A method and system for detecting pore structure of shale gas reservoir

Abstract

The present invention relates to the field of reservoir detection technology, and specifically to a method and system for detecting the pore structure of a shale gas reservoir, comprising the following steps: extracting and analyzing pore volume parameters based on pore structure data of the shale gas reservoir, correcting initial pore permeability parameters, extracting local flow characteristics, and generating a permeability distribution model. In the present invention, by extracting pore volume parameters and distribution density values, the correction of reservoir permeability parameters is enhanced, the accuracy of pore structure analysis is improved, the distribution gradient value of pore morphology is used to analyze regional characteristics in detail, the application of pore connectivity and fluid response characteristics is optimized, the understanding of reservoir dynamic relationships is improved, multi-point operations and dynamic relationship correction are used to optimize fluid dynamic data calculations, the analysis of the dynamic characteristics of connected paths is enhanced, the decision-making efficiency of reservoir management is improved, the fluid path analysis is optimized, the identification of high-permeability paths is enhanced, and the economic benefits and development efficiency of the detection technology are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of reservoir detection technology, and in particular to a method and system for detecting the pore structure of a shale gas reservoir. Background Art

[0002] The field of reservoir detection technology mainly studies the physical properties and fluid migration characteristics of oil and gas reservoirs. Through physical, chemical and numerical simulation methods, the pore structure, permeability and reservoir saturation of the reservoir are characterized and analyzed. This field includes experimental testing technology, geological modeling, well logging analysis and reservoir numerical simulation, aiming to enhance the understanding of reservoir characteristics and provide a scientific basis for the formulation of oil and gas development plans. Reservoir detection technology has a wide range of applications, including conventional oil and gas fields, unconventional oil and gas fields (such as shale gas and tight oil and gas) and geothermal resource development.

[0003] Among them, the detection method of pore structure of shale gas reservoirs is to use a variety of detection technologies to conduct qualitative and quantitative analysis of the pore characteristics of shale gas reservoirs, including the determination of indicators such as pore size distribution, pore surface area and connectivity. Its purpose is to optimize the development plan of shale gas resources and improve the accuracy of reservoir permeability assessment, thereby improving the efficiency and economic benefits of shale gas development. This method is of great significance to the development of unconventional oil and gas resources and has promoted the in-depth development of reservoir characterization technology in practical applications.

[0004] Although existing technologies in reservoir detection cover a variety of means such as experimental testing technology and geological modeling, common methods have limitations when dealing with the pore structure and fluid migration characteristics of unconventional oil and gas fields such as shale gas. Existing technologies lack sufficient accuracy in the application of parameters for pore connectivity and fluid response characteristics, making it difficult to fully capture the dynamic changes of complex reservoirs, resulting in limited accuracy in permeability assessments and resource development plans. Existing technologies rely on traditional well logging analysis and numerical simulations when analyzing reservoir permeability. When dealing with permeability changes in highly heterogeneous reservoirs, they find it difficult to adapt to rapidly changing geological conditions, resulting in the inability to effectively predict the dynamic distribution of fluids in the reservoir. As a result, in actual applications, especially in the development of complex reservoirs, problems such as insufficient understanding of reservoir characteristics and low development efficiency often occur. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for detecting the pore structure of a shale gas reservoir.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for detecting the pore structure of a shale gas reservoir, comprising the following steps:

[0007] S1: Based on the pore structure data of shale gas reservoirs, pore volume parameters are extracted and analyzed, initial pore permeability parameters are corrected, local flow characteristics are extracted, and a permeability distribution model is generated;

[0008] S2: Based on the permeability distribution model, the pore morphology distribution characteristics are analyzed, the pore connectivity and fluid response characteristic parameters are called, the classification characteristic differences are screened, the regional pore boundaries are recalibrated, and the pore morphology regional distribution structure is generated;

[0009] S3: Based on the regional distribution structure of the pore morphology, calculate the pore regional morphology change characteristics, combine the pore volume and flow rate data to calculate the fluid dynamic data in sections, adjust the regional dynamic relationship, and generate the pore dynamic behavior field;

[0010] S4: Based on the pore dynamics behavior field, extract the fluid dynamics distribution value in the region, classify the regions using the pressure gradient and flow velocity difference value, refine the fluid path distribution change trend, and generate a dynamic distribution map of the connected paths;

[0011] S5: Based on the dynamic distribution diagram of the connected paths, analyzing the resistance distribution values of the high and low flow rate paths, extracting the dynamic characteristics of the fluid, combining the permeation resistance trend calculation inside and outside the paths, analyzing the low resistance areas and marking the path areas, and generating a connected path trend analysis matrix;

[0012] S6: Based on the connectivity path trend analysis matrix, extract regional priority sorting rules, analyze dynamic characteristic indicators, classify low resistance and high permeability path distribution results, and generate a key pore structure detection plan.

[0013] As a further solution of the present invention, the permeability distribution model includes boundary data range, local flow feature extraction, and pore distribution density; the pore morphology area distribution structure includes distribution gradient value, connectivity parameter, and fluid response characteristics; the pore dynamics behavior field includes morphology change characteristic value, multi-point operation results, and fluid dynamic calculation; the connection path dynamic distribution diagram includes pressure gradient value, flow velocity difference, and dynamic distribution value; the connection path trend analysis matrix includes resistance distribution value, fluid dynamic characteristics, and low resistance area mark; the pore structure key detection scheme includes low resistance area characteristic value, priority sorting rules, and high permeability path identification.

