Method and apparatus for identifying aggregates in carbonate rock cores

Abstract

This invention provides a method and apparatus for identifying clumps in carbonate rock cores. The method includes: automatically rolling and photographing the carbonate rock core, then stitching the images together to obtain a stitched image; establishing image selection areas based on color differences in the stitched image and labeling the colors of each selection area to obtain delineated clumps; obtaining the clump development locations within the delineated clumps; automatically rolling and photographing the carbonate rock core in a darkroom environment using a fluorescent light source to determine whether the delineated clumps exhibit fluorescence; obtaining the hardness range of the carbonate reservoir and the Richter hardness value of the delineated clumps; and determining the clump type based on the color, fluorescence response, Richter hardness value, and hardness range of the carbonate reservoir. This invention obtains clump information without cutting or damaging the core. Furthermore, this application employs automated scanning and image processing, significantly improving clump identification efficiency, shortening analysis time, and reducing labor costs.

Description

Technical Field

[0001] This invention relates to the field of core mass identification technology, and in particular to a method and apparatus for identifying core masses in carbonate rocks. Background Technology

[0002] Carbonate rocks contain numerous lithological clusters, which are massive formations of different lithological types that have formed within or on the surface of the rock. These clusters can be formed due to hydrothermal fluid activity or geological events during sedimentation and diagenesis, and possess distinct physical and chemical properties. The type and distribution of these clusters can provide crucial information about the rock formation environment, diagenetic history, and potential hydrocarbon reservoir characteristics. Specifically, clusters may include argillaceous clusters, carbonate clusters, tuff clusters, pyrite clusters, and siliceous clusters. Therefore, identifying these clusters is essential for understanding the sedimentary and diagenetic processes of carbonate rocks and their reservoir characteristics.

[0003] Currently, existing technologies for identifying these clumps in carbonate rocks mostly employ microscopic identification. This method requires sampling and preparation of thin sections, but it is both wasteful of the sample and time-consuming, failing to meet the need for rapid identification.

[0004] In addition, existing technologies also employ a combination of core scanning and expert judgment. However, this approach is flawed because core scanning technology primarily records core images and cannot provide in-depth analysis of local lithological changes in the core. Furthermore, expert judgment relies on personal experience and lacks objectivity and universal applicability. Summary of the Invention

[0005] In view of this, the present invention provides a method and apparatus for identifying aggregates in carbonate rock cores to solve at least one of the aforementioned problems.

[0006] To achieve the above objectives, the present invention adopts the following solution:

[0007] According to a first aspect of the present invention, a method for identifying clumps in a carbonate rock core is provided. The method includes: automatically rolling and photographing a carbonate rock core to obtain a first core image and a corresponding first axial distance and a first core rotation angle, wherein the rotation angle is between 0° and 360°; cropping the image other than the core from the first core image to obtain a cropped image, and stitching together cropped images with the same first axial distance and first core rotation angle to obtain several stitched images; establishing image selection areas in the stitched images based on color differences and labeling the colors of each image selection area to obtain each delineated clump; extracting the first axial distance and first core rotation angle corresponding to each image selection area in the stitched images, and determining the median value of the first axial distance and first core rotation angle as the clump development location; and automatically rolling and photographing the carbonate rock core in a darkroom environment using a fluorescent light source to obtain... The second core image and its corresponding second axial distance and second core rotation angle are used. Based on the first axial distance and first core rotation angle corresponding to each image selection area in the stitched image, the second core image corresponding to the same second axial distance and second core rotation angle is found, and the fluorescence reaction of the delineated mass is determined based on the found second core image. The Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the mass development location are obtained, and the hardness range of the carbonate reservoir is obtained based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the mass development location. The Leeb hardness value of the delineated mass is obtained based on the Leeb hardness value at the mass development location. The type of the delineated mass is determined based on the color, fluorescence reaction result, Leeb hardness value, and hardness range of the carbonate reservoir.

[0008] As an embodiment of the present invention, the above method for automatically rolling and photographing carbonate rock cores includes: first, rotating the carbonate rock core one revolution at the starting position of the axial direction of the carbonate rock core to take a photograph, and then moving along the axial direction of the carbonate rock core in 10cm increments and continuing to rotate one revolution to take a photograph, until the photographing operation of the entire carbonate rock core is completed.

[0009] As an embodiment of the present invention, in the above method, when taking pictures of a carbonate rock core by rotating it one revolution, a picture is taken once every 20° of rotation.

