Lamella recognition method, device and storage medium

Abstract

The disclosure provides a kind of lamina identification method, device and storage medium, belong to oil and gas exploration technical field, method includes: the CT image of core sample is segmented, obtains multiple image regions;Wherein, an image region corresponds to the lamina of core sample;According to the XRF element analysis data of core sample, the type of each lamina is identified;According to the lamina information contained in the image region corresponding to each lamina, the lamina of different types of core sample is quantitatively characterized.The embodiment of the disclosure can realize the lamina identification of core sample quickly and accurately.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas exploration technology, and in particular to a method, apparatus and storage medium for laminar flow identification. Background Technology

[0002] As oil and gas exploration progresses, the vast majority of newly discovered oil and gas reservoirs are tight reservoirs such as shale. Tight reservoirs typically exhibit different laminar characteristics, and the type and degree of laminar development have a significant impact on whether they can become favorable reservoirs. Favorable reservoirs usually exhibit laminar structures with high silica content, and the greater the laminar thickness, the more favorable the reservoir.

[0003] In related technologies, the main methods for identifying rock laminae include geological observation and well logging.

[0004] Geological observation primarily involves geologists directly examining full-diameter rock cores or using a magnifying glass with the naked eye to identify the types of laminations on the cores and perform approximate quantitative characterization. This method is relatively intuitive, but it is generally costly, time-consuming, and the quality is difficult to standardize, being largely dependent on the professional competence of the geologists themselves.

[0005] Well logging identification methods are mainly based on well logging curves or images for identification. However, due to the limitations of the resolution of well logging equipment, the accuracy of well logging curves is usually only a few decimeters, while the thickness of some layers may be lower than the accuracy, making them difficult to identify. Summary of the Invention

[0006] This disclosure provides a method, apparatus, and storage medium for texture recognition.

[0007] A first aspect of this disclosure provides a method for laminar texture recognition, the method comprising:

[0008] The CT images of the core sample are segmented to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample.

[0009] Based on the XRF elemental analysis data of the core samples, the types of each of the laminae were identified;

[0010] Based on the laminar information contained in the image region corresponding to each laminar layer, the different types of laminar layers in the core sample are quantitatively characterized.

[0011] In one embodiment, the CT image of the core sample is segmented to obtain multiple image regions, including:

[0012] Based on the gray value distribution of the CT image, multiple gray value peaks and multiple gray value troughs are determined;

[0013] The CT image is segmented based on the midpoint between each adjacent gray value peak and gray value trough to obtain multiple image regions.

[0014] In one embodiment, identifying the type of each laminar layer based on the XRF elemental analysis data of the core sample includes:

[0015] According to the depth direction of the core sample, establish the correspondence between depth and image region in the CT image and element information in the XRF elemental analysis data;

[0016] Based on the established correspondence, the elemental information of each of the texture layers is determined in the XRF elemental analysis data;

[0017] The type of each texture layer is identified based on the element information of each texture layer.

[0018] In one embodiment, the method further includes:

[0019] Different display styles are used to display the different types of textures.

[0020] In one embodiment, the step of quantitatively characterizing different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae includes:

[0021] Based on the laminar information contained in the image region corresponding to each laminar layer, the thickness and development density of different types of laminar layers in the core sample are determined.

[0022] In one embodiment, determining the thickness and development density of different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae includes:

[0023] The thickness of each texture layer is determined based on the texture information contained in the image region corresponding to each texture layer.

[0024] Based on the number of each type of texture layer and the thickness of each texture layer, a statistical value for the thickness of each type of texture layer is determined;

[0025] The development density of each type of laminae is determined based on the number of each type of laminae and the length of the core sample.

[0026] A second aspect of this disclosure provides a texture recognition device, the device comprising:

[0027] The image segmentation module is used to segment the CT image of the core sample to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample.

[0028] The type identification module is used to identify the type of each of the laminae based on the XRF elemental analysis data of the core sample;

[0029] The quantitative characterization module is used to quantitatively characterize different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae.