[0014] As a further solution of the present invention, based on the pore structure data of the shale gas reservoir, the pore volume parameters are extracted and analyzed, the initial pore permeability parameters are corrected, the local flow characteristics are extracted, and the permeability distribution model is generated. Specifically, the steps are:

[0015] S101: Based on the pore structure data of shale gas reservoirs, key pore structure parameters are extracted, pore volume and distribution density are identified and recorded, and a pore characteristic dataset is obtained through data screening and aggregation;

[0016] S102: identifying adjustment requirements for pore permeability parameters, including pore size and connectivity, based on the pore characteristic dataset, optimizing the pore permeability parameters using a quantitative adjustment technique, and obtaining corrected permeability parameters;

[0017] S103: Analyze the boundary data of the permeability field using the corrected permeability parameters, identify key permeability paths and local flow characteristics, and perform data integration to construct a permeability distribution model.

[0018] As a further solution of the present invention, based on the permeability distribution model, the pore morphology distribution characteristics are analyzed, the pore connectivity and fluid response characteristic parameters are called, the classification characteristic differences are screened, and the regional pore boundaries are recalibrated to generate the pore morphology regional distribution structure. Specifically, the steps are:

[0019] S201: Analyze the gradient distribution of pore morphology in the region based on the permeability distribution model, distinguish differentiated pore characteristic regions through quantitative analysis of gradient values, and obtain a pore characteristic zoning map;

[0020] S202: Based on the pore characteristic zoning map, combined with the pore connectivity parameter and the fluid response characteristic parameter, performing differentiated screening of regional pore characteristics, screening regions with different characteristics through parameter comparison and metrological classification, and obtaining a differential region classification result;

[0021] S203: Using the difference region classification result, the pore boundary of each region is corrected, and the regional distribution structure of the pore morphology is obtained by comparative analysis with the real-time observation data.

[0022] As a further solution of the present invention, based on the regional distribution structure of pore morphology, calculating the pore regional morphological change characteristics, combining the pore volume and flow rate data to calculate the fluid dynamic data in sections, adjusting the regional dynamic relationship, and generating the pore dynamic behavior field are specifically as follows:

[0023] S301: Based on the regional distribution structure of pore morphology, analyzing the characteristic value of pore morphology change in each region, using the characteristic value to perform comparative analysis of permeability between pore regions, and obtaining pore region permeability characteristic data by quantitatively processing the permeability data;

[0024] S302: Using the pore region permeability characteristic data, combined with the pore volume and fluid velocity parameters of each region, performing segmented operations on the data, analyzing the fluid dynamic characteristics, and obtaining regional fluid dynamic characteristics results;

[0025] S303: Using the regional fluid dynamic characteristic results, adopting a multivariate regression analysis algorithm, calling the dynamic distribution trend value, correcting the dynamic relationship between regions, and obtaining the pore dynamic behavior field.

[0026] As a further solution of the present invention, the formula using the multivariate regression analysis algorithm is as follows:

[0027] ;

[0028] in, represents the predicted value of the pore dynamics behavior field, represents the average value of the fluid velocity, represents the average temperature of the fluid, represents the concentration of chemical components of the fluid, represents the weight coefficient obtained by data fitting, Represents the density of the fluid.

[0029] As a further solution of the present invention, based on the pore dynamics behavior field, the fluid dynamics distribution value within the region is extracted, the pressure gradient and flow velocity difference values are used to classify the regions, the fluid path distribution change trend is refined, and the steps of generating a dynamic distribution map of the connected paths are specifically as follows:

[0030] S401: extracting the fluid dynamics distribution value within the region based on the pore dynamics behavior field, using data to analyze the fluid pressure gradient value and flow velocity difference, dividing and classifying the region, and obtaining a regional fluid dynamics characteristic classification result;

[0031] S402: Based on the regional fluid dynamic characteristics classification result, performing a distribution change trend analysis of the fluid paths, comparing the flow velocity and pressure data between the differentiated paths, plotting the dynamic changes of the fluid behavior, and obtaining a fluid behavior dynamic change graph;

[0032] S403: using the fluid behavior dynamic change graph, analyzing the boundary area characteristics of the communication path, and generating a dynamic distribution graph of the communication path by comparing and analyzing the boundary data.

[0033] As a further solution of the present invention, based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high and low flow rate paths are analyzed, the dynamic characteristics of the fluid are extracted, and the low resistance areas are analyzed and marked in combination with the permeation resistance trend calculation inside and outside the paths. The steps of generating a connected path trend analysis matrix are specifically as follows:

[0034] S501: Based on the dynamic distribution diagram of the communication paths, analyzing the resistance distribution values of the high-flow-velocity paths and the low-flow-velocity paths, extracting the fluid dynamic characteristics according to the distribution values, and performing fluid dynamic characteristic classification of the regions by analyzing the resistance differences to obtain the fluid dynamic characteristic region division;

[0035] S502: Using the fluid dynamic characteristics regional division, combined with the permeation resistance data inside and outside the path, perform a trend analysis of the fluid dynamic characteristics, and analyze the fluid dynamics in the low resistance area through data comparison to obtain a low resistance dynamic analysis result;

[0036] S503: Using the low-resistance dynamic analysis results, mark the fluid dynamic behavior of the corresponding path area, analyze the dynamic characteristics of the marked area in detail, and generate a connected path trend analysis matrix through integration and comparison of the characteristics.