[0010] As an embodiment of the present invention, the method described above for stitching together cropped images with the same first axial distance and first core rotation angle to obtain several stitched images includes: extracting feature points from the cropped images with the same first axial distance and first core rotation angle and calculating feature point descriptors; using the feature point descriptors to perform feature point matching to find matching feature point pairs between different cropped images; using a random sampling consensus algorithm to calculate the geometric transformation relationship between different cropped images; performing geometric correction and stitching on the cropped images according to the calculated geometric transformation relationship to eliminate overlap and misalignment between images; and performing stitching processing on the stitched images to eliminate stitching marks and obtain the final stitched image.

[0011] As an embodiment of the present invention, the criterion for judging color difference in the above method is whether the difference value in the RGB color space exceeds a preset threshold.

[0012] As an embodiment of the present invention, the method described above for determining the type of the delineated agglomerate based on its color, fluorescence reaction result, Leeb hardness value, and hardness range of the carbonate reservoir includes: based on the color, fluorescence reaction result, Leeb hardness value, and hardness range of the carbonate reservoir, searching a preset agglomerate type lookup table to obtain the agglomerate type corresponding to the delineated agglomerate.

[0013] According to a second aspect of the present invention, a device for identifying clumps in carbonate rock cores is provided. The device includes: a first data acquisition unit, configured to automatically roll and photograph a carbonate rock core, acquiring a first core image and corresponding first axial distance and first core rotation angle, wherein the rotation angle is between 0° and 360°; an image stitching unit, configured to crop images other than the core from the first core image to obtain cropped images, and stitch together cropped images with the same first axial distance and first core rotation angle to obtain several stitched images; a color marking unit, configured to establish image selection areas based on color differences in the stitched images and mark the color of each image selection area to obtain each delineated clump; a development location acquisition unit, configured to extract the first axial distance and first core rotation angle corresponding to each image selection area in the stitched images, and calculate the median value of the first axial distance and first core rotation angle as the clump development location; and a second data acquisition unit, configured to automatically roll the carbonate rock core in a darkroom environment using a fluorescent light source. The system includes a motion and photography unit to acquire a second core image and its corresponding second axial distance and second core rotation angle; a fluorescence reaction determination unit to find second core images with the same second axial distance and second core rotation angle based on the first axial distance and first core rotation angle of each image selection area in the stitched image, and to determine whether the delineated agglomerate has a fluorescence reaction based on the found second core images; a hardness value acquisition unit to acquire the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the agglomerate development location, and to obtain the carbonate reservoir hardness range value based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the agglomerate development location, and to obtain the Leeb hardness value of the delineated agglomerate based on the Leeb hardness value at the agglomerate development location; and a agglomerate type determination unit to determine the type of the delineated agglomerate based on its color, fluorescence reaction result, Leeb hardness value, and carbonate reservoir hardness range value.

[0014] As an embodiment of the present invention, the first data acquisition unit is specifically used to: first, rotate the carbonate rock core one revolution at the starting position of the axial direction of the carbonate rock core to take a picture, and then move along the axial direction of the carbonate rock core in 10cm increments and continue to rotate one revolution to take a picture, until the entire carbonate rock core is photographed; the second data acquisition unit is specifically used to: first, rotate the carbonate rock core one revolution at the starting position of the axial direction of the carbonate rock core in a darkroom environment to take a picture, and then move along the axial direction of the carbonate rock core in 10cm increments and continue to rotate one revolution to take a picture, until the entire carbonate rock core is photographed.

[0015] As an embodiment of the present invention, when taking pictures of the carbonate rock core by rotating it one revolution, the picture is taken once every 20° of rotation.

[0016] As an embodiment of the present invention, the image stitching unit includes: a feature extraction module, used to extract feature points and calculate feature point descriptors from cropped images having the same first axial distance and the same first core rotation angle; a feature matching module, used to perform feature point matching using the feature point descriptors to find matching feature point pairs between different cropped images; a geometric transformation calculation module, used to calculate the geometric transformation relationship between different cropped images using a random sampling consensus algorithm; a correction stitching module, used to perform geometric correction and stitching on the cropped images according to the calculated geometric transformation relationship to eliminate overlap and misalignment between images; and a stitching processing module, used to perform stitching processing on the stitched images to eliminate stitching traces and obtain the final stitched image.

