Shale bitumen maturity analysis methods, systems, devices, media, and procedures
By conducting Raman spectral analysis on bitumen and graptol components in marine shale, a relationship model was established, which solved the accuracy problem of maturity evaluation of marine shale and enabled accurate determination of the over-maturity stage.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-27
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, there is a lack of effective quantitative indicators for evaluating the maturity of marine shale. Especially in the high-maturity stage, the asphalt reflectivity is greatly affected by its formation and optical properties, resulting in low analytical accuracy.
By performing Raman spectroscopy analysis on organic micro-components such as bitumen and graptol in marine shale, a graptol-bitumen relationship model was established, the maturity model was updated, and the bitumen maturity of marine shale with higher maturity was accurately determined.
It improves the accuracy of marine shale bitumen maturity analysis and provides reliable oil and gas resource evaluation data.
Smart Images
Figure CN122109169A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of oil and gas exploration technology, and in particular to a method, system, equipment, medium and procedure for analyzing the maturity of shale bitumen. Background Technology
[0002] Maturity is a crucial indicator for evaluating the resource potential of shale reservoirs and for making development decisions. Current technologies primarily use indicators for maturity measurement such as vitrinite and graptol reflectance, Tmax, the H / CO / C elemental ratio of kerogen, and biomarker compounds. While each indicator has its own applicability, they also have limitations. Currently, the most common indicator reflecting organic matter maturity is vitrinite reflectance (vRo), which serves as a benchmark for classifying the stages of organic matter generation. However, this parameter has significant limitations in its acquisition and effectiveness. Because vitrinite is generally lacking in marine sedimentary strata, vitrinite reflectance cannot be effectively measured, thus preventing direct quantitative characterization of marine shale maturity. In marine shale lacking vitrinite, the equivalent vitrinite reflectance converted from bitumen reflectance is typically used for maturity evaluation. However, asphalt reflectivity is not only related to maturity, but also greatly affected by the origin and optical properties of asphalt. This results in significant variations in asphalt reflectivity at very high maturity levels, which cannot meet the characterization requirements for the maturity of marine shale at higher maturity levels. This may lead to lower accuracy in maturity analysis of shale asphalt. Summary of the Invention
[0003] One objective of this invention is to provide a method for determining the maturity of shale bitumen. This method, through the Raman parameter relationships of different organic microscopic components such as bitumen and graptol in marine shale, more accurately determines the maturity of bitumen in marine shale with high maturity, thereby providing reliable and accurate data parameters for oil and gas resource evaluation.
[0004] In a first aspect, this disclosure provides a method for analyzing the maturity of shale bitumen, comprising: cutting and polishing a pre-screened shale sample set to obtain a polished sample set; performing electron microscopy scanning and sample component analysis on the polished sample set to obtain a sample organic type set; performing Raman spectroscopy measurements on the polished sample set based on the sample organic type set to obtain a sample Raman spectrum set; extracting a sample Raman parameter set from the sample Raman spectrum set and generating a graptol-bitumen relationship model based on the sample Raman parameter set; updating a preset graptolen maturity model based on the graptol-bitumen relationship model to obtain an bitumen maturity model; and performing maturity analysis on a preset shale bitumen sample based on the bitumen maturity model to obtain the bitumen maturity.
[0005] In some embodiments, the step of cutting and polishing a pre-screened shale sample set to obtain a polished sample set includes: cutting the pre-screened shale sample set to obtain a cut sample set; performing a surface grinding operation on the cut sample set to obtain a ground sample set; and performing argon ion polishing on the ground sample set to obtain a polished sample set.
[0006] In some embodiments, the step of performing electron microscopy scanning and sample component analysis on the polished sample set to obtain a sample organic type set includes: performing electron microscopy scanning on the polished sample set to obtain a sample electron microscopy image set; locating organic matter particles in the sample electron microscopy image set to obtain a sample organic matter electron microscopy image set; extracting an organic matter feature set from the sample organic matter electron microscopy image set; and performing organic matter component analysis on the organic matter feature set to obtain a sample organic type set.
