A method and system for analyzing and modeling the pore surface roughness of shale
Through scanning electron microscopy and image processing technology, a pore roughness analysis of mud shale samples was established to establish a molecular model that was in line with reality, solving the limitations of pore surface roughness data and the defects of nanoscale micropore structure modeling in the existing technology, and achieving high-precision pore structure analysis and model establishment.
Patent Information
- Application Number
- CN202310040752.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-01-13
- Publication Date
- 2025-05-27
- Estimated Expiration
- 2043-01-13
AI Technical Summary
The prior art is difficult to fully reflect the pore structure information of mud shale, especially the limitations of pore surface roughness data, and the modeling technology of nanoscale micropore structures lacks a molecular model that suits reality.
Through scanning electron microscopy, mud shale samples are collected and processed at high resolution, combined with image processing software (such as ImageJ) for pore roughness analysis, and a multi-porous mud shale nanoscale molecular pore structure model with roughness characteristics is established.
High-precision roughness analysis and pore structure identification of nano-micro-scale organic matter and clay mineral pores were achieved. The established molecular model can truly reflect the pore structure of mud shale internally, providing a reliable model for subsequent scientific analysis.
Smart Images
Figure CN116245819B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of mudstone pore structure characterization parameter evaluation, and in particular to a mudstone pore surface roughness analysis, modeling method and system. Background Art
[0002] The pore structure of mudstone is highly heterogeneous, and the roughness of the pore surface varies significantly. The large number of nano-scale to micro-scale pore structures in organic matter and clay minerals provide the main storage space for methane; methane mainly accumulates on the surface of the pore structure of mudstone in an adsorbed state and flows in the pore space in a free state. The methane gas content in the two states together constitutes the most important shale gas in the reservoir. Due to the strong heterogeneity of the pore structure of mudstone, the adsorption and transmission process of methane molecules in the pore structure and on its surface is also extremely complex. At the same time, due to the different degrees of interaction between methane molecules and the pore surface, the transmission process and action mechanism of methane gas in the pores of mudstone are also changing rapidly.
[0003] There are many analytical methods and technologies for the pore surface structure of mudstone in the existing technology, which can be mainly divided into three categories: (1) Image analysis technology: mainly using optical, electronic signal, atomic signal and other technologies to observe and analyze the surface of the mudstone pore structure, and the pore structure characteristics obtained are mainly qualitative and semi-quantitative results; (2) Fluid injection technology: mainly through the injection of signal response fluid media, such as mercury, nitrogen, carbon dioxide gas, etc. to obtain structural information such as porosity and surface area. The results obtained from different experiments have a limited scale range and cannot obtain information about closed pore space; (3) Physical signal detection technology: The pore structure is characterized by technical means such as magnetic field, radio frequency field, X-ray and other physical signal detection imaging, mainly obtaining micron-millimeter pore structure information in small-sized samples, and the testing cost is relatively expensive.
[0004] At present, the research on the pore surface roughness of mudstone is generally lacking. Some scholars use shale samples after argon ion polishing, extract only the information of kerogen organic pores in the mudstone under a microscope, perform double integration to obtain the surface area to calculate the roughness, and the obtained roughness data is relatively limited, and the complete pore roughness characteristics cannot be obtained, so it is difficult to fully reflect the pore structure information of mudstone. In addition, the current modeling technology for the nano-scale microscopic pore structure of mudstone is generally in an idealized model, and lacks a molecular model that can reflect the pore surface roughness of actual mudstone samples. Therefore, the present invention proposes a mudstone pore surface roughness analysis, modeling method and system to solve the problems existing in the prior art. Summary of the invention
[0005] In view of the above problems, the purpose of the present invention is to propose a shale pore surface roughness analysis, modeling method and system to solve the problems that the roughness data obtained from the current research on the shale pore surface roughness is relatively limited and the complete pore roughness characteristics cannot be obtained, so it is difficult to fully reflect the shale pore structure information, and the current modeling technology for the nano-scale microscopic pore structure of shale lacks a molecular model that can reflect the pore surface roughness of actual shale samples.