[0017] As an embodiment of the present invention, the criterion for judging the color difference is whether the difference value in the RGB color space exceeds a preset threshold.

[0018] As an embodiment of the present invention, the above-mentioned clumping type determination unit is specifically used to: based on the color, fluorescence reaction result, Leeb hardness value and hardness range value of the delineated clumping and the carbonate reservoir, look up a preset clumping type lookup table to obtain the clumping type corresponding to the delineated clumping.

[0019] According to a third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0021] As can be seen from the above technical solution, the method and apparatus for identifying clumps in carbonate rock cores provided by this invention, firstly, can obtain clump information without cutting or damaging the core, preserving the integrity of the core and facilitating subsequent experiments and analyses. Secondly, this application employs automated scanning and image processing, greatly improving clump identification efficiency, shortening analysis time, and reducing labor costs. Furthermore, this application can not only identify the location and size of clumps but also more accurately determine the type of clumps and their oil content through multi-dimensional information such as color, fluorescence reaction, and hardness. In summary, this application provides an efficient, accurate, low-cost, and non-destructive method for identifying clumps in carbonate rock cores, which has significant application value for oil and gas exploration and development. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0023] Figure 1 This is a schematic flowchart of a method for identifying aggregates in carbonate rock cores provided by an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the image stitching process provided in an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the clumping region identification results provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the structure of a carbonate rock core mass identification device provided in an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of the structure of the image stitching unit provided in the embodiments of this application;

[0028] Figure 6 This is a front view of an implementation device provided in an embodiment of this application;

[0029] Figure 7 This is a side view of an implementation device provided in an embodiment of this application;

[0030] Figure 8 This is a schematic block diagram of the system configuration of the electronic device provided in the embodiments of the invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] like Figure 1 The diagram shown is a flowchart illustrating a method for identifying aggregates in carbonate rock cores according to an embodiment of the present invention. The method includes the following steps:

[0033] Step S101: Perform automatic rolling and photographing operations on the carbonate rock core to obtain the first core image and the corresponding first axial distance and first core rotation angle, wherein the rotation angle is between 0° and 360°.

[0034] In this embodiment, the carbonate rock core used for the experiment is generally prepared in a cylindrical shape. This cylindrical core is easy to operate, measure and analyze, and is also convenient to use in various experimental equipment. The rock on the surface of the cylindrical core can be either smooth and flat or rough and flat.

[0035] In practice, the surface of the carbonate rock core needs to be cleaned with a towel and water to remove mud and dust, and then air-dried. It is then placed on an automatic rolling device, ensuring the core height remains consistent with a difference of less than 10mm. This automatic rolling device allows the core to rotate along its axis with precise control over the rotation angle.

[0036] Preferably, the automatic rolling and photographing operation of the carbonate rock core in this step includes: first, rotating the carbonate rock core one revolution at the starting position of the axial direction of the carbonate rock core to take a photograph, and then moving along the axial direction of the carbonate rock core in 10cm increments and continuing to rotate one revolution to take a photograph, until the photographing operation of the entire carbonate rock core is completed.

[0037] Preferably, in this step, when photographing the carbonate rock core after rotating it one revolution, a photograph is taken every 20° of rotation. In practice, the rotation angle interval can be adjusted according to the size of the core, the complexity of its surface features, and the required precision. A smaller interval results in more images and finer data, but also increases the data processing burden. The 20° interval used in this embodiment was determined experimentally and balances data precision and processing efficiency. Of course, if the core surface features are very complex, or higher precision is required, the rotation angle interval can be further reduced, for example, taking a photograph every 10° or 5°.

[0038] Simultaneously, this step requires recording the current first axial distance (i.e., the distance the camera moves along the axial direction) and the first core rotation angle (0° to 360°). This yields the first core image dataset A, which records the first axial distance and the first core rotation angle corresponding to each image.

[0039] Step S102: Cropping out images other than the core from the first core image to obtain a cropped image, and stitching together cropped images with the same first axial distance and first core rotation angle to obtain several stitched images.

[0040] Since the captured images may include background areas outside the core, they need to be cropped to retain only the core portion. Cropped images with the same first axial distance and first core rotation angle are then stitched together. For example, for an axial distance of 10cm, cropped images with rotation angles of 0°, 10°, 20°...350° are stitched together to form a complete cylindrical core image. This results in several stitched images, each representing a complete circle of the core at different axial distances.