[0037] As a further solution of the present invention, based on the connectivity path trend analysis matrix, the steps of extracting regional priority sorting rules, analyzing dynamic characteristic indicators, classifying the distribution results of low resistance and high permeability paths, and generating a key pore structure detection plan are as follows:

[0038] S601: Based on the connectivity path trend analysis matrix, extract the characteristic values of the low resistance distribution areas of the pore areas, perform sorting operations according to the range of the regional characteristic values, and modify the sorting priority in combination with the fluid dynamic characteristic data. By analyzing the low resistance areas, generate a regional priority sorting result;

[0039] S602: Analyze the dynamic characteristic indicators of each distribution area using the regional priority ranking results, combine the permeability resistance value and dynamic flow rate data of the path, and classify the path distribution into sections. By comparing the internal and external characteristics of each area, generate low resistance and high permeability path classification results;

[0040] S603: Using the low resistance and high permeability path classification results, reanalyze the path distribution characteristics according to the regional sorting information, mark the key path areas based on the resistance and permeability differences between regions, and generate a key pore structure detection plan.

[0041] A shale gas reservoir pore structure detection system, the shale gas reservoir pore structure detection system is used to perform the above-mentioned shale gas reservoir pore structure detection method, the system comprising:

[0042] The pore parameter extraction module measures the pore volume and distribution density based on the pore structure data of the shale gas reservoir, calibrates the initial parameters of the pore permeability, analyzes the permeability field boundary data, compares the local flow characteristics of the pore path, and obtains the permeability distribution model;

[0043] The permeability distribution analysis module uses the permeability distribution model to analyze the distribution gradient of pore morphology, calls pore connectivity parameters and fluid response characteristic parameters, performs screening and classification merging of characteristic differences, recalibrates the pore boundary range, and obtains the regional distribution structure of pore morphology;

[0044] The pore morphology regional analysis module analyzes the morphology change characteristic values of the pore region based on the pore morphology regional distribution structure, performs multi-point calculations on the permeability change characteristics, combines the pore volume and fluid flow rate data, calculates the fluid dynamic data in the region in sections, adjusts the dynamic relationship between regions, and constructs the pore dynamic behavior field;

[0045] The pore dynamics modeling module extracts the fluid dynamics distribution value from the pore dynamics behavior field, uses the fluid pressure gradient and flow velocity difference to analyze the distribution change trend of the fluid path, analyzes the boundary area characteristics of the connected path, and obtains the dynamic distribution map of the connected path;

[0046] The connectivity path trend analysis module analyzes the fluid dynamics and permeation resistance data within the path based on the connectivity path dynamic distribution diagram, distinguishes the resistance characteristics of high and low flow rate paths, marks low resistance area paths, and obtains a key detection plan for the pore structure.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by extracting pore volume parameters and pore distribution density values, the initial parameter correction capability of reservoir permeability is enhanced, the accuracy of quantitative analysis of pore structure is improved, the boundary data range of the permeability field is analyzed and local flow characteristics are extracted, so that the dynamic characteristics of the permeability path are clearer, thereby more effectively guiding the development of shale gas resources. By utilizing the distribution gradient value of pore morphology, the regional distribution characteristics can be analyzed in detail, and the parameter application of pore connectivity and fluid response characteristics can be more accurate, further improving the understanding of reservoir dynamic relationships. This multi-point operation and correction of dynamic relationships between regions optimizes the calculation of fluid dynamic data, enhances the ability to analyze the dynamic characteristics of connected paths, and provides more efficient decision support for reservoir management. By extracting fluid dynamic characteristics and characteristic values of low resistance distribution areas, the fluid migration path analysis is effectively optimized, and the identification capability of high permeability paths is enhanced, thereby significantly improving the economic benefits and development efficiency of reservoir detection technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for detecting the pore structure of a shale gas reservoir, comprising the following steps:

[0060] S1: Based on the pore structure data of shale gas reservoirs, extract pore volume parameters and pore distribution density values, calibrate the initial parameters of pore permeability, analyze the boundary data range of the permeability field, extract the local flow characteristics of the permeability path, and generate a permeability distribution model;

[0061] S2: Based on the permeability distribution model, the distribution gradient value of pore morphology is used to analyze the regional distribution characteristics. The pore connectivity parameters and fluid response characteristic parameters are called, and the range of characteristic differences is screened and classified and merged. The pore boundary range of each region is recalibrated to generate the regional distribution structure of pore morphology.

[0062] S3: Based on the regional distribution structure of pore morphology, the morphological change characteristic values of the pore area are used to perform multi-point calculations on the permeability change characteristics. The pore volume parameters and fluid velocity data are combined to calculate the fluid dynamic data in the region in sections. The dynamic distribution trend value is called, the dynamic relationship between regions is corrected, and the pore dynamic behavior field is generated.

