A method and system for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy

Abstract

The present invention provides a method and system for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy, which belongs to the technical field of crude oil component analysis. In order to solve the problem of how to visualize the spatial distribution and ratio analysis of light and heavy components in crude oil. The present invention excites the crude oil sample with a 488nm laser, collects fluorescence signals in the 490nm-510nm and 680nm-710nm bands, and characterizes the light and heavy components respectively; extracts the fluorescence intensity of each pixel point, calculates the fluorescence intensity of the light and heavy components, and associates R with the crude oil density through a regression model, sets thresholds R1 and R2 to classify the components, and realizes visualization of the spatial distribution of crude oil components on a microscopic scale. This method is quantitatively accurate and the results are intuitive. It overcomes the problems of low spatial resolution and difficulty in component differentiation in the existing technology, and is suitable for reservoir evaluation and development optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of crude oil component analysis, and in particular to a method and system for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy. Background Art

[0002] The analysis of light and heavy components in crude oil is crucial for the research and development of shale oil and other crude oil resources. The ratio of light components (primarily low-molecular-weight components such as saturated hydrocarbons) to heavy components (primarily high-molecular-weight components such as aromatics, resins, and asphaltenes) and their spatial distribution within the core directly impact reservoir recovery and the processing and utilization of oil products.

[0003] Traditional crude oil composition analysis techniques, such as pyrolysis analysis (Rock-Eval), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and conventional fluorescence microscopy, can provide a certain degree of analysis of oil content or composition, but each has its limitations. Pyrolysis analysis and conventional fluorescence microscopy only provide overall oil content or a total fluorescence signal, but cannot obtain quantitative ratios of different components or their spatial distribution information at the core or microscale. NMR methods primarily assess hydrogen content or the saturated hydrocarbon / aromatic hydrocarbon ratio, but have low spatial resolution. While SEM can observe the microstructure of the sample, it lacks direct qualitative analysis of chemical composition. Organic matter identification and imaging solutions based on confocal laser scanning microscopy (CLSM) have been developed abroad, but these are primarily used for organic matter identification and analysis. Integrated visualization methods for the spatial distribution and ratio analysis of light and heavy components in crude oil are currently unavailable. Therefore, a new analytical method is still needed to fill this technological gap. Summary of the Invention

[0004] The technical problems to be solved by the present invention are:

[0005] In order to solve the problem of how to visualize the spatial distribution and proportion analysis of light and heavy components in crude oil.

[0006] The present invention is to solve the above technical problems using the following technical solutions:

[0007] The present invention provides a method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy, comprising the following steps:

[0008] S100, sample preparation, selection of crude oil samples and rock slices;

[0009] S200, CLSM parameter settings and fluorescence imaging: Fluorescence imaging of crude oil samples and rock slices was performed using CLSM to obtain spectral signals of different components in the two samples, including fluorescence signals of light components and fluorescence signals of heavy components. The fluorescence signals of the light components correspond to the collection channel wavelength of 490nm-510nm, and the fluorescence signals of the heavy components correspond to the collection channel wavelength of 680nm-710nm;

[0010] S300, channel fluorescence image preprocessing, correcting, denoising and splicing the light component and heavy component channel fluorescence images obtained in step S200;

[0011] S400, ratio calculation and spatial visualization. After completing the channel fluorescence image preprocessing of step S300, the ratio of light / heavy fluorescence intensity at each pixel is calculated. Two thresholds are determined based on the logarithmic regression model established based on the density of crude oil samples with known density and the light-to-heavy ratio. The light oil area, medium oil area, and heavy oil area of ​​the rock slice are distinguished by comparing the light / heavy fluorescence intensity ratio with the two thresholds, and a pseudo-color distribution map is generated to realize visualization of the light and heavy composition and ratio spatial distribution of the rock slice.