[0041] Preferred, such as Figure 2 As shown, the step of stitching together cropped images with the same first axial distance and first core rotation angle to obtain several stitched images may further include the following sub-steps:

[0042] Step S1021: Extract feature points and calculate feature point descriptors for cropped images that have the same first axial distance and first core rotation angle.

[0043] The goal of this step is to extract representative feature points from each cropped image and compute their descriptors. Commonly used feature point extraction algorithms include Scale Invariant Feature Transform (SIFT) and Speed-Up Robust Feature Transform (SURF). These algorithms can extract feature points that are invariant to transformations such as rotation, scaling, and translation. This step does not limit the specific feature point extraction algorithm used. The computed feature point descriptors are then used to describe the local features of the feature points for subsequent matching.

[0044] Step S1022: Use the feature point descriptor to perform feature point matching and find matching feature point pairs between different cropped images.

[0045] Feature point matching is performed between different cropped images using the calculated feature point descriptors. The goal is to find feature point pairs corresponding to the same physical location in different images. Common matching methods include brute-force matching, KD-tree matching, and Flann matching.

[0046] Step S1023: Calculate the geometric transformation relationship between different cropped images using the random sampling consensus algorithm.

[0047] By utilizing matched feature point pairs, the Random Sample Consensus (RANSAC) algorithm is used to calculate the geometric transformation relationship between different cropped images. The RANSAC algorithm can effectively eliminate the influence of erroneous matching points, thus obtaining a more accurate geometric transformation relationship.

[0048] Step S1024: Based on the calculated geometric transformation relationship, perform geometric correction and stitching on the cropped images to eliminate overlap and misalignment between images.

[0049] Based on the calculated geometric transformation relationships, the cropped images are geometrically corrected and then stitched together. The goal of this step is to eliminate overlaps and misalignments between the images, forming a complete stitched image.

[0050] Step S1025: Perform stitching on the stitched images to eliminate stitching marks and obtain the final stitched image.

[0051] Due to factors such as lighting and exposure, stitched images may exhibit noticeable stitching marks. The goal of stitching is to eliminate these marks, making the stitched image more natural and smooth. Methods such as weighted averaging and Poisson blending can be used for stitching.

[0052] By applying the above sub-steps and combining them with algorithms, the quality of image stitching can be effectively improved, thereby enhancing the performance of the entire cluster recognition method.

[0053] Step S103: In the stitched image, image selection areas are established based on color differences, and the colors of each image selection area are marked to obtain each circled block.

[0054] On the stitched image, different image regions are identified and selected based on color differences; these regions represent potential clumps. Each selected region is then color-coded, for example, using different colors or labels to distinguish different clumps. This results in several defined clumping regions, as shown in [reference needed]. Figure 3 As shown, the red lines circle the identified cluster areas.

[0055] The criterion for judging color difference here is whether the difference value in the RGB color space exceeds a preset threshold. If the calculated color difference value exceeds this threshold, the two colors are considered to be significantly different and can be divided into different image selection areas. The threshold can be adjusted according to the specific application scenario and image characteristics. Too small a threshold will lead to oversegmentation, dividing the same block into multiple regions; too large a threshold will lead to undersegmentation, merging different blocks into one region. In this embodiment, an appropriate threshold can be selected based on experience or experimental results.

[0056] Step S104: Extract the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, and take the median value of the first axial distance and the first core rotation angle as the clumping development position.

[0057] On the core samples, cores of the same lithology at close range generally have a consistent color. Color variations can be used to identify the location of the clumps. Therefore, this step extracts the first axial distance and the first core rotation angle information corresponding to each delineated clump in the stitched image, and uses this as set A. nThe set A to be obtained n The median value of the first axial distance and the first core rotation angle is taken as A. n * A n * This refers to the location information of the cluster development site.

[0058] Step S105: In a darkroom environment, use a fluorescent light source to automatically roll and photograph the carbonate rock core to obtain a second core image and the corresponding second axial distance and second core rotation angle.

[0059] This step is similar to step S101, except that the environment is different. In this step, the rolling and photographing operations are performed in a darkroom environment and after illumination with a fluorescent light source. In a specific embodiment, an opaque black cloth can be placed over the outside of the device to simulate the darkroom environment. Through this step, a second core image dataset B can be obtained. This second core image dataset B records the second axial distance and the second core rotation angle corresponding to each image.

