A method for extracting pore structure based on CT scanning technology and digital image processing

Abstract

This application discloses a method for extracting pore structure based on CT scanning technology and digital image processing, belonging to the field of oil and gas field development technology. This pore structure extraction method combines the method of reconstructing three-dimensional digital rock cores using rock CT scanning and simultaneously employs multiple image processing techniques, including threshold segmentation and watershed algorithms, to extract binary images of the rock skeleton and pore space, as well as binary images of throat distribution characteristics, ultimately obtaining the pore topology of the rock core. This pore structure extraction method avoids, to a certain extent, the strong randomness inherent in conventional methods for extracting planar pore structures, making the reconstructed pore structure more realistic and reliable.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas field development, and in particular to a method for extracting pore structure based on CT scanning technology and digital image processing. Background Art

[0002] Pore ​​structure is a major factor controlling the flow and migration behavior of fluids in reservoir rocks. Pore structure, typically characterized by the distribution of pores within the rock matrix and further quantified through parameters such as porosity and permeability, is crucial for designing and studying reservoir micromodels.

[0003] In the existing technology, the method for extracting the pore structure of the microscopic model includes: based on rock casting thin sections or scanning electron microscopy technology, observing the pores, throats and their interconnected two-dimensional spatial structure in the image under a polarizing microscope, and then directly etching the two-dimensional pore topology structure of the rock thin section onto the microscopic model.

[0004] However, on the one hand, the irregular morphology and tight arrangement of rock particles in thin section images, as well as the overlapping and adhesion between particles, lead to poor segmentation quality of rock skeleton and pore space. On the other hand, the selection of pore structure images such as rock casting thin sections or scanning electron microscopes is highly random and can only reflect the characteristics of two-dimensional planar pore structure. Summary of the Invention

[0005] In view of this, the present application provides a method for extracting pore structure based on CT scanning technology and digital image processing.

[0006] Specifically, the following technical solutions are included:

[0007] A method for extracting pore structure based on CT scanning technology and digital image processing is provided. The method for extracting pore structure includes:

[0008] The core samples are scanned layer by layer based on CT scanning technology to obtain several CT images;

[0009] Performing threshold segmentation on the plurality of CT images respectively to obtain a plurality of pore particle binary images;

[0010] Based on the plurality of pore-grain binary images, a three-dimensional representative unit cell of the core sample is obtained;

[0011] Obtaining a two-dimensional superimposed grayscale image corresponding to the three-dimensional representative unit body based on pixel values ​​of pixel points in the three-dimensional representative unit body;

[0012] Performing threshold segmentation on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space;

[0013] Performing watershed algorithm processing on the two-dimensional superimposed grayscale image to obtain a binary image of throat distribution characteristics;

[0014] Based on the binary map of the rock skeleton and pore space and the binary map of the throat distribution characteristics, the pore structure of the core sample is obtained.

[0015] In some embodiments, the core sample is scanned layer by layer based on CT scanning technology to obtain several CT images, including:

[0016] Obtaining the core sample of the target block;

[0017] Both ends of the core sample are cut flat, and a layer-by-layer CT scanning experiment is performed on the flattened core sample to obtain the plurality of CT images.

[0018] In some embodiments, performing threshold segmentation on the plurality of CT images to obtain a plurality of pore particle binarization images includes:

[0019] Performing grayscale processing on each CT image to obtain a grayscale image of the CT image;

[0020] Noise points in the grayscale image of the CT image are removed, and threshold segmentation is performed on the CT image based on the Otsu method to obtain a pore particle binarization image corresponding to each CT image.

[0021] In some embodiments, obtaining a three-dimensional representative unit cell of the core sample based on the plurality of pore-grain binary images includes:

[0022] The plurality of pore-grain binary images are stacked from top to bottom to obtain a three-dimensional reconstructed digital core;

[0023] A unit volume is selected from the three-dimensional reconstructed digital rock core to determine the three-dimensional representative unit volume of the core sample.