[0012] Furthermore, in step S100, it includes:

[0013] S110. Selection of rock slice samples: thin slices of rock covering heavy and light oil from different depths of typical oil reservoirs, ultrasonically cleaned with deionized water and isopropyl alcohol, and dried for later use to obtain shale samples;

[0014] S120, grinding thickness control, the shale sample is ground into a thin slice with a thickness of 0.04 mm–0.05 mm to obtain a shale thin slice sample;

[0015] S130, the environment is stabilized, and the shale thin section sample prepared in step S120 is placed at a temperature of 20° C. to 25° C. and a relative humidity of 30% to 46% for stable storage;

[0016] S140, repeat the preparation, repeat the above preparation process for different samples, and finally obtain multiple rock slices in stable state.

[0017] Furthermore, in step S200, it includes:

[0018] S210, instrument and excitation source, uses CLSM to select a fixed 488 nm laser line to excite crude oil samples and rock slices;

[0019] S220, detection channel setting, setting two groups of fluorescence detection channels, the wavelength of the fluorescence signal collection channel corresponding to the light component is 490nm-510nm, and the wavelength of the fluorescence signal collection channel corresponding to the heavy component is 680nm-710nm;

[0020] S230, scanning mode setting, select 20× objective lens for XY plane scanning according to the size of crude oil sample and rock slice, and set the scanning resolution to 512×512 pixels;

[0021] S240, imaging acquisition. After completing parameter setting, fluorescence imaging is performed on multiple pre-selected areas on the surface of crude oil samples and rock slices. Two sets of channel images are collected for each pre-selected area to record the fluorescence intensity distribution of light and heavy components of crude oil on each focal plane. After the acquisition is completed, the channel fluorescence image data is exported as input for subsequent analysis.

[0022] Furthermore, in step S300, it includes:

[0023] S310, channel separation, reading the fluorescence image data of the light component channel and the heavy component channel respectively, and converting them into NumPy array or Pandas data table format;

[0024] S320, background correction, background subtraction of channel fluorescence image data, using local median filtering or polynomial smoothing method to eliminate crude oil sample and rock slice autofluorescence and background noise, improve the signal-to-noise ratio; for channel fluorescence image Background estimation value Corrected image background estimation value , calculated as follows:

[0025]

[0026] in, represents the background estimate obtained by sliding window median filtering or polynomial fitting;

[0027] S330, alignment and cropping, registering the two-channel fluorescence images of the light component and the heavy component collected in the same area to ensure spatial alignment between pixels; cropping the edges of the channel fluorescence images to eliminate overlapping and distorted parts;

[0028] Image registration uses the maximum correlation positioning method to calculate the reference image

[0029]

[0030] in: Indicates the reference channel fluorescence image at coordinates The pixel value at ; Indicates the coordinates of the fluorescence image of the channel to be registered The pixel value at ; is the horizontal and vertical offset of the sliding window; For the offset Cross-correlation value under ;

[0031] Take the maximum value position As the optimal translation parameter:

[0032]

[0033] Then Perform translation transformation to complete the registration:

[0034]

[0035] in, represents the channel fluorescence image after registration;

[0036] S340, quality inspection, visual inspection and statistical verification of the panoramic image generated after stitching to ensure that the seams are smooth, there is no obvious displacement, and it is in good agreement with the original image features.

[0037] Furthermore, in step S400, it includes:

[0038] S410, ratio calculation, extracting the light component intensity of each pixel from the spliced ​​light component and heavy component dual-channel fluorescence images and heavy component strength ; Calculate the fluorescence intensity ratio for each pixel , get the ratio matrix;

[0039] S420, threshold classification, determining two thresholds R1 and R2 based on the logarithmic regression model established based on the density and light-heavy ratio of crude oil samples with known density, for classifying the ratio R of the rock slices, where:

[0040] If R > R2, it is determined to be a light oil zone;

[0041] If R1 ≤ R ≤ R2, it is determined to be a medium oil zone;

[0042] If R < R1, it is determined to be a heavy oil area;

[0043] S430, a visualization interface, after completing the classification, is visualized by assigning different colors to the spatial distribution of the light components of the rock slice, the heavy components of the rock slice, and the light-to-heavy ratio of the rock slice.

[0044] The present invention provides a system for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy. The system has program modules corresponding to the above steps and executes the steps of the above method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy when running.