[0063] S4: Based on the pore dynamics behavior field, the fluid dynamic distribution value in the region is extracted. The fluid pressure gradient value and flow velocity difference value are used to classify the region, refine the distribution change trend of the fluid path, analyze the dynamic characteristics between the paths, analyze the boundary area characteristics of the connected paths, and generate a dynamic distribution map of the connected paths;

[0064] S5: Based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high-flow velocity paths and the low-flow velocity paths are analyzed, the fluid dynamic characteristics of the paths are extracted, and the trend calculation is performed based on the permeation resistance data inside and outside the paths. The low-resistance distribution areas are analyzed and the corresponding path areas are marked to generate the connected path trend analysis matrix;

[0065] S6: Based on the connectivity path trend analysis matrix, the characteristic values of the low-resistance distribution areas in the pore area are used to extract the regional priority sorting rules, analyze the dynamic characteristic indicators of each distribution area, reclassify the distribution results of low-resistance paths and high-permeability paths, and generate a key detection plan for the pore structure.

[0066] The permeability distribution model includes boundary data range, local flow feature extraction, and pore distribution density. The pore morphology regional distribution structure includes distribution gradient value, connectivity parameter, and fluid response characteristics. The pore dynamics behavior field includes morphological change characteristic values, multi-point calculation results, and fluid dynamic calculation. The dynamic distribution diagram of the connected path includes pressure gradient value, flow velocity difference, and dynamic distribution value. The connected path trend analysis matrix includes resistance distribution value, fluid dynamic characteristics, and low resistance area mark. The key detection plan for pore structure includes low resistance area characteristic value, priority sorting rules, and high permeability path identification.

[0067] Specifically, if Figure 2 As shown in the figure, based on the pore structure data of the shale gas reservoir, the pore volume parameters are extracted and analyzed, the initial pore permeability parameters are corrected, the local flow characteristics are extracted, and the permeability distribution model is generated as follows:

[0068] S101: Based on the pore structure data of shale gas reservoirs, key pore structure parameters are extracted, pore volume and distribution density are identified and recorded, and a pore characteristic dataset is obtained through data screening and aggregation;

[0069] When extracting key parameters of pore structure, image analysis-based methods can be used to resolve and segment scanning electron microscope data, and the pore morphology can be divided into three types according to the volume range: micropores, mesopores and macropores. The total volume proportion of different types of pores and the average volume of a single pore can be calculated. To identify the pore distribution density, a three-dimensional distribution map of pore density is constructed by analyzing the spatial distribution of pores of different volumes in the reservoir, and the data is used to evaluate the storage capacity of different areas. When screening data, redundant data can be eliminated to simplify the data set based on the conditions that the pore volume is greater than a certain threshold and its number proportion is higher than 5%, and the sufficiency of representative pores in the data set is ensured. Finally, the screened data are classified and summarized to form a pore characteristic data set, laying the foundation for subsequent permeability analysis.

[0070] S102: Based on the pore characteristic dataset, identify the need for adjustment of pore permeability parameters, including pore size and connectivity, apply quantitative adjustment technology to optimize the pore permeability parameters, and obtain corrected permeability parameters;

[0071] When identifying the need for permeability adjustment, the pore characteristic data is matched with the experimental permeability data. Through quantitative analysis of pore size and connectivity, it is determined whether the current permeability parameters deviate from the actual reservoir characteristics. For pore size, a threshold range (such as 0.1-10 microns) can be set to screen out pores that are too small or too large, and their contribution to permeability can be evaluated. For connectivity, the distribution density of the connecting paths between pores can be statistically analyzed, the total proportion of connected pores and the spatial proportion of non-connected pores can be calculated, and the permeability parameters of the areas with lower connected paths can be adjusted. When optimizing the permeability parameters, the adjustment data will be corrected with an increasing step size until the deviation between the corrected permeability parameter and the actual test value is lower than the preset range, thereby ensuring the accuracy of the parameter.

[0072] S103: Analyze the boundary data of the permeability field using the corrected permeability parameters, identify key permeability paths and local flow characteristics, and integrate the data to construct a permeability distribution model;

[0073] Using the corrected permeability parameters, the reservoir area is divided into several grid cells. The corresponding permeability value is calculated for each grid cell. Based on the differences in permeability in different regions, the boundary range of the permeability field is identified. In the identification of key permeability paths, the path is gradually traced in the direction of the maximum permeability, and the distribution of connected pores and the seepage direction on the permeability path are recorded. At the same time, the flow uniformity and heterogeneity within the reservoir are quantified through the statistics of local flow characteristics. After integrating the boundary conditions and flow characteristics of the permeability in each region, spatial interpolation is used to complete the smooth transition of the permeability distribution and construct a permeability distribution model to provide a basis for further reservoir development strategies.

[0074] Specifically, if Figure 3 As shown in the figure, based on the permeability distribution model, the pore morphology distribution characteristics are analyzed, the pore connectivity and fluid response characteristic parameters are called, the classification characteristic differences are screened, the regional pore boundaries are recalibrated, and the steps to generate the pore morphology regional distribution structure are as follows:

[0075] S201: Based on the permeability distribution model, analyze the gradient distribution of pore morphology in the region, distinguish differentiated pore characteristic regions through quantitative analysis of the gradient values, and obtain a pore characteristic zoning map;

[0076] First, it is necessary to extract the basic data in the permeability distribution model, use a high-precision grid division method, and perform uniform interpolation processing on the permeability data in the area. By applying the gradient calculation formula to the interpolated data, the gradient change rate of each grid point is extracted, and a gradient distribution map of the area is constructed. By setting a threshold, regional data points with significant gradient changes are screened. The data points are further clustered using a multi-layer classification algorithm, and the points are classified into a specific gradient range. The partition matrix is formed by mapping the two-dimensional distribution of the clustering results. To ensure the reliability of the regional partition mapping results, the generated partition results are checked for consistency, and repeated or abnormal regional boundary points are corrected to form a pore characteristic partition map that can reflect the pore gradient characteristics of the area.

