Rock core image registration method and device, equipment, storage medium and program product

Abstract

The invention relates to the technical field of image processing, in particular to a core image registration method, device and equipment, a storage medium and a program product, and the core image registration method comprises the following steps: acquiring a first scanning image corresponding to a target core sample; acquiring a second scanning image corresponding to the target rock core sample; and registering the first scanning image and the second scanning image based on the marker by using a feature point matching algorithm and a spatial transformation algorithm to obtain a registered image of the target rock core sample, the registered image being used for analysis processing of the rock core sample. Based on the characteristics of strong identification degree and stability in the scanning process of the marker, the image registration problem caused by displacement or deformation of the rock core sample due to experimental operation can be effectively solved, and the accuracy of rock core image registration before and after the experimental operation is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and is a core image registration method, a core image registration device, an electronic device, a storage medium and a program product. BACKGROUND

[0002] The development of unconventional oil and gas resources (such as shale gas and tight oil) has become an important development direction in the global energy field, and in the development of unconventional oil and gas resources, it is crucial to accurately obtain the micro-pore structure and fracture distribution information of rocks.

[0003] Existing micro-CT technology has become an important means for studying the microstructure of unconventional oil and gas reservoirs because it can provide high-resolution three-dimensional images. However, during multiple scanning processes of micro-CT technology, the core sample may be displaced or deformed due to experimental operations (such as soaking and displacement), resulting in low accuracy of image registration and inaccurate micro-pore structure and fracture distribution information of rocks obtained. Therefore, there is an urgent need to propose a method for improving the accuracy of core image registration.

[0004] The following are related literature on core image processing or registration technology: Patent application document with publication number CN119555617A discloses a method for reconstructing the hyperspectral image of the outer surface of a columnar core, which includes the following steps: step 1: collecting the multi-angle hyperspectral image of the columnar core; step 2: cropping the effective area of the multi-angle hyperspectral image of the columnar core; step 3: performing radiation correction on the multi-angle hyperspectral image of the columnar core; step 4: performing geometric correction on the multi-angle hyperspectral image of the columnar core; step 5: splicing the multi-angle hyperspectral image of the columnar core; and step 6: removing the overlapping area of the spliced image to obtain the reconstruction result of the hyperspectral image of the outer surface of the columnar core. The document processes the hyperspectral image of the core, and mainly processes the hyperspectral image through radiation correction, geometric correction and image splicing, which is not applicable to the registration of three-dimensional images obtained by CT scanning.

[0005] The patent application document with the publication number CN114648563A discloses a core CT scanning image and spliced SEM image registration method: it includes: S1, making a positioning mark; S2, acquiring an X-CT three-dimensional image; S3, acquiring a spliced SEM image of a section; S4, based on the positioning mark, acquiring a height section image corresponding to the spliced SEM image on the X-CT three-dimensional image; S5, registering the spliced SEM image and the section image to obtain a plurality of registration points; S6, determining an inclination axis based on the registration points; S7, rotating the X-CT three-dimensional image around the inclination axis to obtain a rotated image; S8, registering the rotated image and the spliced SEM image, and the section image with the most matching points is the most appropriate section image of the rotation angle. The document mainly registers the spliced SEM image and the section image, which is not suitable for core image registration before and after experimental operation. SUMMARY

[0006] The present application provides a core image registration method, device, equipment, storage medium and program product, which overcomes the shortcomings of the prior art and effectively solves the problem of low accuracy of core image registration before and after experimental operation.

[0007] One of the technical solutions of the present application is realized by the following measures: a core image registration method, comprising: acquiring a first scanning image corresponding to a target core sample; wherein the target core sample is a core sample with a marker fixed on the surface, and the first scanning image is an image obtained by scanning the target core sample before performing experimental operation on the target core sample; acquiring a second scanning image corresponding to the target core sample; wherein the second scanning image is an image obtained by scanning the target core sample after performing experimental operation on the target core sample; using a feature point matching algorithm and a spatial transformation algorithm, registering the first scanning image and the second scanning image based on the marker, to obtain a registered image of the target core sample, wherein the registered image is used for analysis and processing of the core sample.

[0008] The following is a further optimization or / and improvement of the above-mentioned one of the technical solutions of the present application: Before the above-mentioned acquiring a first scanning image corresponding to a target core sample, the method further comprises: using a dispensing method to fix the marker to the surface of the core sample to obtain the target core sample.

[0009] The above-mentioned first scanning image and second scanning image are both three-dimensional images.

[0010] The above-mentioned marker satisfies at least one of the following: The marker is a rutile titanium dioxide particle; The size of the marker is greater than or equal to 10 microns and less than or equal to 20 microns. The number of the marker is greater than or equal to 5 and less than or equal to 8.

[0011] The above uses a feature point matching algorithm and a spatial transformation algorithm to register the first scanning image and the second scanning image based on the marker, to obtain a registered image of the target core sample, including: Using a feature point matching algorithm, the first scanning image and the second scanning image are calculated based on the marker, to obtain a rotation matrix and a translation vector. Using the rotation matrix and the translation vector, the second scanning image is transformed based on a spatial transformation algorithm to obtain a registered image.

