Method and device for identifying block mass in carbonate rock core

Abstract

The invention provides a method and device for identifying a block mass in a carbonate rock core, and the method comprises the steps: carrying out the automatic rolling and photographing operation of the carbonate rock core, and then carrying out the splicing to obtain a spliced image; image selection areas are established in the spliced image according to color differences, and colors of all the image selection areas are marked to obtain all delineated block masses; acquiring a block mass development position in the delineated block mass; carrying out automatic rolling and photographing operation on the carbonate rock core by adopting a fluorescent light source in a camera obscura environment, and judging whether the delineated block mass has a fluorescent reaction or not; the hardness range value of the carbonate reservoir is obtained, and the Leeb hardness value of a delineated block mass is delineated; and determining the type of the block mass based on the color of the delineated block mass, the fluorescence reaction result, the Leeb hardness value and the hardness range value of the carbonate reservoir. According to the method, the block mass information can be obtained without cutting or damaging the rock core, in addition, the automatic scanning and image processing flow is adopted, the block mass recognition efficiency is greatly improved, the analysis time is shortened, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of core lump identification, and particularly to a method and device for identifying lumps in carbonate rock cores. Background Art

[0002] In carbonate rocks, various types of lithological lumps are commonly seen. These lumps refer to massive objects of different lithological types formed inside or on the surface of the rock. These lumps can be formed due to sedimentation, hydrothermal fluid action during diagenesis, or geological events, and have different physical and chemical properties. The types and distributions of the lumps can provide important information about the rock formation environment, diagenetic history, and potential hydrocarbon reservoir characteristics. Specifically, the lumps may include argillaceous lumps, carbonate rock lumps, tuff lumps, pyrite lumps, and siliceous lumps, etc. Therefore, the identification of these lumps is crucial for understanding the sedimentary diagenetic process and reservoir characteristics of carbonate rocks.

[0003] Currently, when identifying these lumps in carbonate rocks in the prior art, the method of microscopic identification is mostly used. This method requires sampling and preparing thin sections. However, on the one hand, this method will consume samples, and on the other hand, the whole process takes a long time and cannot meet the need for rapid identification.

[0004] In addition, the prior art also uses the method of core scanning plus expert judgment. However, on the one hand, core scanning technology mainly records core images and cannot deeply analyze the local lithological changes of the core. On the other hand, expert judgment relies on personal experience and lacks objectivity and universality. Summary of the Invention

[0005] In view of this, the present invention provides a method and device for identifying lumps in carbonate rock cores to solve at least one of the above-mentioned problems.

[0006] To achieve the above object, the present invention adopts the following solutions:

[0007] According to a first aspect of the present invention, a method for identifying lumps in a carbonate rock core is provided. The method includes: performing an automatic rolling and photographing operation on the carbonate rock core to obtain a first core image, a corresponding first axial distance, and a first core rotation angle, where the rotation angle is between 0° and 360°; trimming the image other than the core in the first core image to obtain a trimmed image, and splicing the trimmed images with the same first axial distance and the first core rotation angle to obtain several spliced images; establishing an image selection area in the spliced images according to color differences and marking the colors of each image selection area to obtain each circled lump; extracting the first axial distance and the first core rotation angle corresponding to each image selection area in the spliced images, and obtaining the intermediate value of the first axial distance and the first core rotation angle as the lump development position; performing an automatic rolling and photographing operation on the carbonate rock core using a fluorescent light source in a dark box environment to obtain a second core image, a corresponding second axial distance, and a second core rotation angle; based on the first axial distance and the first core rotation angle corresponding to each image selection area in the spliced images, searching for the second axial distance and the second core rotation angle corresponding to the second core image with the same axial distance and core rotation angle, and judging whether the circled lump has a fluorescence reaction based on the found second core image; obtaining the Richter hardness values at the first axial distance and 0° rotation angle of the carbonate rock core and at the lump development position, and obtaining the carbonate rock reservoir hardness range value based on the Richter hardness values at the first axial distance and 0° rotation angle of the carbonate rock core and at the lump development position, and obtaining the Richter hardness value of the circled lump based on the Richter hardness value at the lump development position; determining the type of the circled lump based on the color, fluorescence reaction result, Richter hardness value, and carbonate rock reservoir hardness range value of the circled lump.

