A shale different lamina light and heavy component quantitative characterization method

Abstract

The embodiment of the application provides a shale different lamina light component and heavy component quantitative characterization method, selects a specified visual field of a target shale, identifies different lamina units of the target shale through a rock thin section optical micrograph of the specified visual field, and circumscribes a lamina boundary according to the different lamina units of the target shale; obtains a laser scanning confocal fluorescence image under the specified visual field; performs image pretreatment, threshold segmentation, binary processing, light component and heavy component effective fluorescence region visualization on the laser scanning confocal fluorescence image; performs superposition analysis on each lamina unit and the effective fluorescence region, and determines an area normalization integral fluorescence intensity and a light-heavy ratio. Through the same visual field superposition analysis under the lamina boundary constraint, the quantitative characterization of the distribution range, the relative enrichment degree and the light-heavy component advantage of the light component and the heavy component in different lamina is realized, and the difference in the occurrence of the light component and the heavy component between different lamina is avoided to be covered by the whole image statistics.

Description

Technical Field

[0001] This application relates to the field of fine characterization of unconventional oil and gas reservoirs and evaluation of shale oil reservoirs, specifically to a method for quantitative characterization of light and heavy components in different shale laminae. Background Technology

[0002] Shale oil reservoirs typically exhibit distinct laminar structures, with significant differences between laminae in mineral composition, organic matter abundance, pore structure, microfracture development, wettability, and fluid occurrence. The distribution, enrichment, and relative proportion of light and heavy components within different laminae directly influence the occurrence state, flowability, and exploitability of shale oil. Therefore, identifying and quantitatively evaluating the differences in the occurrence of light and heavy components within different shale laminae at the microscopic scale is a crucial foundation for identifying dominant oil-bearing laminae, evaluating the differences in the occurrence of light and heavy components, and conducting refined evaluations of shale oil reservoirs.

[0003] In existing technologies, laser scanning confocal microscopy can utilize the fluorescence differences generated by hydrocarbon components under laser excitation to observe and identify light and heavy components in shale oil. By acquiring fluorescence images corresponding to light and heavy components separately, their spatial distribution within the same microscopic field of view can be displayed, and the area of ​​light components, the area of ​​heavy components, or the relative ratio between them can be obtained. This type of method provides an important technical foundation for the study of the microscopic occurrence characteristics of shale oil.

[0004] Furthermore, thin-section optical microscopy can effectively identify laminar types, laminar boundaries, mineral assemblages, microfracture distribution, and matrix structure differences in shale samples. Through thin-section optical microscopy, different microstructural units can be distinguished in shale, such as argillaceous laminae, micritic calcite laminae, spar crystal laminae, and fracture neighborhoods. These structural identification results are of great significance for understanding the differential occurrence of shale oil in different laminae or microstructural units.

[0005] However, in existing technologies, laser scanning confocal images are typically used to display the distribution of light and heavy components across the entire image area, while thin-section optical microscopy images are mostly used to describe structural features such as laminae, mineral composition, and fractures. Although both can serve the study of shale oil microscopic occurrence, in many cases, existing results often remain at the descriptive level of "stronger fluorescence in a certain area," "more oil in a certain type of laminae," or "more light components at a certain location," lacking a standardized processing procedure that maps laminar structure information to fluorescence information of light and heavy components within the same field of view.

[0006] Existing technologies have the foundation to observe the spatial differences between light and heavy components in shale oil using laser scanning confocal images, and also the foundation to identify shale lamellar boundaries and microstructural units using optical microscopic images of rock thin sections. However, a method is still lacking to spatially correlate lamellar boundary information with fluorescence information of light and heavy components in the same field of view, and to conduct regional quantitative statistics and comparisons under different lamellar boundary constraints.

[0007] Existing technical solutions primarily utilize laser scanning confocal microscopy to acquire fluorescence images of the light and heavy components of shale oil. Based on the fluorescence differences among these components, their spatial distribution, area ratio, or light-heavy component ratio is identified and displayed. This type of technology can reflect the spatial differences between the light and heavy components of shale oil at the microscopic scale, providing an important foundation for the study of the microscopic occurrence state of shale oil.

[0008] However, existing technologies typically focus more on statistical analysis of light and heavy components and their ratios within a single laser-scanned confocal image or manually selected local areas. When the same image contains multiple laminar units, fracture neighborhoods, and matrix background, the overall statistical results can only reflect the overall oil-bearing characteristics or the proportion of light and heavy components in that field of view, making it difficult to accurately explain the differences in the distribution of light and heavy components within different laminar units. For example, existing results can usually indicate that a certain field of view has a higher concentration of light components or a higher concentration of heavy components, but it is difficult to further determine whether argillaceous laminar units, cryptocrystalline laminar units, spar crystal laminar units, or fracture neighborhoods play a major controlling role in this result.

[0009] On the other hand, while thin-section optical microscopy images of rocks can effectively identify the boundaries, morphology, and mineral composition differences of different shale layers, these images only provide structural information and cannot further explain the area ratio, enrichment intensity, and relative dominance of light and heavy components within these layers. Therefore, the limitation of existing technologies lies not in the inability to observe the light and heavy components of shale oil, but in the lack of a method for statistical analysis and comparison of light and heavy components in laser scanning confocal images. Summary of the Invention

[0010] To address one of the aforementioned technical deficiencies, this application provides a method for quantitative characterization of light and heavy components in different shale laminations.