[0012] The above uses a feature point matching algorithm to calculate the first scanning image and the second scanning image based on the marker, to obtain a rotation matrix and a translation vector, including: Using a feature point matching algorithm, the first scanning image and the second scanning image are calculated based on the marker, to obtain a first centroid and a second centroid. According to the first centroid and the second centroid, a covariance matrix is obtained. The covariance matrix is singular value decomposed, and the rotation matrix is calculated according to the singular value decomposition result. The first centroid, the second centroid and the rotation matrix are substituted into a translation vector calculation formula to obtain the translation vector.

[0013] The above said obtaining a first scanning image corresponding to a target core sample, including: Based on a target parameter, a scanning operation is performed on the target core sample to obtain the first scanning image; the target parameter at least includes: scanning angle, exposure time, magnification; The above said obtaining a second scanning image corresponding to the target core sample, including: An experimental operation is performed on the target core sample. Based on the target parameter, a scanning operation is performed on the target core sample to obtain the second scanning image.

[0014] The second technical solution of the present application is realized by the following measures: a core image registration device, comprising: The first scanning image module is configured to acquire a first scanning image corresponding to a target core sample, wherein the target core sample is a core sample with a marker fixed on the surface thereof, and the first scanning image is an image obtained by scanning the target core sample before performing an experimental operation on the target core sample. The second scanning image module is configured to acquire a second scanning image corresponding to the target core sample, wherein the second scanning image is an image obtained by scanning the target core sample after performing the experimental operation on the target core sample. The registration module is configured to perform registration on the first scanning image and the second scanning image based on the marker by using a feature point matching algorithm and a spatial transformation algorithm, to obtain a registered image of the target core sample, and the registered image is used for analysis and processing of the core sample.

[0015] The third technical solution of the present application is achieved by the following measures: an equipment, comprising a memory, a processor and a computer processing program stored on the memory and executable on the processor, wherein the computer processing program is configured to implement the core image registration method.

[0016] The fourth technical solution of the present application is achieved by the following measures: a storage medium, wherein the storage medium stores a computer processing program, and the computer processing program is executed by a processor to implement the core image registration method.

[0017] The fifth technical solution of the present application is achieved by the following measures: a program product, wherein instructions in the program product are executed by a processor of an electronic device to enable the electronic device to perform the core image registration method.

[0018] The core sample in the present application can have a marker fixed on the surface thereof, and by acquiring scanning images of the core sample with the marker fixed on the surface thereof before and after an experimental operation, the obtained images are matched and transformed to obtain a registered image of the target core sample, so as to realize registration of core images. In this way, based on the characteristics of the marker having strong identification and stability in the scanning process, the problem of low accuracy of image registration caused by displacement or deformation of the core sample due to the experimental operation can be effectively solved, and the accuracy of image registration of the core sample before and after the experimental operation is significantly improved. Further, by using the registered image of the target core sample, micro information of the core sample can be obtained, and the influence of experimental processing on the core sample can be analyzed. BRIEF DESCRIPTION OF DRAWINGS

[0019] FIG. 1 is a flowchart of a core image registration method according to an embodiment of the present application. Figure 1 FIG. 1 is a flowchart of a core image registration method according to an embodiment of the present application.

[0020] FIG. 1 is a flowchart of a core image registration method according to an embodiment of the present application.Figure 2 It is a slice (XY direction) of a scanned image of a core sample with markers fixed on its surface.

[0021] Appendix Figure 3 It is a slice (YZ direction) of a scanned image of a core sample with markers fixed on its surface.

[0022] Appendix Figure 4 It is a slice of the first scan image of the target core sample before the experiment.

[0023] Appendix Figure 5 It is a slice of the registered image.

[0024] Appendix Figure 6 It is a three-dimensional image of the skeleton expansion zone (i.e., clay expansion zone) of the target core sample after the experiment.

[0025] Appendix Figure 7 This is a schematic diagram of the core image registration device provided in the embodiments of this application.

[0026] Appendix Figure 8 This is one of the structural schematic diagrams of the electronic device provided in the embodiments of this application. Detailed Implementation

[0027] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0028] In this invention, it should be noted that the terms "first," "second," "third," etc., are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the modules or elements referred to must have a specific order and operation, nor should they be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. They are used to distinguish similar objects.

[0029] In this invention, image registration can be performed using either two-dimensional or three-dimensional images. For example, both the first and second scanned images can be two-dimensional images, or both can be three-dimensional images. Of course, it is particularly applicable to three-dimensional images.

[0030] The present invention will be further described below with reference to embodiments: Example 1: As Figure 1 As shown, a core image registration method includes the following steps: Step 101: Obtain the first scan image corresponding to the target core sample; wherein, the target core sample is a core sample with markers fixed on its surface, and the first scan image is an image obtained by scanning the target core sample before performing experimental operations on the target core sample; In step 102, a second scanning image corresponding to the target core sample is acquired; wherein the second scanning image is an image obtained by scanning the target core sample after performing an experimental operation on the target core sample. In step 103, a feature point matching algorithm and a space transformation algorithm are used to register the first scanning image and the second scanning image based on the marker, so as to obtain a registered image of the target core sample, and the registered image is used for analysis and processing of the core sample.