[0045] The present invention provides a computer-readable storage medium, which stores a computer program. The computer program is configured to implement the steps of a method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy when called by a processor.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This method uses crude oil samples of known density to establish a regression model and determine thresholds R1 and R2. This model verifies the correlation between the R value and the density of the oil sample, providing a basis for classification. The formula for calculating the fluorescence intensity ratio R clearly reflects the proportional relationship between the two bands' light intensities, which has clear physical and chemical significance. The method can appropriately adjust the band range and threshold parameters based on the specific sample and experimental conditions to accommodate the analysis needs of different types of crude oil. The visual design makes the distinction between light and heavy components more intuitive, filling a gap in the spatial distribution and ratio analysis of light and heavy components in crude oil. This method offers the advantages of accurate quantitative analysis and intuitive results, overcoming the low spatial resolution and difficulty in component differentiation found in existing technologies, making it suitable for reservoir assessment and development optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy in an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of the sample preparation and fluorescence collection process in an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the fluorescence receiving wavelength range of light components and heavy components in an embodiment of the present invention;

[0051] Figure 4 This is a fluorescence image of light components obtained by exciting a crude oil sample at 490-510 nm using a 488 nm laser line in an embodiment of the present invention;

[0052] Figure 5 This is a fluorescence image of heavy components obtained by exciting a crude oil sample at 680-710 nm using a 488 nm laser line in an embodiment of the present invention;

[0053] Figure 6: is a regression relationship diagram of the ratio R of light components to heavy components and crude oil density in an embodiment of the present invention;

[0054] Figure 7 Schematic diagram of the spatial distribution of light component-rich regions in an embodiment of the present invention, wherein the red highlights are pixel locations where the ratio R is greater than the threshold R2;

[0055] Figure 8 Schematic diagram of the spatial distribution of heavy component-rich regions in an embodiment of the present invention, wherein the blue highlights are pixel locations where the ratio R is less than the threshold R1;

[0056] Figure 9 : This is a thermal diagram of the spatial distribution of the light-to-heavy component ratio R in the crude oil sample according to the embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] Specific implementation plan 1: Combined Figures 1 to 9 As shown, the present invention provides a method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy, comprising the following steps:

[0059] S100, sample preparation, select representative crude oil samples and rock slices, combined with Figure 2 As shown, in order to ensure the representativeness and repeatability of the samples, this implementation plan selected 26 bottles of crude oil samples with different densities from the Fuyu oil layer in the Songliao Basin (as crude oil samples for obtaining the threshold of light and heavy components), and selected siltstone samples from the Qingyi 1st member of the Songliao Basin to make rock slices as the samples to be tested, specifically including:

[0060] S110. Sample selection: siltstone samples were collected from thin sections of rock covering heavy and light oil at different depths in typical oil reservoirs. The thin sections were ultrasonically cleaned with deionized water and isopropyl alcohol and dried for later use to obtain shale samples.

[0061] S120, grinding thickness control, the mud shale sample is ground into a thin slice with a thickness of 0.04mm-0.05mm, ensuring that the optical uniformity of the test area is consistent, and the mud shale thin slice sample is obtained;

[0062] S130, stabilizing the environment by placing the shale thin section sample prepared in step S120 at a temperature of 20°C-25°C and a relative humidity of 30%-46% to stabilize the environment to prevent the state change of the shale thin section sample from affecting subsequent measurements;

[0063] S140, repeating the preparation process for different samples to ensure the repeatability and stability of the verification method, and ultimately obtaining multiple representative and stable rock slices;

[0064] S200, CLSM parameter settings and fluorescence imaging, use high-resolution CLSM (Leica TCS SP8 high-performance confocal laser scanning microscope) to perform fluorescence imaging on crude oil samples and rock slices to obtain spectral signals of different components in crude oil, including:

[0065] S210, instrument and excitation source, uses a Leica TCS SP8 laser confocal microscope, with a maximum imaging resolution of approximately 200 nm. A fixed 488 nm laser line is selected to excite the sample to be tested. This excitation wavelength can effectively stimulate the fluorescence emission of light and heavy components in the sample to be tested.