[0030] In one embodiment, the image segmentation module is used for:

[0031] Based on the gray value distribution of the CT image, multiple gray value peaks and multiple gray value troughs are determined;

[0032] The CT image is segmented based on the midpoint between each adjacent gray value peak and gray value trough to obtain multiple image regions.

[0033] In one embodiment, the type recognition module is used for:

[0034] According to the depth direction of the core sample, establish the correspondence between depth and image region in the CT image and element information in the XRF elemental analysis data;

[0035] Based on the established correspondence, the elemental information of each of the texture layers is determined in the XRF elemental analysis data;

[0036] The type of each texture layer is identified based on the element information of each texture layer.

[0037] In one embodiment, the apparatus further includes:

[0038] The display module is used to display different types of textures using different display styles.

[0039] In one embodiment, the quantitative characterization module is used for:

[0040] Based on the laminar information contained in the image region corresponding to each laminar layer, the thickness and development density of different types of laminar layers in the core sample are determined.

[0041] In one embodiment, the quantitative characterization module is used for:

[0042] The thickness of each texture layer is determined based on the texture information contained in the image region corresponding to each texture layer.

[0043] Based on the number of each type of texture layer and the thickness of each texture layer, a statistical value for the thickness of each type of texture layer is determined;

[0044] The development density of each type of laminae is determined based on the number of each type of laminae and the length of the core sample.

[0045] A third aspect of this disclosure provides a computer 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 steps of the texture recognition method according to any one of the first aspects.

[0046] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the textural recognition method according to any one of the first aspects.

[0047] This disclosure provides a method, apparatus, and storage medium for laminar texture identification. By segmenting CT images of a core sample, multiple image regions are obtained; each image region corresponds to a laminar texture of the core sample. This allows for the differentiation of different laminar textures using CT images, and the identification of each laminar texture type by combining XRF elemental analysis data of the core sample. Furthermore, based on the laminar texture information contained in the image regions corresponding to each laminar texture, quantitative characterization of different types of laminar textures in the core sample is performed, thereby enabling rapid and accurate qualitative and quantitative analysis of the laminar texture of the core sample. Attached Figure Description

[0048] Figure 1 A schematic flowchart of a texture recognition method provided in an embodiment of this disclosure;

[0049] Figure 2 A schematic flowchart of a texture recognition method provided in an embodiment of this disclosure;

[0050] Figure 3 A schematic flowchart of a texture recognition method provided in an embodiment of this disclosure;

[0051] Figure 4 A schematic flowchart of a texture recognition method provided in an embodiment of this disclosure;

[0052] Figure 5 A schematic flowchart of a texture recognition method provided in an embodiment of this disclosure;

[0053] Figure 6a This is a schematic diagram of the CT scan imaging principle of the texture recognition method shown in the embodiments of this disclosure;

[0054] Figure 6b This is a comparison diagram of a core CT scan image and a core photograph shown in an embodiment of this disclosure.

[0055] Figure 6cThis is a schematic diagram of the texture segmentation in the texture recognition method provided in the embodiments of this disclosure;

[0056] Figure 6d This is a schematic diagram of the texture recognition method provided in the embodiments of this disclosure;

[0057] Figure 7 This is a schematic diagram of the structure of a texture recognition device provided in an embodiment of the present disclosure;

[0058] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure. Unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be arbitrarily combined with each other. The steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0060] It is understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0061] Among related technologies, laminar flow identification methods mainly include geological observation and well logging identification.

[0062] Geological observation primarily involves geologists directly examining full-diameter rock cores or using a magnifying glass with the naked eye to identify the types of laminations on the cores and perform approximate quantitative characterization. This method is relatively intuitive, but it is generally costly, time-consuming, and the quality is difficult to standardize, being largely dependent on the professional competence of the geologists themselves.

[0063] Well logging identification methods primarily rely on well logging curves or images for identification. A common curve is the FMI (Fade-Morphology Microresistivity) image, which quantitatively characterizes laminar features based on differences in rock resistivity and other numerical values. It also combines other types of well logging curves (e.g., natural gamma ray curves) to determine rock type. The advantage of this method is the ease of data acquisition and the relatively intuitive results. The disadvantage is its limited accuracy; the accuracy of well logging curves is typically a few decimeters, while the thickness of some laminar lines may be below this accuracy, making them difficult to identify.