[0077] S202: Based on the pore characteristic zoning mapping, combined with the pore connectivity parameter and the fluid response characteristic parameter, performing differential screening of regional pore characteristics, screening regions with different characteristics through parameter comparison and metrological classification, and obtaining differential region classification results;

[0078] Through multiple calculations to analyze the differentiated pore characteristics within the region, according to the formula:

[0079] ;

[0080] Calculate the comprehensive score of the difference characteristics within the region, where represents the comprehensive score of regional pore characteristics differences, Representative The weighted value of the pore connectivity within a partition, Representative The fluid response characteristic index within each partition is Represents the number of partitions;

[0081] Obtain pore connectivity data and derive the connectivity factor (in m²) based on the relationship between flow rate and pressure difference through permeability experiments;

[0082] The fluid response characteristic parameters are derived from viscosity tests and osmotic pressure experiments, and the unit of the measured fluid characteristic coefficient is Pa;

[0083] The pore connectivity and fluid response characteristic parameters in each partition are linearly weighted to calculate the comprehensive score. The weight coefficient is selected based on the normalized variance of each indicator.

[0084] Set five partitions, calculate the pore connectivity parameters to be [0.8, 0.6, 0.9, 0.5, 0.7] m², and the fluid response characteristics to be [1000, 800, 1200, 600, 900] Pa, and substitute into the formula:

[0085] ;

[0086] ;

[0087] The calculation results show that 3290 is the comprehensive score of regional difference characteristics, reflecting that the pore characteristics in this region are significantly different and further optimization and screening are needed.

[0088] S203: Using the difference region classification results, calibrate the pore boundaries of each region, and obtain the regional distribution structure of pore morphology through comparative analysis with real-time observation data;

[0089] First, the spatial point cloud data of the boundaries of each region in the real-time observation data are extracted, and the three-dimensional coordinates of the boundary points are calculated using the spatial interpolation method. Then, the initial distribution of the boundary points is smoothed to remove obviously discrete noise points. The deviation value of each point from the ideal boundary of the region is calculated through a statistically based boundary difference model. A correction model is constructed using a kernel function centered on the mean deviation value. For points whose deviation values exceed a certain threshold, their belonging regions are recalculated. At the same time, the shared nodes of adjacent boundaries between regions are geometrically optimized and reconstructed to ensure that the corrected boundary points meet the continuity and consistency requirements. The boundary correction model is used to complete the correction of the regional boundaries and update the regional distribution structure map of the pore morphology.

[0090] Specifically, if Figure 4 As shown in the figure, based on the regional distribution structure of pore morphology, the pore regional morphology change characteristics are calculated, the fluid dynamics data is calculated segmented by combining the pore volume and flow velocity data, and the regional dynamic relationship is adjusted to generate the pore dynamics behavior field. The specific steps are:

[0091] S301: Based on the regional distribution structure of pore morphology, the characteristic value of pore morphology change in each region is analyzed, and the characteristic value is used to perform comparative analysis of permeability between pore regions. By quantitatively processing the permeability data, the permeability characteristic data of the pore region is obtained;

[0092] The basic parameters of pore morphology, including pore area, perimeter, shape factor and fractal dimension, are extracted through image analysis technology. The eigenvalue matrix is established by normalizing the basic parameters. The matrix is reduced in dimension through principal component analysis to extract the main eigenvalues. The permeability differences between regions are then calculated based on the extracted main eigenvalues. The correlation between permeability and pore morphology characteristics is used to determine the permeability characteristics of differentiated regions by fitting the permeability change curve. A regional permeability comparison analysis table is constructed based on the permeability eigenvalue data. The permeability change trends of each region are compared to obtain the permeability characteristic data of the pore area.

[0093] S302: Using the pore area permeability characteristic data, combined with the pore volume and fluid flow rate parameters of each area, performing segmented operations on the data, analyzing the fluid dynamic characteristics, and obtaining regional fluid dynamic characteristics results;

[0094] First, the fluid velocity field of each area is generated according to the spatial distribution of the pore characteristic parameters. The fluid throughput per unit time is calculated by multiplying the flow velocity and the pore volume. The area is divided into several grids to improve the calculation accuracy. The fluid throughput data of each grid is analyzed by segmented integration. The change pattern of flow velocity with time is calculated according to differentiated time periods, and the dynamic characteristics are analyzed based on the time period division. Finally, the dynamic parameters calculated for each grid are integrated into the regional level, and the regional fluid dynamic characteristics results are generated by mathematical model fitting.