[0024] In some embodiments, selecting a unit volume from the three-dimensional reconstructed digital rock core to determine the three-dimensional representative unit volume of the core sample includes:

[0025] Using porosity as an evaluation parameter, determining whether the porosity of the selected unit body is consistent with the porosity of the core sample;

[0026] If the porosity of the selected unit body is inconsistent with the porosity of the core sample, increase the pixel range of the unit body until the porosity of the unit body after the pixel range is increased is consistent with the porosity of the core sample, and determine the corresponding unit body as the three-dimensional representative unit body of the core sample; or

[0027] If the porosity of the selected unit body is inconsistent with the porosity of the core sample, reselecting a unit body until the porosity of the reselected unit body is consistent with the porosity of the core sample, and determining the corresponding unit body as the three-dimensional representative unit body of the core sample;

[0028] If the porosity of the selected unit body is consistent with the porosity of the core sample, the unit body is determined to be the three-dimensional representative unit body of the core sample.

[0029] In some embodiments, obtaining a two-dimensional superimposed grayscale image corresponding to the three-dimensional representative unit volume based on the pixel values ​​of the pixel points in the three-dimensional representative unit volume includes:

[0030] Based on the pixel values ​​of the pixel points in the three-dimensional representative unit volume, the three-dimensional representative unit volume is weightedly projected onto the XOY plane layer by layer to obtain a two-dimensional superposition map of the rock skeleton and pore space;

[0031] The two-dimensional superposition image of the rock skeleton and the pore space is gray-scale processed to obtain the two-dimensional superposition gray-scale image corresponding to the three-dimensional representative unit body.

[0032] In some embodiments, performing threshold segmentation on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space includes:

[0033] A distribution histogram of the two-dimensional superimposed grayscale image is drawn; and a trough value of the distribution histogram is used as a threshold value to perform threshold segmentation on the two-dimensional superimposed grayscale image to determine a binary image of the rock skeleton and pore space.

[0034] In some embodiments, the step of performing threshold segmentation on the two-dimensional superimposed grayscale image using the trough value of the distribution histogram as a threshold to determine the binary image of the rock skeleton and pore space includes:

[0035] Using the trough value of the distribution histogram as a threshold, threshold segmentation is performed on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space to be processed;

[0036] Calculating the planar distribution porosity of the binary image of the rock skeleton and pore space to be processed;

[0037] Determining whether the planar distribution porosity is consistent with the porosity of the core sample;

[0038] If the planar distribution porosity is inconsistent with the porosity of the core sample, adjusting the threshold of the distribution histogram until the planar distribution porosity of the binary image of the rock skeleton and pore space segmented by the adjusted threshold matches the porosity of the core sample, and determining the binary image of the rock skeleton and pore space segmented by the adjusted threshold as the binary image of the rock skeleton and pore space;

[0039] If the binarized image of the rock skeleton and pore space to be processed is consistent with the porosity of the core sample, the binarized image of the rock skeleton and pore space to be processed is determined to be the binarized image of the rock skeleton and pore space.

[0040] In some embodiments, the performing of a watershed algorithm on the two-dimensional superimposed grayscale image to obtain a binary image of the laryngeal distribution characteristics includes:

[0041] Performing watershed algorithm processing on the two-dimensional superimposed grayscale image to obtain a throat distribution feature map;

[0042] Threshold segmentation is performed on the laryngeal tract distribution feature map to obtain a binary map of the laryngeal tract distribution feature.

[0043] In some embodiments, obtaining the pore structure of the core sample based on the binarized image of the rock skeleton and pore space and the binarized image of the throat distribution characteristics includes:

[0044] The binary map of the rock skeleton and pore space and the binary map of the throat distribution characteristics are added together to obtain the pore structure of the core sample.

[0045] The beneficial effects of the technical solutions provided by the embodiments of the present application include at least:

[0046] The pore structure extraction method provided in the embodiments of this application combines a method for reconstructing three-dimensional digital rock cores from rock CT scans with multiple image processing techniques, including threshold segmentation and a watershed algorithm, to extract binary images of the rock skeleton and pore space, as well as a binary image of throat distribution characteristics, ultimately obtaining the core's pore topology. This pore structure extraction method avoids the strong randomness inherent in conventional methods for extracting planar pore structures, making the reconstructed pore structure more realistic and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 A flowchart of a method for extracting pore structure based on CT scanning technology and digital image processing provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a core sample for CT scanning provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of a core CT image obtained after layer-by-layer scanning of a core sample provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of a pore-particle binary image obtained by threshold segmentation of a core CT image provided in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of a three-dimensional representative unit cell of a core sample provided in an embodiment of the present application;