[0066] S220, detection channel setting, set up two groups of fluorescence detection channels, combined Figure 3 As shown, the ordinate is the normalized emission intensity (unitless), the abscissa is the wavelength (nm), the wavelength of the fluorescence signal collection channel corresponding to the light component is 490nm–510nm, and the wavelength of the fluorescence signal collection channel corresponding to the heavy component is 680nm–710nm; CLSM can distinguish light components from heavy components based on the differences in the fluorescence bands of different components; through the above channel settings, the fluorescence distribution information of the two components can be obtained simultaneously;

[0067] S230, Scan mode settings: Select a 20× objective lens for XY plane scanning based on the sample size to be measured, and set the scanning resolution to 512×512 pixels to balance spatial resolution and acquisition speed. Line Scan or Frame Scan modes can be used to reduce noise. Laser power and photomultiplier tube (PMT) gain are optimized according to the sample fluorescence intensity to avoid signal saturation or excessive weakness. See Table 1 for details of related equipment and parameters.

[0068] Table 1 Statistics of laser confocal microscope scanning parameters

[0069]

[0070] S240, imaging acquisition, after completing the parameter setting, fluorescence imaging is performed on multiple preselected areas on the surface of the sample to be tested; two sets of channel images (LCs channel and HCs channel) are collected for each preselected area, and the fluorescence intensity distribution of the light component and heavy component of the sample to be tested on each focal plane is recorded, as shown in the attached figure. Figure 4 and attached Figure 5 After the acquisition is completed, the channel fluorescence image data (such as TIFF format) is exported through the microscope supporting software as input for subsequent analysis;

[0071] S300, fluorescence image preprocessing, importing the light component and heavy component channel fluorescence images obtained by CLSM into the independently developed image processing software system based on Python language for correction, denoising and splicing processing; specifically including:

[0072] S310, channel separation, reads the fluorescence image data of the light component channel (490nm–510 nm) and the heavy component channel (680nm–710 nm) respectively, and converts them into NumPy arrays or Pandas data tables for subsequent processing;

[0073] S320, background correction, background subtraction of the channel fluorescence image data, local median filtering or polynomial smoothing method can be used to eliminate the autofluorescence and background noise of the sample to be tested, and improve the signal-to-noise ratio; for example, for the channel fluorescence image Background estimation value Corrected image background estimation value , can be calculated as follows:

[0074]

[0075] in, represents the background estimate obtained by sliding window median filtering or polynomial fitting;

[0076] S330, alignment and cropping, accurately aligning the two-channel fluorescence images of the light component and the heavy component collected in the same area to ensure spatial alignment between pixels; if necessary, cropping the edges of the channel fluorescence images to eliminate overlapping and distorted parts;

[0077] Image registration can use the maximum correlation positioning method to calculate the cross-correlation matrix between the reference image and the image to be registered:

[0078]

[0079] in: Indicates the reference channel fluorescence image at coordinates The pixel value at ; Indicates the coordinates of the fluorescence image of the channel to be registered The pixel value at ; is the horizontal and vertical offset of the sliding window; For the offset Cross-correlation value under ;

[0080] Take the maximum value position As the optimal translation parameter:

[0081]

[0082] Among them, max· is the maximum value;

[0083] Then Perform translation transformation to complete the registration:

[0084]

[0085] in: represents the channel fluorescence image after registration;

[0086] S340: Quality inspection: Visual inspection and statistical verification of the stitched panoramic image to ensure smooth seams, no obvious displacement, and good consistency with the original image features;

[0087] S400, ratio calculation and spatial visualization, after completing the channel fluorescence image preprocessing, calculates the ratio of light / heavy fluorescence intensity at each pixel in the software system and generates a pseudo-color distribution map; the software builds a user interface based on PyQt5, uses Pandas and NumPy internally to process data, and uses Matplotlib to render images; specifically,

[0088] S410, ratio calculation, extracting the light component intensity of each pixel from the spliced ​​light component and heavy component dual-channel fluorescence images and heavy component strength ; Calculate the fluorescence intensity ratio for each pixel , and obtain the ratio matrix; this calculation can be achieved by using NumPy array operations for efficient batch processing;