[0095] S303: Using the regional fluid dynamic characteristics results, a multivariate regression analysis algorithm is employed to call the dynamic distribution trend value, correct the dynamic relationship between regions, and obtain the pore dynamic behavior field;

[0096] The formula for the multivariate regression analysis algorithm is as follows:

[0097] ;

[0098] in, represents the predicted value of the pore dynamics behavior field, represents the average value of the fluid velocity, represents the average temperature of the fluid, represents the concentration of chemical components of the fluid, represents the weight coefficient obtained by data fitting, represents the density of the fluid;

[0099] This formula considers multiple important physical and chemical parameters. By adjusting weight coefficients and introducing square root and absolute value operations, it improves the model's flexibility and prediction accuracy. This method can not only more accurately describe fluid dynamic characteristics in complex geological environments, but is also applicable to similar engineering and scientific research situations.

[0100] Detailed explanation of the formula and the process of formula calculation and derivation:

[0101] The average value of the fluid velocity ( ): By arranging flow velocity sensors in different areas, the fluid velocity is continuously monitored, and the data is recorded and the average value is calculated. The unit is meters per second (m / s). Depending on the fluid type and pipeline conditions, the average velocity is between 0.1 and 5 m / s. The average velocity obtained by monitoring is set to 2.5 m / s;

[0102] The average value of the fluid temperature ( ): Install temperature sensors in different areas, continuously record temperature data, calculate the average value, in degrees Celsius (℃), and set the measured average temperature to 50℃ between 20℃ and 80℃ according to the fluid characteristics;

[0103] Fluid chemical composition concentration ( ): Collect a fluid sample and use a chemical analysis method to determine the concentration of a specific component in milligrams per liter (mg / L), which is between 0 and 1000 mg / L depending on the fluid's use. The concentration obtained by analysis is set to 200 mg / L;

[0104] Fluid density ( ): Directly measure with a density meter, or consult a standard density table based on fluid composition and temperature. The unit is kilograms per cubic meter (kg / m3). Depending on the fluid type, the density is between 800 and 1200 kg / m3. Set the measured density to 1000 kg / m3.

[0105] Weight coefficient ( ): Through historical data and expert experience, the influence of each parameter on the pore dynamics behavior field is determined. According to different working conditions and goals, the weight coefficient can be adjusted and set. , , .

[0106] Compute linear combinations:

[0107] ;

[0108] ;

[0109] Take the absolute value and square root:

[0110] ;

[0111] Compute the square root of the density:

[0112] ;

[0113] Calculate the final result:

[0114] ;

[0115] The results show that based on the measured fluid velocity, temperature, chemical composition concentration and density, the pore dynamics behavior field value obtained through multivariate regression analysis is 0.237.

[0116] Specifically, if Figure 5 As shown in the figure, based on the pore dynamics behavior field, the fluid dynamics distribution value in the region is extracted, the pressure gradient and velocity difference values are used to classify the regions, the fluid path distribution trend is refined, and the steps of generating the dynamic distribution map of the connected paths are as follows:

[0117] S401: Based on the pore dynamics behavior field, the fluid dynamics distribution value in the region is extracted, and the fluid pressure gradient value and flow velocity difference are analyzed by data to divide and classify the region, thereby obtaining the classification results of the regional fluid dynamic characteristics;

[0118] Dynamic monitoring technology is used to obtain fluid pressure and velocity data at each time point. The spatial distribution of pressure and velocity is used to calculate the fluid pressure gradient value and flow velocity difference. The pressure gradient is calculated by the spatial differential of the pressure value, and the flow velocity difference is calculated by the difference between the maximum and minimum velocity values in the area. The gradient values and difference values calculated above are normalized to improve the accuracy of the classification analysis. Subsequently, a classification algorithm is used to perform cluster analysis on the gradient and difference values, and regions with similar characteristics are divided into one category. The differences between categories are statistically analyzed to form the classification results of regional fluid dynamic characteristics.

[0119] S402: Based on the regional fluid dynamic characteristics classification results, perform a distribution change trend analysis of the fluid paths, compare the flow velocity and pressure data between the differentiated paths, plot the dynamic changes of the fluid behavior, and obtain a fluid behavior dynamic change diagram;

[0120] When analyzing the distribution change trend of fluid paths, we first segment the path distribution in the regional classification results and calibrate the average flow velocity and pressure data of each path segment. The change trend of the path flow velocity over time is expressed as a time series. The flow velocity change curve of the path is fitted through regression analysis. The spatial distribution map of the path pressure difference is used to further plot the dynamic changes of the fluid behavior in the region. The flow velocity change data between paths are compared and analyzed. The time correlation characteristics between paths are obtained by constructing a dynamic change matrix. Finally, the flow velocity change trend and pressure distribution are combined to generate a dynamic change map of fluid behavior.

[0121] S403: Using the fluid behavior dynamic change graph, analyzing the boundary area characteristics of the connected path, and generating a dynamic distribution graph of the connected path through comparison and analysis of the boundary data;

[0122] According to the formula:

[0123] ;

[0124] Generate a dynamic distribution graph of connected paths, where represents the dynamic feature intensity of the boundary area, For the The pressure difference of the segment path, in Pascals, For the The length of the path segment in meters, For the The flow rate of the segment path, in meters per second, is the number of path segments;

[0125] The pressure difference is monitored by a manometer and calculated by recording the pressure values at the starting and end points of each path, and the unit is Pascal;

[0126] The path length is directly measured in meters from the three-dimensional distribution map of the path;

[0127] The flow velocity is the average velocity of each path obtained through tracer experiments, in meters per second;

[0128] Assume there are three paths, the pressure difference is [500, 600, 400] Pa, the path length is [10, 15, 8] m, and the flow rate is [1.2, 1.5, 0.9] m / s. Substitute into the formula:

[0129] ;

[0130] Calculation steps:

[0131] ;

[0132] The calculation results show that 165 is the dynamic feature intensity of the boundary area, which reflects the dynamic distribution relationship of the connected path, indicating that the dynamic characteristics of the path boundary have been successfully analyzed and used to generate the distribution map.