[0011] According to a first aspect of the embodiments of this application, a method for quantitatively characterizing the light and heavy components of different laminae in shale is provided, including: Select a specified field of view of the target shale, identify different lamellar units of the target shale through optical microscopic images of thin sections of rock in the specified field of view, and delineate the lamellar boundaries based on the different lamellar units of the target shale. Acquire laser scanning confocal fluorescence images within a specified field of view; Image preprocessing, threshold segmentation, binarization, and visualization of effective fluorescence regions of light and heavy components are performed on laser scanning confocal fluorescence images. By overlaying analysis of each lamellar unit with the effective fluorescence region, the area-normalized integral fluorescence intensity and light-to-heavy ratio were determined, and the differences in the occurrence of light and heavy components in different shale lamellars were quantitatively identified.

[0012] In an optional embodiment of this application, acquiring the laser scanning confocal fluorescence image within a specified field of view includes: Fluorescence images of the light component and the heavy component were acquired in a specified field of view using laser scanning confocal microscopy, denoted as I0 and I1, respectively. L and I H , among which, I L Represents the fluorescence image of the light component, I H This image shows the fluorescence of the heavy component. The identification of light and heavy components relies on preset fluorescence detection channels in a laser scanning confocal microscope. Different detection channels correspond to fluorescence responses within different wavelength ranges. Light components primarily correspond to fluorescence responses within a shorter wavelength range, while heavy components primarily correspond to fluorescence responses within a longer wavelength range. The differences in responses obtained through different fluorescence channels allow for the differentiation between light and heavy components.

[0013] In an optional embodiment of this application, the image preprocessing of the laser scanning confocal fluorescence image includes: Fluorescence image of light component I L Fluorescence images of heavy components I H Denoising, background subtraction, image cropping, image registration, flat-field correction, and intensity normalization were performed to obtain the preprocessed fluorescence image I′ of the light component. L Fluorescence images of heavy components I′ H .

[0014] In an optional embodiment of this application, the threshold segmentation and binarization processing of the laser scanning confocal fluorescence image includes: Fluorescence image I′ of the preprocessed light component L Fluorescence images of heavy components I′ H Threshold segmentation was performed separately, and the effective fluorescence regions of the light component and the heavy component were extracted by binarization.

[0015] In an optional embodiment of this application, the visualization of the effective fluorescence regions of light and heavy components in the laser scanning confocal fluorescence image includes: The effective fluorescence regions of the light and heavy components obtained after binarization are displayed with different color values ​​to characterize the distribution location and spread characteristics of the light and heavy components in the same field of view.

[0016] In an optional embodiment of this application, the step of performing overlay analysis on each laminar unit and the effective fluorescence region to determine the area-normalized integrated fluorescence intensity and the light-to-heavy ratio includes: For the k-th laminar unit Ω k By overlaying the effective fluorescence regions of the light and heavy components obtained after binarization, the effective fluorescence regions of the light and heavy components within the boundary of the kth laminar unit are obtained. The integrated fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit is determined by the following expression:

[0017]

[0018] Among them, S L,k S is the integrated fluorescence intensity of the effective fluorescence region of the light component within the boundary of the k-th laminar unit. H,k Ω represents the integrated fluorescence intensity of the effective fluorescence region of the heavy component within the boundary of the k-th laminar unit. L,k Ω represents the effective fluorescence region of the lightweight component within the boundary of the k-th laminar unit. H,k I′ represents the effective fluorescence region of the heavy component within the boundary of the k-th laminar unit. L,i and I′ H,i The fluorescence intensities of the light and heavy components after pretreatment are shown, respectively. The area-normalized integral fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit is determined by the following expression:

[0019]

[0020] Among them, Q L,k and Q H,k A represents the area-normalized integral fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit, respectively; k S represents the total area of ​​the k-th laminar unit; L,k and S H,k , respectively, are the integrated fluorescence intensities of the effective fluorescence regions of the light and heavy components within the k-th laminar unit.

[0021] The light-to-heavy ratio of the k-th laminar unit is determined by the following expression:

[0022] Among them, R L / H,k is the weight ratio of the k-th laminar unit.

[0023] In an optional embodiment of this application, the quantitative identification of differences in the occurrence of light and heavy components within different shale laminations includes: By comparing the area ratio of light components, area ratio of heavy components, area-normalized integral fluorescence intensity, and light-to-heavy ratio differences among different lamellae in the same laser scanning confocal fluorescence image, the advantages of light components and heavy components in different lamellae can be determined. By comparing the results of different views or repeated tests of the same laminae of the target shale under the same experimental conditions, the light-to-heavy ratio is extracted to determine the repeatability of the quantitative characterization results. By comparing the changes in the light-to-heavy ratio of the same laminae under different samples or different experimental conditions, the stability and differences of the dominance of light components or heavy components in the laminae are evaluated.

[0024] According to a second aspect of the embodiments of this application, a device for quantitative characterizing the light and heavy components of different laminae in shale is provided, comprising: The identification module is used to select a specified field of view of the target shale, identify different lamellar units of the target shale through optical microscopic images of thin sections of rock in the specified field of view, and delineate the lamellar boundaries based on the different lamellar units of the target shale. The acquisition module is used to acquire laser scanning confocal fluorescence images within a specified field of view; The image preprocessing module is used to perform image preprocessing, threshold segmentation, binarization, and visualization of the effective fluorescence regions of light and heavy components on laser scanning confocal fluorescence images. The determination module is used to perform overlay analysis on each lamellar unit and the effective fluorescence region, determine the area-normalized integral fluorescence intensity and the light-to-heavy ratio, and quantitatively identify the differences in the occurrence of light and heavy components in different shale lamellars.

[0025] According to a third aspect of the embodiments of this application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, it receives optical microscopic images of thin sections of a target shale, fluorescence images of light components, and fluorescence images of heavy components acquired in the same field of view, and sequentially performs laminar boundary determination, image preprocessing and spatial registration, threshold segmentation and binarization, overlay analysis of laminar regions and effective fluorescence regions, and quantitative parameter calculation, and outputs quantitative characterization results of light and heavy components of different laminar layers of the shale.