[0064] Figure 1 This is a flowchart illustrating a texture recognition method provided in an embodiment of the present disclosure, as shown below. Figure 1 As shown, the method may include:

[0065] 101. The CT image of the core sample is segmented to obtain multiple image regions; one image region corresponds to one layer of the core sample.

[0066] 102. Based on the XRF elemental analysis data of the core samples, identify the types of each laminar layer;

[0067] 103. Based on the laminar information contained in the image regions corresponding to each laminar layer, the different types of laminar layers in the core sample are quantitatively characterized.

[0068] In this embodiment, the core sample can be a core extracted from a reservoir or formation. For example, a full-diameter core drilled underground using special coring tools can be used as the core sample. The full-diameter core sample has a diameter of 60mm to 100mm and is the largest geological sample directly obtained from underground oil and gas reservoirs, containing rich information and being more representative. Furthermore, the core sample can also be a small rock sample, such as one smaller than the full-diameter core sample; the specific core sample is not limited here.

[0069] The CT images were obtained by performing CT scans on core samples arranged in order of depth from shallow to deep.

[0070] In some examples, CT (Computed Tomography) images of core samples can be obtained by performing CT scans on the core samples using single-energy or dual-energy X-rays. These CT images can be stored in DICOM (Digital Imaging and Communications in Medicine, an international standard for medical images and related information) format.

[0071] In some examples, CT images of core samples can be preprocessed before segmentation. This preprocessing may include image denoising and / or image enhancement. For example, image erosion and / or image dilation can be used to denoise the CT images. Alternatively, frequency domain enhancement algorithms can be used to enhance the CT images. Thus, by preprocessing the CT images of core samples, the quality of the CT images can be improved without losing image information, thereby facilitating more accurate segmentation of the CT images of the core samples into different image regions corresponding to different layers of the core sample.

[0072] In this embodiment, since CT images have a high degree of fidelity to the internal structure of the core sample and high resolution, different image regions can be obtained by segmenting the CT images of the core sample. Different image regions correspond to different layers of the core sample, which makes it easy to accurately distinguish the layers of the core sample.

[0073] In some examples, in step 101 above, the midpoint between the local maximum and minimum values ​​can be selected based on the differences in grayscale values ​​in the CT images of the core sample to determine the contour boundaries of each layer, and different image regions corresponding to different layers can be determined in the CT images based on the contour boundaries of each layer.

[0074] In some examples, X-ray fluorescence (XRF) elemental analysis data of core samples can be obtained by acquiring data from core samples using a desktop XRF elemental analyzer or a handheld XRF elemental analyzer. The data sampling density can be adjusted according to actual needs; for example, the sampling density can be one data point per 1 cm to 10 cm.

[0075] In some examples, in step 102 above, XRF element analysis data of the target texture corresponding to the selected target image region in the CT image can be obtained based on the selection operation applied to the CT image, and the type of the target texture can be identified based on the XRF element analysis data of the target texture. The target image region can be any one of multiple image regions segmented from the CT image.

[0076] In other examples, a correspondence can be established between image regions in the CT image and element information in the XRF element analysis data. Based on this correspondence and the element information in the XRF element analysis data, the type of texture corresponding to each image region can be determined.

[0077] In some examples, the types of textures may include, but are not limited to: siliceous textures, calcareous textures, mottled textures, ferrous textures, carbonaceous textures, and / or clay textures.

[0078] It is understood that in this embodiment, multiple image regions are obtained by segmenting the CT image of the core sample, so that each image region has laminar information. By further combining the XRF elemental analysis data of the core sample, the types of different laminar structures can be identified.

[0079] In some examples, in step 103 above, the thickness, density and other parameters of different types of textures can be calculated based on the texture information contained in the image region corresponding to each texture, so as to achieve quantitative characterization of different types of textures in the core sample.