[0031] The embodiment of the present application first acquires a first scanning image corresponding to a target core sample; secondly, acquires a second scanning image corresponding to the target core sample; and finally, uses a feature point matching algorithm and a space transformation algorithm to register the first scanning image and the second scanning image based on the marker, so as to obtain a registered image of the target core sample, and the registered image is used for analysis and processing of the core sample. The core sample to be analyzed in the present application can have a marker fixed on the surface thereof. By acquiring scanning images of the core sample before and after experimental operation, the obtained images are subjected to feature point matching and space transformation to obtain a registered image of the target core sample, so as to realize registration of the core image. In this way, based on the characteristics of the marker having strong identification and stability in the scanning process, the image registration problem caused by displacement or deformation of the core sample due to experimental operation can be effectively solved, so as to significantly improve the accuracy of image registration. Further, through the registered image of the target core sample, micro information of the core sample can be obtained, which is convenient for analyzing the influence of experimental processing on the core sample.

[0032] In step 101, the target core sample is scanned before experimental operation to obtain a first scanning image.

[0033] In the embodiment, it should be noted that the target core sample can be a core sample used for experiments, and the surface of the target core sample has a marker fixed thereon, which is convenient for subsequent image registration. The first scanning image can be an image obtained by scanning the target core sample before experimental operation, which records the original state of the core sample.

[0034] The marker can be an object for marking the surface of the core sample, and can be an object capable of forming a high-contrast marker point in a CT image. The marker can be an object having the stability characteristic in the scanning process, and can be titanium dioxide particles, and further can be rutile titanium dioxide particles, but the present application does not limit the specific form of the marker. In addition, the number of markers is controllable and uniformly distributed on the surface of the core sample.

[0035] In step 102, after the experimental operation on the target core sample is completed, the target core sample is scanned again to obtain a second scanning image. The second scanning image records the state change of the target core sample after the experiment.

[0036] After the first scanning image and the second scanning image are obtained, image preprocessing can be performed on the first scanning image and the second scanning image obtained each time. The image preprocessing includes denoising, contrast enhancement, and other preprocessing operations to improve the image quality. After image preprocessing, the first scanning image and the second scanning image are registered.

[0037] In step 103, the first scanning image and the second scanning image are registered based on the markers on the surface of the core sample using a feature point matching algorithm and a spatial transformation algorithm to obtain a registered image. The registered image aligns the images before and after the experiment, and the registered image is used for subsequent analysis and processing. Further, the analysis and processing can extract information such as micro-pore structure and crack distribution of the core sample from the registered image to provide data support for subsequent quantitative analysis. The specific steps of analysis and processing can be as follows: 1. Image segmentation: using image processing technology to segment the registered series of three-dimensional images to identify pore and crack information. 2. Feature extraction: extracting geometric features of pores and cracks, such as size, shape, distribution, etc. 3. Data analysis: statistically analyzing the extracted features to generate a detailed report for further research.

[0038] In this embodiment, it should be noted that the feature point matching algorithm can be an algorithm for detecting feature points in an image and matching them. The feature point matching algorithm can be a least squares-based feature point matching algorithm, or any other algorithm that can achieve the same function. The spatial transformation algorithm can be an algorithm for converting an image from one coordinate system to another coordinate system. The spatial transformation algorithm can be a rigid body transformation algorithm, or any other algorithm that can achieve the same function. The spatial transformation algorithm can adjust the position, direction, and size of the image based on the results of the feature point matching to align the two images. The registered image can be the result of aligning the images before and after the experimental operation after feature point matching and spatial transformation, and can be used for subsequent core sample analysis and processing.

[0039] In addition, assuming that the second scanning image is an image collected at time t, the registered image can be considered as an image of the core sample at time t without displacement or deformation due to experimental operation, and each second scanning image collected at different times will have a corresponding registered image.

[0040] Exemplarily, assume that there is a target core sample with some markers (such as rutile titanium dioxide particles or small colored markers) fixed on its surface. Before the experiment, the target core sample is scanned to obtain a first scan image. Then, a series of experimental operations are performed on the target core sample, such as applying pressure, injecting fluid, fluid immersion, etc. After the experiment, the target core sample is scanned again to obtain a second scan image.

[0041] Next, a feature point matching algorithm is used to find the corresponding feature points of the markers in the first scan image and the second scan image, where the feature points are the corresponding positions of the markers in the first scan image and the second scan image. Then, a spatial transformation algorithm is used to adjust the position, direction and size of the second scan image according to the matching relationship of these feature points, so that it is aligned with the first scan image, and a registered image is obtained. In this way, by comparing the registered image, the changes of the core sample before and after the experiment can be analyzed, such as the generation of cracks, the change of pores, etc., thereby providing a basis for geological research and engineering application.

[0042] In the above embodiment, the method further comprises, before obtaining the first scan image corresponding to the target core sample: The marker is fixed to the surface of the core sample by using a dispensing method to obtain the target core sample.

[0043] First, find the core sample to be analyzed, and then use a dispensing method to fix the marker on the surface of the core sample to obtain the target core sample. Using the dispensing method can ensure the stability of the marker during scanning, and at the same time, the dispensing method is flexible and suitable for laboratory environment. By fixing the marker on the surface of the core sample and then performing image registration, the state of the core sample before and after the experiment can be more accurately compared, thereby more accurately analyzing the changes of the core sample.