[0026] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored; when the computer program is executed by a processor, it implements the above-described image processing, region overlay analysis and quantitative parameter calculation processes.

[0027] The quantitative characterization method for light and heavy components in different shale laminae provided in this application embodiment selects a specified field of view of the target shale. Different laminar units of the target shale are identified using optical microscopy images of thin sections within the specified field of view, and laminar boundaries are delineated based on these units. Laser scanning confocal fluorescence images are acquired within the specified field of view. These images undergo image preprocessing, threshold segmentation, and binarization to extract the effective fluorescence regions of light and heavy components. Overlay analysis is performed on each laminar unit and the effective fluorescence region to determine the area-normalized integral fluorescence intensity and the light-to-heavy ratio. By determining the boundaries of different laminae using optical microscopy images of thin sections and overlaying the laminar regions with laser scanning confocal fluorescence images within the same field of view, quantitative characterization of the distribution range, relative enrichment, and dominance of light and heavy components within different laminae is achieved, avoiding the statistical masking of differences in the occurrence of different laminae by the overall image statistics. Attached Figure Description

[0028] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating the quantitative characterization method for light and heavy components of shale with different laminations provided in this application embodiment; Figure 2 Flowchart of a method for quantitative characterization of light and heavy components in different shale laminations provided in another embodiment of this application; Figure 3 This is a schematic diagram illustrating the process of identifying and superimposing light and heavy components in an interbedded shale sample with micritic calcite and argillaceous laminae, provided in an embodiment of this application. Figure 3 (a) For texture identification and boundary delineation, the white area represents the target texture that has been identified and included in the calculation, and the black area represents the non-target area that has not been selected; Figure 3 (b) The acquired laser scanning confocal fluorescence image; Figure 3 (c) shows the effective fluorescence regions of the light and heavy components extracted by threshold segmentation and binarization. Figure 3 (d) is a schematic diagram of the superposition of the laminar region and the binarized fluorescence region; Figure 4 The dashed line R represents the average light-to-weight ratio of the mud-like lamination, micritic calcite lamination, powder calcite lamination, and fibrous calcite lamination provided in the embodiments of this application. L / H=1 is the normalized equilibrium line for light and heavy components; when R L / H When R > 1, it indicates that the light component is relatively dominant; when R L / H When <1, it indicates that the heavy component is relatively dominant; Figure 5 A schematic diagram of the structure of the quantitative characterization device for light and heavy components of different shale laminations provided in this application embodiment; Figure 6 This is a schematic diagram of the computer device structure provided in an embodiment of this application. Detailed Implementation

[0029] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0030] See Figure 1 and Figure 2 This application provides a method for quantitative characterization of light and heavy components in different shale laminations, including: S110, Select a specified field of view of the target shale, identify different lamellar units of the target shale through optical microscopic images of thin sections of rock in the specified field of view, and delineate the lamellar boundaries according to the different lamellar units of the target shale. S120, acquire laser scanning confocal fluorescence images under the specified field of view; S130 performs image preprocessing, threshold segmentation, binarization, and visualization of the effective fluorescence regions of light and heavy components on laser scanning confocal fluorescence images. S140, by superimposing analysis of each lamellar unit and the effective fluorescence region, determined the area-normalized integral fluorescence intensity and the light-to-heavy ratio, and quantitatively identified the differences in the occurrence of light and heavy components in different shale lamellars.

[0031] In an optional embodiment of this application, step S110, selecting the designated field of view of the target shale, includes: The target shale sample was prepared into a thin section suitable for optical microscopy and laser scanning confocal microscopy. The microscopic field of view with clear and representative laminar structure in the thin section was selected as the designated field of view, and the optical microscopic image of the rock thin section and the subsequent laser scanning confocal fluorescence image were ensured to be located in the same field of view. Among them, the magnification, resolution, scanning range and image acquisition parameters were kept consistent between different samples or different fields of view to ensure the comparability of subsequent results.

[0032] In an optional embodiment of this application, step S110, which involves identifying different lamellar units of the target shale through optical microscopic images of rock thin sections within a specified field of view, and delineating lamellar boundaries based on these different lamellar units, includes: Based on optical microscopic images of thin sections of rock under the same field of view, different lamellar units in shale samples are identified and their boundaries are delineated according to characteristics such as color, lamellar morphology, mineral composition, structural differences, and visible fracture distribution. These lamellar units include one or more of the following: argillaceous lamellars, micritic calcite lamellars, microcrystalline calcite lamellars, and fibrous calcite lamellars. Let the region and area corresponding to the k-th lamellar unit be denoted as ... and , where k = 1, 2, ..., n, and n is the number of laminar units identified in the same field of view.

[0033] In an optional embodiment of this application, step S120, acquiring the laser scanning confocal fluorescence image in the specified field of view, includes: Fluorescence images of the light and heavy components were acquired using laser scanning confocal microscopy within a specified field of view, denoted as IL and IH, respectively. IL represents the fluorescence image of the light component, and IH represents the fluorescence image of the heavy component. Image acquisition of the light and heavy components was performed using pre-defined corresponding fluorescence channels. Excitation, detection, and image acquisition parameters were kept consistent for the same set of samples for comparison to ensure a consistent basis for subsequent comparisons.

[0034] In an optional embodiment of this application, step S130, the image preprocessing of the laser scanning confocal fluorescence image, includes: The fluorescence images IL (light fraction) and IH (heavy fraction) were subjected to denoising, background subtraction, image cropping, image registration, flat-field correction, and intensity normalization to obtain preprocessed fluorescence images I′L (light fraction) and I′H (heavy fraction). For the same set of images to be compared, consistent acquisition parameters and intensity normalization rules were used. The preprocessed images maintained spatial correspondence with the optical micrographs of rock thin sections to ensure accurate overlay analysis of different laminar regions with the light and heavy fraction fluorescence regions.