[0060] Step S106: Based on the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, find the second core image corresponding to the second axial distance and the second core rotation angle with the same axial distance and core rotation angle, and determine whether the delineated mass has a fluorescent reaction based on the found second core image.

[0061] This step is based on A. n The location information set is used to find the corresponding location of B in B. n Photo, and then based on that B n The photograph is used to determine whether the corresponding delineated aggregate exhibits a fluorescent reaction. The presence of a fluorescent reaction indicates that the aggregate may contain oil or hydrocarbons, while the absence of a fluorescent reaction indicates that the aggregate does not contain organic matter.

[0062] Step S107: Obtain the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the agglomeration, and obtain the hardness range of the carbonate reservoir based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the agglomeration, and obtain the Leeb hardness value for delineating the agglomeration based on the Leeb hardness value at the location of the agglomeration.

[0063] If in step S101 the camera moves along the axis of the carbonate rock core in 10cm increments, then this step involves setting hardness test points at the starting position, every 10cm, and at a 0° rotation angle, while also setting them at the location of the mass development (i.e., A). n *Set up hardness test points. Then, count the Leeb hardness value at the 0° test point to obtain the hardness range value F of the carbonate reservoir. At the same time, in order to remove outliers, the maximum and minimum values ​​of the Leeb hardness value at the 0° test point can be removed.

[0064] Step S108: Determine the type of the delineated mass based on its color, fluorescence reaction results, Leeb hardness value, and hardness range of the carbonate reservoir.

[0065] In this step, the type of each delineated mass is determined by comprehensively considering the color of the delineated mass, the fluorescence reaction results, the Richter hardness value, and the hardness range of the carbonate reservoir.

[0066] Preferably, this step may further include: based on the color, fluorescence reaction results, Richter hardness value, and hardness range of the delineated agglomerates, searching a preset agglomerate type lookup table to obtain the agglomerate type corresponding to the delineated agglomerates. For example, Table 1 below can be seen as a reference table for this agglomerate type lookup table. However, Table 1 is only an example and does not cover all cases. In other special cases, the agglomerate type can be defined as a high-hardness agglomerate containing metallic elements.

[0067] Table 1

[0068] color Fluorescence reaction Leeb hardness type Various shades Both are acceptable Less than 315 muddy lumps Various shades Both are acceptable F range carbonate rock masses Various shades Both are acceptable 420～520 tuff nodules Yellow tones none 810～850 Pyrite nodules Various shades Both are acceptable Greater than 850 Siliceous aggregates black Both are acceptable invalid Hole

[0069] As can be seen from the above technical solution, the method for identifying clumps in carbonate rock cores provided by this invention, firstly, obtains clump information without cutting or damaging the core, preserving the integrity of the core and facilitating subsequent experiments and analyses. Secondly, this application employs automated scanning and image processing, significantly improving clump identification efficiency, shortening analysis time, and reducing labor costs. Furthermore, this application can not only identify the location and size of clumps but also more accurately determine the type of clump and its oil content through multi-dimensional information such as color, fluorescence response, and hardness. In summary, this application provides an efficient, accurate, low-cost, and non-destructive method for identifying clumps in carbonate rock cores, which has significant application value for oil and gas exploration and development.

[0070] like Figure 4 The diagram shows a structural schematic of a carbonate rock core mass identification device provided in an embodiment of this application. The device includes: a first data acquisition unit 410, an image stitching unit 420, a color marking unit 430, a development location acquisition unit 440, a second data acquisition unit 450, a fluorescence reaction determination unit 460, a hardness value acquisition unit 470, and a mass type determination unit 480, which are connected sequentially.

[0071] The first data acquisition unit 410 is used to automatically roll and photograph the carbonate rock core to acquire the first core image and the corresponding first axial distance and first core rotation angle, wherein the rotation angle is between 0° and 360°.

[0072] The image stitching unit 420 is used to crop out images other than the core from the first core image to obtain a cropped image, and to stitch together cropped images with the same first axial distance and the same first core rotation angle to obtain a number of stitched images.

[0073] Color annotation unit 430 is used to establish image selection areas in the stitched image based on color differences and to annotate the color of each image selection area to obtain each circled block.

[0074] The development location acquisition unit 440 is used to extract the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, and to obtain the median value of the first axial distance and the first core rotation angle as the development location of the agglomerate.