[0080] In the laminar texture identification method provided in this embodiment, multiple image regions are obtained by segmenting the CT image of the core sample; wherein, one image region corresponds to one laminar texture of the core sample; thus, different laminar textures of the core sample can be distinguished by using the CT image, and the type of each laminar texture can be identified by combining the XRF elemental analysis data of the core sample; and the different types of laminar textures of the core sample can be quantitatively characterized according to the laminar texture information contained in the image region corresponding to each laminar texture, thereby enabling rapid and accurate qualitative and quantitative identification of laminar textures of the core sample.

[0081] In one embodiment, such as Figure 2 As shown, in step 101 above, the CT image of the core sample is segmented to obtain multiple image regions, which may include:

[0082] 201: Based on the gray value distribution of CT images, determine multiple gray value peaks and multiple gray value troughs;

[0083] The grayscale distribution of a CT image refers to the distribution of grayscale values ​​in the CT image, which can be represented by a grayscale histogram.

[0084] Here, the gray value distribution of CT images can be used to determine the changes in local gray value peaks and troughs, where changes in gray value peaks and troughs can reflect changes in the mineral composition of the core.

[0085] 202: The CT image is segmented based on the midpoint between each adjacent gray value peak and gray value trough to obtain multiple image regions.

[0086] In some examples, the midpoint between each adjacent gray value peak and gray value trough can be determined, the contour boundary of each texture layer can be determined based on the determined midpoint, and different image regions corresponding to different texture layers can be determined in the CT image based on the contour boundary of each texture layer.

[0087] In some examples, determining the contour boundaries of each texture layer based on a determined midpoint may include:

[0088] For each adjacent gray value peak and gray value trough midpoint, taking the pixel corresponding to the midpoint as the starting point, along the vertical direction of the depth direction of the core sample, find the pixel with the maximum gray value gradient corresponding to the pixel. Based on the multiple pixels found, determine the contour boundary of the texture passing through the midpoint.

[0089] In one embodiment, such as Figure 3 As shown, in step 102 above, identifying the type of each laminar layer based on the XRF elemental analysis data of the core sample may include:

[0090] 301: Establish the correspondence between depth and image regions in CT images and elemental information in XRF elemental analysis data according to the depth direction of the core sample.

[0091] 302: Based on the established correspondence, determine the elemental information of each texture layer in the XRF elemental analysis data.

[0092] Here, the elemental information of the texture can include: the types of elements contained in the texture and the content of each element.

[0093] Specifically, based on the correspondence between image regions and element information in CT images, the element information of the texture corresponding to each image region is determined in the XRF element analysis data.

[0094] 303: Identify the type of each texture layer based on the element information of each texture layer.

[0095] Specifically, the type of each texture can be determined based on the element information of each texture layer and the correspondence between the element information and the texture type.

[0096] In one embodiment, the method further includes:

[0097] Different display styles are used for different types of textures.

[0098] In some examples, different fill colors or grayscale values ​​are used to display different types of textures.

[0099] In other examples, different fill patterns are used to display different types of textures. The fill lines within these different fill patterns are of different types.

[0100] In this embodiment of the disclosure, by displaying different types of textures using different display styles, different types of textures can be presented to the user more intuitively.

[0101] In one embodiment, step 103 above, which involves quantitatively characterizing different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae, includes:

[0102] Based on the laminar information contained in the image region corresponding to each laminar layer, the thickness and development density of different types of laminar layers in the core sample are determined.

[0103] In this embodiment, by determining the thickness and development density of different types of laminae in the core sample, a strong basis can be provided for the selection of the dominant reservoir interval.

[0104] In one embodiment, such as Figure 4 As shown, the thickness of each texture layer is determined based on the texture information contained in the image region corresponding to each texture layer, which may include:

[0105] 401: Determine the thickness of each texture layer based on the texture information contained in the image region corresponding to each texture layer.

[0106] In some examples, the texture information contained in the image region can be the pixel information contained in the image region.

[0107] In some examples, in step 401 above, for each of the textures, the pixel distance between each pixel on the upper boundary and the corresponding pixel on the lower boundary of the texture can be determined according to the depth direction of the core sample, and the multiple pixel distances can be converted into multiple physical distances according to the transformation relationship between the image coordinate system and the actual coordinate system, and the thickness of the texture can be determined based on the multiple physical distances.

