A method for determining shale microcomponent types and molecular structure characterization based on optical microscope-AFM-IR combination

Abstract

The present application relates to the field of optical microscope-AFM-IR combined technique, in particular to a method for determining shale microcomponent type and its molecular structure characterization based on optical microscope-AFM-IR combined technique. The present application combines AFM-IR and optical microscope, not only avoids the limitation of single technology, but also constructs a feasible method for revealing the relationship between the morphology of microcomponent and its chemical properties, and provides a new method for the study of shale microcomponent heterogeneity.

Description

Technical Field

[0001] This invention relates to the field of optical microscopy-AFM-IR coupling technology, specifically to a method for determining the type of microscopic components of shale and characterizing its molecular structure based on optical microscopy-AFM-IR coupling. Background Technology

[0002] In recent years, conventional oil and gas production has been declining worldwide, and shale oil and gas has become an important replacement area for oil and gas resources. Consequently, organic matter in shale formations has also attracted widespread attention. Organic matter can be divided into micro-components from different sources, and the differences in the chemical structure of different micro-components are key factors affecting the hydrocarbon generation capacity of organic matter. Therefore, identifying the types of micro-components and characterizing their chemical structures is of great significance for revealing the hydrocarbon generation mechanism of organic matter. Summary of the Invention

[0003] The purpose of this invention is to provide a method for determining the types of microscopic components and characterizing their molecular structures in shale based on optical microscopy-AFM-IR coupled technology. The method provided by this invention can accurately identify each microscopic component and characterize its chemical structure under optical microscopy-AFM-IR coupled experiments.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] This invention provides a method for determining the types of microscopic components and characterizing the molecular structure of shale based on optical microscopy-AFM-IR, comprising the following steps:

[0006] (1) Identification of each micro-component: The micro-components in the organic-rich shale sample were identified by optical microscope. The area with rich micro-component types was selected for local splicing and scanning to obtain the white light field splicing distribution map and the fluorescence field splicing distribution map of the micro-components in the whole rock light section. The area with rich micro-component types has ≥3 types of micro-components.

[0007] Marking and locating each microscopic component: Number the white light field-view splicing distribution map and the fluorescence field-view splicing distribution map of the whole rock light section microscopic components, record the sequence number and position of the typical microscopic component in the white light field-view splicing distribution map and the fluorescence field-view splicing distribution map of the whole rock light section microscopic components, and mark reference points on the edge of each typical microscopic component;

[0008] (2) After finding the typical micro-components under the objective lens of AFM-IR, scan each typical micro-component in tapping mode using AFM-IR to obtain the surface morphology map of each typical micro-component. Based on the surface morphology map, confirm and mark the typical micro-components a second time.

[0009] (3) Scan the surface morphology map in step (2) in contact mode to obtain the infrared spectrum and chemical functional group distribution map of each typical micro-component of the secondary confirmation mark;

[0010] (4) The infrared spectrum and chemical functional group distribution map are preprocessed, and the molecular structure characteristics of typical micro-components in the shale sample are obtained based on the preprocessed infrared spectrum and chemical functional group distribution map.

[0011] The preprocessing involves denoising the infrared spectrum and adjusting the color mark intensity, respectively.

[0012] Preferably, the typical microscopic components include primary microscopic components and solid bitumen; the primary microscopic components include one or more of the saprophytic group, chitinous group, vitrinite group and inertinite group, the saprophytic group includes algae; the algae include structural algae and layered algae;

[0013] The solid asphalt includes one or more of pre-oil asphalt, post-oil asphalt, and tar pitch.

[0014] Preferably, the denoising process includes background energy normalization and visualization data processing performed sequentially; the visualization data processing includes curve smoothing, baseline correction, and Gaussian-Lorentz function peak fitting performed sequentially.

[0015] The curve smoothing and baseline correction were performed using Analysis Studio software and Origin software, respectively.

[0016] The color scale intensity adjustment is achieved by adjusting the color scale intensity of the chemical functional group distribution map using Analysis Studio software.

[0017] Preferably, the scanning method in the contact mode is a combination of point and area scanning; during scanning, the infrared laser power is 80-100 mW; and the wavelength range of the point and area scanning in the contact mode is 800-1800 cm⁻¹. -1 .