[0044] In this embodiment, it should be noted that the dispensing method can be a method of fixing an object to the surface of another object by using glue. In core sample analysis, the dispensing method is used to fix the marker on the surface of the core sample to ensure that the marker remains stable during the experiment.

[0045] According to the experimental requirements, select the core sample to be analyzed. Use the dispensing method to fix the marker to the surface of the core sample. The dispensing method can ensure that the marker is firmly attached to the surface of the core sample and will not fall off during subsequent experimental operations.

[0046] Exemplarily, the rutile titanium dioxide particles are fixed on the surface of the core sample by dispensing. A small amount of glue (such as epoxy resin or nano glue) is dispensed on the surface of the sample by a micro dispensing device (such as a micro dispensing needle or a micro syringe), and the particles are placed on the glue dots one by one to ensure uniform distribution and controllable number (5 to 8).

[0047] In an embodiment, the marker further satisfies at least one of the following: The marker is a rutile titanium dioxide particle; The size of the marker is greater than or equal to 10 microns and less than or equal to 20 microns; The number of the marker is greater than or equal to 5 and less than or equal to 8.

[0048] In an embodiment, the marker further satisfies at least one of the following: the material of the marker is a rutile titanium dioxide particle; the size of the marker is in a preset range, preferably, the size range is greater than or equal to 10 microns and less than or equal to 20 microns; and the number of the marker is in a preset range, preferably, the range is greater than or equal to 5 and less than or equal to 8. The rutile titanium dioxide particle has excellent X-ray absorption capacity and can form a high-contrast marker point in the CT image, which helps to improve the accuracy of the core image registration method. Because the core sample used in general rock scanning has a diameter range of 3 to 25 millimeters, the corresponding CT scanning resolution is 3 to 15 microns, therefore, the flexible selection of the marker with a size of 10 to 20 microns is more conducive to registration. In addition, because the core sample is irregular and has multiple surfaces, the selection of 5 to 8 markers uniformly fixed on the surface of the core sample also helps to register the core image.

[0049] The core sample with the marker fixed on the surface is shown in FIGS. 1 to 3. Figure 2 、 Figure 3 As shown in FIGS. 1 to 3, Figure 2 、 Figure 3 In FIGS. 1 to 3, the white bright spots pointed by the red arrows are the markers.

[0050] In an embodiment, the method for registering the first scanning image and the second scanning image based on the marker by using the feature point matching algorithm and the space transformation algorithm to obtain the registered image of the target core sample comprises the following steps: Calculating the first scanning image and the second scanning image based on the marker by using the feature point matching algorithm to obtain a rotation matrix and a translation vector; Transforming the second scanning image based on the space transformation algorithm by using the rotation matrix and the translation vector to obtain the registered image.

[0051] In this embodiment, the rotation matrix and the translation vector between the first scan image and the second scan image are calculated by using the feature point matching algorithm, and then the rotation matrix and the translation vector are applied to the second scan image to realize the alignment of the images before and after the experiment operation, and the registered images are obtained. Through the feature point matching algorithm and the spatial transformation algorithm, the images before and after the experiment can be accurately aligned, ensuring the accuracy of image registration. Secondly, the spatial transformation using the rotation matrix and the translation vector can effectively handle the rotation and translation changes in the image, improving the robustness of registration. In addition, the registered images can be directly used for subsequent analysis, reducing the error caused by image misalignment and improving the analysis efficiency.

[0052] In this embodiment, it should be noted that the rotation matrix can be a matrix for describing the rotation of the image, and the rotation matrix can represent the rotation angle and direction of the image in space. The translation vector can be a vector for describing the translation of the image, and the translation vector can represent the translation distance of the image in space.

[0053] The feature points corresponding to the markers in the first scan image and the second scan image are detected using the feature point matching algorithm. The corresponding relationship between the feature points is calculated by the matching algorithm, and the rotation matrix and the translation vector are obtained. The rotation matrix and the translation vector calculated are used to perform spatial transformation on the second scan image. The second scan image is adjusted to the position aligned with the first scan image by the spatial transformation algorithm, and the registered image is obtained.

[0054] In some implementations, the corresponding feature points between the first scan image and the second scan image can be found by the feature point matching algorithm, and then combined with the rigid body transformation algorithm based on the least squares method to realize image alignment and obtain the registered image.

[0055] In some implementations, the corresponding feature points between the first scan image and the second scan image can be found by the feature point matching algorithm (such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF)), and then combined with the rigid body transformation algorithm or the non-rigid body transformation algorithm to realize image alignment and obtain the registered image.

[0056] Embodiment 5: As an optimization of the above-mentioned embodiment 4, the use of the feature point matching algorithm to calculate the first scan image and the second scan image based on the markers to obtain the rotation matrix and the translation vector comprises: The first scan image and the second scan image are calculated based on the markers respectively by using the feature point matching algorithm to obtain the first centroid and the second centroid; According to the first centroid and the second centroid, a covariance matrix is obtained; The covariance matrix is singular value decomposed, and a rotation matrix is calculated according to a singular value decomposition result. The first centroid, the second centroid and the rotation matrix are substituted into a translation vector calculation formula to obtain the translation vector.