[0053] Figure 6 A schematic diagram of a two-dimensional superimposed grayscale image obtained by stacking three-dimensional representative unit cells provided in an embodiment of the present application;

[0054] Figure 7 A schematic diagram of a binary image of rock skeleton and pore space obtained by threshold segmentation of a two-dimensional superimposed grayscale image provided in an embodiment of the present application;

[0055] Figure 8 A schematic diagram of throat distribution characteristics obtained by performing watershed algorithm processing on a two-dimensional superimposed grayscale image provided in an embodiment of the present application;

[0056] Figure 9 A schematic diagram of a binary map of throat distribution characteristics provided in an embodiment of the present application;

[0057] Figure 10 A schematic diagram of the pore structure of a core sample provided in an embodiment of the present application.

[0058] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0059] This application extracts rock property information from CT images of rock cores and constructs digital 3D images of reservoir rocks. It focuses on organically combining multiple image processing technologies to obtain the pore topology of the real rock core and convert its attribute morphological characteristics into a microscopic model.

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] The present invention provides a method for extracting pore structure based on CT scanning technology and digital image processing. Figure 1 As shown, the method for extracting the pore structure includes:

[0062] Step 101: Scan the core sample layer by layer based on CT scanning technology to obtain several CT images;

[0063] Step 102: performing threshold segmentation on a plurality of CT images to obtain a plurality of pore-particle binary images;

[0064] Step 103: Based on a plurality of pore-grain binary images, a three-dimensional representative unit cell of the core sample is obtained;

[0065] Step 104: obtaining a two-dimensional superimposed grayscale image corresponding to the three-dimensional representative unit body based on the pixel values ​​of the pixel points in the three-dimensional representative unit body;

[0066] Step 105: Threshold segmentation is performed on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space;

[0067] Step 106: performing watershed algorithm processing on the two-dimensional superimposed grayscale image to obtain a binary image of the throat distribution characteristics;

[0068] Step 107: Based on the binary map of the rock skeleton and pore space and the binary map of throat distribution characteristics, the pore structure of the core sample is obtained.

[0069] The pore structure extraction method provided in the embodiments of this application combines a method for reconstructing three-dimensional digital rock cores from rock CT scans with multiple image processing techniques, including threshold segmentation and a watershed algorithm, to extract binary images of the rock skeleton and pore space, as well as a binary image of throat distribution characteristics, ultimately obtaining the core's pore topology. This pore structure extraction method avoids the strong randomness inherent in conventional methods for extracting planar pore structures, making the reconstructed pore structure more realistic and reliable.

[0070] In addition, the pore structure extracted by the method of the embodiment of the present application can be used for etching of microfluidic chips to perform microfluidic visualization experiments.

[0071] For step 101, in the embodiment of the present application, it is necessary to first obtain core samples of the target block. Specifically, multiple cores of the target block are obtained, and basic physical parameters such as porosity and average permeability of the multiple cores are measured respectively. Then, the average porosity, average permeability and other physical parameters of the measured multiple cores are calculated, and the core with the closest average porosity and average permeability is selected as the core sample of the target block, such as Figure 2 As shown, the core sample is generally cylindrical. For example, the core sample is a cylindrical body with a diameter of 2.5 cm and a length of 7-8 cm.

[0072] Among them, the porosity of the core can be measured with reference to the industry standard "SY / T-6298-1997 Rock Porosity Measuring Instrument", and the permeability of the core can be measured with reference to the industry standard "SY / T 6810-2010 Rock Gas Permeability Measuring Instrument Calibration Method Standard".

[0073] In addition, in order to obtain more complete scanning data, it is usually necessary to perform wire cutting on the core sample before CT scanning, and then perform layer-by-layer CT scanning after cutting the two ends of the core sample flat. Based on the resolution requirements, a corresponding number of CT images can be obtained. For example, after CT scanning the core sample of the target block, 1200 high-resolution CT images are obtained. The obtained CT images can be as follows Figure 3 Of course, the number of CT images obtained may also be 2400, 3000, etc., and this embodiment of the present application does not limit this.

[0074] For step 102, a number of CT images can be batch-threshold segmented and binarized based on Python+OpenCV programming to segment the rock skeleton and pore space of each CT image.