[0018] Preferably, during the spot scanning, single-point spectra are acquired for each microscopic group of the positioning mark, and multiple spectral acquisitions are performed for each sample point;

[0019] The scanning range during the area scan is (20~50)μm×(20~50)μm; the step size is 20 nm; and the image resolution is 512×512 pixels.

[0020] Preferably, prior to scanning in contact mode, the method further includes scanning a 1450 cm² area. -1 1600 cm -1 1710 cm -1The incident frequency of the infrared spectrum was tuned to the contact resonance of the shale sample in each band.

[0021] Preferably, the scanning rate in the tapping mode is 0.5-2 Hz and the resolution is 512×512 pixels.

[0022] Preferably, the optical microscope is a polarizing microscope equipped with a fluorescence module, and the identification of microscopic components is performed in white light and fluorescence modes;

[0023] When observed in fluorescence mode, the excitation wavelength is 365 nm; the emission wavelength is 420-700 nm.

[0024] Preferably, before identifying the microscopic components, the shale sample is further subjected to polishing and epoxy resin embedding and curing.

[0025] Preferably, after obtaining the molecular structure characteristics of typical micro-components in the shale sample, the method further includes using infrared spectra to calculate the A factor and C factor of each typical micro-component; wherein the A factor is the ratio of the spectral absorption peak intensity of lipids to that of aromatic ring functional groups; and the C factor is the ratio of the spectral absorption peak intensity of oxygen-containing functional groups to that of aromatic functional groups.

[0026] By using factors A and C, we analyzed the differences in molecular structure and heterogeneity of different micro-components in organic-rich shale.

[0027] The present invention has the following beneficial effects:

[0028] This invention utilizes optical-AFM-IR coupled technology. First, it records the precise location information of different micro-components under an optical microscope. Then, it obtains the distribution characteristics of various functional groups among the labeled micro-components under AFM-IR, thereby determining the chemical structural characteristics of different micro-components. This method can improve and advance the study of the formation mechanism of high-quality continental shale reservoirs, and has significant implications and application value for revealing the enrichment law of continental shale and predicting oil and gas distribution. Furthermore, this method can achieve quantitative analysis of the surface chemical structure of different micro-components under in-situ conditions, quantitatively characterizing the cleavage of aliphatic CH bonds and the aromatization process of organic matter in different micro-components. Combined with optical microscopy-AFM-IR technology, it can detect the chemical composition of materials with resolutions far below the conventional optical diffraction limit, while providing distribution maps of different components, thus revealing the chemical structural characteristics of different micro-components and providing a new method for studying the heterogeneity of shale organic matter. This invention combines AFM-IR with traditional optical microscopy, which not only avoids the limitations of a single technology, but also constructs a highly feasible method to reveal the relationship between the morphology of micro-components and their chemical properties, providing a new approach for the study of the heterogeneity of shale micro-components. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 Microscopic component identification, labeling, and localization diagram of organic-rich shale samples under an optical microscope;

[0031] Figure 2 White light / fluorescence microscopic images of the microscopic components;

[0032] Figure 3 The microscopic components in shale are characterized by white light, fluorescence, surface morphology, and AFM-IR spectra, as well as the distribution of functional groups.

[0033] Figure 4 This is a pseudo-Van Kreveln diagram constructed based on the A and C factors of infrared spectroscopy. Detailed Implementation

[0034] This invention provides a method for determining the type of microscopic components and / or characterizing the molecular structure of shale based on optical microscopy-AFM-IR, comprising the following steps:

[0035] (1) Identification of each micro-component: The micro-components in the organic-rich shale sample were identified by optical microscope. The area with rich micro-component types was selected for local splicing and scanning to obtain the white light field splicing distribution map and the fluorescence field splicing distribution map of the micro-components in the whole rock light section. The area with rich micro-component types has ≥3 types of micro-components.

[0036] Marking and locating each microscopic component: Number the white light field-view splicing distribution map and the fluorescence field-view splicing distribution map of the whole rock light section microscopic components, record the sequence number and position of the typical microscopic component in the white light field-view splicing distribution map and the fluorescence field-view splicing distribution map of the whole rock light section microscopic components, and mark reference points on the edge of each typical microscopic component;

[0037] (2) After finding the typical micro-components under the objective lens of AFM-IR, scan each typical micro-component in tapping mode using AFM-IR to obtain the surface morphology map of each typical micro-component. Based on the surface morphology map, confirm and mark the typical micro-components a second time.