[0008] As an embodiment of the present invention, the automatic rolling and photographing operation on the carbonate rock core in the above method includes: first, performing a photographing operation by rotating the carbonate rock core one week at the axial starting position of the carbonate rock core, and then moving along the axis of the carbonate rock core in steps of 10 cm and continuing to rotate one week for photographing until the photographing operation of the entire carbonate rock core is completed.

[0009] As an embodiment of the present invention, when performing a photographing operation by rotating the carbonate rock core one week in the above method, a photograph is taken every 20°.

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

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

[0012] As an embodiment of the present invention, in the above method, determining the type of the delineated blob based on the color, fluorescence reaction result, Richter hardness value, and carbonate reservoir hardness range value of the delineated blob includes: based on the color, fluorescence reaction result, Richter hardness value, and carbonate reservoir hardness range value of the delineated blob, looking up a preset blob type look-up table to obtain the blob type corresponding to the delineated blob.

[0013] According to a second aspect of the present invention, there is provided a device for identifying lumps in a carbonate rock core. The device includes: a first data acquisition unit for automatically rolling and photographing the carbonate rock core to obtain a first core image and corresponding first axial distance and first core rotation angle, where the rotation angle is between 0° and 360°; an image stitching unit for cropping the image other than the core in the first core image to obtain a cropped image, and stitching the cropped images with the same first axial distance and first core rotation angle to obtain several stitched images; a color annotation unit for establishing an image selection area in the stitched image according to color differences and annotating the colors of each image selection area to obtain each delineated lump; a development position acquisition unit for extracting the first axial distance and first core rotation angle corresponding to each image selection area in the stitched image, and obtaining the median value of the first axial distance and first core rotation angle as the lump development position; a second data acquisition unit for automatically rolling and photographing the carbonate rock core with a fluorescent light source in a dark box environment to obtain a second core image and corresponding second axial distance and second core rotation angle; a fluorescence reaction determination unit for, based on the first axial distance and first core rotation angle corresponding to each image selection area in the stitched image, searching for 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 judging whether the delineated lump has a fluorescence reaction based on the found second core image; a hardness value acquisition unit for obtaining the Leeb hardness values at the first axial distance and 0° rotation angle of the carbonate rock core and at the lump development position, and obtaining the Leeb hardness range value 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 lump development position, and obtaining the Leeb hardness value of the delineated lump based on the Leeb hardness value at the lump development position; a lump type determination unit for determining the type of the delineated lump based on the color, fluorescence reaction result, Leeb hardness value and Leeb hardness range value of the carbonate rock reservoir of the delineated lump.

[0014] As an embodiment of the present invention, the above first data acquisition unit is specifically configured to: first perform a photographing operation by rotating the carbonate rock core one week at the axial starting position of the carbonate rock core, and then move along the axis of the carbonate rock core in steps of 10 cm and continue to rotate one week for photographing until the photographing operation of the entire carbonate rock core is completed; the above second data acquisition unit is specifically configured to: in a dark box environment, first perform a photographing operation by rotating the carbonate rock core one week at the axial starting position of the carbonate rock core, and then move along the axis of the carbonate rock core in steps of 10 cm and continue to rotate one week for photographing until the photographing operation of the entire carbonate rock core is completed.

[0015] As an embodiment of the present invention, when taking pictures of the carbonate rock core for one full rotation, pictures are taken every 20° of rotation.

[0016] As an embodiment of the present invention, the above-mentioned image stitching unit includes: a feature extraction module for extracting feature points from the cropped images with the same first axial distance and the first core rotation angle and calculating feature point descriptors; a feature matching module for using the feature point descriptors to perform feature point matching to find pairs of matching feature points between different cropped images; a geometric transformation calculation module for calculating the geometric transformation relationship between different cropped images using the random sample consensus algorithm; a calibration and stitching module for performing geometric calibration and stitching on the cropped images according to the calculated geometric transformation relationship to eliminate overlap and misalignment between the images; and a stitching processing module for performing stitching processing on the stitched image to eliminate stitching marks and obtain the final stitched image.