[0075] The second data acquisition unit 450 uses a fluorescent light source in a darkroom environment to automatically roll and photograph the carbonate rock core, acquiring the second core image and the corresponding second axial distance and second core rotation angle.

[0076] The fluorescence reaction determination unit 460 is used to find a second core image corresponding to the same second axial distance and second core rotation angle based on the first axial distance and first core rotation angle of each image selection area in the stitched image, and to determine whether the delineated mass has a fluorescence reaction based on the found second core image.

[0077] The hardness value acquisition unit 470 is used to acquire the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the mass development, and to obtain the hardness range of the carbonate reservoir based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the mass development, and to obtain the Leeb hardness value for delineating the mass based on the Leeb hardness value at the location of the mass development.

[0078] The clumping type determination unit 480 is used to determine the type of the delineated clumping based on the color, fluorescence reaction results, Leeb hardness value, and hardness range of the carbonate reservoir.

[0079] Preferably, the automatic rolling and photographing operation of the carbonate rock core by the first data acquisition unit 410 and the second data acquisition unit 450 includes: first, rotating the carbonate rock core one revolution at the starting position of the axial direction of the carbonate rock core to take a photograph, and then moving along the axial direction of the carbonate rock core in 10cm increments and continuing to rotate one revolution to take a photograph, until the photographing operation of the entire carbonate rock core is completed.

[0080] Preferably, when taking pictures of the carbonate rock core by rotating it one revolution, a picture is taken every 20° of rotation.

[0081] Preferred, such as Figure 5 As shown, the image stitching unit 420 includes:

[0082] The feature extraction module 421 is used to extract feature points and calculate feature point descriptors from cropped images that have the same first axial distance and the first core rotation angle.

[0083] The feature matching module 422 is used to perform feature point matching using the feature point descriptor to find matching feature point pairs between different cropped images.

[0084] The geometric transformation calculation module 423 is used to calculate the geometric transformation relationship between different cropped images using a random sampling consensus algorithm.

[0085] The correction and stitching module 424 is used to perform geometric correction and stitching on the cropped image based on the calculated geometric transformation relationship, so as to eliminate the overlap and misalignment between the images.

[0086] The stitching module 425 is used to stitch the spliced ​​images to eliminate stitching marks and obtain the final spliced ​​image.

[0087] Preferably, the criterion for judging color differences in the color labeling unit 430 is whether the difference value in the RGB color space exceeds a preset threshold.

[0088] Preferably, the above-mentioned clumping type determination unit 480 is specifically used to: based on the color, fluorescence reaction results, Leeb hardness value, and hardness range of the delineated clumping and the carbonate reservoir, look up a preset clumping type lookup table to obtain the clumping type corresponding to the delineated clumping.

[0089] As can be seen from the above technical solution, the carbonate rock core clumping identification device provided by this invention, firstly, obtains clumping information without cutting or damaging the core, preserving the integrity of the core and facilitating subsequent experiments and analyses. Secondly, this application employs automated scanning and image processing, significantly improving clumping identification efficiency, shortening analysis time, and reducing labor costs. Furthermore, this application can not only identify the location and size of clumping but also more accurately determine the type of clumping and its oil content through multi-dimensional information such as color, fluorescence response, and hardness. In summary, this application provides an efficient, accurate, low-cost, and non-destructive method for identifying clumping in carbonate rock cores, which has significant application value for oil and gas exploration and development.

[0090] Finally, this application also provides a method for execution Figure 1 The specific implementation apparatus of the corresponding method is as follows: Figure 6 and Figure 7 As shown, these are the front view and side view of the implementing device, respectively. Figure 6 and Figure 7 As shown, the implementation device consists of several parts: a core placement and rotation mechanism, an imaging system, a support structure, and a control and data processing system. Among them:

[0091] Core placement and rotation mechanism: Consists of a motor, rollers, and a conveyor belt. The motor drives the conveyor belt and rollers to rotate, causing the core to rotate so that images can be captured from different angles. The roller design ensures the smoothness and accuracy of the core rotation.

[0092] Imaging system: Includes a camera, a fluorescent / white light source, and a hardness tester. The camera is used to capture images of the core. The fluorescent / white light source provides illumination, with the fluorescent source exciting the fluorescent substances in the core and the white light source used for routine imaging. The hardness tester is used to measure the Leeb hardness value of the core. The positions and angles of the camera, light source, and hardness tester are carefully designed to ensure high-quality images and accurate hardness data. As can be seen from the front view, the two light sources are symmetrically placed on both sides of the core, providing uniform illumination of the core surface.