[0038] (3) Scan the surface morphology map in step (2) in contact mode to obtain the infrared spectrum and chemical functional group distribution map of each typical micro-component of the secondary confirmation mark;

[0039] (4) The infrared spectrum and chemical functional group distribution map are preprocessed, and the molecular structure characteristics of the micro-components in the shale sample are obtained based on the preprocessed infrared spectrum and chemical functional group distribution map.

[0040] The preprocessing involves denoising the infrared spectrum and adjusting the color mark intensity, respectively.

[0041] This invention identifies microscopic components in organic-rich shale samples using an optical microscope, obtaining a whole-rock light section microscopic component white-light field-of-view mosaic distribution map and a whole-rock light section microscopic component fluorescence field-of-view mosaic distribution map (identifying each microscopic component); the whole-rock light section microscopic component white-light field-of-view mosaic distribution map and the whole-rock light section microscopic component fluorescence field-of-view mosaic distribution map are numbered, and the sequence number and position of the typical microscopic component in the whole-rock light section microscopic component white-light field-of-view mosaic distribution map and the whole-rock light section microscopic component fluorescence field-of-view mosaic distribution map are recorded, and reference points are marked on the edges of each microscopic component (marking and locating each microscopic component).

[0042] In one embodiment of the present invention, the microstructures include primary microstructures and solid bitumen; the primary microstructures include one or more of algae, vitrinite, and inertinite; the algae include layered algae and structural algae; the solid bitumen includes one or more of pre-oil bitumen, post-oil bitumen, and tar bitumen. In this invention, the classification scheme and nomenclature of the microstructures are based on the International Society for Petrology (ICCP). The primary microstructures mainly include algae, vitrinite, and inertinite. Algae mainly appear during the low-maturity to peak hydrocarbon generation period, and their fluorescence color changes from pale green to yellow with maturity, appearing brown under white light. They are elongated or laminated, parallel to the bedding planes. Inertinite and vitrinite appear throughout the entire thermal evolution stage. Inertinite appears yellowish-white to bright white under white light, is non-fluorescent, and is angular or subangular in shape, possessing a cellular structure. Vitrinite appears gray under white light, exhibits no fluorescence, and has a reflectivity between that of solid bitumen and inertinite. It occurs as dispersed particles, thin layers, or lenses. Pre-oil bitumen appears during the low-maturity to peak hydrocarbon generation stage, displaying reddish-brown or black fluorescence. Under white light, it appears black to dark gray, is relatively homogeneous, and mostly exhibits the morphology of primary microscopic components, with a few showing characteristics of filling pores and fissures. Post-oil bitumen exists from the early to late stages of oil generation, shows no fluorescence, and appears dark gray to gray under white light. Its main characteristics are filling pores and fissures and encapsulating authigenic minerals. Pyrite exists during the high-maturity stage, shows no fluorescence, and appears light gray to bright white under white light. It fills pores and fissures and encapsulates authigenic minerals.

[0043] As one embodiment of the present invention, before identifying the shale sample using the optical microscope, the shale sample is further subjected to polishing and epoxy resin embedding and curing.

[0044] In one embodiment of the present invention, the optical microscope can be a polarizing microscope equipped with a fluorescence module, which can realize observation in both white light and fluorescence modes. An example is the Zeiss Axio Imager M2m. In another embodiment of the present invention, when observing in fluorescence mode, the excitation wavelength can be 365 nm; the emission wavelength can be 420-700 nm.

[0045] After locating the microscopic components under the objective lens of AFM-IR, the present invention scans each located microscopic component in tapping mode using AFM-IR to obtain a surface morphology image of each microscopic component. Based on the surface morphology image, the marked microscopic components are then confirmed for secondary verification.

[0046] In one embodiment of the present invention, the scanning rate of the tapping mode can be 0.5-2 Hz, and the resolution can be 512×512 pixels.

[0047] As one embodiment of the present invention, before scanning in contact mode, a 1450 cm² scan is also included. -1 1600 cm -1 1710 cm -1 The incident frequency of the infrared spectrum was tuned to the contact resonance of the shale sample in each band to enhance the signal and improve the signal-to-noise ratio.

[0048] Building upon the nanometer-level resolution of AFM, AFM-IR combines infrared spectroscopy with chemical analysis capabilities, offering significant advantages such as requiring fewer samples, being non-destructive, and being rapid. It breaks through the optical diffraction limit, achieving infrared spectroscopy and imaging at an ultra-high spatial resolution of 10 nm.