[0035] In an optional embodiment of this application, step S130, which involves thresholding and binarizing the laser scanning confocal fluorescence image, includes: Fluorescence image I′ of the preprocessed light component L Fluorescence images of heavy components I′ H Threshold segmentation was performed separately, and the effective fluorescence regions of the light component and the heavy component were extracted by binarization.

[0036] In an optional embodiment of this application, the threshold for identifying lightweight components is set to T. L The threshold for identifying heavy components is T. H Then the binarization results of the light and heavy components are expressed as follows: M L (x,y) =

[0037] M H (x,y) =

[0038] Wherein, ML(x,y) represents the binarization result of the light component, and MH(x,y) represents the binarization result of the heavy component; I′L(x,y) and I′H(x,y) are the preprocessed fluorescence intensities of pixel (x,y) in the fluorescence images of the light and heavy components, respectively; TL and TH are the identification thresholds for the light and heavy components, respectively. When the binarization result is 1, it indicates that the pixel belongs to the effective fluorescence region of the corresponding component; when the binarization result is 0, it indicates that the pixel is background or an invalid signal. The threshold can be determined by manual thresholding, Otsu automatic thresholding, or adaptive thresholding; for the same set of images to be compared, a consistent threshold determination rule should be used to ensure the comparability and repeatability of the results. Through the above processing, continuous color fluorescence images can be converted into effective fluorescence regions of the light and heavy components for region overlay analysis and quantitative calculation.

[0039] In an optional embodiment of this application, step S130, which involves visualizing the effective fluorescence regions of the light and heavy components in the laser-scanned confocal fluorescence image, includes: The effective fluorescence regions of the light and heavy components obtained after binarization are displayed with different color values ​​to characterize the distribution and spread characteristics of the light and heavy components within the same field of view. Specifically, the identification results of the light components can be assigned orange, and the identification results of the heavy components can be assigned cyan. This color assignment is only used for result display, region verification, and overlay analysis, and does not change the original fluorescence intensity information. Subsequent calculations of integrated fluorescence intensity and area-normalized integrated fluorescence intensity are still based on the fluorescence intensity image after background subtraction and intensity normalization.

[0040] In an optional embodiment of this application, step S140, which involves performing an overlay analysis on each laminar unit and the effective fluorescence region to determine the area-normalized integrated fluorescence intensity and the light-to-heavy ratio, includes: For the k-th laminar unit Ω kBy overlaying the effective fluorescence regions of the light and heavy components obtained after binarization, the effective fluorescence regions of the light and heavy components within the boundary of the k-th laminar unit are obtained, and are represented as follows: Ω L,k =Ω k ∩M L Ω H,k =Ω k ∩M H Among them, Ω L,k Ω represents the effective fluorescence region of the lightweight component within the k-th laminar unit. H,k Ω represents the effective fluorescence region of the heavy component within the k-th laminar unit; k For the k-th lamellae region, M L and M H The results show the binarized identification of light and heavy components, respectively. The effective fluorescence areas of the light and heavy components within the k-th laminar unit are respectively represented as: A L,k =Area(Ω L,k ) A H,k =Area(Ω H,k ) Among them, A L,k A represents the effective fluorescence area of ​​the lightweight component within the k-th laminar unit. H,k The effective fluorescence area of ​​the heavy component within the k-th laminar unit. For the k-th laminar unit, let its total area be A. k The effective fluorescence region area of ​​the light component is A. L,k The effective fluorescence region area of ​​the heavy component is A. H,k The area ratios of the light and heavy components are respectively:

[0041]

[0042] Among them, P L,k and P H,k These are used to characterize the distribution range of light and heavy components within the k-th laminar unit, respectively. The integrated fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit is determined by the following expression:

[0043]

[0044] Among them, SL,k S is the integrated fluorescence intensity of the effective fluorescence region of the light component within the boundary of the k-th laminar unit. H,k Ω represents the integrated fluorescence intensity of the effective fluorescence region of the heavy component within the boundary of the k-th laminar unit. L,k Ω represents the effective fluorescence region of the lightweight component within the boundary of the k-th laminar unit. H,k I′ represents the effective fluorescence region of the heavy component within the boundary of the k-th laminar unit. L,i and I′ H,i These represent the fluorescence intensities of the light and heavy components after preprocessing, i.e., the fluorescence intensities of the light and heavy components after background subtraction and normalization at the corresponding pixel points. The area-normalized integral fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit is determined by the following expression:

[0045]

[0046] Among them, Q L,k and Q H,k , respectively, are the area-normalized integral fluorescence intensities of the light and heavy components within the boundary of the k-th laminar unit, used to characterize the relative fluorescence enrichment of the light and heavy components per unit laminar area; A k S represents the total area of ​​the k-th laminar unit; L,k and S H,k , respectively, are the integrated fluorescence intensities of the effective fluorescence regions of the light and heavy components within the k-th laminar unit.

[0047] The light-to-heavy ratio of the k-th laminar unit is determined by the following expression:

[0048] Among them, R L / H,k R represents the weight ratio of the k-th laminar unit. L / H,k Used to characterize the fluorescence dominance of the lighter component relative to the heavier component within the k-th laminar unit. When R L / H,k When R > 1, it indicates that the light components are relatively dominant in this laminar layer under a unified collection and normalization system; when R L / H,k When R < 1, it indicates that the heavy component is relatively dominant; when R L / H,k When the ratio is approximately 1, it indicates that the light and heavy components are relatively close. The light-to-heavy ratio is a relative quantitative parameter based on the fluorescence response and is not directly equivalent to the absolute mass ratio of the light and heavy components.