[0006] In order to achieve the purpose of the present invention, the present invention is implemented by the following technical scheme: a shale pore surface roughness analysis and modeling method, comprising the following steps:
[0007] Step 1: first prepare a mud shale sample according to actual analysis needs, then cut a fresh section of the prepared mud shale sample, then coat the surface of the mud shale sample except the fresh section with a conductive glue to obtain a mud shale sample half-wrapped by the conductive glue, then plate the surface of the mud shale sample half-wrapped by the conductive glue with gold particles, and then fix the mud shale sample treated with gold particles on the observation table of a scanning electron microscope;
[0008] Step 2: first set the scanning parameters of the scanning electron microscope instrument on the scanning electron microscope observation platform, then scan the fresh cross section of the shale sample under the set fixed scanning parameters, and obtain more than one set of fresh cross section photos of the shale sample based on the continuous moving scanning shooting method;
[0009] Step 3: Continuously splicing the fresh cross-section photos of the shale samples taken in step 2 to form a large-area shale cross-section photo;
[0010] Step 4: Use image processing software to process the large-area shale cross-section photos obtained by splicing in step 3, and obtain pore roughness three-dimensional stereo images, roughness parameter values and pore structure data of the large-area shale cross-section photos;
[0011] Step 5: Based on the pore roughness three-dimensional stereo image, roughness parameter value and pore structure data of the large-area shale cross-section photos obtained in step 4, the actual shale sample roughness dominant characteristics and pore size distribution dominant distribution range are obtained, and a multi-aperture shale nano-scale molecular pore structure model with roughness is established.
[0012] A further improvement is that in step 1, the main component of the conductive adhesive is silver powder, and the surface gold-plating particle treatment operation of the shale sample is carried out in a vacuum environment.
[0013] A further improvement is that in step 2, the scanning electron microscope instrument is a field emission scanning electron microscope, and the set fixed scanning parameters include fixed magnification, fixed contrast and fixed brightness.
[0014] A further improvement is that in step 4, the specific steps of image processing of large-area shale cross-section photos are:
[0015] S1. First, adjust the grayscale value interception range of the large-area shale cross-section photo, and extract the pores in the large-area shale cross-section to obtain the pore morphology photo;
[0016] S2, then setting a filtering range for the area and roundness of the pore structure in the pore morphology photograph, and extracting the real pore grayscale value and radius data of the pore structure after filtering;
[0017] S3, obtaining a three-dimensional distribution map of the gray value roughness distribution of a large area of shale section;
[0018] S4. Then, the pore morphology photograph is subjected to roughness analysis using image processing software, and a gray value distribution characteristic curve diagram reflecting the roughness is obtained through the image processing software. At the same time, a three-dimensional image of the pore roughness and a roughness parameter value are obtained using the image processing software.
[0019] A further improvement is that in S4, the image processing software used is ImageJ software, the pore morphology photos are subjected to roughness analysis using the SurfCharJ plug-in in ImageJ software, and the gray value distribution characteristic curve graph reflecting the roughness is obtained using the PlotProfile tool in ImageJ software.
[0020] A further improvement is that in step five, the main pore structures of the established multi-pore shale nano-scale molecular pore structure model are organic pores and clay mineral pores, and the multi-pore shale nano-scale molecular pore structure model is used to reflect the pore characteristics of the shale section.
[0021] A further improvement is that in step 5, when establishing the model, according to the molecular formula of the unit cell of the mudstone sample, an organic matter unit molecular structure and three clay mineral unit molecular structures are provided, and the molecular formulas are: graphite organic matter C, Na-montmorillonite Na 0.75 (Si 7.75 Al 0.25 )(Al 3.5 Mg 0.5 ) 20 (OH) 4 、K-illite K(Si 7 Al)Al 4 O 20 (OH) 4 , Kaolinite Al 4 Si 4 O 10 (OH) 8.
[0022] A shale pore surface roughness analysis and modeling system comprises a parameter determination module for determining the shale pore roughness and pore size structure, a parameter processing module for processing the shale pore roughness and pore size respectively, and a model building module for building a molecular structure model of the reaction pore characteristics, wherein the parameter determination module is connected to the model building module via the parameter processing module.