[0049] The present invention scans the surface morphology map in step (2) in contact mode to obtain the infrared spectrum map and chemical functional group distribution map of each microscopic component of the secondary confirmation mark.

[0050] In one embodiment of the present invention, the scanning in the contact mode adopts a combination of point and surface scanning. In another embodiment of the present invention, during the point scanning, single-point spectra are collected for each microscopic component of the secondary confirmation mark, and multiple spectral acquisitions are performed for each sample point. Specifically, each sample point can be acquired 3 times, with a single integration time of 200 ms, and the average signal intensity is selected, which can enhance the accuracy of the results.

[0051] In one embodiment of the present invention, during the area scanning, the infrared laser power is 80~100 mW; the scanning area range is (20-50) μm×(20-50) μm, the step size is 20 nm; and the image resolution is 512×512 pixels.

[0052] The present invention preprocesses the infrared spectrum and chemical functional group distribution map, and obtains the molecular structure characteristics of the micro-components in the shale sample based on the preprocessed infrared spectrum and chemical functional group distribution map;

[0053] The preprocessing involves denoising the infrared spectrum and adjusting the color mark intensity, respectively.

[0054] In one embodiment of the present invention, the denoising process includes background energy normalization and visualization data processing performed sequentially; the visualization data processing includes curve smoothing, baseline correction, and Gaussian-Lorentz function peak fitting until convergence; the curve smoothing and baseline correction are performed using Analysis Studio software and Origin software, respectively.

[0055] In one embodiment of the present invention, the color mark intensity is adjusted by using Analysis Studio software to adjust the color mark intensity of the chemical functional group distribution map.

[0056] As one embodiment of the present invention, the present invention can also be based on the molecular structure characteristics of the microscopic components in the obtained shale sample, and further analyzed by aliphatic (1450 cm⁻¹) -1 C=O (1710 cm) -1 C=C (1600 cm) -1 The absorption peak intensity is used to determine the degree of aromatization and hydrocarbon generation potential of organic matter in different occurrence forms.

[0057] In one embodiment of the present invention, after obtaining the infrared spectrum, the method further includes calculating the A factor and C factor of each microscopic component using the infrared spectral data; wherein the A factor is the ratio of the spectral absorption peak intensity of lipids to that of aromatic ring functional groups; and the C factor is the ratio of the spectral absorption peak intensity of oxygen-containing functional groups (1710 cm⁻¹). -1 ) and aromatic functional groups (1600 cm -1 The ratio of the spectral absorption peak intensities of the shale to the molecular structure differences and heterogeneity of different micro-components in organic-rich shale was analyzed using factors A and C.

[0058] To further illustrate the present invention, the following detailed description of the invention's solutions, in conjunction with the accompanying drawings and embodiments, is provided, but should not be construed as limiting the scope of protection of the present invention.

[0059] The nano-infrared spectroscopy system in Example 1 was a Bruker NanoIR3, and the AFM sample stage was equipped with a temperature control system (25±0.5℃); the polarizing microscope equipped with a fluorescence module was a Zeiss Axio Imager M2m.

[0060] Example 1

[0061] (1) Take organic shale core samples (diameter ≤ 1 cm) and polish them with argon ion polishing until the surface smoothness is < 100 nm roughness; then, embed and cure the samples with epoxy resin. The cured samples (length, width and height are all in the range of 1-2 cm) are placed in the sample chamber.

[0062] (3) Under an optical microscope, use a 20x objective lens to survey the sample. Select areas with abundant micro-component types and perform 100 local serpentine mosaic scans to obtain a white-light field-of-view mosaic distribution map and a fluorescence field-of-view mosaic distribution map of the micro-components on the whole-rock light section. Name the first image 1, the second image 2, and so on. Record the sequence number and position of the typical micro-components in the images. Use a diamond etching instrument to mark reference points on the edge of the sample. Mark 2-3 test points for each micro-component in each sample. Mark 5-10 test points for different components in each sample.

[0063] (2) The samples were observed using a Zeiss Axio Imager M2m. Under white light and fluorescence (excitation wavelength 365 nm, emission wavelength 420-700 nm), the primary microscopic components and the color, morphology, and fluorescence color of the solid asphalt were observed using a 20x objective lens. Figure 2 .