[0017] As an embodiment of the present invention, the judgment criterion for the above-mentioned 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 blob type determination unit is specifically used for: based on the color, fluorescence reaction result, Leeb hardness value of the delineated blob and the carbonate rock reservoir hardness range value, looking up a preset blob type look-up table to obtain the blob type corresponding to the delineated blob.

[0019] According to the third aspect of the present invention, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0020] According to the fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above method are implemented.

[0021] As can be seen from the above technical solutions, for the method and device for identifying blobs in carbonate rock cores provided by the present invention, firstly, without cutting or damaging the core, blob information can be obtained, retaining the integrity of the core, which is beneficial to subsequent other experiments and analyses. Secondly, the present application adopts an automated scanning and image processing process, greatly improving the blob identification efficiency, shortening the analysis time, and reducing the labor cost. In addition, the present application can not only identify the position and size of the blobs, but also more accurately judge the blob type and its oil content through multi-dimensional information such as color, fluorescence reaction, and hardness. In short, the present application provides an efficient, accurate, low-cost, and non-destructive method for identifying blobs in carbonate rock cores, which has important application value for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings. In the accompanying drawings:

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

[0024] Figure 2 is a schematic flowchart of image stitching provided by an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of the identification result of the clump area provided by an embodiment of the present application;

[0026] Figure 4 is a schematic structural diagram of a device for identifying clumps in a carbonate rock core provided by an embodiment of the present application;

[0027] Figure 5 is a schematic structural diagram of an image stitching unit provided by an embodiment of the present application;

[0028] Figure 6 is a front view of an implementation device provided by an embodiment of the present application;

[0029] Figure 7 is a side view of an implementation device provided by an embodiment of the present application;

[0030] Figure 8 is a schematic block diagram of the system composition of an electronic device provided by an embodiment of the invention. Detailed implementation manners

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the following will further describe the embodiments of the present invention in detail 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 not to limit the present invention.

[0032] As Figure 1 shown is a schematic flowchart of a method for identifying clumps in a carbonate rock core provided by an embodiment of the present invention. The method includes the following steps:

[0033] Step S101: Automatically roll and photograph the carbonate rock core to obtain a first core image, as well as the corresponding first axial distance and the first core rotation angle, where the rotation angle is between 0° and 360°.

[0034] In this embodiment, the carbonate rock core used for experiments is generally prepared in a cylindrical shape. This cylindrical core is convenient for operation, measurement, and analysis, and is also easy to use in various experimental equipment. The rock on the surface of the cylindrical core can be either smooth or uneven.

[0035] During specific implementation, it is necessary to clean the mud and dust on the surface of the carbonate rock core with a towel and clean water, and air-dry it. Then, place it on an automatic rolling device. At this time, it is necessary to keep the height of the core consistent, with a height difference of less than 10 mm. This automatic rolling device can make the core rotate along its axis direction and precisely control the rotation angle.

[0036] Preferably, the automatic rolling and photographing operation of the carbonate rock core in this step includes: first, take a photograph of the carbonate rock core by rotating it one week at the axial starting position of the carbonate rock core, and then move along the axis of the carbonate rock core in steps of 10 cm and continue to rotate it one week for photographing until the photographing operation of the entire carbonate rock core is completed.

[0037] Further preferably, when taking a photograph by rotating the carbonate rock core one week in this step, take a photograph every 20°. In actual operation, the selection of the rotation angle interval can be adjusted according to the size of the core, the complexity of the surface features, and the required accuracy. The smaller the interval, the more image numbers are obtained, and the more refined the data, but at the same time, it also increases the burden of data processing. The interval of 20° adopted in this embodiment is determined through experiments and can balance data accuracy and processing efficiency. Of course, if the surface features of the core are very complex or higher accuracy is required, the rotation angle interval can be further reduced, such as taking a photograph every 10° or 5°.

[0038] At the same time, this step needs to record the current first axial distance (i.e., the distance that the camera moves along the axis) and the first core rotation angle (from 0° to 360°). In this way, the first core image dataset A is obtained, and the first axial distance and the first core rotation angle corresponding to each image are recorded in the first core image dataset A.