[0057] In the embodiment, the first centroid and the second centroid between the first scan image and the second scan image are calculated by using a feature point matching algorithm, and then the covariance matrix is calculated. The covariance matrix is singular value decomposed, and then the rotation matrix is calculated according to the singular value decomposition result. Finally, the translation vector is obtained according to the first centroid, the second centroid and the rotation matrix. The rotation matrix and the translation vector are accurately calculated through the feature point matching algorithm and the singular value decomposition, which can ensure the high precision of image registration. The method based on the centroid and the covariance matrix can effectively process the noise and outliers in the image, and improve the robustness of registration. In addition, the calculation efficiency can be optimized by using the singular value decomposition method.

[0058] In the embodiment, it should be noted that the centroid can be a weighted average position of the feature points, and the centroid can be used to describe the center position of the feature points. The feature points can be corresponding positions of the markers in the first scan image and the second scan image. The covariance matrix can be used to describe the linear relationship between two groups of data, and is an important tool for calculating the rotation matrix. The singular value decomposition can be a matrix decomposition method for calculating the rotation matrix.

[0059] The feature points corresponding to the markers in the first scan image and the second scan image are detected by using the feature point matching algorithm. According to the positions of the feature points, the first centroid of the first scan image and the second centroid of the second scan image are calculated. Then, a centroid removal operation can be performed, specifically, each feature point is subtracted by the corresponding centroid to obtain a first point set and a second point set. Based on the first point set and the second point set, the covariance matrix between the two point sets is calculated. The covariance matrix is singular value decomposed (Singular Value Decomposition, SVD), and the rotation matrix is calculated according to the singular value decomposition result. Finally, the first centroid, the second centroid and the rotation matrix are substituted into the calculation formula of the translation vector to obtain the translation vector.

[0060] Embodiment 6: As an optimization of the above-mentioned embodiments, the obtaining of the first scan image corresponding to the target core sample comprises: Performing a scanning operation on the target core sample based on a target parameter to obtain the first scan image; the target parameter at least includes a scanning angle, an exposure time and a magnification; The obtaining of the second scan image corresponding to the target core sample comprises: performing an experimental operation on the target core sample; performing a scanning operation on the target core sample based on the target parameter, to obtain the second scanning image.

[0061] In this embodiment, the acquisition of the first scanning image and the second scanning image requires scanning the target core sample before and after the experimental operation based on the same target parameter. By scanning the target core sample using the same target parameter (scanning angle, exposure time, magnification), the consistency of the first scanning image and the second scanning image in imaging conditions is ensured, which helps to reduce the image differences caused by different scanning parameters, thereby improving the accuracy and reliability of image registration. The images before and after the experimental operation are acquired under the same scanning conditions, which can more accurately reflect the changes of the core sample during the experimental process and provide more reliable data support for subsequent analysis and processing.

[0062] In this embodiment, it should be noted that the target parameter can be a parameter that needs to be set during scanning, including scanning angle, exposure time, magnification, etc. The target parameter can directly affect the quality and characteristics of the scanning image. The scanning angle can be the angle of the scanning device relative to the core sample during scanning. Different scanning angles can capture different characteristics of the sample. The exposure time can be the exposure time of the scanning device when acquiring the image. The length of the exposure time will affect the brightness and contrast of the image. The magnification can be the magnification of the scanning image. Different magnifications can be used to observe the details of the core sample at different scales. The experimental operation can be a series of experimental treatments on the target core sample, such as applying pressure, injecting fluid, fluid immersion, etc., the purpose is to study the changes of the core sample under these operations.

[0063] According to the experimental requirements, determine the scanning angle, exposure time, magnification and other parameters. Before the experimental operation, scan the target core sample using the above target parameters to obtain the first scanning image. The first scanning image records the initial state of the core sample. Perform a predetermined experimental operation on the target core sample, such as applying pressure, injecting fluid, fluid immersion, etc. After the experimental operation is completed, scan the target core sample using the same target parameters as when obtaining the first scanning image to obtain the second scanning image. The second scanning image records the state of the core sample after the experiment.

[0064] For ease of understanding, the present application provides an example of a core image registration method in the registration process.

[0065] (1) Particle selection and fixation: Particle selection: Select rutile titanium dioxide (TiO2) particles with a particle size of 10-20 microns as markers (the sample diameter range commonly used for rock scanning is 3-25 mm, and the corresponding CT scanning resolution is 3-15 microns, so selecting rutile particles with a particle size of 10-20 microns is more conducive to pixel-level registration).

[0066] Particle fixation: Rutile titanium dioxide particles are precisely fixed on the surface of the core sample using dispensing method. A small amount of glue (such as epoxy resin or nano glue) is dispensed on the surface of the core sample using a micro dispensing device (such as a micro dispensing needle or a micro syringe), and the particles are placed one by one on the glue spot, ensuring uniform distribution and controllable number (5-8).

[0067] (2) Micro-CT scanning: Scanning strategy: The core sample with fixed markers is scanned multiple times. Each time, record the scanning parameters (such as scanning angle, exposure time, magnification), and ensure consistent scanning conditions.