[0108] For example, the maximum or average value of multiple physical distances can be used to determine the thickness of the texture.

[0109] 402: Determine the statistical value of the thickness of each type of texture based on the number of textures of each type and the thickness of each texture.

[0110] Here, the statistical values ​​of the layer thickness include, but are not limited to: the average, maximum and / or minimum values ​​of the layer thickness.

[0111] For example, assuming the texture type is silicon texture, the total thickness of silicon texture, the average thickness of silicon texture, etc. can be determined based on the number of silicon textures and the thickness of each silicon texture, and the maximum and minimum values ​​of all silicon texture thicknesses can be determined.

[0112] 403: Determine the development density of each type of laminae based on the number of each type and the length of the core sample.

[0113] Here, the length of the core sample can be obtained in advance by actually measuring the core sample.

[0114] Specifically, for each type of lamination, the development density of that type of lamination can be obtained by dividing the number of laminations of that type by the length of the core sample.

[0115] The technical solutions provided in this disclosure will now be described in conjunction with specific embodiments.

[0116] To address the challenges of identifying tight reservoir laminae in related technologies, including difficulties, low precision, and poor accuracy, this disclosure provides a laminae identification method. This method involves X-ray CT scanning of full-diameter core samples to obtain three-dimensional CT images of continuous cores, quantifying the laminae, and combining this with elemental analysis results from X-ray fluorescence (XRF) spectroscopy of the cores to identify laminae types. This method characterizes the thickness and development density of different laminae types, thus providing a strong basis for selecting advantageous reservoir intervals.

[0117] This disclosure provides a method for identifying rock laminae, the method may include:

[0118] Step 1: Acquisition of full-diameter core CT images

[0119] This step may include: core scanning and whole-core CT image processing.

[0120] Core scanning: First, the cores to be scanned are removed from the core box according to depth from deep to shallow and placed on the core sample stage. Adjacent cores are then spliced ​​and aligned based on various characteristics of the cross-section (such as minerals, structure, texture, cracks, striations, and cross-sectional shape). Broken cores can be wrapped in plastic film packaging (e.g., polyethylene film) before being placed in the core tray. Then, a CT scan of the full-diameter core is performed using medical or industrial CT to obtain CT scan images of the core.

[0121] Whole core CT image processing: Reconstructing core CT scan images to ensure clear images without artifacts.

[0122] Step 2: Elemental analysis of full-diameter core and classification of laminar flow patterns

[0123] Data can be collected from core samples using desktop XRF elemental analysis equipment or handheld XRF elemental analyzers. The sampling density can be adjusted according to actual needs. For example, the sampling density can be one data point every 1cm-10cm.

[0124] Based on the collected elemental information, the type of texture is determined.

[0125] Step 3: Quantitative analysis of different types of laminae

[0126] First, based on the grayscale values ​​of CT images, different individual texture layers are distinguished, and then the images are standardized.

[0127] The image standardization process includes: removing the background portion (e.g., tray) of the core sample in the image, removing the pores between different core samples, stretching the rock photograph, and ensuring that the thickness direction of the layers is perpendicular to the long axis of the core sample, with a fixed thickness.

[0128] Subsequently, based on the elemental analysis results, different striae were classified. Quantitative analysis was then performed on the classified striae, including the striae type, total number of striae, number of each striae, maximum thickness, minimum thickness, average thickness, and developmental density in the CT images.

[0129] Next, the technical solutions provided by the embodiments of this disclosure will be further described with reference to specific examples.

[0130] This disclosure describes the use of CT scanning and XRF elemental analysis on a full-diameter shale core sample, and the use of specialized image processing software and a pre-written Matlab program for image processing and calculation.

[0131] In practice, the CT scanning equipment can be the CereTom NL3000 from Neurological, the XRF elemental analysis uses the S1 TITAN 800 handheld elemental analyzer from Bruker, and the image processing uses the Pergeos 2021.1 digital core analysis software from Thermo Fisher Scientific and the DECT Matlab program developed by Digital Rock Technology.