[0039] Step S102: Crop the images in the first core image except for the core to obtain cropped images, and splice the cropped images with the same first axial distance and the first core rotation angle to obtain several spliced images.

[0040] Since the captured images may contain background areas outside the core, it is necessary to crop the images to retain only the core part. The cropped images with the same first axial distance and the first core rotation angle are stitched together. For example, at an axial distance of 10 cm, the cropped images with rotation angles of 0°, 10°, 20°... 350° are stitched into a complete cylindrical core image. Finally, several stitched images are obtained, and each stitched image represents a complete circle of the core at different axial distances.

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

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

[0043] The goal of this step is to extract representative feature points from each cropped image and calculate their descriptors. Commonly used feature point extraction algorithms include Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), etc. 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. The calculated feature point descriptors are used to describe the local features of the feature points for subsequent matching.

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

[0045] Using the calculated feature point descriptors, perform feature point matching between different cropped images. The goal is to find feature point pairs corresponding to the same physical position in different images. Commonly used matching methods include brute-force matching, KD-tree matching, and Flann matching, etc.

[0046] Step S1023: Use the Random Sample Consensus algorithm to calculate the geometric transformation relationship between different cropped images.

[0047] Using the matching feature point pairs, use the Random Sample Consensus (RANSAC) algorithm to calculate the geometric transformation relationship between different cropped images. The RANSAC algorithm can effectively exclude the influence of mis-matched points, thereby obtaining a more accurate geometric transformation relationship.

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

[0049] According to the calculated geometric transformation relationship, geometric correction is performed on the cropped images, and they are stitched together. The goal of this step is to eliminate the overlap and misalignment between the images and form a complete stitched image.

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

[0051] Due to the influence of factors such as lighting and exposure, obvious stitching marks may exist in the stitched image. The goal of stitching processing is to eliminate these marks and make the stitched image more natural and smooth. For example, methods such as weighted average method and Poisson fusion can be used for stitching processing.

[0052] By the application of the above sub-steps and the combination of algorithms, the quality of image stitching can be effectively improved, and thus the performance of the entire blob recognition method can be improved.

[0053] Step S103: Establish image selection areas in the stitched image according to color differences and label the colors of each image selection area to obtain each delineated blob.

[0054] On the stitched image, different image areas are identified and selected according to color differences. These areas represent potential blobs. Each selection area is color-labeled, for example, using different colors or labels to distinguish different blobs. In this way, several delineated blob areas are obtained. For example, it can be seen Figure 3 as shown, where the areas circled by the red lines are the identified blob areas.

[0055] Here, the judgment criterion for color difference is whether the difference value in the RGB color space exceeds a preset threshold. If the calculated color difference value exceeds this threshold, it is considered that there is a significant difference between these two colors, and they can be divided into different image selection areas. The selection of the threshold can be adjusted according to the specific application scenario and image characteristics. Too small a threshold will lead to over-segmentation, dividing the same blob into multiple areas; too large a threshold will lead to under-segmentation, merging different blobs into one area. In this embodiment, a suitable threshold can be selected according to 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 obtain the intermediate value of the first axial distance and the first core rotation angle as the blob development position.

[0057] On the core, the colors of the core with the same lithology are basically the same in the short distance. Through the color change, the development position of the blob can be identified. Therefore, in this step, the first axial distance and the first core rotation angle information corresponding to each delineated blob in the stitched image are extracted and used as set A n, and the obtained set A n Take the median of the first axial distance and the first core rotation angle in it as A n * , A n * That is the position information of the position where the clumps develop.

[0058] Step S105: In a dark box environment, use a fluorescent light source to perform automatic rolling and photographing operations on 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, the difference is the environment where the two are located. In this step, it is in a dark box environment and rolling and photographing operations are performed after being irradiated with a fluorescent light source. In the specific embodiment, an opaque black cloth can be covered outside the device to simulate a dark box environment. Through this step, a second core image dataset B can be obtained, and the second axial distance and the second core rotation angle corresponding to each image are recorded in the second core image dataset B.