[0089] S420, threshold classification, based on the density of crude oil samples with known density (referring to crude oil samples, it can be understood that the purpose of preparing crude oil is to determine the thresholds of light and heavy components, that is, R1 and R2, and the purpose of rock slice preparation is to collect the spectral signals of rock slices based on the threshold determination, and input the appropriate threshold values ​​on the visualization system according to the two threshold values. For example, if the threshold value determined is R1 equal to 5, if the minimum value input on the system is 5 and the maximum value is any number greater than 5, such as 8, then only the signal of the light component will be displayed on this image) and the light-heavy ratio (Table 2), a logarithmic regression model (referring to Figure 6 The logarithmic formula in the formula can be understood as a fitting function. By comparing the linear fitting function, exponential fitting function and logarithmic fitting function, it is found that the R square determined by the logarithmic fitting function is the highest, so the logarithmic regression model is used to determine the fitting formula) to determine two thresholds R1 and R2 for classifying the ratio R (see Appendix). Figure 6 ),in:

[0090] If R > R2, it is determined to be a light oil zone;

[0091] If R1 ≤ R ≤ R2, it is determined to be a medium oil zone;

[0092] If R < R1, it is determined to be a heavy oil area;

[0093] The statistical analysis yielded threshold values ​​of R1 = 0.96 and R2 = 1.23 (Appendix Figure 6 When the pixel ratio R is greater than R2, it can be determined that the area is rich in light components; when R is less than R1, it can be determined that the area is rich in heavy components;

[0094] Table 2 Statistics of crude oil density and light-heavy ratio

[0095]

[0096] S430, visualization interface, through the PyQt5 interface, users can customize the light-heavy ratio threshold according to their needs to realize the light component of crude oil (rock slice) (attached Figure 7 ), crude oil (rock slice) heavy components (attached Figure 8 ) and crude oil (rock slice) weight ratio (attached Figure 9 )’s spatial distribution visualization; users can also interactively view the ratios of different areas and export the final image; the software supports saving analysis results in common image formats (such as PNG, TIFF) and data tables to facilitate subsequent analysis.

[0097] Specific implementation scheme 2: The present invention provides a system for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy. The system has program modules corresponding to the above steps, and executes the steps in the above-mentioned method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy during operation.

[0098] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0099] Specific implementation scheme three: The present invention provides a computer-readable storage medium, which stores a computer program. The computer program is configured to implement the steps of a method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy when called by a processor.