[0007] In some embodiments, extracting the sample Raman parameter set from the sample Raman spectrum set includes: performing background separation on the sample Raman spectrum set to obtain a denoised Raman spectrum set; selecting denoised Raman spectral images from the denoised Raman spectral set one by one as target denoised spectral images, performing defect peak fitting on the target denoised spectral images to obtain defect peak intensities; performing graphite peak fitting on the target denoised spectral images to obtain graphite peak intensities; performing a ratio calculation on the defect peak intensities and the graphite peak intensities to obtain target sample Raman parameters; and compiling the target sample Raman parameters of each denoised Raman spectral image in the denoised Raman spectral set into a sample Raman parameter set.
[0008] In some embodiments, generating a graptol-asphalt relationship model based on the sample Raman parameter set includes: extracting a sample organic type set corresponding to the sample Raman parameter set; extracting graptol type and asphalt type from the sample organic type set; establishing a Raman parameter relationship coordinate system based on the asphalt type and the graptol type; performing parameter positioning on the Raman parameter relationship coordinate system based on the sample Raman parameter set to obtain a Raman parameter coordinate point set; and performing function fitting on the Raman parameter coordinate point set to obtain the graptol-asphalt relationship model.
[0009] In some embodiments, the step of performing maturity analysis on a preset shale asphalt sample according to the asphalt maturity model to obtain asphalt maturity includes: cutting and polishing the preset shale asphalt sample to obtain a polished asphalt sample; performing Raman spectroscopy on the polished asphalt sample to obtain an asphalt Raman spectral image; extracting asphalt Raman parameters from the asphalt Raman spectral image; and calculating the maturity of the asphalt Raman parameters according to the asphalt maturity model to obtain the asphalt maturity.
[0010] Secondly, this disclosure provides a shale asphalt maturity analysis system, comprising: a sample polishing module for cutting and polishing a pre-screened shale sample set to obtain a polished sample set; an electron microscopy scanning module for scanning the polished sample set with an electron microscope and analyzing its components to obtain a sample organic type set; a spectral measurement module for performing Raman spectral measurements on the polished sample set based on the sample organic type set to obtain a sample Raman spectral atlas; a relationship calculation module for extracting a sample Raman parameter set from the sample Raman spectral atlas and generating a graptol-asphalt relationship model based on the sample Raman parameter set; and a maturity analysis module for updating a preset graptol maturity model based on the graptol-asphalt relationship model to obtain an asphalt maturity model, and performing maturity analysis on preset shale asphalt samples based on the asphalt maturity model to obtain the asphalt maturity.
[0011] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the shale bitumen maturity analysis method described in the above aspects.
[0012] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the shale bitumen maturity analysis method described above.
[0013] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the shale bitumen maturity analysis method described above.
[0014] This disclosure provides a shale bitumen maturity analysis method, system, equipment, medium, and program. By performing noise reduction filtering and image standardization operations, it can reduce noise in insulator images and standardize the images to a preset input standard, facilitating subsequent defect identification operations through the model. By performing multi-channel convolution and residual connection, it can flexibly capture the features of insulator images of different sizes and channels, and maintain information flow through residual connection, thereby improving the detail of image features.
[0015] By performing global attention encoding on the insulator image feature set, global feature modeling can be achieved, capturing the relationship between each position in the image and global information, resulting in image features with higher robustness. Furthermore, the steps of self-attention computation are reduced, improving the efficiency of feature extraction. By performing channel fusion and spatial fusion, long-distance dependencies in image features can be captured, thereby enhancing the feature details and global information of image features and improving the accuracy of subsequent defect detection.
[0016] By performing layer-by-layer feature decoding, defect features can be upsampled layer by layer, and feature fusion can be performed by combining attention mechanism to improve the accuracy of defect detection, thereby improving the efficiency of safety status detection. Attached Figure Description
[0017] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:
[0018] Figure 1 A flowchart illustrating the shale bitumen maturity analysis method according to Embodiment 1 of this disclosure is shown.
[0019] Figure 2 An electron microscope image of the graptolite in region F1 of Embodiment 1 of this disclosure is shown.
[0020] Figure 3 An electron microscope image of the graptolite in the MY1 region of Embodiment 1 of this disclosure is shown.
[0021] Figure 4 An electron microscope image of the graptolite in region JY8 of Embodiment 1 of this disclosure is shown.
[0022] Figure 5 An electron microscope image of the asphalt in region F1 of Embodiment 1 of this disclosure is shown.