[0060] Step S106: Based on the first axial distance and the first core rotation angle corresponding to each image selection area in the spliced 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 judge whether the circled clump has a fluorescence reaction based on the found second core image.

[0061] This step is to find the B n photo at the corresponding position in B based on the positioning information set of A n , and then judge whether the corresponding circled clump has a fluorescence reaction based on this B n photo. The presence of a fluorescence reaction indicates that the clump may contain oil or hydrocarbon substances, while the absence of a fluorescence reaction indicates that the clump 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 position where the clumps develop, and obtain the hardness range value 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 position where the clumps develop, and obtain the Leeb hardness value of the circled clump based on the Leeb hardness value at the position where the clumps develop.

[0063] If the camera moves along the axial direction of the carbonate rock core in step S101 with a step size of 10 cm, then in this step, hardness test points are set at the starting position, every 10 cm and 0° rotation angle, and also at the position where the clumps develop (that is, A n *)Set hardness test points. Then count the Leeb hardness values of the 0° test points to obtain the hardness range value F of the carbonate rock reservoir. At the same time, in order to remove outliers, the maximum and minimum values of the Leeb hardness values of the 0° test points can be removed.

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

[0065] In this step, comprehensively consider the color, fluorescence reaction result, Leeb hardness value, and the hardness range value of the carbonate rock reservoir of the circled mass to determine the type of each circled mass.

[0066] Preferably, this step may further include: based on the color, fluorescence reaction result, Leeb hardness value, and the hardness range value of the carbonate rock reservoir of the circled mass, look up a preset mass type comparison table to obtain the mass type corresponding to the circled mass. The mass type comparison table here can be referred to as Table 1 below. Of course, Table 1 is only for example and does not cover all cases. For other special cases, the mass type can be defined as a high-hardness metal element-containing mass.

[0067] Table 1

[0068] Color Fluorescence reaction Leeb hardness Type All kinds of hues All can Less than 315 Argillaceous mass All kinds of hues All can F range Carbonate rock mass All kinds of hues All can 420～520 Tuff mass Yellow hue None 810～850 Pyrite mass All kinds of hues All can Greater than 850 Siliceous mass Black All can Invalid Hole

[0069] As can be seen from the above technical solutions, for the mass identification method in the carbonate rock core provided by the present invention, first, without cutting or damaging the core, the mass information can be obtained, and the integrity of the core is retained, which is beneficial to subsequent other experiments and analyses. Second, the present application adopts an automated scanning and image processing process, greatly improving the mass identification efficiency, shortening the analysis time, and reducing the labor cost. In addition, the present application can not only identify the position and size of the mass, but also more accurately judge the mass type and its oil content through multi-dimensional information such as color, fluorescence reaction, and hardness. In short, the present application provides an efficient, accurate, low-cost, and non-destructive mass identification method for carbonate rock cores, which has important application value for oil and gas exploration and development.

[0070] As Figure 4 shown is a schematic structural diagram of a mass identification device for carbonate rock cores provided by an embodiment of the present application. The device includes: a first data acquisition unit 410, an image stitching unit 420, a color annotation unit 430, a development position 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 in sequence. Among them:

[0071] The first data acquisition unit 410 is configured to perform automatic rolling and photographing operations on the carbonate rock core, obtain a first core image, as well as a corresponding first axial distance and a first core rotation angle, where the rotation angle is between 0° and 360°.

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

[0073] The color annotation unit 430 is configured to establish an image selection area in the stitched image according to color differences and annotate the color of each image selection area to obtain each enclosed mass.

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

[0075] The second data acquisition unit 450 performs automatic rolling and photographing operations on the carbonate rock core using a fluorescent light source in a dark box environment, and obtains a second core image, as well as a corresponding second axial distance and a second core rotation angle.

[0076] The fluorescence reaction determination unit 460 is configured to, 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 enclosed mass has a fluorescence reaction based on the found second core image.

[0077] The hardness value acquisition unit 470 is configured to obtain the Richter hardness values at the first axial distance and the 0° rotation angle of the carbonate rock core, as well as at the development position of the mass, and obtain the hardness range value of the carbonate rock reservoir based on the Richter hardness values at the first axial distance and the 0° rotation angle of the carbonate rock core, as well as at the development position of the mass, and obtain the Richter hardness value of the enclosed mass based on the Richter hardness value at the development position of the mass.

