纹层数计算出图像的平均亮断点数，平均亮断点数的倒数即为图像暗纹层的连续度。
  8. 根据权利要求7所述的表征方法，其特征在于，所述步骤S6中，对图像亮纹层的连续度和图像暗纹层的连续度求平均，即为图像亮暗纹层的连续度。
  9. 根据权利要求1或3所述的表征方法，其特征在于，所述步骤S4中，统计图像每一行的亮像素点，得到波形图；选取第一阈值，高于第一阈值的波峰为初始波峰，判定为有纹层，否者判定为无纹层。
  10. 根据权利要求9所述的表征方法，其特征在于，设定第二阈值，通过相邻的两个初始波峰找到波谷的位置；如果初始波峰与波谷的高程差大于第二阈值，选取该波谷为有效波谷；如果初始波峰与波谷的高程差小于或等于第二阈值，那么同时移除该初始波峰和相应的波谷，从而得到进一步筛选后的波峰，即有效波峰，并最终确定有效波谷。
  11. 根据权利要求10所述的表征方法，其特征在于，根据有效波峰和有效波谷设定亮纹层和暗纹层的带宽，得到各个亮纹层和暗纹层的宽度。
  12. 根据权利要求10所述的表征方法，其特征在于，设置亮暗纹层界限的值为波峰的2/5，从而得到各个亮纹层的宽度和各个暗纹层的宽度。
  13. 根据权利要求11或12所述的表征方法，其特征在于，确定亮纹层连续度的过程为：统计每个亮纹层的每行像素点的暗断点数，再根据每个亮纹层的总行数进一步求出平均断点数，最后根据亮纹层数计算出图像的平均暗断点数，平均暗断点数的倒数即为图像亮纹层的连续度；确定暗纹层连续度的过程为：统计每个暗纹层的每行像素点的亮断点数，再根据每个暗纹层的总行数进一步求出平均断点数，最后根据暗纹层数计算出图像的平均亮断点数，平均亮断点数的倒数即为图像暗纹层的连续度；对图像亮纹层的连续度和图像暗纹层的连 续度求平均，即为图像亮暗纹层的连续度。
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109003248B (zh) 2018-07-23 2020-12-08 中国石油大学(华东) 一种细粒沉积岩纹层结构的表征方法
CN113586019B (zh) * 2020-04-30 2024-01-30 中国石油天然气股份有限公司 页岩气储层的压裂优化方法、装置和计算机存储介质
CN111951347B (zh) * 2020-08-24 2021-03-12 重庆科技学院 一种页岩油气储层砂质纹层参数提取方法
CN113808190B (zh) * 2021-09-23 2023-07-28 西南石油大学 一种基于电成像测井图像的页岩纹层信息定量提取方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105572757A (zh) * 2015-11-03 2016-05-11 山东科技大学 一种描述煤系细粒沉积结构的方法
KR101653115B1 (ko) * 2015-11-20 2016-09-01 제주대학교 산학협력단 쇄설성 퇴적암의 암상 분류방법 및 이를 이용한 셰일가스 저류층의 탐색방법
CN105954492A (zh) * 2016-04-28 2016-09-21 西南石油大学 页岩纹层定量表征方法
CN109003248A (zh) * 2018-07-23 2018-12-14 中国石油大学(华东) 一种细粒沉积岩纹层结构的表征方法

Family Cites Families (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7020307B2 (en) * 2002-02-15 2006-03-28 Inco Limited Rock fragmentation analysis system
KR20080016847A (ko) * 2005-05-13 2008-02-22 트리패스 이미징, 인코포레이티드 크로모겐 분리에 기초한 영상 분석 방법
US8103102B2 (en) * 2006-12-13 2012-01-24 Adobe Systems Incorporated Robust feature extraction for color and grayscale images
US8742372B2 (en) * 2009-07-20 2014-06-03 Bt Imaging Pty Ltd Separation of doping density and minority carrier lifetime in photoluminescence measurements on semiconductor materials
US8577135B2 (en) * 2009-11-17 2013-11-05 Tandent Vision Science, Inc. System and method for detection of specularity in an image
CN101908142A (zh) * 2010-08-04 2010-12-08 丁天 一种基于特征分析的视频火焰检测方法
EP2833123A4 (en) * 2012-03-30 2015-12-09 Konica Minolta Inc MEDICAL IMAGE PROCESSOR AND PROGRAM
JP5677356B2 (ja) * 2012-04-04 2015-02-25 キヤノン株式会社 マスクパターンの生成方法
US9140117B2 (en) * 2012-07-13 2015-09-22 Ingrain, Inc. Method for evaluating relative permeability for fractional multi-phase, multi-component fluid flow through porous media
CN104780822B (zh) * 2012-07-19 2017-06-23 独立行政法人国立长寿医疗研究中心 牙菌斑、牙龈和牙槽骨的测量和显示方法及装置
US9939548B2 (en) * 2014-02-24 2018-04-10 Saudi Arabian Oil Company Systems, methods, and computer medium to produce efficient, consistent, and high-confidence image-based electrofacies analysis in stratigraphic interpretations across multiple wells
CN104007484A (zh) * 2014-06-06 2014-08-27 董春梅 一种泥页岩的分类方法
CA3211036A1 (en) * 2015-04-23 2016-10-27 Bd Kiestra B.V. A method and system for automated microbial colony counting from streaked sample on plated media
EP3848509A1 (en) * 2015-07-21 2021-07-14 Kabushiki Kaisha Toshiba Crack analysis device, crack analysis method, and crack analysis program
WO2017139367A1 (en) * 2016-02-08 2017-08-17 Imago Systems, Inc. System and method for the visualization and characterization of objects in images
CN105809692B (zh) * 2016-03-10 2017-05-03 中国石油大学(华东) 一种页岩结构的定量表征方法
JP6217886B1 (ja) * 2016-03-14 2017-10-25 日本電気株式会社 物体管理装置
CN105869060A (zh) * 2016-04-06 2016-08-17 山东省煤田地质规划勘察研究院 一种细粒岩微细纹层分类方法
US10181391B2 (en) * 2016-05-26 2019-01-15 Nanojehm Inc. Image processing system and method of processing images
JP6777147B2 (ja) * 2016-06-30 2020-10-28 株式会社ニコン 画像選択装置、画像選択プログラム、演算装置、及び表示装置
US20200043159A1 (en) * 2016-10-03 2020-02-06 Nikon Corporation Analysis device, analysis method, and program
US10423820B2 (en) * 2017-09-13 2019-09-24 General Electric Company Systems and methods for automatic generation of training sets for machine interpretation of images

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105572757A (zh) * 2015-11-03 2016-05-11 山东科技大学 一种描述煤系细粒沉积结构的方法
KR101653115B1 (ko) * 2015-11-20 2016-09-01 제주대학교 산학협력단 쇄설성 퇴적암의 암상 분류방법 및 이를 이용한 셰일가스 저류층의 탐색방법
CN105954492A (zh) * 2016-04-28 2016-09-21 西南石油大学 页岩纹层定量表征方法
CN109003248A (zh) * 2018-07-23 2018-12-14 中国石油大学(华东) 一种细粒沉积岩纹层结构的表征方法

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