[0075] Specifically, for each CT image, the grayscale value of the CT image is optimized, and the grayscale value range is mapped from 0-130 to 0-255 to increase the contrast of the image; the grayscale value of the CT image after contrast enhancement is equalized to avoid the grayscale value being concentrated in a narrow range, making it easier to distinguish the pore space and the rock skeleton; the grayscale value of the equalized CT image is Gaussian filtered to remove the noise in the image, and then the grayscale distribution histogram of the CT image is drawn. Figure 1 It is generally unimodal in distribution. The Otsu method can be used to binarize the image segmentation threshold so that the inter-class variance of the foreground (pore space) and background (rock skeleton) images is maximized.

[0076] The Otsu method is considered the optimal algorithm for threshold selection in image segmentation. It is computationally simple and unaffected by image brightness and contrast, making it widely used in digital image processing. It separates an image into background and foreground components based on its grayscale characteristics. Because variance is a measure of the uniformity of grayscale distribution, the greater the inter-class variance between the background and foreground, the greater the difference between the two components of the image. Misclassifying part of the foreground as background or vice versa results in a smaller difference between the two components. Therefore, the segmentation that maximizes the inter-class variance minimizes the probability of misclassification.

[0077] Each CT image is processed according to the above method to segment the pore particle binary image of each CT image, such as Figure 4 As shown, the black area is the rock skeleton distribution and the white area is the pore space distribution.

[0078] In this embodiment, the rock skeleton and pore space in the rock microstructure are reasonably segmented, ensuring the consistency of the binarization threshold segmentation effect of each grayscale image.

[0079] In the embodiment of the present application, core samples that can reflect the characteristics of the target block are first screened out, and then the core samples are cut flat and subjected to a CT scanning test to obtain several CT images. The CT images are then threshold segmented to obtain several pore particle binarization images to provide data for subsequent three-dimensional reconstruction.

[0080] For step 103, after obtaining several binary pore-grain maps corresponding to the core sample, the several binary pore-grain maps can be stacked from top to bottom to obtain a three-dimensional reconstructed digital core; then, a unit body is selected from the three-dimensional reconstructed digital core to determine the three-dimensional representative unit body of the core sample.

[0081] When stacking multiple binary pore-grain maps, the spacing between two adjacent binary pore-grain maps can be set to a value equal to the actual size of a single pixel. For example, if the resolution of each pixel in a CT scan image is 5 μm, and a voxel actually corresponds to a cube with a length, width, and height of 5 μm, setting the spacing between two adjacent binary pore-grain maps to 5 μm, and stacking the binary pore-grain maps corresponding to the multiple CT images from top to bottom, a 3D reconstructed digital core can be obtained.

[0082] A unit cell is selected from the 3D reconstructed digital core. Porosity is used as an evaluation parameter to determine whether the porosity of the selected unit cell is consistent with that of the core sample. For example, a unit cell of 500 pixels in length, 500 pixels in width, and 200 pixels in height can be selected from the 3D digital core. The porosity of the unit cell can be calculated as the percentage of all voxels marked as pores after threshold segmentation, representing the porosity of the rock sample.

[0083] If the porosity of the selected unit body is consistent with the porosity of the core sample, the unit body is directly determined to be the three-dimensional representative unit body of the core sample; it can be understood that the average value of the physical quantity of the representative unit body in the seepage field can approximately replace the characteristic value of the entire seepage field.

[0084] If the porosity of the selected unit volume is inconsistent with the porosity of the core sample, the three-dimensional representative unit volume of the core sample can be determined by the following two examples.

[0085] In one example, the pixel range of the unit body is increased. For example, the selected unit body of 500 pixels in length × 500 pixels in width × 200 pixels in height is increased to a unit body of 550 pixels in length × 550 pixels in width × 300 pixels in height, until the porosity of the unit body after the pixel range is increased is consistent with the porosity of the core sample, and the corresponding unit body is determined as the three-dimensional representative unit body of the core sample.

[0086] In another example, the unit body is reselected, that is, the position of the unit body in the 3D reconstructed digital core is changed until the porosity of the reselected unit body is consistent with the porosity of the core sample, and the corresponding unit body is determined as the 3D representative unit body of the core sample;

[0087] The embodiment of the present application is composed of several pore particle binary images (such as Figure 4 The three-dimensional representative unit of the core sample obtained by stacking (as shown) is as follows Figure 5 As shown, black represents the rock skeleton (image pixel value is 0) and white represents the pore space (image pixel value is 1).

