A three-dimensional pore type characterization method based on image fusion

Abstract

A three-dimensional pore type characterization method based on image fusion includes the following steps: scanning a selected analysis area using a focused ion beam scanning electron microscope to obtain backscattered images and secondary electron images; performing image processing on the backscattered and secondary electron images using software to segment and assign values ​​to the ranges of organic pores, inorganic pores, organic matter, and particles in the two images; finally, multiplying the processed images, and separating organic and inorganic pores based on different thresholds in the resulting image. The beneficial effects of this invention are: by utilizing the different imaging principles of the two sets of images (secondary electron and backscattered images), their respective advantages are utilized during threshold segmentation, resulting in different phases obtained from the two sets of images. By assigning values ​​to different phases in the two sets of images and then performing numerical calculations on the two sets of images, different pore types can be distinguished, and quantitative calculations and analyses of different pore types can be performed.

Description

Technical Field

[0001] This invention relates to the field of digital core imaging, and more specifically to a three-dimensional pore type characterization method based on image fusion. Background Technology

[0002] Scanning electron microscopy (SEM) is a widely used technique in shale porosity evaluation and pore structure analysis. Currently, focusing ion beam SEM is an effective means of identifying nanoscale pores for unconventional oil and gas reservoirs in China. By scanning and segmenting selected analytical regions of the sample, the total porosity and organic matter content of that region can be quantitatively calculated. However, the problem of classifying nanoscale pore types in unconventional reservoirs has not yet been effectively solved. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides a three-dimensional pore type characterization method based on image fusion.

[0004] The objective of this invention is achieved through the following technical solution.

[0005] A three-dimensional pore type characterization method based on image fusion includes the following steps:

[0006] S1. The selected analytical region is scanned using a focused ion beam scanning electron microscope to obtain backscattered images and secondary electron images;

[0007] S2. Select suitable regions from the two sets of images for pore type analysis;

[0008] S3. Use software to crop, threshold segment, and binarize the secondary electronic image to obtain a two-phase image, and assign values ​​to the threshold values ​​of the organic pore and inorganic pore phases and the particle phase in the image respectively;

[0009] S4. Use software to crop, threshold segment, and binarize the backscattered electron image to obtain a two-phase image. Set the thresholds for the organic pore, inorganic pore, and organic phases and the particle phase in the image respectively.

[0010] S5. By screening the thresholds set in step S4 for organic pores, inorganic pores and organic phase in the three-dimensional image obtained by backscattering image segmentation, the largest unit in the organic pores, inorganic pores and organic phase is identified as the organic phase, and the thresholds of other smaller units are assigned to distinguish them.

[0011] S6. The backscattered image after screening is re-segmented. The organic matter region determined in step S5 is assigned as organic pores and organic phase, and the other regions are assigned as particles and inorganic pore phase.

[0012] S7. Multiply the secondary electron thresholding result image by the backscattered electron thresholding result image. In the final result image, organic pores and inorganic pores can be separated according to different thresholds.

[0013] In steps S3 and S4, the secondary electron image and backscattered electron image are cropped and then smoothed and denoised.

[0014] In step S3, the threshold values ​​for organic and inorganic pore phases are set to 2, and the threshold value for particulate phase is set to 4.

[0015] In step S4, the threshold values ​​for organic pores, inorganic pores, and organic phase are set to 0, and the threshold value for particulate phase is set to 255.

[0016] Step S5 converts the backscattered image segmentation result RAW format file into DAT format file. It can be seen that each value in the DAT format file corresponds to the threshold of each continuous voxel in the RAW format file. By screening the threshold around the value with a threshold of 0, the largest unit in the organic pore, inorganic pore and organic phase is found. Other phases with smaller volume and a threshold of 0 are assigned a value of 255. After editing, the original DAT file is replaced and converted into a raw format file.

[0017] Set the threshold values ​​for organic pores and organic phases to 2, and assign a value of 3 to particles and inorganic pores.

[0018] In step S7, the secondary electron threshold result image is multiplied by the backscattered electron threshold result image. In the final result image, the phase with a threshold of 4 is organic pores, the phase with a threshold of 6 is inorganic pores, the phase with a threshold of 8 is organic matter, and the phase with a threshold of 12 is particles.

[0019] The beneficial effects of this invention are as follows: Based on existing technology, this invention further classifies pores into organic and inorganic pores, enabling precise classification of pore types and quantitative calculation during actual analysis, effectively improving the analysis of different types of reservoir space. This scheme obtains two sets of images—secondary electron and backscattered images—from core scanning images. Due to the different imaging principles of the two sets of images, their respective advantages are utilized during threshold segmentation, resulting in different phases identified by the two sets of images. By assigning values ​​to the different phases in the two sets of images and then performing numerical calculations on the two sets of images, different pore types can be distinguished, and quantitative calculations and analyses of different pore types can be performed. Attached Figure Description

[0020] Figure 1 It is a secondary electronic image;

[0021] Figure 2 It is a backscattered electron image;