[0068] Data acquisition: Obtain three-dimensional image data at different scanning stages, including before the experiment (i.e. the first scanning image), after the experiment (such as soaking, displacement) (i.e. the second scanning image), etc. Among them, soaking is vacuum fluid soaking of core sample; displacement is to wrap the core sample and inject fluid.

[0069] (3) Image processing and registration: Preprocessing: Perform denoising, contrast enhancement and other preprocessing operations on the three-dimensional images obtained by each scanning to improve image quality. This step ensures the accuracy of the subsequent feature point matching and registration process.

[0070] Feature point matching algorithm and spatial transformation algorithm: It is assumed that the relative position of 5-8 rutile microparticles and the core sample is basically unchanged, and the size of the outer edge of the rock skeleton is basically unchanged. Since it is not possible to achieve micron-level alignment every time when taking out the container for soaking and manually placing it on the CT holder for scanning, the scanning data body (especially the three-dimensional coordinates of the 5-8 bright spots) in the obtained scanning image cannot be completely consistent. When registering, ignore part of the skeleton entity, in order to register these feature points (i.e. 5-8 bright spots), the image can only be rotated, translated, and cannot be deformed. The least squares-based rigid body transformation is the simplest and most convenient.

[0071] Marker point extraction: Extract the rutile titanium dioxide particles (5-8) fixed on the surface of the core sample from the three-dimensional image of each scanning as marker points.

[0072] Centroid calculation: Calculate the centroid of all the marker points in the reference scan image (first scan image) and the target scan image (second scan image) respectively, i.e. the first centroid C1 and the second centroid C2. The first centroid C1 is specifically the coordinates of the centroid of all the marker points in the first scan image; the second centroid C2 is specifically the coordinates of the centroid of all the marker points in the second scan image.

[0073] In the formula, C1 is the coordinates of the centroid of all the marker points in the first scan image; C2 is the coordinates of the centroid of all the marker points in the second scan image; N is the total number of marker points; is the coordinates of the i-th marker point in the first scan image, and the coordinates of all the marker points in the first scan image form a reference point set . is the coordinates of the i-th marker point in the second scan image, and the coordinates of all the marker points in the second scan image form a target point set .

[0074] Centroid removal: Subtract the centroid coordinates of each marker point from the corresponding centroid coordinates to obtain new point sets Q1 and Q2.

[0075] In the formula, C1 is the coordinates of the centroid of all the marker points in the first scan image; C2 is the coordinates of the centroid of all the marker points in the second scan image; i is the marker point; is the coordinates of the i-th marker point in the reference point set ; is the centroid-removed coordinates of the reference point set, and the centroid-removed coordinates of all the reference point sets form a first point set Q1; is the coordinates of the i-th marker point in the target point set ; is the centroid-removed coordinates of the target point set, and the centroid-removed coordinates of all the target point sets form a second point set Q2.

[0076] Covariance matrix calculation: Calculate the covariance matrix H between the two centroid-removed point sets.

[0077] In the formula, H is the covariance matrix; is the centroid-removed coordinates of the reference point set; is the transpose of the centroid-removed coordinates of the target point set ; i is the marker point; N is the total number of marker points.

[0078] Singular value decomposition (SVD): Perform singular value decomposition on the covariance matrix H.

[0079] H is the covariance matrix; U is the orthogonal matrix after singular value decomposition; V is the diagonal matrix after singular value decomposition.

[0080] The column vectors of the orthogonal matrix U are called the left singular vectors of H, the column vectors of the diagonal matrix V are called the right singular vectors of H, and the elements on the diagonal of the diagonal matrix V are the singular values of H.

[0081] Rotation matrix calculation: calculate the optimal rotation matrix R according to the SVD result.

[0082] where R is the rotation matrix; V is the diagonal matrix after singular value decomposition; is the transpose of the orthogonal matrix U after singular value decomposition.

[0083] Translation vector calculation: calculate the translation vector t according to the translation vector calculation formula: where t is the translation vector; R is the rotation matrix; C1 is the coordinate of the centroid of all marker points in the first scan image; C2 is the coordinate of the centroid of all marker points in the second scan image.

[0084] Through the above steps, the optimal rotation matrix R and translation vector t can be obtained.

[0085] Application of transformation: apply the rotation matrix R and translation vector t calculated above to the entire three-dimensional image data body of the second scan image (including the rock skeleton part). Specifically, for any point P of the second scan image, its transformed coordinates P' can be calculated by the following formula: After the second scan image is coordinate-transformed according to the formula, the registered image is obtained. Through the coordinate transformation, the coincidence between the marker points in the reference scan image (i.e. the first scan image) and the marker points in the target scan image (the second scan image) is the highest, so that the three-dimensional alignment of the data bodies before and after the target core sample experiment is realized.

[0086] (4) Result verification and analysis: Registration accuracy evaluation: quantitative indicators such as structural similarity index (Structural Similarity Index, SSIM) and peak signal-to-noise ratio (Peak Signal-to-Noise Ratio, PSNR) can be used to evaluate the accuracy of the registration result.

[0087] (5) Microstructure extraction: From the registered images, information such as micro-pore structure, fracture distribution, etc. of the target core sample is extracted to provide data support for subsequent quantitative analysis. The specific steps are as follows: Image segmentation: using image processing technology to segment the registered series of three-dimensional images to identify the pore and fracture regions.