[0049] The heavy component index is determined using the following expression:

[0050] Among them, HI k Used to characterize the relative enrichment of heavy components within the k-th laminar unit.

[0051] This application proposes a quantitative characterization parameter system using different lamellar units as statistical objects. Specifically, this includes: the area ratio of light components, the area ratio of heavy components, the integrated fluorescence intensity of light components, the integrated fluorescence intensity of heavy components, the area-normalized integrated fluorescence intensity, and the light-to-heavy ratio. The area ratio characterizes the distribution range of light and heavy components in different lamellar units; the integrated fluorescence intensity characterizes the total fluorescence response of the components; the area-normalized integrated fluorescence intensity characterizes the relative enrichment of light and heavy components per unit lamellar area; and the light-to-heavy ratio determines whether light components are dominant relative to heavy components in a given lamellar unit. Through these parameters, subjective image judgments such as "a certain lamellar unit looks brighter" or "a certain area seems to be more light or heavy" can be transformed into verifiable and comparable quantitative results. The quantitative characterization described in this application is a relative quantification of fluorescence response based on a unified acquisition and image processing system, used to compare the relative enrichment and dominance of light and heavy components between different lamellar units, and is not directly used to calculate the absolute mass content of light or heavy components.

[0052] In an optional embodiment of this application, step S140, which involves quantitatively identifying the differences in the occurrence of light and heavy components within different shale laminations, includes: By comparing the area ratios of light and heavy components, the area-normalized integrated fluorescence intensity, and the light-to-heavy ratios among different laminae in the same laser-scanned confocal fluorescence image, the dominance of light and heavy components in different laminae can be determined. For example, when the light-to-heavy ratio of a clayey laminae is greater than 1, it indicates that the light component is relatively dominant in that laminae; when the light-to-heavy ratio of a clayey laminae is less than 1, it indicates that the heavy component is relatively dominant in that laminae. By comparing the results of different views or repeated tests of the same laminae of the target shale under the same experimental conditions, the light-to-heavy ratio is extracted to determine the repeatability of the quantitative characterization results. By comparing the changes in the light-to-heavy ratio of the same laminae under different samples or different experimental conditions, the stability and differences of the dominance of light components or heavy components in the laminae are evaluated.

[0053] This application enables quantitative statistical analysis of light and heavy component information in laser scanning confocal images within specific laminar units, avoiding the problem of masking heterogeneity differences between different laminar units by analyzing the entire image. It achieves quantitative identification of the distribution range, enrichment degree, and relative dominance of light and heavy components within different shale laminar units, and provides a basis for identifying dominant oil-bearing laminar units, analyzing differences in the occurrence of light and heavy components, and fine evaluation of shale oil reservoirs.

[0054] In an optional embodiment of this application, a shale sample with alternating layers of micritic calcite and argillaceous laminae is used as the target shale to further interpret the quantitative characterization method for light and heavy components in different shale laminae presented in this application. The sample is cut, ground, and prepared into a thin section suitable for optical microscopy and laser scanning confocal microscopy. During sample preparation, the observation surface is kept flat and clean to avoid surface contamination, scratches, or uneven polishing that could interfere with subsequent fluorescence identification. After sample preparation, the entire section is first observed under an optical microscope. A representative microscopic field of view with a clear laminar structure, alternating micritic calcite and argillaceous laminae, is selected as the analysis area. The position, magnification, image resolution, and observation range of this field of view are recorded, ensuring that the subsequent laser scanning confocal fluorescence image corresponds to the same field of view as the optical microscopy image of the rock thin section.

[0055] Within a selected field of view, optical microscopic images of thin rock sections were acquired. Based on color differences, laminar morphology, mineral composition, structural features, and laminar continuity in the images, micritic calcite laminae and argillaceous laminae were identified. Micritic calcite laminae typically appear as relatively bright, well-continuous carbonate bands, while argillaceous laminae typically appear as darker bands rich in argillaceous components. Based on these identification results, the boundaries of micritic calcite laminae and argillaceous laminae were delineated, forming spatially defined regions for subsequent statistical analysis of light and heavy components.

[0056] Subsequently, under the same field of view, fluorescence images of the light and heavy components were acquired using laser scanning confocal microscopy. During image acquisition, consistency was maintained in the scanning range, pixel resolution, magnification, and acquisition parameters to ensure a good spatial correspondence between the fluorescence images of the light and heavy components and the optical micrographs of the rock thin sections. The acquired fluorescence images of the light and heavy components underwent image cropping, background subtraction, noise removal, flattening correction, image registration, and intensity normalization. Image cropping was used to unify the analysis range, background subtraction to remove ineffective fluorescent background, and image registration to ensure that the lamellar boundaries in the optical micrographs accurately correspond to the confocal fluorescence images. A consistent intensity normalization rule was applied to the same set of images to be compared to reduce the impact of non-target brightness differences on subsequent relative quantitative results.

[0057] After preprocessing, thresholding and binarization were performed on the fluorescence images of the light and heavy components to extract the effective fluorescence regions of the light and heavy components. The threshold was determined based on the difference between the effective fluorescence signal and the background signal. For ease of observation and verification, the identification results of the light component were assigned orange, and the identification results of the heavy component were assigned cyan, thus visually displaying the distribution and spread characteristics of the light and heavy components in the micritic calcite and argillaceous laminae. The color assignment is only used for result display and region verification and does not participate in subsequent fluorescence intensity calculations; subsequent calculations of area ratio, integrated fluorescence intensity, area-normalized integrated fluorescence intensity, and light-to-heavy ratio are still based on the fluorescence intensity images after background subtraction and intensity normalization.