[0100] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0101] Although the present invention is disclosed as above, the scope of protection disclosed by the present invention is not limited thereto. Those skilled in the art of the present invention may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy, characterized in that: The following steps are involved: S100, sample preparation, selection of crude oil samples and rock slices; S200, CLSM parameter settings and fluorescence imaging: Fluorescence imaging of crude oil samples and rock slices was performed using CLSM to obtain spectral signals of different components in the two samples, including fluorescence signals of light components and fluorescence signals of heavy components. The fluorescence signals of the light components correspond to the collection channel wavelength of 490nm-510nm, and the fluorescence signals of the heavy components correspond to the collection channel wavelength of 680nm-710nm; S300, channel fluorescence image preprocessing, correcting, denoising and splicing the light component and heavy component channel fluorescence images obtained in step S200; include, S310, channel separation, reading the fluorescence image data of the light component channel and the heavy component channel respectively, and converting them into NumPy array or Pandas data table format; S320, background correction, background subtraction of channel fluorescence image data, using local median filtering or polynomial smoothing method to eliminate crude oil sample and rock slice autofluorescence and background noise, improve the signal-to-noise ratio; for channel fluorescence image Background estimation value Corrected image background estimation value , calculated as follows: ； in, represents the background estimate obtained by sliding window median filtering or polynomial fitting; S330, alignment and cropping, registering the two-channel fluorescence images of the light component and the heavy component collected in the same area to ensure spatial alignment between pixels; cropping the edges of the channel fluorescence images to eliminate overlapping and distorted parts; Image registration uses the maximum correlation positioning method to calculate the cross-correlation matrix between the reference image and the image to be registered: ； in: Indicates the reference channel fluorescence image at coordinates The pixel value at ; Indicates the coordinates of the fluorescence image of the channel to be registered The pixel value at ; is the horizontal and vertical offset of the sliding window; For the offset Cross-correlation value under ; Take the maximum value position As the optimal translation parameter: ； Then Perform translation transformation to complete the registration: ； in, represents the channel fluorescence image after registration; S340: Quality inspection: Visual inspection and statistical verification of the stitched panoramic image to ensure smooth seams, no obvious displacement, and good consistency with the original image features; S400, ratio calculation and spatial visualization, after completing the channel fluorescence image preprocessing of step S300, calculate the ratio of light / heavy fluorescence intensity at each pixel point, determine two thresholds based on the logarithmic regression model established between the density of crude oil samples with known density and the light-heavy ratio, distinguish the light oil area, medium oil area and heavy oil area of ​​the rock slice by comparing the light / heavy fluorescence intensity ratio with the two thresholds, and generate a pseudo-color distribution map to realize visualization of the light-heavy composition and ratio spatial distribution of the rock slice; including, S410, ratio calculation, extracting the light component intensity of each pixel from the spliced ​​light component and heavy component dual-channel fluorescence images and heavy component strength ; Calculate the fluorescence intensity ratio for each pixel , get the ratio matrix; S420, threshold classification, determining two thresholds R1 and R2 based on the logarithmic regression model established based on the density and light-heavy ratio of crude oil samples with known density, for classifying the ratio R of the rock slices, where: If R > R2, it is determined to be a light oil zone; If R1 ≤ R ≤ R2, it is determined to be a medium oil zone; If R < R1, it is determined to be a heavy oil area; S430, a visualization interface, after completing the classification, is visualized by assigning different colors to the spatial distribution of the light components of the rock slice, the heavy components of the rock slice, and the light-to-heavy ratio of the rock slice.

2. The method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy according to claim 1, characterized in that: In step S100, it includes: S110. Selection of rock slice samples: thin slices of rock covering heavy and light oil from different depths of typical oil reservoirs, ultrasonically cleaned with deionized water and isopropyl alcohol, and dried for later use to obtain shale samples; S120, grinding thickness control, the shale sample is ground into a thin slice with a thickness of 0.04 mm–0.05 mm to obtain a shale thin slice sample; S130, the environment is stabilized, and the shale thin section sample prepared in step S120 is placed at a temperature of 20° C. to 25° C. and a relative humidity of 30% to 46% for stable storage; S140, repeat the preparation, repeat the above preparation process for different samples, and finally obtain multiple rock slices in stable state.

3. The method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy according to claim 2, characterized in that: In step S200, it includes: S210, instrument and excitation source, uses CLSM to select a fixed 488 nm laser line to excite crude oil samples and rock slices; S220, detection channel setting, setting two groups of fluorescence detection channels, the wavelength of the fluorescence signal collection channel corresponding to the light component is 490nm-510nm, and the wavelength of the fluorescence signal collection channel corresponding to the heavy component is 680nm-710nm; S230, scanning mode setting, select 20× objective lens for XY plane scanning according to the size of crude oil sample and rock slice, and set the scanning resolution to 512×512 pixels; S240, imaging acquisition: After completing parameter settings, fluorescence imaging is performed on multiple preselected areas on the surface of the crude oil sample and rock slice; two sets of channel images are collected for each preselected area to record the fluorescence intensity distribution of the light and heavy components of the crude oil at each focal plane; After acquisition is completed, the channel fluorescence image data is exported as input for subsequent analysis.

4. A CLSM laser spectroscopy-based visualization system for the spatial distribution of light and heavy components and ratios of shale oil, characterized by: The system has a program module corresponding to the steps described in any one of claims 1 to 3 above, and when running, executes the steps in the above-mentioned method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of a method for visualizing the spatial distribution of light and heavy components and ratios of shale oil based on CLSM laser spectroscopy according to any one of claims 1 to 3 when called by a processor.

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