[0023] The beneficial effects of the present invention are as follows: based on high-resolution, large-field-of-view scanning electron microscope photographs, the present invention can quickly perform roughness analysis and pore structure identification on nano- to micro-scale organic pores and clay mineral pores, with high recognition accuracy, and reduce the influence of artificial point selection factors. Based on the pore surface roughness and pore structure characteristics of shale samples, a molecular structure model that is more consistent with the actual pore characteristics of shale is established. The model can truly reflect the internal pore structure organization of shale, providing a more reliable model for subsequent scientific analysis and research using the model. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 1 is a schematic diagram of a flow chart of a shale pore surface roughness analysis and modeling method in an embodiment of the present invention;
[0025] Figure 2 This is a scanning electron microscope image of a fresh cross section of a Longtan Formation shale sample in an embodiment of the present invention;
[0026] Figure 3 This is a scanning electron microscope image of a shale sample of the Longtan Formation after large-area splicing in an embodiment of the present invention;
[0027] Figure 4 is a roughness intensity variation curve of the Longtan Formation shale sample in an embodiment of the present invention;
[0028] Figure 5 It is a three-dimensional stereogram of pore roughness of the Longtan Formation shale sample in the embodiment of the present invention;
[0029] Figure 6 is a pore size distribution diagram of a Longtan Formation shale sample in an embodiment of the present invention;
[0030] Figure 7 It is a molecular model diagram of organic pore roughness of Longtan Formation shale sample in an embodiment of the present invention. DETAILED DESCRIPTION
[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0032] Embodiment 1
[0033] See also Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 This embodiment provides a method for analyzing and modeling the surface roughness of pores in shale, comprising the following steps:
[0034] Step 1: Prepare shale samples
[0035] The Longtan Formation mud shale samples were selected as the objects, and the samples were prepared. Fresh sections of the mud shale samples were cut, and the surfaces of the cut mud shale except the fresh sections were coated with conductive glue whose main component was silver powder to obtain the treated samples. The surfaces of the mud shale samples were plated with gold particles in a vacuum environment, and then fixed on the observation platform of a scanning electron microscope.
[0036] Step 2: Obtain fresh cross-section photos of shale samples
[0037] Using a field emission scanning electron microscope (FE-SEM) on a scanning electron microscope observation platform, the fresh cross section of the shale sample was imaged at a fixed magnification (400 million times), fixed contrast, and fixed brightness (e.g. Figure 2 As shown in FIG. 1 , a plurality of fresh cross-section photos of shale samples are obtained based on the method of continuous mobile scanning and shooting;
[0038] Step 3: Splicing large-area shale cross-section photos
[0039] Multiple groups of fresh cross-section photos of shale samples are continuously spliced together to form large-area shale cross-section photos (such as Figure 3 shown);
[0040] Step 4: Roughness analysis of pore morphology photos
[0041] The pores in the large-area shale section are extracted to obtain pore morphology photos. Then, the filtering range is set for the area and roundness of the pore structure in the pore morphology photos. After filtering, the real pore gray value and radius data of the pore structure are extracted to obtain the three-dimensional distribution map of the gray value roughness distribution of the large-area shale section. Then, the pore morphology photos are analyzed for roughness using the SurfCharJ plug-in in the ImageJ software, and the intensity change curve of the response roughness is obtained by the PlotProfile tool in the ImageJ software (such as Figure 4 As shown in Figure 2), ImageJ software was used to obtain a three-dimensional image of pore roughness (as shown in Figure 2). Figure 5 As shown in the figure, the roughness variance of the pore interface of the shale sample is calculated to be 22.8, the roughness mean difference is 15.6, the roughness peak is 6.0, the roughness skewness is -1.4, the roughness valley is -99.9, the maximum peak height of the roughness is 110.5, the cumulative height of the roughness is 210.4, and the average height of the roughness is -0.4;
[0042] Step 5: Obtain pore structure related parameters
[0043] At the same time, the pore structure parameters of the shale sample were obtained, including a pore area of 6.05 μm2, a surface porosity of 0.213%, an average radius of 53 nm, and a main pore size distribution between 20 and 40 nm (e.g. Figure 6 shown);
[0044] Step 6: Establish pore molecular model
[0045] Based on the pore surface roughness and pore structure parameters of the shale sample, a molecular model of organic matter (represented by graphite carbon) was established (e.g. Figure 7 The molecular model reflects the main characteristics of the pore surface roughness of the Longtan Formation shale sample. The height difference of the roughness in the pore structure is 7.1nm (the pore diameters of the upper and lower parts are 40nm and 32.9nm respectively);
[0046] When building the model, according to the molecular formula of the unit cell of the shale sample, an organic matter unit molecular structure and three clay mineral unit molecular structures are provided, and the molecular formulas are: graphite organic matter C, Na-montmorillonite Na 0.75 (Si 7.75 Al 0.25 )(Al 3.5 Mg 0.5 ) 20 (OH) 4 、K-illite K(Si 7 Al)Al 4 O 20 (OH) 4 , Kaolinite Al4 Si 4 O 10 (OH) 8 ;
[0047] In addition, a clay mineral pore molecular model with different roughness can be established according to the corresponding clay mineral molecular formula and molecular structure mentioned above.
[0048] Embodiment 2
[0049] This embodiment provides a shale pore surface roughness analysis and modeling system, including a parameter determination module, a parameter processing module and a model building module, wherein the parameter determination module is connected to the model building module through the parameter processing module, wherein:
[0050] Parameter determination module: used to determine the pore roughness and pore size structure of shale;
[0051] Parameter processing module: used to process the pore roughness and pore size of shale respectively;
[0052] Model building module: used to build molecular structure models of reactive pore characteristics.