[0023] Figure 6 An electron microscope image of the asphalt in the MY1 region of Embodiment 1 of this disclosure is shown.
[0024] Figure 7 The image shows an electron microscope (EM) image of the asphalt in region JY8 in Embodiment 1 of this disclosure.
[0025] Figure 8 The Raman spectrum of graptolite in region F1 of Embodiment 1 of this disclosure is shown.
[0026] Figure 9 The Raman spectrum of graptolite in the MY1 region of Embodiment 1 of this disclosure is shown.
[0027] Figure 10 The Raman spectrum of graptolite in region JY8 of Embodiment 1 of this disclosure is shown.
[0028] Figure 11 The Raman spectrum of the asphalt in region F1 of Embodiment 1 of this disclosure is shown.
[0029] Figure 12 The Raman spectrum of the bitumen in region MY1 in Embodiment 1 of this disclosure is shown.
[0030] Figure 13 The Raman spectrum of the asphalt in region JY8 of Embodiment 1 of this disclosure is shown.
[0031] Figure 14 The diagram shows a function representation of the graptol-asphalt relationship model in Embodiment 1 of this disclosure.
[0032] Figure 15 The diagram shows the functional modules of the shale bitumen maturity analysis system according to Embodiment 2 of this disclosure.
[0033] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation
[0034] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.
[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0036] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0037] Example 1
[0038] Figure 1 This is a schematic flowchart illustrating a shale bitumen maturity analysis method provided in an embodiment of this disclosure. Figure 1As shown, a method for analyzing the maturity of shale bitumen includes:
[0039] S1. The pre-screened shale sample set is cut and polished to obtain a polished sample set.
[0040] In detail, the shale samples in the shale sample collection are highly overmature marine shale samples from multiple different regions. Marine shale is a type of shale formed in ancient marine sedimentary environments, rich in organic matter, and usually accompanied by fine-grained sediments such as silt and clay. Highly overmature marine shale samples refer to shale formed from sedimentary organic matter that has undergone extremely high thermal evolution. They mainly refer to shale with a marine sedimentary environment as the background. Due to long-term geological thermal history, the organic matter inside has reached an overmature stage, that is, it exceeds the thermal evolution window for the formation of conventional oil and gas. The high overmaturity refers to the vitrinite reflectance Ro of the organic matter being greater than 1.2%.
[0041] In this embodiment of the invention, the step of cutting and polishing a pre-screened shale sample set to obtain a polished sample set includes: cutting the pre-screened shale sample set to obtain a cut sample set; performing a surface grinding operation on the cut sample set to obtain a ground sample set; and performing argon ion polishing on the ground sample set to obtain a polished sample set.
[0042] Specifically, the shale samples in the shale sample collection can be black shale samples from the Longmaxi Formation in the Sichuan Basin, namely, high-maturity shale samples rich in graptol and bitumen from three regions in the Sichuan Basin. The sample cutting refers to cutting each shale sample in the shale sample collection into blocks, which can be done using methods such as high-pressure water jet cutting, mechanical saw cutting, or diamond wire saw cutting.
[0043] Specifically, the surface grinding refers to the grinding operation on the cutting surfaces of each cutting sample in the cutting sample set. The surface grinding operation can be performed by methods such as sandpaper grinding, diamond wheel grinding, and fine grinding with a polishing machine.
[0044] In detail, argon ion polishing is a technique that uses a high-energy argon ion beam to finely process the surface of materials. It is mainly used to prepare high-quality sample surfaces for subsequent microscopic analysis, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM). Argon ion polishing can remove rough layers from sample surfaces with nanometer-level precision, obtaining smooth, flat surfaces free from mechanical damage. This facilitates the effective differentiation of the microscopic components of organic matter in shale.
[0045] In this embodiment of the invention, by cutting and polishing a pre-screened shale sample set, samples of specific shapes and sizes can be obtained, exposing the target area of interest while reducing surface roughness and improving the accuracy of subsequent electron microscopy and Raman spectroscopy measurements.
[0046] S2. Perform electron microscopy scanning and sample component analysis on the polished sample set to obtain the sample organic type set.
[0047] In detail, in order to effectively distinguish the microscopic component types of organic matter in shale, it is necessary to observe the morphological characteristics of different organic matter particles in shale using an electron microscope to identify the microscopic component types of the organic matter particles.