[0132] This disclosure provides a method for texture recognition, such as... Figure 5 As shown, the method may include:

[0133] Step S1: Full-diameter core X-ray CT scan.

[0134] Before performing CT scans on the full-diameter core samples, the full-diameter core samples are first sorted.

[0135] 1) Air calibration: Turn on CereTom NL3000 and enter the air calibration interface. The air calibration time is about 15 minutes. The instrument needs to be calibrated before each turn-on.

[0136] 2) Sample Placement: Place the core sample on the sample stage. Confirm that the top and bottom orientation, depth, and angle of the core sample are correct, and confirm that the core sample is centered on the sample stage and within the scanning area. Figure 6aAs shown, the core sample is placed between the detector and the collimator, and the X-ray source provides X-rays. The principle of CT scanning is as follows: X-rays are emitted from the X-ray tube inside the CT equipment. After being collimated by the collimator, the X-rays pass through the scanned core sample and reach the detector. By measuring the amount of transmitted X-rays, the data is digitized and calculated to determine the absorption coefficient of each unit volume of the irradiated tissue layer. These absorption coefficients can form different digital matrices. A high-speed computer inside the machine performs digital-to-analog conversion, which can be displayed on a screen or captured as a photograph. The reconstructed image can also provide the X-ray attenuation coefficient for each pixel, usually expressed as a CT value.

[0137] 3) Core scanning: First, set the parameters: scanning voltage 100kV, scanning current 7mA, filament current 2.65A, and slice thickness 0.6mm. After completing the parameter settings, start scanning;

[0138] 4) CT image quality control: After completing the CT scan of the core, ensure that the image is clear and without tomography, and that there is no significant difference in gray value of the slice image in different directions. If ring artifacts appear, perform air correction and rescan for testing.

[0139] Combination Figure 6b As shown, the scanned images are compared with the core photographs to ensure a one-to-one correspondence. Figure 6b The two images in the picture, from left to right, are a core photograph and a longitudinal section of a CT image.

[0140] Step S2: X-ray fluorescence elemental scan.

[0141] Place the handheld elemental analyzer probe firmly against the core sample for 2 minutes, then retrieve the instrument. The instrument will automatically record the elemental information; save the file by setting the filename to the depth point.

[0142] Step S3: CT image processing and segmentation.

[0143] 1) Image standardization: Import the scanned CT data into PerGeos software, remove the sample tray and the gaps at the beginning and middle of the CT image to form a standardized core CT image.

[0144] 2) Individual layer differentiation: The standardized CT images are imported into the Matlab program DECT, combined with... Figure 6c As shown, the midpoint between the local maximum and minimum values ​​can be selected based on the difference in grayscale values ​​to determine the boundary surface of a single layer, thereby distinguishing different layers in the core.

[0145] Step S4: Determine the texture type based on the element results.

[0146] Texture type classification: Based on XRF elemental analysis data, the type of different textures is determined, and different types of textures are filled with different colors or grayscale values. For example... Figure 6d The three images shown, from left to right, are a core photograph, a standardized CT image, and a CT image incorporating elemental analysis results.

[0147] The type of different textures can be determined according to Table 1 below.

[0148] Table 1: Classification of Texture Types

[0149]

[0150]

[0151] It is understood that each element in the table above exists independently. These elements are listed in the same table as an example, but this does not mean that all elements in the table must exist simultaneously as shown in the table. The value of each element is independent of the values ​​of any other element in the table. Therefore, those skilled in the art will understand that the value of each element in this table is an independent embodiment.

[0152] Step S5: Quantitative characterization of the CT images.

[0153] Quantitative analysis of different types of laminae: The number and thickness of each type of laminae were calculated, and the maximum, minimum, and average thicknesses of each type of laminae were statistically analyzed. Simultaneously, the development density of each type of laminae was calculated by dividing the number of each type of laminae by the length of the core (in meters). Two types of laminae were statistically analyzed in this sample: siliceous laminae and clayey laminae. Specific statistical information is shown in Table 2 below.