[0058] The micritic calcite lamellar regions and argillaceous lamellar regions delineated in the optical micrographs of rock thin sections were correlated with the effective fluorescence regions of the light and heavy components, respectively, to obtain the distribution results of the light and heavy components within different lamellar structures. The specific process is as follows: Figure 3 As shown.

[0059] For the k-th laminar unit, let its laminar region be Ω. k The binarized identification result of the light component is M. L The binarized identification result of the heavy component is M. H Then, the effective fluorescence regions of the light and heavy components within the laminae are respectively: Ω L,k =Ω k ∩M L Ω H,k =Ω k ∩M H Among them, Ω L,k Ω represents the effective fluorescence region of the lightweight component within the k-th laminar unit. H,k This represents the effective fluorescence region of the heavy component within the k-th laminar unit.

[0060] Further calculations were performed on the area ratio, integrated fluorescence intensity, area-normalized integrated fluorescence intensity, light-to-heavy ratio, and heavy component index of each laminar unit. For the k-th laminar unit, let its total area be A. k The effective fluorescence region area of ​​the light component is A. L,k The effective fluorescence region area of ​​the heavy component is A. H,k The area ratios of the light and heavy components are respectively:

[0061]

[0062] Let the integrated fluorescence intensities of the light and heavy components within the k-th laminar unit be S, respectively. L,k and S H,k ,but:

[0063]

[0064] Among them, I ′ L,i and I ′ H,i These represent the background subtraction and normalized fluorescence intensities of the light and heavy components at the corresponding pixels.

[0065] Based on this, the area-normalized integral fluorescence intensity is calculated:

[0066]

[0067] Further calculation of the light-to-weight ratio:

[0068] If necessary, further calculate the heavy component index:

[0069] Following the above method, quantitative calculations were performed on the micritic calcite laminae and argillaceous laminae, and the quantitative identification results of light and heavy components in the micritic calcite laminae and argillaceous laminae are shown in Table 1.

[0070] Table 1

[0071] Among them, F A , k This represents the proportion of the corresponding ridge area to the total field of view; P L and P H These represent the area percentages of the light and heavy components within the corresponding lamellae, respectively. and The average fluorescence intensities of the light and heavy components are respectively; Q L and Q H These represent the area-normalized integral fluorescence intensities of the light and heavy components, respectively; R L / H is the ratio of light to heavy components; HI is the index of the heavy component.

[0072] As shown in Table 1, the light-to-heavy ratio R of the micritic calcite lamellar layers L / H The density index (HI) is 1.08, which is greater than 1, and the heavy component index (HI) is 0.48, which is less than 0.5, indicating that the light component is relatively dominant in this lamination. The light-to-heavy ratio (R) of the argillaceous lamination is... L / HThe index is 0.66, less than 1, and the heavy component index (HI) is 0.60, higher than 0.5, indicating that heavy components are relatively dominant in this laminar flow. The overall light-to-heavy ratio R in the field of view... L / H The value is 0.77, which is less than 1, and the heavy component index HI is 0.57, which is higher than 0.5. This indicates that if only the whole chart is used for statistics, only the overall heavy component result can be obtained, which is difficult to reveal the difference in the occurrence of light and heavy components between the micritic calcite laminae and the argillaceous laminae.

[0073] Furthermore, to demonstrate the applicability of this application in the comparative evaluation of differences in the occurrence of light and heavy components in different lamellar structures, in addition to the aforementioned samples of interbedded micritic calcite and argillaceous lamellar structures, the same image acquisition, lamellar boundary delineation, threshold segmentation, binarization processing, regional overlay analysis, and parameter calculation procedures were used to process multiple shale thin section samples or multiple representative fields of view. For the same lamellar type developed in different samples, its light-to-heavy ratio R was extracted respectively. L / H The average light-to-heavy ratio of each lamellar type was calculated to evaluate the relative dominance of light and heavy components in different lamellar types. For example... Figure 4 As shown, the average light-to-weight ratio of the argillaceous laminae is 0.48, which is less than 1, indicating that the heavy component is dominant. The average light-to-weight ratios of the micritic calcite laminae, microcrystalline calcite laminae, and fibrous calcite laminae are 1.15, 1.72, and 1.44, respectively, all greater than 1, indicating that the light component is dominant. Among them, the microcrystalline calcite laminae has the highest average light-to-weight ratio, indicating that its light component advantage is the most obvious.

[0074] The above results demonstrate that this application can not only quantitatively identify the differences in the occurrence of light and heavy components in different laminae within a single thin section or a single field of view, but also extract the light-to-heavy ratio of the same laminae type across multiple samples or multiple fields of view and perform lateral comparisons. This method reduces the influence of randomness in single image results and further determines whether the dominance of light or heavy components in different laminae types is stable and reproducible, providing a quantitative basis for identifying dominant oil-bearing laminae, evaluating the differences in the occurrence of light and heavy components, and conducting fine-grained evaluations of shale oil reservoirs.

[0075] This application does not simply obtain the results of laser scanning confocal imaging, nor does it simply display the spatial distribution of light and heavy components. Instead, it provides quantitative identification and comparison results of different laminar units in shale within the same field of view. First, it uses optical microscopic images of rock thin sections under the same field of view to identify different laminar types and delineate laminar boundaries. Then, within the range defined by these boundaries, it statistically analyzes the effective fluorescence information of light and heavy components obtained from laser scanning confocal fluorescence images, further calculating the area ratio, integrated fluorescence intensity, area-normalized integrated fluorescence intensity, and light-to-heavy ratio. This enables a digital comparison of the differences in the occurrence of light and heavy components between different laminar layers. Therefore, the processing flow of this application—"defining laminar boundaries using optical microscopic images of rock thin sections, and then quantitatively statistically analyzing and comparing the fluorescence results of light and heavy components in laser scanning confocal imaging within the corresponding laminar layers"—is not a simple "joint observation" or "visualization of light and heavy components."