[0093] Control and data processing system: This includes storage, controllers, and displays. The storage and controllers control the operation of the device and store acquired images and data. The displays show the images and data and provide a human-machine interface. Data cables connect the camera, hardness tester, and control system, enabling real-time data transmission and processing.

[0094] Support structure: Composed of slide rails and frames, it supports and secures the aforementioned components, ensuring the stability and reliability of the device. The side view clearly shows the structure of the slide rails, which guide the camera and hardness tester along the core axis.

[0095] The device is compact in design and fully functional, and can automatically perform operations such as core rotation, imaging and hardness testing, improving the efficiency and accuracy of clumping identification in carbonate rock cores.

[0096] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0097] This invention also provides a computer-readable storage medium storing a computer program for performing the above-described methods.

[0098] like Figure 8 The electronic device 600 may also include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily need to include these components. Figure 8 All components shown; in addition, electronic device 600 may also include Figure 8 The components shown can be referenced in the prior art.

[0099] like Figure 8 The central processing unit 100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operation of various components of the electronic device 600.

[0100] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0101] Input unit 120 provides input to central processing unit 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to electronic device 600. Display 160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0102] The memory 140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 140 can also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application and function programs or processes for executing the operation of the electronic device 600 via the central processing unit 100.

[0103] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0104] The communication module 110 is a transmitter / receiver that transmits and receives signals via the antenna 111. The communication module 110 (transmitter / receiver) is coupled to the central processing unit 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0105] Based on different communication technologies, multiple communication modules 110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 110 (transmitter / receiver) is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby enabling typical telecommunications functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 130 is coupled to a central processing unit 100, enabling on-device recording via the microphone 132 and on-device playback of stored audio via the speaker 131.

[0106] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for identifying aggregates in carbonate rock cores, characterized in that, The method includes: Automatic rolling and photographing operations are performed on carbonate rock cores to obtain a first core image and the corresponding first axial distance and first core rotation angle, wherein the rotation angle is between 0° and 360°. Cropped images are obtained by cropping out images other than the core from the first core image, and then cropped images with the same first axial distance and first core rotation angle are stitched together to obtain several stitched images. In the stitched image, image selection areas are established based on color differences, and the colors of each image selection area are marked to obtain each defined block; Extract the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, and take the median value of the first axial distance and the first core rotation angle as the location of the mass development. In a darkroom environment, a fluorescent light source is used to automatically roll and photograph the carbonate rock core to obtain a second core image as well as the corresponding second axial distance and second core rotation angle. Based on the first axial distance and first core rotation angle corresponding to each image selection area in the stitched image, find the second core image corresponding to the second axial distance and second core rotation angle with the same axial distance and core rotation angle, and determine whether the delineated mass has a fluorescent reaction based on the found second core image; The Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the mass development are obtained. Based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the mass development, the hardness range of the carbonate rock reservoir is obtained. Based on the Leeb hardness values ​​at the location of the mass development, the Leeb hardness value of the delineated mass is obtained. The type of the delineated mass is determined based on its color, fluorescence reaction results, Leeb hardness value, and the hardness range of the carbonate reservoir.

2. The method for identifying aggregates in carbonate rock cores as described in claim 1, characterized in that, The automatic rolling and photographing operation of the carbonate rock core includes: first, rotating the carbonate rock core one revolution at the starting position of the axial direction of the carbonate rock core to take a photograph, and then moving along the axial direction of the carbonate rock core in 10cm increments and continuing to rotate one revolution to take a photograph, until the photographing operation of the entire carbonate rock core is completed.

3. The method for identifying aggregates in carbonate rock cores as described in claim 2, characterized in that, When taking pictures of a carbonate rock core by rotating it one revolution, take a picture every 20° of rotation.

4. The method for identifying aggregates in carbonate rock cores as described in claim 1, characterized in that, The process of stitching together cropped images with the same first axial distance and first core rotation angle to obtain several stitched images includes: Feature points are extracted and feature point descriptors are calculated for cropped images with the same first axial distance and first core rotation angle; The feature point descriptor is used to perform feature point matching to find matching feature point pairs between different cropped images; The geometric transformation relationship between different cropped images is calculated using the random sampling consensus algorithm. Based on the calculated geometric transformation relationship, the cropped images are geometrically corrected and stitched together to eliminate overlap and misalignment between images; The stitched images are then stitched together to remove stitching marks, resulting in the final stitched image.