[0088] In the embodiment of the present application, a three-dimensional digital core is reconstructed by using several pore particle binary maps (for example, 1,200). Since the number of pore particle binary maps used to reconstruct the three-dimensional digital core is sufficient to exceed the representative unit body, the pore structure characteristics of the selected block can be reflected to a certain extent.

[0089] In addition, the embodiment of the present application uses porosity as a constraint condition to determine the three-dimensional representative unit volume (REV), so that the extraction result of the pore structure is more accurate and closer to the pore and particle size distribution, permeability and other properties of the real reservoir.

[0090] For step 104, based on the pixel values ​​of the pixel points in the three-dimensional representative unit body, the three-dimensional representative unit body can be weightedly projected onto the XOY plane layer by layer to obtain a two-dimensional superposition image of the rock skeleton and the pore space; then the two-dimensional superposition image of the rock skeleton and the pore space is grayscale processed to obtain a two-dimensional superposition grayscale image corresponding to the three-dimensional representative unit body.

[0091] For example, the three-dimensional representative unit cell can be projected onto the XOY plane layer by layer by weighted average, with the weight of each layer f i All are set to 1;

[0092] Among them, the weighted average of each pixel The specific expression is:

[0093]

[0094] Where x i is the horizontal coordinate of a pixel point in the i-th layer, y i is the vertical coordinate of a pixel point in the i-th layer, f i is the weight of the i-th layer, and i=1,...,k, where k is the number of all layers of the three-dimensional representative unit body, for example, k is 1200.

[0095] By weightedly projecting the three-dimensional representative unit body onto the XOY plane layer by layer, a two-dimensional overlay diagram of the rock skeleton and pore space distribution is obtained. For example, the darker the color (for example, close to 1), the higher the probability that the pixel position is the pore space, and the lighter the color (for example, close to 0), the higher the probability that the pixel position is the rock skeleton.

[0096] The two-dimensional superposition image of the rock skeleton and pore space distribution is gray-scale processed to obtain the two-dimensional superposition gray-scale image corresponding to the three-dimensional representative unit body (such as Figure 6 As shown), to facilitate subsequent threshold segmentation and watershed algorithm processing.

[0097] In this embodiment, the three-dimensional representative unit body is projected onto a plane, which maps the distribution probability of the pore space and the rock skeleton at a certain pixel point in two dimensions. It is an intuitive reflection of the three-dimensional pore structure of the real core in the two-dimensional plane, avoiding the strong randomness of the conventional method of extracting the planar pore structure.

[0098] For step 105, the grayscale value of the two-dimensional superimposed grayscale image is optimized to increase the contrast of the image. Then, a distribution histogram of the two-dimensional superimposed grayscale image is drawn. The trough value of the distribution histogram is used as the threshold value, and the two-dimensional superimposed grayscale image is threshold segmented to obtain a binary image of the rock skeleton and pore space, as shown in FIG. Figure 7 shown.

[0099] Specifically, the trough value of the distribution histogram is used as a threshold value to perform threshold segmentation on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space, which may include the following steps:

[0100] The trough value of the distribution histogram is used as the threshold value to perform threshold segmentation on the two-dimensional superimposed grayscale image to obtain the binary image of the rock skeleton and pore space to be processed.

[0101] The planar distribution porosity of the binary image of the rock skeleton and pore space to be processed is calculated. For example, the planar distribution porosity can be calculated by the area ratio of the pore space in the binary image.

[0102] Determine whether the planar distribution porosity is consistent with the porosity of the core. If the planar distribution porosity is inconsistent with the porosity of the core, adjust the threshold of the distribution histogram until the planar distribution porosity of the binary image of the rock skeleton and pore space segmented by the adjusted threshold matches the porosity of the core, and determine the binary image of the rock skeleton and pore space segmented by the adjusted threshold as the binary image of the rock skeleton and pore space;

[0103] If the binary image of the rock skeleton and pore space to be processed is consistent with the porosity of the core, the binary image of the rock skeleton and pore space to be processed is determined to be the binary image of the rock skeleton and pore space.