[0048] In this embodiment of the invention, the step of performing electron microscopy scanning and sample component analysis on the polished sample set to obtain a sample organic type set includes: performing electron microscopy scanning on the polished sample set to obtain a sample electron microscopy image set; locating organic matter particles in the sample electron microscopy image set to obtain a sample organic matter electron microscopy image set; extracting an organic matter feature set from the sample organic matter electron microscopy image set; and performing organic matter component analysis on the organic matter feature set to obtain a sample organic type set.
[0049] For details, refer to Figures 2 to 7 The image shown is an electron microscope image of each sample in the sample electron microscope image set. Organic matter particles can be located using YOLO network, U-Net network or Mask R-CNN network, and organic matter feature sets can be extracted using networks such as ResNet, DenseNet or Swin Transformer.
[0050] Specifically, the organic matter component analysis refers to identifying the type of organic matter corresponding to the electron micrograph of the sample's organic matter based on the organic matter feature set. Each sample organic matter type in the sample organic matter type set includes information about the region where the sample is located and the corresponding type of organic matter, for example:
[0051] Figure 2 The organic type of the sample is graptol from the F1 region. Figure 3 The organic type of the sample is graptol from the MY1 region. Figure 4 The organic type of the sample is graptol from the JY8 region. Figure 5 The organic type of the sample was asphalt from region F1. Figure 6 The organic type of the sample is asphalt from region MY1. Figure 7 The organic type of the sample is asphalt from region JY8.
[0052] In this embodiment of the invention, by performing electron microscopy scanning and sample component analysis on the polished sample set, it is possible to help determine the type of organic matter in each polished sample in the polished sample set, thereby facilitating the subsequent identification of Raman spectra of different organic matter from various regions.
[0053] S3. Raman spectroscopy measurements are performed on the polished sample set according to the organic type set of the samples to obtain a sample Raman spectrum set.
[0054] In detail, Raman spectroscopy is an analytical technique based on molecular scattering spectroscopy used to study the vibrational, rotational, and other low-frequency modes of molecules in matter. By detecting the frequency difference between incident and scattered light, known as the Raman shift, Raman spectroscopy provides unique information about the molecular structure and chemical composition of a substance.
[0055] Specifically, the Raman shift refers to the interaction between monochromatic light and the molecules of a sample. Most of this interaction is elastic scattering, meaning the scattered light has the same frequency as the incident light. However, a small portion of photons undergo inelastic scattering due to energy changes caused by molecular vibrations, rotations, or electronic transitions. This type of scattering is called Raman scattering, and the frequency of the Raman scattered light will shift relative to the incident light. This frequency shift is called the Raman shift.
[0056] In this embodiment of the invention, the step of performing Raman spectroscopy measurements on the polished sample set according to the sample organic type set to obtain a sample Raman spectrum atlas refers to performing Raman spectroscopy measurements on a portion of the corresponding polished sample set according to each sample organic type in the sample organic type set to obtain the corresponding sample Raman spectrum image, and then compiling all the sample Raman spectrum images into a sample Raman spectrum atlas.
[0057] Specifically, when performing Raman spectroscopy measurements, a 532nm laser, a 100× objective lens, a laser power of 8mW, a spatial resolution of 360nm, a spectral resolution of 1cm⁻¹, and a scanning range of 95–4000cm⁻¹ are used. The Raman spectral parameters are calculated by the spectral analysis software provided with the instrument.
[0058] For details, refer to Figures 8 to 13 The image shown is a set of Raman spectra corresponding to the organic type set of the samples, where:
[0059] Figure 8 Raman spectra of samples with organic type F1 graptolite. Figure 9 Raman spectra of samples containing graptolite from the MY1 region, of organic type. Figure 10 Raman spectra of samples containing graptolite from the JY8 region, of organic type. Figure 11 Raman spectra of asphalt samples with organic type F1. Figure 12 Raman spectra of asphalt samples with organic type MY1 region. Figure 13 Raman spectral image of asphalt sample with organic type JY8 region.
[0060] In this embodiment of the invention, by performing Raman spectroscopy measurements on the polished sample set according to the sample organic type set, a sample Raman spectrum set is obtained, which can clearly analyze the microstructure and chemical composition of the sample. The maturity of asphalt can be quantitatively evaluated through the characteristics of Raman spectroscopy, thereby improving the accuracy of asphalt maturity analysis.