[0076] This application addresses the need for evaluating the heterogeneity of oil-bearing components in different shale laminae. It transforms the traditional method of judging the distribution characteristics of light and heavy components in laser scanning confocal images, which primarily relies on differences in brightness, color, and local fluorescence intensity, into quantitative comparative results under the same field of view and the same laminae classification standard. By introducing thin-section optical microscopy images to define the boundaries of different laminae and statistically analyzing the information of light and heavy components in the laser scanning confocal fluorescence images within the corresponding laminae, this application can not only determine the distribution range of light and heavy components in different laminae but also further evaluate their relative enrichment degree per unit laminae area and the dominance of light and heavy components, thereby achieving precise identification of the differences in the occurrence of light and heavy components among different shale laminae.

[0077] Compared to simple confocal fluorescence image visualization, this application transforms the question of "which type of laminae is richer in light components and which type of laminae is richer in heavy components" into verifiable and comparable numerical evidence by using parameters such as area ratio, integrated fluorescence intensity, area-normalized integrated fluorescence intensity, and light-heavy ratio. Compared to traditional whole-map statistics or local experience-based judgments, this method effectively avoids the problem of averaging information from different laminae, improving the accuracy and reliability of identifying differences in light and heavy component occurrence in different shale laminae. Furthermore, this method can also be used for lateral comparison of the light-heavy ratio of the same laminae type under different viewpoints, different samples, or different experimental conditions, providing technical support for the identification of dominant oil-bearing laminae, analysis of differences in light and heavy component occurrence, and fine evaluation of shale oil reservoirs.

[0078] See Figure 5 This application provides a device for quantitative characterization of light and heavy components in different shale laminations, comprising: The identification module 510 is used to select a specified field of view of the target shale, identify different lamellar units of the target shale through optical microscopic images of thin sections of rock in the specified field of view, and delineate the lamellar boundaries according to the different lamellar units of the target shale. The acquisition module 520 is used to acquire laser scanning confocal fluorescence images under a specified field of view; Image preprocessing module 530 is used to perform image preprocessing, threshold segmentation, binarization, and visualization of effective fluorescence regions of light and heavy components on laser scanning confocal fluorescence images; The determination module 540 is used to perform superposition analysis of each lamellar unit and the effective fluorescence region, determine the area-normalized integral fluorescence intensity and the light-to-heavy ratio, and quantitatively identify the differences in the occurrence of light and heavy components in different lamellars of shale.

[0079] This application, based on a single field of view, first identifies and delineates different lamellar units in shale using optical microscopic images of rock thin sections. Then, it acquires laser-scanning confocal fluorescence images of the light and heavy components within the same field of view. Next, it analyzes the correspondence between different lamellar units and the effective fluorescence regions of the light and heavy components. Finally, it calculates the area ratio, integrated fluorescence intensity, area-normalized integrated fluorescence intensity, and light-to-heavy ratio of the light and heavy components within different lamellar units. Compared to existing technologies, this approach not only compares the differences in light and heavy components between different lamellar units within the same laser-scanning confocal fluorescence image but also further compares the differences in the light-to-heavy ratio of the same lamellar type within different laser-scanning confocal fluorescence images. For different fields of view or repeated tests under the same experimental conditions, it can be used to evaluate the reproducibility of the results; for different samples or different experimental conditions, it can be used to evaluate the stability and differences in the advantages of light and heavy components within the same lamellar unit.

[0080] See Figure 6 This application provides a computer device, including: Memory; A processor; and a computer program stored in the memory and executable by the processor.

[0081] The computer device receives optical microscopic images of thin rock sections, fluorescence images of light components, and fluorescence images of heavy components acquired under the same field of view of the target shale. The processor executes the computer program to perform lamellar boundary determination, image preprocessing and spatial registration, threshold segmentation and binarization, and overlay analysis of lamellar regions and effective fluorescence regions on the above images. It also calculates the area ratio, area-normalized integral fluorescence intensity, and light-to-heavy ratio of light and heavy components in different lamellar regions, and outputs quantitative characterization results of light and heavy components in different lamellar regions.

[0082] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the above-described image processing, region overlay analysis, and quantitative parameter calculation processes.

[0083] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as C, VHDL, Verilog, the object-oriented programming language Java, and the interpreted scripting language JavaScript.

[0084] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that the image processing, spatial registration, thresholding and binarization processing, region overlay analysis, and quantitative parameter calculation steps performed by a computer device in this application can be implemented by computer program instructions, thereby achieving the functions specified in the corresponding flowcharts and / or blocks. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to create a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate functions for implementing the processes... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0088] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0089] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0090] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0091] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for quantitative characterizing the light and heavy components of different laminae in shale, characterized in that, include: Select a specified field of view of the target shale, identify different lamellar units of the target shale through optical microscopic images of thin sections of rock in the specified field of view, and delineate the lamellar boundaries based on the different lamellar units of the target shale. Acquire laser scanning confocal fluorescence images within a specified field of view; Image preprocessing, threshold segmentation, binarization, and visualization of effective fluorescence regions of light and heavy components are performed on laser scanning confocal fluorescence images. By overlaying analysis of each lamellar unit with the effective fluorescence region, the area-normalized integral fluorescence intensity and light-to-heavy ratio were determined, and the differences in the occurrence of light and heavy components in different shale lamellars were quantitatively identified.

2. The method according to claim 1, characterized in that, The acquisition of the laser scanning confocal fluorescence image under the specified field of view includes: Fluorescence images of light and heavy components were acquired in a specified field of view using laser scanning confocal microscopy.