5. The method for identifying aggregates in carbonate rock cores as described in claim 1, characterized in that, The criterion for judging color differences is whether the difference value in the RGB color space exceeds a preset threshold.

6. The method for identifying aggregates in carbonate rock cores as described in claim 1, characterized in that, The determination of the type of the delineated mass based on its color, fluorescence reaction results, Leeb hardness value, and hardness range of the carbonate reservoir includes: Based on the color, fluorescence reaction results, Leeb hardness value, and hardness range of the delineated clumps, a preset clump type lookup table is consulted to obtain the clump type corresponding to the delineated clumps.

7. A device for identifying aggregates in carbonate rock cores, characterized in that, The device includes: The first data acquisition unit is used to automatically roll and photograph the carbonate rock core to acquire the first core image and the corresponding first axial distance and first core rotation angle, wherein the rotation angle is between 0° and 360°. The image stitching unit is used to crop out images other than the core from the first core image to obtain a cropped image, and to stitch together cropped images with the same first axial distance and the same first core rotation angle to obtain several stitched images. The color annotation unit is used to establish image selection areas in the stitched image based on color differences and to annotate the color of each image selection area to obtain each circled block. The development location acquisition unit is used to extract the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, and to obtain the median value of the first axial distance and the first core rotation angle as the development location of the agglomerate. The second data acquisition unit uses a fluorescent light source in a darkroom environment to automatically roll and photograph the carbonate rock core, thereby acquiring the second core image and the corresponding second axial distance and second core rotation angle. The fluorescence reaction determination unit is used to find the second core image corresponding to the same second axial distance and second core rotation angle based on the first axial distance and first core rotation angle of each image selection area in the stitched image, and to determine whether the delineated block has a fluorescence reaction based on the found second core image. The hardness value acquisition unit is used to acquire the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the mass development, and to obtain the hardness range of the carbonate rock reservoir based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate rock core and at the location of the mass development, and to obtain the Leeb hardness value for delineating the mass based on the Leeb hardness value at the location of the mass development. The clumping type determination unit is used to determine the type of the delineated clumping based on its color, fluorescence reaction results, Leeb hardness value, and hardness range of the carbonate reservoir.

8. The carbonate rock core mass identification device as described in claim 7, characterized in that, The first data acquisition unit is specifically used to: first take a picture of the carbonate rock core by rotating it around the starting position of the carbonate rock core axis, and then move along the carbonate rock core axis in 10cm increments and continue to rotate it around the starting position to take a picture, until the entire carbonate rock core is photographed. The second data acquisition unit is specifically used to: first, in a darkroom environment, rotate the carbonate rock core one revolution at the starting position of the carbonate rock core axis to take pictures, and then move along the carbonate rock core axis in 10cm increments and continue to rotate one revolution to take pictures, until the entire carbonate rock core is photographed.

9. The carbonate rock core mass identification device as described in claim 8, characterized in that, When taking pictures of a carbonate rock core by rotating it one revolution, take a picture every 20° of rotation.

10. The carbonate rock core mass identification device as described in claim 7, characterized in that, The image stitching unit includes: The feature extraction module is used to extract feature points and calculate feature point descriptors from cropped images that have the same first axial distance and the first core rotation angle. The feature matching module is used to perform feature point matching using the feature point descriptor to find matching feature point pairs between different cropped images; The geometric transformation calculation module is used to calculate the geometric transformation relationship between different cropped images using a random sampling consensus algorithm. The correction and stitching module is used to perform geometric correction and stitching on the cropped images based on the calculated geometric transformation relationship, so as to eliminate the overlap and misalignment between the images; The stitching module is used to stitch the spliced ​​images, eliminate stitching marks, and obtain the final spliced ​​image.

11. The carbonate rock core mass identification device as described in claim 7, characterized in that, The criterion for judging color differences is whether the difference value in the RGB color space exceeds a preset threshold.

12. The carbonate rock core mass identification device as described in claim 7, characterized in that, The clumping type determination unit is specifically used to: based on the color, fluorescence reaction results, Leeb hardness value, and hardness range of the delineated clumping and the carbonate reservoir, look up a preset clumping type lookup table to obtain the clumping type corresponding to the delineated clumping.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images