[0078] The mass type determination unit 480 is configured to determine the type of the enclosed mass based on the color, fluorescence reaction result, Richter hardness value, and carbonate rock reservoir hardness range value of the enclosed mass.

[0079] Preferably, the automatic rolling and photographing operations of the carbonate core by the first data acquisition unit 410 and the second data acquisition unit 450 include: first, taking a photograph of the carbonate core by rotating it one week at the axial starting position of the carbonate core, and then moving along the axial direction of the carbonate core in steps of 10 cm and continuing to rotate it one week for photographing until the photographing operation of the entire carbonate core is completed.

[0080] Preferably, when taking a photograph by rotating the carbonate core one week, a photograph is taken every 20°.

[0081] Preferably, as Figure 5 shown, the image stitching unit 420 includes:

[0082] A feature extraction module 421, configured to extract feature points from the cropped images with the same first axial distance and the first core rotation angle and calculate feature point descriptors.

[0083] A feature matching module 422, configured to perform feature point matching by using the feature point descriptors to find pairs of matching feature points between different cropped images.

[0084] A geometric transformation calculation module 423, configured to calculate the geometric transformation relationship between different cropped images by using the random sample consensus algorithm.

[0085] A calibration and stitching module 424, configured to perform geometric calibration and stitching on the cropped images according to the calculated geometric transformation relationship to eliminate the overlap and misalignment between the images.

[0086] A stitching processing module 425, configured to perform stitching processing on the stitched image to eliminate the stitching marks and obtain the final stitched image.

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

[0088] Preferably, the blob type determination unit 480 is specifically configured to: based on the color, fluorescence reaction result, Richter hardness value, and carbonate reservoir hardness range value of the delineated blob, look up a preset blob type look-up table to obtain the blob type corresponding to the delineated blob.

[0089] As can be seen from the above technical solutions, the apparatus for identifying clumps in carbonate rock cores provided by the present invention can, first of all, obtain clump information without cutting or damaging the core, preserving the integrity of the core, which is beneficial for subsequent other experiments and analyses. Secondly, the present application adopts an automated scanning and image processing process, greatly improving the efficiency of clump identification, shortening the analysis time, and reducing the labor cost. In addition, the present application can not only identify the position and size of the clumps, but also more accurately determine the type of clumps and their oil-bearing properties through multi-dimensional information such as color, fluorescence reaction, and hardness. In short, the present application provides an efficient, accurate, low-cost, and non-destructive method for identifying clumps in carbonate rock cores, which has important application value for oil and gas exploration and development.

[0090] Finally, the present application also provides a specific implementation apparatus for executing Figure 1 the corresponding method. The implementation apparatus is as Figure 6 shown in Figure 7 and Figure 6 shown in Figure 7 which are the front view and side view of the implementation apparatus respectively. As shown by

[0091] The core placement and rotation mechanism: It consists of a motor, rollers, and a conveyor belt. The motor drives the conveyor belt and rollers to rotate, driving the core to rotate so as to capture images from different angles. The design of the rollers can ensure the smoothness and accuracy of the core rotation.

[0092] The imaging system: It includes a camera, a fluorescence / white light source, and a hardness tester. The camera is used to capture images of the core. The fluorescence / white light source provides illumination, where the fluorescence source is used to excite the fluorescent substances in the core, and the white light source is used for conventional imaging. The hardness tester is used to measure the Richter 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 are obtained. As can be seen from the front view, the two light sources are symmetrically placed on both sides of the core, which can evenly illuminate the surface of the core.

[0093] The control and data processing system: It includes a storage and controller and a display. The storage and controller is used to control the operation of the apparatus and store the captured images and data. The display is used to display the images and data and provide a man-machine interaction interface. The data line connects the camera, hardness tester, and control system to achieve real-time transmission and processing of data.