[0104] In an embodiment of the present application, threshold segmentation is performed based on the probability size of the two-dimensional plane mapped by the three-dimensional digital core to obtain a binary map of the rock particle and pore space distribution, which can reflect the spatial characteristics of the three-dimensional digital core.

[0105] Regarding step 106 , since the pore spaces in the binary image after threshold segmentation are discontinuous between each other, in order to meet the requirements of fluid flow and migration between microfluidic chips, it is also necessary to extract the rock pore throat properties.

[0106] To make the pore structure extraction more accurate, the 2D superimposed grayscale image can be opened. This involves first eroding and then dilating the image. This process smoothes the object's outline, disconnects narrow necks, and eliminates small noise, resulting in a smoother 2D superimposed grayscale image.

[0107] In order to avoid the interference of noise points or other factors, Gaussian smoothing operation can be performed on the two-dimensional superimposed grayscale image to erase the local minimum points in the image, thereby preventing the image from being divided too finely and obtaining densely packed small areas.

[0108] Specifically, the two-dimensional superimposed grayscale image is processed by a watershed algorithm to obtain a binary image of the throat distribution characteristics, which may include the following steps:

[0109] All pixels in the grayscale image are classified according to their grayscale values, and a geodesic distance (geodesic distance is the distance of the shortest path (executable path) between two points on the earth's surface) threshold is set;

[0110] Find the pixel with the smallest gray value (marked as the lowest gray value point by default), and let the segmentation threshold increase from the minimum value, with these points as the starting points;

[0111] As the segmentation threshold increases, it will encounter neighboring pixels. The geodesic distance from these pixels to the starting point (the lowest grayscale value) is measured. If it is less than the set threshold, these pixels are submerged. Otherwise, a dam is set on these pixels. In this way, these neighboring pixels are classified, and the throat distribution characteristics are obtained, such as Figure 8 shown.

[0112] Furthermore, the throat distribution feature map is segmented by threshold value to obtain a binary map of the throat distribution feature, such as Figure 9 shown.

[0113] In an embodiment of the present application, a watershed algorithm is applied to the two-dimensional superimposed grayscale image to extract a binary image of the throat distribution characteristics to connect relatively independent pore spaces, thereby achieving the purpose of interconnecting fluids in the pore structure.

[0114] For step 107, the binary image of rock skeleton and pore space (such as Figure 7 As shown in ) and the binary map of throat distribution characteristics (as shown in Figure 9 The pore structure of the core is obtained by adding the pores of the core, that is, the real core pore topology, as shown in Figure 10 shown.

[0115] In an embodiment of the present application, the binary map of the rock skeleton and pore space is added to the binary map of the throat distribution characteristics to obtain the real core pore topology structure of the superposition of the two. This pore structure can be used for etching microfluidic chips to conduct microfluidic (fluid flow and migration) visualization experiments.

[0116] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the present invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only.

[0117] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for extracting pore structure based on CT scanning technology and digital image processing, characterized in that: include: The core samples are scanned layer by layer based on CT scanning technology to obtain several CT images; Performing threshold segmentation on the plurality of CT images respectively to obtain a plurality of pore particle binary images; Based on the plurality of pore-grain binary images, a three-dimensional representative unit cell of the core sample is obtained; Obtaining a two-dimensional superimposed grayscale image corresponding to the three-dimensional representative unit body based on pixel values ​​of pixel points in the three-dimensional representative unit body; Performing threshold segmentation on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space; Performing watershed algorithm processing on the two-dimensional superimposed grayscale image to obtain a binary image of throat distribution characteristics; Based on the binary map of the rock skeleton and pore space and the binary map of the throat distribution characteristics, the pore structure of the core sample is obtained.

2. The method for extracting pore structure according to claim 1, characterized in that: The core sample is scanned layer by layer based on the CT scanning technology to obtain several CT images, including: Obtaining the core sample of the target block; Both ends of the core sample are cut flat, and a layer-by-layer CT scanning experiment is performed on the flattened core sample to obtain the plurality of CT images.

3. The method for extracting pore structure according to claim 1, characterized in that: The threshold segmentation is performed on the plurality of CT images respectively to obtain a plurality of pore particle binary images, including: Performing grayscale processing on each CT image to obtain a grayscale image of the CT image; Noise points in the grayscale image of the CT image are removed, and threshold segmentation is performed on the CT image based on the Otsu method to obtain a pore particle binarization image corresponding to each CT image.