3. The method according to claim 2, characterized in that, The image preprocessing of the laser scanning confocal fluorescence image includes: Fluorescence image of light component I L Fluorescence images of heavy components I H Denoising, background subtraction, image cropping, image registration, flat-field correction, and intensity normalization were performed to obtain the preprocessed fluorescence image I′ of the light component. L Fluorescence images of heavy components I′ H .

4. The method according to claim 3, characterized in that, The threshold segmentation and binarization processing of the laser scanning confocal fluorescence image includes: Fluorescence image I′ of the preprocessed light component L Fluorescence images of heavy components I′ H Threshold segmentation was performed separately, and the effective fluorescence regions of the light component and the heavy component were extracted by binarization.

5. The method according to claim 4, characterized in that, The visualization of the effective fluorescence regions of light and heavy components in laser-scanned confocal fluorescence images includes: The effective fluorescence regions of the light and heavy components obtained after binarization are displayed with different color values ​​to characterize the distribution location and spread characteristics of the light and heavy components in the same field of view.

6. The method according to claim 4, characterized in that, The step of performing overlay analysis on each laminar unit and the effective fluorescence region to determine the area-normalized integrated fluorescence intensity and the light-to-heavy ratio includes: For the k-th laminar unit Ω k By overlaying the effective fluorescence regions of the light and heavy components obtained after binarization, the effective fluorescence regions of the light and heavy components within the boundary of the kth laminar unit are obtained. The integrated fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit is determined by the following expression: ； ； Among them, S L,k S is the integrated fluorescence intensity of the effective fluorescence region of the light component within the boundary of the k-th laminar unit. H,k Ω represents the integrated fluorescence intensity of the effective fluorescence region of the heavy component within the boundary of the k-th laminar unit. L,k Ω represents the effective fluorescence region of the lightweight component within the boundary of the k-th laminar unit. H,k I′ represents the effective fluorescence region of the heavy component within the boundary of the k-th laminar unit. L,i and I′ H,i The fluorescence intensities of the light and heavy components after pretreatment are shown, respectively. The area-normalized integral fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit is determined by the following expression: ； ； Among them, Q L,k and Q H,k A represents the area-normalized integral fluorescence intensity of the light and heavy components within the boundary of the k-th laminar unit, respectively; k S represents the total area of ​​the k-th laminar unit; L,k and S H,k These are the integrated fluorescence intensities of the effective fluorescence regions of the light and heavy components within the kth laminar unit, respectively. The light-to-heavy ratio of the k-th laminar unit is determined by the following expression: ； Among them, R L / H,k is the weight ratio of the k-th laminar unit.

7. The method according to claim 1, characterized in that, The quantitative identification of differences in the occurrence of light and heavy components within different shale laminae includes: By comparing the area ratio of light components, area ratio of heavy components, area-normalized integral fluorescence intensity, and light-to-heavy ratio differences among different lamellae in the same laser scanning confocal fluorescence image, the advantages of light components and heavy components in different lamellae can be determined. By comparing the results of different views or repeated tests of the same laminae of the target shale under the same experimental conditions, the light-to-heavy ratio is extracted to determine the repeatability of the quantitative characterization results. By comparing the changes in the light-to-heavy ratio of the same laminae under different samples or different experimental conditions, the stability and differences of the dominance of light components or heavy components in the laminae are evaluated.

8. A device for quantitative characterizing the light and heavy components of different shale laminations, characterized in that, include: The identification module is used to select a specified field of view of the target shale, identify different lamellar units of the target shale through optical microscopic images of thin sections of rock in the specified field of view, and delineate the lamellar boundaries based on the different lamellar units of the target shale. The acquisition module is used to acquire laser scanning confocal fluorescence images within a specified field of view; The image preprocessing module is used to perform image preprocessing, threshold segmentation, binarization, and visualization of the effective fluorescence regions of light and heavy components on laser scanning confocal fluorescence images. The determination module is used to perform overlay analysis on each lamellar unit and the effective fluorescence region, determine the area-normalized integral fluorescence intensity and the light-to-heavy ratio, and quantitatively identify the differences in the occurrence of light and heavy components in different shale lamellars.

9. A computer device, characterized in that, include: Memory; processor; as well as Computer programs; Wherein, when the processor executes the computer program, it is used to: Receive optical microscopic images of thin sections of rock, fluorescence images of light components, and fluorescence images of heavy components that have been acquired in the same field of view of the target shale; Different laminar units and their laminar boundaries were determined based on the optical micrographs of the rock thin sections. The fluorescence images of the light and heavy components are preprocessed to maintain their spatial correspondence with the optical micrographs of the rock thin sections. Thresholding and binarization were performed on the preprocessed fluorescence images of light and heavy components to extract the effective fluorescence regions of light and heavy components. Each laminar unit was overlaid with the effective fluorescence regions of the light and heavy components for analysis. The area ratio, area-normalized integral fluorescence intensity, and light-to-heavy ratio of light and heavy components in each lamellar unit are calculated, and the quantitative characterization results of light and heavy components in different shale lamellars are output.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, is used to: Receive optical microscopic images of thin sections of rock, fluorescence images of light components, and fluorescence images of heavy components that have been acquired in the same field of view of the target shale; Different laminar units and their laminar boundaries were determined based on the optical micrographs of the rock thin sections. The fluorescence images of the light and heavy components are preprocessed to maintain their spatial correspondence with the optical micrographs of the rock thin sections. Thresholding and binarization were performed on the preprocessed fluorescence images of light and heavy components to extract the effective fluorescence regions of light and heavy components. Each laminar unit was overlaid with the effective fluorescence regions of the light and heavy components for analysis. The area ratio, area-normalized integral fluorescence intensity, and light-to-heavy ratio of light and heavy components in each lamellar unit are calculated, and the quantitative characterization results of light and heavy components in different shale lamellars are output.

Images