[0094] The support structure: It consists of a slide rail and a frame, etc., and is used to support and fix the above-mentioned various components to ensure the stability and reliability of the apparatus. The side view clearly shows the structure of the slide rail, which can guide the camera and hardness tester to move along the axial direction of the core.

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

[0096] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above method is implemented.

[0097] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for executing the above method.

[0098] As Figure 8 , the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It should be noted that the electronic device 600 does not necessarily have to include Figure 8 all the components shown; in addition, the electronic device 600 may further include Figure 8 components not shown, and reference may be made to the prior art.

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

[0100] Among them, the memory 140, for example, may be one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. The above information related to failures can be stored, and in addition, programs for executing relevant information can also be stored. And the central processing unit 100 can execute the program stored in the memory 140 to implement information storage or processing, etc.

[0101] The input unit 120 provides inputs to the central processing unit 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to supply power to the electronic device 600. The display 160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0102] The memory 140 can be a solid-state memory, for example, a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that stores information even when power is off, can be selectively erased and has more data. An example of this memory is sometimes referred to as an EPROM, etc. 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 can include an application / function storage unit 142 for storing applications and function programs or the processes for operating the electronic device 600 by the central processor 100.

[0103] The memory 140 can 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 can include various drivers for the communication functions of the electronic device and / or for performing other functions of the electronic device (such as a messaging application, an address book application, 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 processor 100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0105] Based on different communication technologies, multiple communication modules 110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module 110 (transmitter / receiver) is also coupled to the speaker 131 and the microphone 132 via the audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby implementing the usual telecommunication functions. The audio processor 130 can include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that recording can be performed on the local machine through the microphone 132 and the sound stored on the local machine can be played through the speaker 131.

[0106] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. 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. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0107] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0110] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for identifying clusters in carbonate rock cores, characterized in that: The method comprises: Automatically rolling and photographing the carbonate 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°; trimming the images other than the core in the first core image to obtain a trimmed image, and splicing the trimmed images having the same first axial distance and the first core rotation angle to obtain a plurality of spliced ​​images; In the stitched image, image selection areas are established according to color differences and the colors of the image selection areas are marked to obtain the delineated clumps; Extracting the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, and obtaining the middle value of the first axial distance and the first core rotation angle as the cluster development position; Automatically rolling and photographing the carbonate core using a fluorescent light source in a dark box environment to obtain a second core image and a corresponding second axial distance and a second core rotation angle; Based on the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, searching for a second core image corresponding to a second axial distance and a second core rotation angle having the same axial distance and core rotation angle, and judging whether the delineated mass has a fluorescent reaction based on the found second core image; Obtaining the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate core and at the location where the cluster is developed, and obtaining the carbonate reservoir hardness range value based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate core and at the location where the cluster is developed, and obtaining the Leeb hardness value of the delineated cluster based on the Leeb hardness value at the location where the cluster is developed; The type of the delineated mass is determined based on the color of the delineated mass, the fluorescence reaction result, the Leeb hardness value, and the carbonate reservoir hardness range value.

2. The method for identifying clusters in carbonate rock cores according to claim 1, characterized in that: The automatic rolling and photographing operation of the carbonate rock core includes: first rotating the carbonate rock core for one circle at the axial starting position of the carbonate rock core for photographing, then moving along the axial direction of the carbonate rock core with a step length of 10 cm and then continuing to rotate for one circle for photographing, until the photographing operation of the entire carbonate rock core is completed.

3. The method for identifying clusters in carbonate rock cores according to claim 2, characterized in that: When the carbonate rock core is rotated once for photographing, a photograph is taken every 20°.

4. The method for identifying clusters in carbonate rock cores according to claim 1, characterized in that: The step of stitching the cropped images having the same first axial distance and the first core rotation angle to obtain a plurality of stitched images comprises: Extracting feature points from the cropped images having the same first axial distance and the first core rotation angle and calculating feature point descriptors; Using the feature point descriptors to perform feature point matching, and finding matching feature point pairs between different cropped images; The geometric transformation relationship between different cropped images is calculated using a random sampling consensus algorithm; According to the calculated geometric transformation relationship, the cropped images are geometrically corrected and spliced ​​to eliminate the overlap and misalignment between images; The stitched images are stitched to eliminate stitching marks and obtain the final stitched image.