[0053] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for analyzing and modeling the roughness of pore surfaces in shale. It is characterized in that The following steps are involved: Step 1: first prepare a mud shale sample according to actual analysis needs, then cut a fresh section of the prepared mud shale sample, then coat the surface of the mud shale sample except the fresh section with a conductive glue to obtain a mud shale sample half-wrapped by the conductive glue, then plate the surface of the mud shale sample half-wrapped by the conductive glue with gold particles, and then fix the mud shale sample treated with gold particles on the observation table of a scanning electron microscope; Step 2: first set the scanning parameters of the scanning electron microscope instrument on the scanning electron microscope observation platform, then scan the fresh cross section of the shale sample under the set fixed scanning parameters, and obtain more than one set of fresh cross section photos of the shale sample based on the continuous moving scanning shooting method; Step 3: Continuously splicing the fresh cross-section photos of the shale samples taken in step 2 to form a large-area shale cross-section photo; Step 4: Use image processing software to process the large-area shale cross-section photos obtained by splicing in step 3, and obtain the pore roughness three-dimensional stereo image, roughness parameter value and pore structure data of the large-area shale cross-section photos. The specific steps of image processing of the large-area shale cross-section photos are as follows: S1. First, adjust the grayscale value interception range of the large-area shale cross-section photo, and extract the pores in the large-area shale cross-section to obtain the pore morphology photo; S2, then setting a filtering range for the area and roundness of the pore structure in the pore morphology photograph, and extracting the real pore grayscale value and radius data of the pore structure after filtering; S3, obtaining a three-dimensional distribution map of the gray value roughness distribution of a large area of shale section; S4, then use image processing software to perform roughness analysis on the pore morphology photos, and obtain a gray value distribution characteristic curve graph reflecting the roughness through the image processing software, and simultaneously use the image processing software to obtain a pore roughness three-dimensional stereo image and a roughness parameter value; Step 5: Based on the pore roughness three-dimensional stereo image, roughness parameter value and pore structure data of the large-area shale cross-section photos obtained in step 4, the actual shale sample roughness dominant characteristics and pore size distribution dominant distribution range are obtained, and a multi-aperture shale nano-scale molecular pore structure model with roughness is established.
2. A shale pore surface roughness analysis and modeling method according to claim 1, Features: In the step 1, the main component of the conductive adhesive is silver powder, and the surface gold-plating particle treatment operation of the shale sample is carried out in a vacuum environment.
3. A shale pore surface roughness analysis and modeling method according to claim 1, Features: In the step 2, the scanning electron microscope instrument is a field emission scanning electron microscope, and the set fixed scanning parameters include fixed magnification, fixed contrast and fixed brightness.
4. A shale pore surface roughness analysis and modeling method according to claim 1, Features: In S4, the image processing software used is ImageJ software. The pore morphology photos are analyzed for roughness using the SurfCharJ plugin in ImageJ software, and the gray value distribution characteristic curve reflecting roughness is obtained using the PlotProfile tool in ImageJ software.
5. A method for analyzing and modeling the surface roughness of shale pores according to claim 1, characterized in that: In the fifth step, the main pore structures of the multi-aperture shale nano-scale molecular pore structure model are organic matter pores and clay mineral pores, and the pore characteristics of the shale cross-section are reflected by the multi-aperture shale nano-scale molecular pore structure model.
6. A method for analyzing and modeling the surface roughness of shale pores according to claim 1, characterized in that: In the fifth step, when establishing the model, according to the unit cell molecular formula of the shale sample, an organic matter unit molecular structure and three clay mineral unit molecular structures are provided. The molecular formula of the organic matter unit molecular structure is: graphite organic matter C, and the molecular formulas of the three clay mineral unit molecular structures are respectively: Na-montmorillonite Na 0.75 (Si 7.75 Al 0.25 )(Al 3.5 Mg 0.5 )O 20 (OH) 4 , K-illite K(Si 7 Al)Al 4 O 20 (OH) 4 , kaolinite Al 4 Si 4 O 10 (OH) 8 .
7. A system applied to the method for analyzing and modeling the surface roughness of shale pores according to any one of claims 1-6, characterized in that: It includes a parameter determination module for determining the pore roughness and pore size structure of shale, a parameter processing module for processing the pore roughness and pore size of shale respectively, and a model establishment module for establishing a molecular structure model reflecting pore characteristics. The parameter determination module is connected to the model establishment module through the parameter processing module.