[0064] (4) Using Bruker NanoIR3, first use a polystyrene standard (characteristic absorption peak at 1600 cm⁻¹) as an example. -1 Calibrate the infrared laser wavelength to ensure a wavenumber error of <2 cm. -1 .

[0065] (5) Based on the optical mirror coordinates and the diamond etching reference point, locate the target area, scan the target area in tapping mode, scan rate 0.5 Hz, resolution 512×512 pixels, and obtain the surface morphology images of each microstructure. Figure 3 (A3, B3, C3 in the text), based on surface morphology Figure 2 Secondary confirmation of labeled micro-components

[0066] (6) Scan each relabeled microscopic component using contact mode (using a combination of point and area scanning). Before scanning, a 1450 cm⁻¹ microstructure was prepared. -1 (Aliphatic CH), 1600 cm -1 (Aromatic C=C), 1710 cm -1 (C=O) Local resonance enhancement was performed separately, and the laser modulation frequency was adjusted to 250 kHz.

[0067] (7) Collect the spectrum three times at each point, with a single integration time of 200 ms, and select the average value to obtain the infrared spectrum; set the area scan to 5 μm × 5 μm region and step size of 20 nm to obtain the distribution map of chemical functional groups.

[0068] (8) The obtained infrared spectra and chemical functional group distribution maps were denoised. The specific steps were energy normalization, baseline removal, and smoothing. Energy normalization refers to dividing all spectra by the background spectrum of the gold film collected in the same batch. Baseline removal was performed using Origin software. A suitable baseline mode was selected according to different curve shapes, and the curves were automatically smoothed using Analysis Studio software. Origin software was used to smooth the 800-1800 cm⁻¹ curves. -1 Gaussian-Lorentz mixture fitting is performed on the interval, and the parameters are fitted until convergence through a least squares iterative process (maximum number of iterations 500, tolerance 1e-15) to determine the spectral parameters.

[0069] (9) The degree of aromatization and hydrocarbon generation potential of different micro-components are determined by the absorption peak intensities of aliphatic, C=O, and C=C. Surface scans are used to observe the heterogeneity differences between the same micro-component and adjacent micro-components, as well as to characterize the chemical structure of the same micro-component.

[0070] (10) Calculate factor A and factor C respectively to quantify the abundance of aliphatic and carbonyl / carboxyl groups, where factor A is... I 1450cm -1 / ( I 1450cm -1 + I 1600cm -1 The C factor is I 1720 cm-1 / (I1720cm -1 +I1600cm -1 A pseudo-van-Creyville plot was created using factor A as the horizontal axis and factor C as the vertical axis. (See...) Figure 4 This is used to explain the chemical structural characteristics of different microscopic components. From Figure 4 It can be seen that algae have the highest A and C factors among all microscopic components. Compared with algae, solid bitumen shows a more significant decrease in C factor and a slight decrease in A factor, indicating that solid bitumen still has the ability to generate hydrocarbons, which is consistent with the characteristic of hydrocarbon-generating parent material first losing C=O functional groups during evolution. The content of A and C factors in inertines is not high, indicating that inertines have a higher degree of aromatization and poor hydrocarbon generation potential. The difference in hydrogen and oxygen content between algae and solid bitumen is relatively large, while the difference in hydrogen and oxygen content between inertines is not significant. This is because inertines are basically unaffected by thermal evolution, and their internal functional groups have hardly undergone strong changes.

[0071] Figure 3 This involves analyzing the microscopic components of shale using white light, fluorescence, surface morphology images, and AFM-IR spectra, as well as the distribution of functional groups. Figure 3 It can be seen that the inert group appears bright white under white light. It exhibits no fluorescence at 1600 cm⁻¹. -1 The signal is strong and very uniform; the algae appear brownish-brown under white light and fluoresce orange at 1450 cm⁻¹. -1 and 1600 cm -1 1450 cm -1 The distribution is very uniform; solid asphalt appears dark brown to black under white light and dark brown to black under fluorescence, at 1450 cm. -1 The signal is strong and relatively uniform.