[0061] S4. Extract the sample Raman parameter set from the sample Raman spectrum set, and generate a graptolite-asphalt relationship model based on the sample Raman parameter set.
[0062] In detail, the Raman parameters of each sample in the sample Raman parameter set correspond to the Raman parameters of each sample Raman spectrum image in the sample Raman spectrum image set. The Raman parameter refers to the ratio of the intensity of the defect peak (D peak) to the intensity of the graphite peak (G peak) in the spectrum.
[0063] Specifically, the D peak is caused by scattering due to defects in the material, such as boundaries, incomplete crystal structures, or impurities. The D peak is a type of indirect Raman scattering, which requires the interaction of electrons and phonons at the defect location; the G peak and sp 2 The plane vibration correlation of hybrid carbon atoms is the Raman activity caused by the plane vibration of carbon atoms in the graphite structure.
[0064] In this embodiment of the invention, extracting the sample Raman parameter set from the sample Raman spectrum set includes: performing background separation on the sample Raman spectrum set to obtain a denoised Raman spectrum set; selecting each denoised Raman spectrum image in the denoised Raman spectrum set as a target denoised spectrum image, performing defect peak fitting on the target denoised spectrum image to obtain the defect peak intensity; performing graphite peak fitting on the target denoised spectrum image to obtain the graphite peak intensity; performing a ratio calculation on the defect peak intensity and the graphite peak intensity to obtain the target sample Raman parameters; and compiling the target sample Raman parameters of each denoised Raman spectrum image in the denoised Raman spectrum set into a sample Raman parameter set.
[0065] Specifically, background separation operations can be performed using methods such as polynomial baseline correction, Gaussian fitting, and wavelet transform, using libraries like MATLAB's scipy and numpy in Python. Defect peak fitting and graphite peak fitting can be performed using the Lorentzian function combined with nonlinear least squares.
[0066] In detail, the ratio calculation refers to calculating the ratio of the defect peak intensity to the graphite peak intensity, and using the corresponding ratio as the Raman parameter of the target sample.
[0067] Specifically, generating the graptol-asphalt relationship model based on the sample Raman parameter set includes: extracting the sample organic type set corresponding to the sample Raman parameter set; extracting the graptol type and asphalt type from the sample organic type set; establishing a Raman parameter relationship coordinate system based on the asphalt type and the graptol type; performing parameter positioning on the Raman parameter relationship coordinate system based on the sample Raman parameter set to obtain a Raman parameter coordinate point set; and performing function fitting on the Raman parameter coordinate point set to obtain the graptol-asphalt relationship model.
[0068] Specifically, the graptolite type refers to the sample organic type related to graptolite in the sample organic type set, such as graptolite in region F1, graptolite in region MY1, and graptolite in region JY8. The bitumen type refers to the sample organic type related to bitumen in the sample organic type set, such as bitumen in region F1, bitumen in region MY1, and bitumen in region JY8.
[0069] Specifically, establishing a Raman parameter relationship coordinate system based on the asphalt type and the graptol type refers to establishing a Raman parameter relationship coordinate system with the asphalt Raman parameter of the asphalt type as the abscissa and the graptol Raman parameter of the graptol type as the ordinate.
[0070] In detail, function fitting can be performed using methods such as least squares, weighted least squares, or regularized least squares, as described above. Figure 14 The figure shown is the graptolite-asphalt relationship model in the Raman parameter coordinate point set.
[0071] In this embodiment of the invention, by extracting the sample Raman parameter set from the sample Raman spectrum set and generating a graptol-asphalt relationship model based on the sample Raman parameter set, the relationship between the Raman parameters of organic matter in different regions can be obtained, thereby improving the accuracy of subsequent Raman maturity analysis.
[0072] S5. Update the preset graptol maturity model according to the graptol-asphalt relationship model to obtain the asphalt maturity model, and perform maturity analysis on the preset shale asphalt sample according to the asphalt maturity model to obtain the asphalt maturity.
[0073] Specifically, the graptolite maturity model refers to an empirical formula for calculating the Raman maturity of graptolite, such as graptolite maturity equals 1.67 multiplied by the graptolite Raman parameter, and then adding 2.58 to the product. The shale bitumen sample refers to a shale sample that needs to be analyzed for bitumen maturity.