[0133] Specifically, if Figure 6 As shown in the figure, based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high and low flow velocity paths are analyzed, the dynamic characteristics of the fluid are extracted, and the low resistance areas are analyzed and marked in combination with the permeation resistance trend calculation inside and outside the path. The specific steps for generating the connected path trend analysis matrix are as follows:

[0134] S501: Based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high-flow velocity paths and the low-flow velocity paths are analyzed, the fluid dynamic characteristics are extracted according to the distribution values, and the fluid dynamic characteristics classification of the regions is performed by analyzing the resistance differences to obtain the fluid dynamic characteristics regional division;

[0135] By collecting the pressure change value and fluid velocity data in the path, the resistance value in the path is calculated using the fluid dynamics formula, and the fluid resistance formula is used to calculate the resistance. The resistance value is calculated by dividing the difference between the starting pressure and the end pressure of the path by the fluid flow rate in the path. After normalizing the resistance distribution value, the resistance characteristic value of each path is extracted, and the resistance difference between the high-flow rate path and the low-flow rate path is compared. The resistance characteristics are divided into high and low resistance areas through statistical clustering methods, and the fluid characteristic data are associated. The regional fluid dynamic characteristics are classified in combination with the differentiated resistance areas, and finally the fluid dynamic characteristic regional division is formed.

[0136] S502: Using the fluid dynamic characteristics regional division, combined with the penetration resistance data inside and outside the path, perform the fluid dynamic characteristics trend analysis, and analyze the fluid dynamics in the low resistance area through data comparison to obtain the low resistance dynamic analysis results;

[0137] When analyzing the trend of fluid dynamic characteristics, the dynamic resistance distribution characteristics of the path are calculated by measuring the osmotic pressure and fluid flow rate parameters inside and outside the path. The trend data of the dynamic resistance changing with time is used to generate a time distribution diagram, and the fluid flow rate and pressure values of the path with the least resistance are compared to analyze the dynamic change law of the low-resistance path. The change law is summarized into a data table through a dynamic statistical model. Finally, combined with the regional classification information, the fluid dynamic behavior of the low-resistance area is obtained, and the data is presented in a specific way to form the low-resistance dynamic analysis results.

[0138] S503: Using the low-resistance dynamic analysis results, mark the fluid dynamic behavior of the corresponding path area, analyze the dynamic characteristics of the marked area in detail, and generate a connectivity path trend analysis matrix through integration and comparison of the characteristics;

[0139] According to the formula:

[0140] ;

[0141] Generate a connectivity path trend analysis matrix, where Represents the path calibration value of the trend analysis matrix, is the flow velocity at the starting point of the path, in meters per second, is the flow velocity at the end of the path in meters per second, is the average pressure along the path in Pascals, is the penetration resistance of the path;

[0142] The flow velocity data at the start and end points of the path are obtained experimentally, in meters per second;

[0143] Pressure is the average pressure value along the path monitored by the sensor, in Pascals;

[0144] The permeation resistance is calculated using the resistance formula of the path, combining the path fluid parameters and experimentally measured data;

[0145] Assume that the flow rate at the starting point of the path is 2 meters per second, the flow rate at the end point is 1.5 meters per second, the average pressure of the path is 1000 Pascal, and the penetration resistance is 5 Pascal per cubic meter. Substitute into the formula:

[0146] ;

[0147] Calculation steps:

[0148] ;

[0149] The calculation results show that 100 is the calibration value of the trend analysis matrix of the path, which reflects the fluid dynamic behavior characteristics of the low-resistance path area. This result provides a basis for the generation of the trend analysis matrix of the connected path.

[0150] Specifically, if Figure 7 As shown in the figure, based on the connectivity path trend analysis matrix, the steps of extracting regional priority sorting rules, analyzing dynamic characteristic indicators, classifying the distribution results of low resistance and high permeability paths, and generating a key pore structure detection plan are as follows:

[0151] S601: Based on the connectivity path trend analysis matrix, extract the characteristic values of the low resistance distribution areas in the pore area, perform sorting operations according to the range of the regional characteristic values, and modify the sorting priority in combination with the fluid dynamic characteristic data. By analyzing the low resistance areas, generate the regional priority sorting results;

[0152] The average, maximum and minimum values of the resistance in each region are calculated through the dynamic distribution diagram of the regional resistance data. The dynamic characteristic parameters are extracted in combination with the flow velocity change trend, and a distribution histogram is constructed for the eigenvalue data. The data are grouped according to the interval range of the eigenvalue size. The regional sorting matrix is calculated using the grouped data, and the regions are preliminarily sorted in the order of the eigenvalue range from small to large. Through comparative analysis with the fluid dynamic characteristic data, the priority deviation caused by sudden change in flow velocity or abnormal regional data in the sorting process is corrected. The priority sorting result is updated using matrix operations to generate the regional priority sorting result.