4. The method for extracting pore structure according to claim 1, characterized in that: The method of obtaining a three-dimensional representative unit cell of the core sample based on the plurality of pore-grain binary images includes: The plurality of pore-grain binary images are stacked from top to bottom to obtain a three-dimensional reconstructed digital core; A unit volume is selected from the three-dimensional reconstructed digital rock core to determine the three-dimensional representative unit volume of the core sample.

5. The method for extracting pore structure according to claim 4, characterized in that: The selecting a unit body from the three-dimensional reconstructed digital rock core to determine the three-dimensional representative unit body of the core sample includes: Using porosity as an evaluation parameter, determining whether the porosity of the selected unit body is consistent with the porosity of the core sample; If the porosity of the selected unit body is inconsistent with the porosity of the core sample, increase the pixel range of the unit body until the porosity of the unit body after the pixel range is increased is consistent with the porosity of the core sample, and determine the corresponding unit body as the three-dimensional representative unit body of the core sample; or If the porosity of the selected unit body is inconsistent with the porosity of the core sample, reselecting a unit body until the porosity of the reselected unit body is consistent with the porosity of the core sample, and determining the corresponding unit body as the three-dimensional representative unit body of the core sample; If the porosity of the selected unit body is consistent with the porosity of the core sample, the unit body is determined to be the three-dimensional representative unit body of the core sample.

6. The method for extracting pore structure according to claim 1, characterized in that: The obtaining, based on the pixel values ​​of the pixel points in the three-dimensional representative unit body, a two-dimensional superimposed grayscale image corresponding to the three-dimensional representative unit body includes: Based on the pixel values ​​of the pixel points in the three-dimensional representative unit volume, the three-dimensional representative unit volume is weightedly projected onto the XOY plane layer by layer to obtain a two-dimensional superposition map of the rock skeleton and pore space; The two-dimensional superposition image of the rock skeleton and the pore space is gray-scale processed to obtain the two-dimensional superposition gray-scale image corresponding to the three-dimensional representative unit body.

7. The method for extracting pore structure according to claim 1, characterized in that: The step of performing threshold segmentation on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space includes: A distribution histogram of the two-dimensional superimposed grayscale image is drawn; and a trough value of the distribution histogram is used as a threshold value to perform threshold segmentation on the two-dimensional superimposed grayscale image to determine a binary image of the rock skeleton and pore space.

8. The method for extracting pore structure according to claim 7, characterized in that: The step of performing threshold segmentation on the two-dimensional superimposed grayscale image using the trough value of the distribution histogram as a threshold to determine the binary image of the rock skeleton and pore space includes: Using the trough value of the distribution histogram as a threshold, threshold segmentation is performed on the two-dimensional superimposed grayscale image to obtain a binary image of the rock skeleton and pore space to be processed; Calculating the planar distribution porosity of the binary image of the rock skeleton and pore space to be processed; Determining whether the planar distribution porosity is consistent with the porosity of the core sample; If the planar distribution porosity is inconsistent with the porosity of the core sample, adjusting the threshold of the distribution histogram until the planar distribution porosity of the binary image of the rock skeleton and pore space segmented by the adjusted threshold matches the porosity of the core sample, and determining the binary image of the rock skeleton and pore space segmented by the adjusted threshold as the binary image of the rock skeleton and pore space; If the binarized image of the rock skeleton and pore space to be processed is consistent with the porosity of the core sample, the binarized image of the rock skeleton and pore space to be processed is determined to be the binarized image of the rock skeleton and pore space.

9. The method for extracting pore structure according to claim 1, characterized in that: The step of performing watershed algorithm processing on the two-dimensional superimposed grayscale image to obtain a binary image of the throat distribution characteristics includes: Performing watershed algorithm processing on the two-dimensional superimposed grayscale image to obtain a throat distribution feature map; Threshold segmentation is performed on the laryngeal tract distribution feature map to obtain a binary map of the laryngeal tract distribution feature.

10. The method for extracting pore structure according to claim 1, characterized in that: The method of obtaining the pore structure of the core sample based on the binarized image of the rock skeleton and pore space and the binarized image of the throat distribution characteristics includes: The binary map of the rock skeleton and pore space and the binary map of the throat distribution characteristics are added together to obtain the pore structure of the core sample.

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