5. The method for identifying clusters in carbonate rock cores according to claim 1, characterized in that: The color difference judgment standard is whether the difference value in the RGB color space exceeds a preset threshold.

6. The method for identifying clusters in carbonate rock cores according to claim 1, characterized in that: The method of determining the type of the delineated mass based on the color, fluorescence reaction result, Leeb hardness value and carbonate reservoir hardness range value of the delineated mass includes: Based on the color of the delineated mass, the fluorescence reaction result, the Leeb hardness value and the carbonate reservoir hardness range value, a preset mass type comparison table is searched to obtain the mass type corresponding to the delineated mass.

7. A device for identifying clusters in carbonate rock cores, characterized in that: The device comprises: A first data acquisition unit is used to automatically scroll and photograph the carbonate core to acquire 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°; an image stitching unit, configured to trim the image other than the core in the first core image to obtain a trimmed image, and stitch the trimmed images having the same first axial distance and the first core rotation angle to obtain a plurality of stitched images; A color marking unit, used for establishing image selection areas in the stitched image according to color differences and marking the colors of each image selection area to obtain each delineated mass; A development position 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 obtain the middle value of the first axial distance and the first core rotation angle as the cluster development position; a second data acquisition unit, which uses a fluorescent light source to automatically roll and photograph the carbonate core in a dark box environment to acquire a second core image and a corresponding second axial distance and a second core rotation angle; A fluorescence reaction determination unit is used to search for a second core image corresponding to a second axial distance and a second core rotation angle having the same axial distance and core rotation angle based on the first axial distance and the first core rotation angle corresponding to each image selection area in the stitched image, and determine whether the delineated mass has a fluorescence reaction based on the searched second core image; a hardness value acquisition unit, for acquiring the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate core and at the location where the cluster is developed, and obtaining the carbonate reservoir hardness range value based on the Leeb hardness values ​​at the first axial distance and 0° rotation angle of the carbonate core and at the location where the cluster is developed, and obtaining the Leeb hardness value of the delineated cluster based on the Leeb hardness value at the location where the cluster is developed; The block type determination unit is used to determine the type of the delineated block based on the color of the delineated block, the fluorescence reaction result, the Leeb hardness value and the carbonate reservoir hardness range value.

8. The device for identifying clusters in carbonate rock cores according to claim 7, characterized in that: The first data acquisition unit is specifically used to: first rotate the carbonate rock core for one circle at the axial starting position of the carbonate rock core to take a picture, then move along the axial direction of the carbonate rock core with a step length of 10 cm and continue to rotate for one circle to take a picture, until the picture taking operation of the entire carbonate rock core is completed; The second data acquisition unit is specifically used to: in a dark box environment, first rotate the carbonate rock core for one circle at the axial starting position of the carbonate rock core to take a picture, then move along the axial direction of the carbonate rock core in steps of 10 cm and continue to rotate for one circle to take a picture, until the photographing operation of the entire carbonate rock core is completed.

9. The device for identifying clusters in carbonate rock cores according to claim 8, characterized in that: When the carbonate rock core is rotated once for photographing, a photograph is taken every 20°.

10. The device for identifying clusters in carbonate rock cores according to claim 7, characterized in that: The image stitching unit comprises: A feature extraction module, used for extracting feature points from the cropped images having the same first axial distance and the first core rotation angle and calculating feature point descriptors; 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 is used to calculate the geometric transformation relationship between different cropped images using a random sampling consistency algorithm; A correction and stitching module is 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; The stitching processing module is used to stitch the stitched images to eliminate the stitching marks and obtain the final stitched image.

11. The device for identifying clusters in carbonate rock cores according to claim 7, characterized in that: The color difference judgment standard is whether the difference value in the RGB color space exceeds a preset threshold.

12. The device for identifying clusters in carbonate rock cores according to claim 7, characterized in that: The mass type determination unit is specifically used to: search a preset mass type comparison table based on the color, fluorescence reaction result, Leeb hardness value and carbonate reservoir hardness range value of the delineated mass to obtain the mass type corresponding to the delineated mass.

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, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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