[0088] Feature extraction: extracting the geometric features of pores and fractures, such as size, shape, distribution, etc.

[0089] Data analysis: statistical analysis is performed on the extracted features to generate a detailed report for further research.

[0090] According to the above core image registration method, the registration process of the target core sample before and after the experiment (high clay content rock water sensitivity experiment) is illustrated.

[0091] Before the high clay content rock water sensitivity experiment, the target core sample is scanned to obtain the first scan image, and the slice (i.e. two-dimensional image) of the first scan image is shown in Figure 4 The target core sample after the experiment is scanned again to obtain the second scan image, and the second scan image is registered with the first scan image according to the above core image registration method to obtain the registered image, and the slice of the registered image is shown in Figure 5 By comparing Figure 4 , Figure 5 It can be seen that after the rock water sensitivity experiment, the pores of the target core sample change, because the clay in the pores expands, and the expansion area is shown in Figure 5 the red area.

[0092] At the same time, by comparing Figure 4 and Figure 5 It can be seen that the pore positions (see Figure 4 , Figure 5 black area) of the target core sample are highly aligned, indicating that the registration accuracy is good.

[0093] The gray-scale images of the registered image and the first scan image are respectively threshold segmented, and then the two images are subtracted to obtain the three-dimensional image of the skeleton expansion area (i.e. clay expansion area) of the target core sample after the experiment (see Figure 6 ), thereby clearly and intuitively displaying the three-dimensional information of clay expansion of the target core sample after the high clay content rock water sensitivity experiment.

[0094] Example 7: As shown in Figure 7 , a core image registration device comprises: The first scanning image module 201 is configured to acquire a first scanning image corresponding to a target core sample; the target core sample is a core sample with a marker fixed on the surface, and the first scanning image is an image obtained by scanning the target core sample before performing an experimental operation on the target core sample. The second scanning image module 202 is configured to acquire a second scanning image corresponding to the target core sample; the second scanning image is an image obtained by scanning the target core sample after performing an experimental operation on the target core sample. The registration module 203 is configured to perform registration on the first scanning image and the second scanning image based on the marker by using a feature point matching algorithm and a spatial transformation algorithm, to obtain a registered image of the target core sample, and the registered image is used for analysis and processing of the core sample.

[0095] In some embodiments, the electronic device 400 can further include a computer program configured to implement the core image registration method. Figure 8

[0096] As shown in Figure 8 The device 400 further includes, but is not limited to, a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, and the like.

[0097] Those skilled in the art can understand that the electronic device 400 can further include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 410 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. Figure 8 The electronic device structure shown in the above embodiment does not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than those shown, or combine certain components, or have a different arrangement of components, which will not be described here.

[0098] The processor 410 is configured to: acquire a first scanning image corresponding to a target core sample; the target core sample is a core sample with a marker fixed on the surface, and the first scanning image is an image obtained by scanning the target core sample before performing an experimental operation on the target core sample. ​acquire a second scanning image corresponding to the target core sample, wherein the second scanning image is an image obtained by scanning the target core sample after performing an experimental operation on the target core sample; register the first scanning image and the second scanning image based on the markers by using a feature point matching algorithm and a spatial transformation algorithm, to obtain a registered image of the target core sample, and the registered image is used for analysis and processing of the core sample.

[0099] In this embodiment, the processor 410 is further configured to: fix the markers to the surface of the core sample by using a dispensing method to obtain the target core sample.

[0100] In this embodiment, the processor 410 is further configured to: The marker is a rutile titanium dioxide particle; The size of the marker is greater than or equal to 10 microns and less than or equal to 20 microns; The number of markers is greater than or equal to 5 and less than or equal to 8.

[0101] In this embodiment, the processor 410 is further configured to: calculate the first scanning image and the second scanning image based on the markers by using a feature point matching algorithm to obtain a rotation matrix and a translation vector; transform the second scanning image based on a spatial transformation algorithm by using the rotation matrix and the translation vector to obtain a registered image.

[0102] In this embodiment, the processor 410 is further configured to: calculate the first scanning image and the second scanning image based on the markers by using a feature point matching algorithm to obtain a first centroid and a second centroid; obtain a covariance matrix according to the first centroid and the second centroid; singular value decomposition of the covariance matrix, and calculate the rotation matrix according to the result of singular value decomposition; substitute the first centroid, the second centroid and the rotation matrix into a translation vector calculation formula to obtain the translation vector.

[0103] In this embodiment, the processor 410 is further configured to: perform a scanning operation on the target core sample based on a target parameter to obtain the first scanning image, wherein the target parameter at least includes a scanning angle, an exposure time and a magnification; The acquisition of the second scanning image corresponding to the target core sample comprises: performing an experimental operation on the target core sample; performing a scanning operation on the target core sample based on the target parameter, to obtain the second scanning image.