[0072] Although the above embodiments have provided a detailed description of the present invention, they are only some embodiments of the present invention, not all embodiments. Other embodiments can be obtained based on these embodiments without creative intent, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for determining the type and molecular structure of shale microcomponents based on optical microscope-AFM-IR, comprising the following steps: (1) identifying each microcomponent: identifying the microcomponents in the organic-rich shale sample by optical microscope, selecting a region rich in microcomponent types for local stitching scanning, obtaining the white light view stitching distribution map of the whole rock optical microcomponent and the fluorescence view stitching distribution map of the whole rock optical microcomponent; the number of microcomponent types in the region rich in microcomponent types is ≥3; labeling and locating each microcomponent: numbering the white light view stitching distribution map of the whole rock optical microcomponent and the fluorescence view stitching distribution map of the whole rock optical microcomponent, recording the serial number and position of the whole rock optical microcomponent white light view stitching distribution map and the whole rock optical microcomponent fluorescence view stitching distribution map where the typical microcomponent is located, and labeling the reference points on the edge of each typical microcomponent; (2) after finding the located typical microcomponent under the objective lens of AFM-IR, scanning each located typical microcomponent in tapping mode by AFM-IR, obtaining the surface topography map of each typical microcomponent, and secondarily confirming the labeled typical microcomponent according to the surface topography map; (3) scanning the surface topography map in step (2) in contact mode to obtain the infrared spectrum and chemical functional group distribution of each typical microcomponent confirmed secondarily; (4) preprocessing the infrared spectrum and chemical functional group distribution, and obtaining the molecular structure characteristics of the typical microcomponent in the shale sample according to the preprocessed infrared spectrum and chemical functional group distribution; the preprocessing is denoising and color scale intensity adjustment of the infrared spectrum and chemical functional group distribution, respectively.

2. The method of claim 1, wherein, The typical microcomponent includes primary microcomponent and solid bitumen; the primary microcomponent includes one or more of sapropel group, chitinous group, vitrinite group and inertinite group, and the sapropel group includes algal body; the algal body includes structural algal body and lamellar algal body; the solid bitumen includes one or more of oil pre-bitumen, oil post-bitumen and coke bitumen.

3. The method of claim 1, wherein, The denoising includes background energy normalization and visual data processing in sequence; the visual data processing includes curve smoothing, baseline correction and Gaussian-Lorentz function peak fitting in sequence; the curve smoothing and baseline correction are completed by Analysis Studio software and Origin software, respectively; the color scale intensity adjustment is adjusting the color scale intensity of the chemical functional group distribution by Analysis Studio software.

4. The method of claim 1, wherein, The scanning mode of the contact mode is a combination of point scanning and area scanning; the power of the infrared laser is 80-100 mW during scanning; the wave band of the point scanning and area scanning in the contact mode is 800-1800 cm -1 .

5. The method of claim 4, wherein, During the point scanning, a single point spectrum is collected for each located and labeled microcomponent, and multiple spectra are collected for each sample point. The scanning range during the area scanning is (20-50) μm×(20-50) μm; the step length is 20 nm; and the image resolution is 512×512 pixels.

6. The method of claim 1, wherein, In the contact mode, before scanning, also includes the 1450 cm -1 , 1600 cm -1 , 1710 cm -1 Waveband respectively with shale sample contact resonance of infrared spectrum incident frequency to the resonance.

7. The method of claim 1, wherein, The scanning rate in tapping mode is 0.5-2 Hz, and the resolution is 512×512 pixels.

8. The method of claim 1, wherein, The optical microscope is a polarized microscope equipped with a fluorescence module, and the identification of the microcomponents in the organic-rich shale sample by the optical microscope is performed in white light and fluorescence modes. When observed in the fluorescence mode, the excitation light wavelength is 365 nm, and the emission light wavelength is 420-700 nm.

9. The method of claim 1, wherein, Before the identification of the microcomponents in the organic-rich shale sample by the optical microscope, the shale sample is subjected to polishing treatment and epoxy resin embedding and curing.

10. The method of claim 1, wherein, After the molecular structure characteristics of the typical microcomponents in the shale sample are obtained, the A factor and the C factor of each typical microcomponent are calculated by using the infrared spectrum, the A factor is the ratio of the spectral absorption peak intensity of lipids to that of aromatic ring functional groups, and the C factor is the ratio of the spectral absorption peak intensity of oxygen-containing functional groups to that of aromatic functional groups. The molecular structure differences and the heterogeneity of different microcomponents in the organic-rich shale are analyzed by the A factor and the C factor.

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