[0022] Figure 3This is a schematic diagram of selecting suitable areas from secondary electron images for pore type analysis;

[0023] Figure 4 This is a schematic diagram of selecting suitable areas from backscattered images for pore type analysis;

[0024] Figure 5 This is a schematic diagram of the secondary electronic image preprocessing process;

[0025] Figure 6 This is a schematic diagram of the backscattered image preprocessing process;

[0026] Figure 7 This is a schematic diagram of a three-dimensional display after backscattered image segmentation;

[0027] Figure 8 This is a schematic diagram of the operation of removing inorganic pores from the zero phase through volume screening;

[0028] Figure 9 It is a 3D representation of the backscattered image after it has been re-segmented;

[0029] Figure 10 This is a schematic diagram of the electron image and backscatter image processing;

[0030] Figure 11 This is a flowchart of an analytical method for three-dimensional pore type classification and quantitative calculation based on SE and BSE images from FIBSEM;

[0031] Figure 12 This is a schematic diagram of the process of pore network extraction and quantitative analysis;

[0032] Figure 13 It is a statistical diagram showing the distribution of organic and inorganic pores. Detailed Implementation

[0033] The technical solution of the present invention will be further described below through specific embodiments.

[0034] Example

[0035] A three-dimensional pore type characterization method based on image fusion includes the following steps:

[0036] S1. The selected analytical region is scanned using a focused ion beam scanning electron microscope to obtain backscattered images and secondary electron images;

[0037] S2. Select suitable regions from the two sets of images for pore type analysis;

[0038] S3. Use software to crop, threshold segment, and binarize the secondary electronic image to obtain a two-phase image, and assign values ​​to the threshold values ​​of the organic pore and inorganic pore phases and the particle phase in the image respectively;

[0039] S4. Use software to crop, threshold segment, and binarize the backscattered electron image to obtain a two-phase image. Set the thresholds for the organic pore, inorganic pore, and organic phases and the particle phase in the image respectively.

[0040] S5. By screening the thresholds set in step S4 for organic pores, inorganic pores and organic phase in the three-dimensional image obtained by backscattering image segmentation, the largest unit in the organic pores, inorganic pores and organic phase is identified as the organic phase, and the thresholds of other smaller units are assigned to distinguish them.

[0041] S6. The backscattered image after screening is re-segmented. The organic matter region determined in step S5 is assigned as organic pores and organic phase, and the other regions are assigned as particles and inorganic pore phase.

[0042] S7. Multiply the secondary electron thresholding result image by the backscattered electron thresholding result image. In the final result image, organic pores and inorganic pores can be separated according to different thresholds.

[0043] In steps S3 and S4, the secondary electron image and backscattered electron image are cropped and then smoothed and denoised.

[0044] In step S3, the threshold values ​​for organic and inorganic pore phases are set to 2, and the threshold value for particulate phase is set to 4.

[0045] In step S4, the threshold values ​​for organic pores, inorganic pores, and organic phase are set to 0, and the threshold value for particulate phase is set to 255.

[0046] Step S5 converts the backscattered image segmentation result RAW format file into DAT format file. It can be seen that each value in the DAT format file corresponds to the threshold of each continuous voxel in the RAW format file. By screening the threshold around the value with a threshold of 0, the largest unit in the organic pore, inorganic pore and organic phase is found. Other phases with smaller volume and a threshold of 0 are assigned a value of 255. After editing, the original DAT file is replaced and converted into a raw format file.

[0047] Set the threshold values ​​for organic pores and organic phases to 2, and assign a value of 3 to particles and inorganic pores.

[0048] In step S7, the secondary electron threshold result image is multiplied by the backscattered electron threshold result image. In the final result image, the phase with a threshold of 4 is organic pores, the phase with a threshold of 6 is inorganic pores, the phase with a threshold of 8 is organic matter, and the phase with a threshold of 12 is particles.

[0049] The working principle of this invention is as follows: Figure 1 and Figure 2 As shown, in this embodiment, step S1 uses a SEISS CrossBeam-540 instrument to scan the selected analysis area using a focused ion beam scanning electron microscope to obtain backscattered electron images and secondary electron images.

[0050] like Figure 3 and Figure 4 As shown, step S2 involves selecting suitable regions from the two sets of images for pore type analysis; the area within the frame represents the selection and analysis portion.

[0051] like Figure 5 and Figure 6 As shown, this embodiment uses ImageJ software for operation. Steps S3 and S4 use image processing software to perform cropping, smoothing and denoising, thresholding, and image binarization on the image to obtain two-phase images. The left side of the figure is the selected image, the upper middle image is the image after cropping, the lower middle image is the image after denoising, the upper right image is the image after thresholding, and the lower right image is the image after binarization.