[0104] It should be understood that in the embodiments of the present application, the input unit 404 can include a graphics processor (GPU) 4041 and a microphone 4042. The graphics processor 4041 processes image data of a still picture or a video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 406 can include a display panel 4061, which can be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 407 includes at least one of a touch panel 4071 and other input devices 4072. The touch panel 4071 is also called a touch screen. The touch panel 4071 can include two parts of a touch detection device and a touch controller. The other input devices 4072 can include, but are not limited to, a physical keyboard, function keys (such as volume control keys, on-off keys, etc.), a trackball, a mouse, a joystick, and the like, which will not be described here.

[0105] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area can store an operating system, application programs or instructions required by at least one function (such as a sound playing function, an image playing function, etc.), and the like. In addition, the memory 409 can include a volatile memory or a non-volatile memory, or the memory 409 can include both a volatile memory and a non-volatile memory. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synch link DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM). The memory 409 in the embodiments of the present application includes but is not limited to these and any other suitable types of memory.

[0106] The processor 410 can include one or more processing units; optionally, the processor 410 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and an application program, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 410.

[0107] Embodiment 9: A storage medium, a computer processing program is stored on the storage medium, and the computer processing program is executed by a processor to implement the core image registration method.

[0108] Embodiment 10: A program product, instructions in the program product are executed by a processor of an electronic device to enable the electronic device to perform the core image registration method.

[0109] The above technical features respectively constitute various embodiments of the present application, which have strong adaptability and implementation effects, and unnecessary technical features can be added or reduced according to actual needs to meet the needs of different situations.

Claims

1. A method of core image registration, the method comprising: The method comprises: obtaining a first scanning image corresponding to a target core sample; wherein the target core sample is a core sample with a marker fixed on the surface thereof, and the first scanning image is an image obtained by scanning the target core sample before performing an experimental operation on the target core sample; obtaining a second scanning image corresponding to the target core sample; wherein the second scanning image is an image obtained by scanning the target core sample after performing the experimental operation on the target core sample; using a feature point matching algorithm and a spatial transformation algorithm, registering the first scanning image and the second scanning image based on the marker, to obtain a registered image of the target core sample, wherein the registered image is used for analysis and processing of the core sample.

2. The core image registration method of claim 1, wherein, Before obtaining the first scanning image corresponding to the target core sample, the method further comprises: using a dispensing method to fix the marker to the surface of the core sample to obtain the target core sample; or / and, the first scanning image and the second scanning image are both three-dimensional images.

3. The core image registration method of claim 1 or 2, wherein, The marker satisfies at least one of the following conditions: the marker is a rutile titanium dioxide particle; the size of the marker is greater than or equal to 10 microns and less than or equal to 20 microns; the number of the markers is greater than or equal to 5 and less than or equal to 8.

4. The core image registration method of claim 1 or 2, wherein, The use of the feature point matching algorithm and the spatial transformation algorithm to register the first scanning image and the second scanning image based on the marker to obtain the registered image of the target core sample comprises: using the feature point matching algorithm to calculate the first scanning image and the second scanning image based on the marker to obtain a rotation matrix and a translation vector; using the rotation matrix and the translation vector to transform the second scanning image based on the spatial transformation algorithm to obtain the registered image.

5. The core image registration method of claim 4, wherein, The use of the feature point matching algorithm to calculate the first scanning image and the second scanning image based on the marker to obtain a rotation matrix and a translation vector comprises: using the feature point matching algorithm to calculate the first scanning image and the second scanning image based on the marker to obtain a first centroid and a second centroid; obtaining a covariance matrix according to the first centroid and the second centroid; performing singular value decomposition on the covariance matrix, and calculating the rotation matrix according to the result of the singular value decomposition; substituting the first centroid, the second centroid and the rotation matrix into a translation vector calculation formula to obtain the translation vector.

6. The core image registration method of claim 5, wherein, The obtaining of the first scanning image corresponding to the target core sample comprises: performing a scanning operation on the target core sample based on a target parameter to obtain the first scanning image, wherein the target parameter at least includes a scanning angle, an exposure time and a magnification; or / and, the obtaining of the second scanning image corresponding to the target core sample comprises: performing an experimental operation on the target core sample; performing a scanning operation on the target core sample based on the target parameter to obtain the second scanning image.

7. A core image registration apparatus for implementing the core image registration method according to any one of claims 1 to 6, characterized by The method comprises: The first scanning image module is configured to acquire a first scanning image corresponding to a target core sample, wherein the target core sample is a core sample with a marker fixed on a surface thereof, and the first scanning image is an image obtained by scanning the target core sample before performing an experimental operation on the target core sample. The second scanning image module is configured to acquire a second scanning image corresponding to the target core sample, wherein the second scanning image is an image obtained by scanning the target core sample after performing the experimental operation on the target core sample. The registration module is configured to perform registration on the first scanning image and the second scanning image based on the marker by using a feature point matching algorithm and a spatial transformation algorithm, to obtain a registered image of the target core sample, and the registered image is used for analysis and processing of the core sample.

8. An electronic device, comprising: The device comprises a memory, a processor, and a computer processing program stored on the memory and running on the processor, and the computer processing program is configured to implement the core image registration method according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium stores a computer processing program, and the computer processing program is executed by a processor to implement the core image registration method according to any one of claims 1 to 6.

10. A program product, characterized by The instructions in the program product are executed by a processor of an electronic device, so that the electronic device performs the core image registration method according to any one of claims 1 to 6.

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