[0154] Table 2: Statistics of Texture Parameters

[0155]

[0156]

[0157] It is understood that each element in the table above exists independently. These elements are listed in the same table as an example, but this does not mean that all elements in the table must exist simultaneously as shown in the table. The value of each element is independent of the values ​​of any other element in the table. Therefore, those skilled in the art will understand that the value of each element in this table is an independent embodiment.

[0158] In summary, the technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects:

[0159] 1) By performing CT scans on full-diameter core samples, rock images with millimeter-level precision can be obtained, significantly improving identification accuracy compared to traditional well logging identification methods. Furthermore, CT images directly reflect density information in three-dimensional space, offering higher accuracy than traditional methods that rely on core images for quantitative laminar flow analysis.

[0160] 2) By combining X-ray elemental analysis, rock types can be further subdivided on full-diameter core CT images, and the accuracy and timeliness of the classification are greatly improved compared with traditional methods.

[0161] 3) The technical solution provided in this disclosure is easy to operate and can be directly analyzed on-site in the core repository. It can quickly and accurately complete the qualitative and quantitative analysis of laminar lines, which greatly reduces time and economic costs compared with traditional methods.

[0162] Figure 7 This is a schematic diagram of the structure of a texture recognition device provided in an embodiment of this disclosure, as shown below. Figure 7 As shown, the device may include:

[0163] The image segmentation module 701 is used to segment the CT image of the core sample to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample.

[0164] The type identification module 702 is used to identify the type of each of the laminae based on the XRF elemental analysis data of the core sample;

[0165] The quantitative characterization module 703 is used to quantitatively characterize the different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae.

[0166] In one embodiment, the image segmentation module is used for:

[0167] Based on the gray value distribution of the CT image, multiple gray value peaks and multiple gray value troughs are determined;

[0168] The CT image is segmented based on the midpoint between each adjacent gray value peak and gray value trough to obtain multiple image regions.

[0169] In one embodiment, the type recognition module is used for:

[0170] According to the depth direction of the core sample, establish the correspondence between depth and image region in the CT image and element information in the XRF elemental analysis data;

[0171] Based on the established correspondence, the elemental information of each of the texture layers is determined in the XRF elemental analysis data;

[0172] The type of each texture layer is identified based on the element information of each texture layer.

[0173] In one embodiment, the apparatus further includes:

[0174] The display module is used to display different types of textures using different display styles.

[0175] In one embodiment, the quantitative characterization module is used for:

[0176] Based on the laminar information contained in the image region corresponding to each laminar layer, the thickness and development density of different types of laminar layers in the core sample are determined.

[0177] In one embodiment, the quantitative characterization module is used for:

[0178] The thickness of each texture layer is determined based on the texture information contained in the image region corresponding to each texture layer.

[0179] Based on the number of each type of texture layer and the thickness of each texture layer, a statistical value for the thickness of each type of texture layer is determined;

[0180] The development density of each type of laminae is determined based on the number of each type of laminae and the length of the core sample.

[0181] It should be noted that the above embodiments of the texture recognition device, when executing the texture recognition method, are only illustrated by the division of the above-described program modules. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the texture recognition device and the texture recognition method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0182] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure; as shown below. Figure 8 As shown, the computer device 800 includes a memory 801 and a processor 802. The memory 801 stores a computer program, and the processor 802 is configured to run the computer program to perform the following operations:

[0183] The CT images of the core sample are segmented to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample.

[0184] Based on the XRF elemental analysis data of the core samples, the types of each of the laminae were identified;

[0185] Based on the laminar information contained in the image region corresponding to each laminar layer, the different types of laminar layers in the core sample are quantitatively characterized.

[0186] When the processor runs a computer program, it implements the corresponding processes in the various methods of the embodiments of this disclosure. For the sake of brevity, these will not be described in detail here.

[0187] In practical applications, the computer device 800 may also include at least one network interface 803. The various components in the computer device 800 are coupled together via a bus system 804. It is understood that the bus system 804 is used to implement communication between these components. In addition to a data bus, the bus system 804 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 8 All buses are labeled as bus system 804. The number of processors 801 can be at least one. Network interface 803 is used for wired or wireless communication between computer device 800 and other devices.