[0074] In detail, updating the preset graptol maturity model based on the graptol-asphalt relationship model to obtain the asphalt maturity model means substituting the graptol-asphalt relationship model into the preset graptol maturity model to obtain the asphalt maturity model. For example, in the graptol-asphalt relationship model, the graptol Raman parameter is equal to the asphalt Raman parameter multiplied by 1.6982, and the product is subtracted by 1.0086. In the graptol maturity model, the graptol maturity is equal to 1.67 multiplied by the graptol Raman parameter, and the product is added by 2.58. Therefore, the final asphalt maturity model is asphalt maturity equal to 2.83 multiplied by the graptol Raman parameter, and the product is added by 0.89.
[0075] Specifically, the step of performing maturity analysis on a preset shale asphalt sample according to the asphalt maturity model to obtain the asphalt maturity includes: cutting and polishing the preset shale asphalt sample to obtain a polished asphalt sample; performing Raman spectroscopy on the polished asphalt sample to obtain an asphalt Raman spectral image; extracting asphalt Raman parameters from the asphalt Raman spectral image; and calculating the maturity of the asphalt Raman parameters according to the asphalt maturity model to obtain the asphalt maturity.
[0076] In detail, the maturity calculation refers to substituting the asphalt Raman parameters into the asphalt maturity model to obtain the final asphalt maturity.
[0077] In this embodiment of the invention, the maturity of asphalt is obtained by performing maturity analysis on a preset shale asphalt sample according to the asphalt maturity model. There are no requirements for the size and shape of the asphalt, which has a wider range of applications. Moreover, based on the Raman parameter relationship between asphalt and graptol in marine shale, the maturity of marine shale asphalt with higher maturity can be determined more accurately, thereby improving the accuracy of asphalt maturity analysis.
[0078] Example 2
[0079] Based on the above embodiments, Figure 15 This is a functional block diagram of a shale bitumen maturity analysis system provided in an embodiment of this disclosure. Figure 15 As shown, a shale bitumen maturity analysis system includes:
[0080] The shale bitumen maturity analysis system 500 described in this embodiment can be installed in an electronic device. Depending on the functions implemented, the shale bitumen maturity analysis system 500 may include a sample polishing module 501, an electron microscopy scanning module 502, a spectral measurement module 503, a relationship calculation module 504, and a maturity analysis module 505. The module described in this disclosure can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.
[0081] In this embodiment, the functions of each module / unit are as follows:
[0082] The sample polishing module 501 is used to cut and polish the pre-screened shale sample set to obtain a polished sample set.
[0083] The electron microscopy scanning module 502 is used to perform electron microscopy scanning and sample component analysis on the polished sample set to obtain a sample organic type set.
[0084] The spectral measurement module 503 is used to perform Raman spectral measurements on the polished sample set according to the sample organic type set, and obtain a sample Raman spectrum set.
[0085] The relationship calculation module 504 is used to extract the sample Raman parameter set from the sample Raman spectrum set and generate a graptol asphalt relationship model based on the sample Raman parameter set.
[0086] The maturity analysis module 505 is used to update the preset graptol maturity model according to the graptol-asphalt relationship model to obtain the asphalt maturity model, and to perform maturity analysis on the preset shale asphalt sample according to the asphalt maturity model to obtain the asphalt maturity.
[0087] In detail, each module in the shale bitumen maturity analysis system 500 described in this embodiment of the present disclosure uses the same technical means as the shale bitumen maturity analysis method described in Embodiment 1, and can produce the same technical effect, which will not be repeated here.
[0088] Example 3
[0089] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.
[0090] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.
[0091] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.
[0092] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.
[0093] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).
[0094] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.
[0095] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).
[0096] The processor can communicate with external devices via the I / O bus through wired or wireless networks.
[0097] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.
[0098] In the embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0099] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0100] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.