[0052] like Figure 7 As shown, Figure 7 The image shows a schematic diagram of a 3D representation after backscattered image segmentation. Observations reveal that in the 3D volume obtained from backscattered image segmentation, the organic matter portion constitutes the largest connected unit, which is not connected to the inorganic porous units obtained from the backscattered image segmentation. Figure 8 As shown, step S5 converts the RAW format file of the backscattered image segmentation result into a DAT format file. It can be seen that each value in the DAT format file corresponds to the threshold of each consecutive voxel in the RAW format file. By screening the threshold values ​​around 0, the largest unit in the organic pore, inorganic pore, and organic matter phases is identified. Other smaller phases with a threshold of 0 are assigned a value of 255. After editing, the original DAT file is replaced, and the file is converted back to RAW format. The purpose of the above steps is to select the largest unit in the organic pore, inorganic pore, and organic matter phases from the first segmentation as the organic matter region and complete the re-segmentation. Figure 9 A 3D representation of the backscattered image after re-segmentation.

[0053] like Figure 10 As shown, in this embodiment, the secondary electron image segmentation result assigns a value of 2 to the organic and inorganic porous phases and a value of 4 to the particle phase. The backscattered image segmentation result assigns a value of 2 to the organic matter and organic porous phases and a value of 3 to the particle phase. Multiplying the secondary electron thresholding result image by the backscattered electron thresholding result image, in the final result image, the phase with a threshold of 4 is the organic pore, the phase with a threshold of 6 is the inorganic pore, the phase with a threshold of 8 is the organic matter, and the phase with a threshold of 12 is the particle. Thus, organic pores and inorganic pores can be separated according to different thresholds.

[0054] like Figure 11 The diagram shown is a flowchart of the present invention. Figure 12 As shown, the right side of the figure is a schematic diagram of the three-dimensional structure of organic pores, inorganic pores, and their mixtures, while the middle part of the figure is a schematic diagram of the structure for mesh calculation. The right side and... Figure 13 The attached figure is a statistical diagram showing the distribution of organic and inorganic pores. Figure 12 The top left of the right image shows a 3D representation of the backscattered image after segmentation; the bottom left shows a 3D representation of the organic pores; the bottom right shows a 3D representation of the inorganic pores; and the top right shows a 3D representation of the combination of organic and inorganic pores.

[0055] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A three-dimensional pore type characterization method based on image fusion, characterized in that, Includes the following steps: S1. The selected analytical region is scanned using a focused ion beam scanning electron microscope to obtain backscattered images and secondary electron images; S2. Select suitable regions from the two sets of images for pore type analysis; S3. Use software to crop, threshold segment, and binarize the secondary electronic image to obtain a two-phase image, and assign values ​​to the threshold values ​​of the organic pore and inorganic pore phases and the particle phase in the image respectively; S4. Use software to crop, threshold segment, and binarize the backscattered electron image to obtain a two-phase image. Set the thresholds for the organic pore, inorganic pore, and organic phases and the particle phase in the image respectively. S5. By screening the thresholds set in step S4 for organic pores, inorganic pores and organic phase in the three-dimensional image obtained by backscatter image segmentation, the largest unit in organic pores, inorganic pores and organic phase is identified as the organic phase, and the thresholds of other smaller units are assigned to distinguish them. S6. The backscattered image after screening is re-segmented. The organic matter region determined in step S5 is assigned as organic pores and organic phase, and the other regions are assigned as particles and inorganic pore phase. S7. Multiply the secondary electron thresholding result image by the backscattered electron thresholding result image. In the final result image, organic pores and inorganic pores can be separated according to different thresholds.

2. The three-dimensional pore type characterization method based on image fusion according to claim 1, characterized in that: In steps S3 and S4, the secondary electron image and backscattered electron image are cropped and then smoothed and denoised.

3. The three-dimensional pore type characterization method based on image fusion according to claim 1, characterized in that: In step S3, the threshold values ​​for organic and inorganic pore phases are set to 2, and the threshold value for particulate phase is set to 4.

4. The three-dimensional pore type characterization method based on image fusion according to claim 3, characterized in that: In step S4, the threshold values ​​for organic pores, inorganic pores, and organic phase are set to 0, and the threshold value for particulate phase is set to 255.

5. The three-dimensional pore type characterization method based on image fusion according to claim 4, characterized in that: Step S5 converts the backscattered image segmentation result RAW format file into DAT format file. It can be seen that each value in the DAT format file corresponds to the threshold of each continuous voxel in the RAW format file. By screening the threshold around the value with a threshold of 0, the largest unit in the organic pore, inorganic pore and organic phase is found. Other phases with smaller volume and a threshold of 0 are assigned a value of 255. After editing, the original DAT file is replaced and converted into a raw format file.

6. The three-dimensional pore type characterization method based on image fusion according to claim 5, characterized in that: Set the threshold values ​​for organic pores and organic phases to 2, and assign a value of 3 to particles and inorganic pores.

7. The three-dimensional pore type characterization method based on image fusion according to claim 6, characterized in that: In step S7, the secondary electron threshold result image is multiplied by the backscattered electron threshold result image. In the final result image, the phase with a threshold of 4 is organic pores, the phase with a threshold of 6 is inorganic pores, the phase with a threshold of 8 is organic matter, and the phase with a threshold of 12 is particles.

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