[0188] The memory 802 in this embodiment is used to store various types of data to support the operation of the computer device 800.

[0189] The methods disclosed in the above embodiments of this disclosure can be applied to or implemented by processor 801. Processor 801 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 801 or by instructions in software form. The processor 801 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 801 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 802. Processor 801 reads information from memory 802 and combines it with its hardware to complete the steps of the aforementioned method.

[0190] In an exemplary embodiment, the computer device 800 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0191] This disclosure also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the following operations when run:

[0192] The CT images of the core sample are segmented to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample.

[0193] Based on the XRF elemental analysis data of the core samples, the types of each of the laminae were identified;

[0194] Based on the laminar information contained in the image region corresponding to each laminar layer, the different types of laminar layers in the core sample are quantitatively characterized.

[0195] The computer program is executed by the processor to implement the corresponding processes in the various methods of the embodiments of this disclosure, which will not be described in detail here for the sake of brevity.

[0196] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0197] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0198] In addition, each functional unit in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0199] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this disclosure, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0201] Furthermore, the technical solutions described in the embodiments of this disclosure can be combined arbitrarily without conflict.

[0202] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for identifying texture layers, characterized in that, The method includes: The CT images of the core sample are segmented to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample. Based on the XRF elemental analysis data of the core samples, the types of each of the laminae were identified; Based on the laminar information contained in the image region corresponding to each laminar layer, the different types of laminar layers in the core sample are quantitatively characterized. The step of identifying the type of each laminar layer based on the XRF elemental analysis data of the core sample includes: According to the depth direction of the core sample, establish the correspondence between depth and image region in the CT image and element information in the XRF elemental analysis data; Based on the established correspondence, the elemental information of each of the texture layers is determined in the XRF elemental analysis data; The type of each texture layer is identified based on the element information of each texture layer.

2. The method according to claim 1, characterized in that, The CT images of the core samples are segmented to obtain multiple image regions, including: Based on the gray value distribution of the CT image, multiple gray value peaks and multiple gray value troughs are determined; The CT image is segmented based on the midpoint between each adjacent gray value peak and gray value trough to obtain multiple image regions.

3. The method according to any one of claims 1 to 2, characterized in that, The method further includes: Different display styles are used to display the different types of textures.

4. The method according to any one of claims 1 to 2, characterized in that, The step of quantitatively characterizing different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae includes: Based on the laminar information contained in the image region corresponding to each laminar layer, the thickness and development density of different types of laminar layers in the core sample are determined.

5. The method according to claim 4, characterized in that, The step of determining the thickness and development density of different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae includes: The thickness of each texture layer is determined based on the texture information contained in the image region corresponding to each texture layer. Based on the number of each type of texture layer and the thickness of each texture layer, a statistical value for the thickness of each type of texture layer is determined; The development density of each type of laminae is determined based on the number of each type of laminae and the length of the core sample.

6. A texture recognition device, characterized in that, The device includes: The image segmentation module is used to segment the CT image of the core sample to obtain multiple image regions; wherein, one image region corresponds to one layer of the core sample. The type identification module is used to establish a correspondence between the depth of the core sample and the image region in the CT image and the elemental information in the XRF elemental analysis data of the core sample according to the depth direction; determine the elemental information of each of the textures in the XRF elemental analysis data according to the established correspondence; and identify the type of each texture according to the elemental information of each texture. The quantitative characterization module is used to quantitatively characterize different types of laminae in the core sample based on the laminae information contained in the image region corresponding to each laminae.

7. The apparatus according to claim 6, characterized in that, The image segmentation module is used for: Based on the gray value distribution of the CT image, multiple gray value peaks and multiple gray value troughs are determined; The CT image is segmented based on the midpoint between each adjacent gray value peak and gray value trough to obtain multiple image regions.

8. A computer 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 program, it implements the steps of the textural recognition method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the textural recognition method according to any one of claims 1 to 5.

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