Claims
1. A method for analyzing the maturity of shale bitumen, characterized in that, include: The pre-screened shale sample set was cut and polished to obtain a polished sample set; Electron microscopy and sample composition analysis were performed on the polished sample set to obtain a sample organic type set; Raman spectroscopy measurements were performed on the polished sample set based on the organic type set of the samples to obtain a sample Raman spectrum set. Extract the sample Raman parameter set from the sample Raman spectrum set, and generate a graptol-asphalt relationship model based on the sample Raman parameter set; The preset graptol maturity model is updated based on the graptol-asphalt relationship model to obtain the asphalt maturity model. The maturity of the preset shale asphalt sample is then analyzed based on the asphalt maturity model to obtain the asphalt maturity.
2. The method for analyzing the maturity of shale bitumen according to claim 1, characterized in that, The process of cutting and polishing pre-screened shale sample sets to obtain polished sample sets includes: The pre-screened shale sample set was cut to obtain the cut sample set; The cut sample set is then subjected to a surface grinding operation to obtain a ground sample set; The ground sample set was subjected to argon ion polishing to obtain a polished sample set.
3. The method for analyzing the maturity of shale bitumen according to claim 1, characterized in that, The process of performing electron microscopy scanning and sample component analysis on the polished sample set yields a sample organic type set, including: The polished sample set was scanned by electron microscopy to obtain a sample electron micrograph set; Organic particles were located in the electron microscopy image set of the sample to obtain an electron microscopy image set of organic matter in the sample; Organic matter feature set was extracted from the electron microscopy images of the organic matter in the sample; Organic matter component analysis was performed on the organic matter feature set to obtain the sample organic type set.
4. The shale bitumen maturity analysis method according to claim 1, characterized in that, The step of extracting the sample Raman parameter set from the sample Raman spectrum set includes: Background separation was performed on the Raman spectra of the samples to obtain a noise-reduced Raman spectra. Each denoised Raman spectral image in the denoised Raman spectral image set is selected as the target denoised spectral image, and the defect peak is fitted to the target denoised spectral image to obtain the defect peak intensity. The graphite peak intensity is obtained by fitting the denoised spectral image of the target. The Raman parameters of the target sample are obtained by calculating the ratio between the intensity of the defect peak and the intensity of the graphite peak. The target sample Raman parameters of each denoised Raman spectral image in the denoised Raman spectral set are compiled into a sample Raman parameter set.
5. The method for analyzing the maturity of shale bitumen according to claim 1, characterized in that, The step of generating a graptolite-asphalt relationship model based on the sample Raman parameter set includes: Extract the sample organic type set corresponding to the sample Raman parameter set; Graptolite and bitumen types were extracted from the organic type set of the samples; Establish a Raman parameter relationship coordinate system based on the asphalt type and the graptol type; Based on the sample Raman parameter set, the Raman parameter relationship coordinate system is used for parameter positioning to obtain the Raman parameter coordinate point set; By performing function fitting on the set of Raman parameter coordinate points, a graptolite-asphalt relationship model is obtained.
6. The method for analyzing the maturity of shale bitumen according to claim 1, characterized in that, The step of performing maturity analysis on a preset shale asphalt sample based on the asphalt maturity model to obtain the asphalt maturity includes: The pre-designed shale asphalt sample is cut and polished to obtain a polished asphalt sample; Raman spectroscopy was performed on the polished asphalt sample to obtain the asphalt Raman spectral image; Extract the pitch Raman parameters from the pitch Raman spectrum image; The maturity of the asphalt is obtained by calculating the maturity of the asphalt Raman parameters based on the asphalt maturity model.
7. A shale bitumen maturity analysis system, characterized in that, include: The sample polishing module is used to cut and polish the pre-screened shale sample set to obtain a polished sample set. An electron microscopy scanning module is used to perform electron microscopy scanning and sample component analysis on the polished sample set to obtain a sample organic type set. The spectral measurement module is used to perform Raman spectral measurements on the polished sample set according to the sample organic type set, and obtain a sample Raman spectrum set. The relationship calculation module is used to extract the sample Raman parameter set from the sample Raman spectrum set and generate a graptol asphalt relationship model based on the sample Raman parameter set. The maturity analysis module is used to update the preset graptol maturity model according to the graptol-asphalt relationship model to obtain the asphalt maturity model, and to perform maturity analysis on the preset shale asphalt samples according to the asphalt maturity model to obtain the asphalt maturity.
8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the shale bitumen maturity analysis method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the shale bitumen maturity analysis method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the shale bitumen maturity analysis method according to any one of claims 1 to 6.