[0153] S602: Using the regional priority ranking results, analyze the dynamic characteristic indicators of each distribution area, combine the path's permeability resistance value and dynamic flow rate data, and classify the path distribution into sections. By comparing the internal and external characteristics of each area, generate low resistance and high permeability path classification results;

[0154] Based on the permeability resistance value, fluid flow rate and regional geometric characteristic data within the partition, the distribution parameters inside and outside the path are compared and analyzed, the flow velocity distribution curve of each path segment is extracted, and the segmented classification is carried out in combination with the permeability resistance change trend. The classification algorithm is used to divide the path into two types: low-resistance path and high-permeability path. By comparing the resistance difference inside and outside the path, its change characteristics on the time axis are analyzed. Combined with the high-permeability path classification data, the accuracy of the regional classification is verified, and the classification results of low-resistance and high-permeability paths are generated, providing a detailed classification basis for subsequent dynamic analysis.

[0155] S603: Using the low resistance and high permeability path classification results, reanalyze the path distribution characteristics based on the regional ranking information, mark the key path areas based on the resistance and permeability differences between regions, and generate a key pore structure detection plan;

[0156] According to the formula:

[0157] ;

[0158] Mark the critical path area and generate the key detection plan for the pore structure, where: is the critical path calibration value, is the pressure at the end of the path, in Pascals, is the pressure at the starting point of the path, in Pascals, is the path flow velocity in meters per second, is the cross-sectional area of the path, in square meters;

[0159] The pressure at the starting and ending points of the path is collected in real time by the installed pressure sensors, and the initial and final pressure values of each path are recorded in Pascals;

[0160] The flow velocity of the path is monitored by the fluid velocity measuring equipment set up in the experiment, and the measurement data is extracted in meters per second;

[0161] The path cross-sectional area is obtained by geometric calculation method in the three-dimensional model of the pore area, and is calculated by integration based on the two-dimensional projection area of the cross-sectional morphology, with the unit being square meters;

[0162] Assume the pressure at the end of the path is 800 Pascals, the pressure at the starting point is 1200 Pascals, the flow rate of the path is 2 meters per second, and the cross-sectional area is 0.05 square meters. Substitute into the formula:

[0163] ;

[0164] Calculation steps:

[0165] ;

[0166] The calculation results show that -16000 is the critical path calibration value, indicating that the path has large pressure gradient changes and significant dynamic flow velocity characteristics. Combined with regional sorting and resistance permeability difference analysis, the critical path areas with high priority are marked, providing basic data basis for key pore structure detection, and optimizing detection efficiency through the sorting matrix.

[0167] like Figure 8 As shown, a shale gas reservoir pore structure detection system includes:

[0168] The pore parameter extraction module measures the pore volume and distribution density based on the pore structure data of the shale gas reservoir, calibrates the initial parameters of the pore permeability, analyzes the permeability field boundary data, compares the local flow characteristics of the pore path, and obtains the permeability distribution model;

[0169] The permeability distribution analysis module uses the permeability distribution model to analyze the distribution gradient of pore morphology, call pore connectivity parameters and fluid response characteristic parameters, perform screening and classification of characteristic differences, recalibrate the pore boundary range, and obtain the regional distribution structure of pore morphology;

[0170] The pore morphology regional analysis module analyzes the morphological change characteristic values of the pore region based on the pore morphology regional distribution structure, performs multi-point calculations on the permeability change characteristics, combines pore volume and fluid velocity data, calculates the fluid dynamic data within the region in sections, adjusts the dynamic relationship between regions, and constructs the pore dynamic behavior field;

[0171] The pore dynamics modeling module extracts the fluid dynamics distribution value from the pore dynamics behavior field, uses the fluid pressure gradient and flow velocity difference to analyze the distribution change trend of the fluid path, analyzes the boundary area characteristics of the connected path, and obtains the dynamic distribution map of the connected path;

[0172] The connectivity path trend analysis module analyzes the fluid dynamics and permeation resistance data within the path based on the connectivity path dynamic distribution diagram, distinguishes the resistance characteristics of high and low flow rate paths, marks low resistance area paths, and obtains a key detection plan for pore structure.

[0173] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for detecting the pore structure of a shale gas reservoir, characterized in that: The following steps are involved: Based on the pore structure data of shale gas reservoirs, pore volume parameters are extracted and analyzed, initial pore permeability parameters are corrected, local flow characteristics are extracted, and a permeability distribution model is generated; Based on the permeability distribution model, the pore morphology distribution characteristics are analyzed, the pore connectivity and fluid response characteristic parameters are called, the classification characteristic differences are screened, the regional pore boundaries are recalibrated, and the pore morphology regional distribution structure is generated; Based on the regional distribution structure of pore morphology, the pore regional morphology change characteristics are calculated, the fluid dynamics data is calculated segmentally in combination with the pore volume and flow velocity data, the regional dynamic relationship is adjusted, and the pore dynamics behavior field is generated; The steps of the pore dynamics behavior field are specifically as follows: Based on the regional distribution structure of pore morphology, the characteristic value of pore morphology change in each region is analyzed, and the characteristic value is used to perform comparative analysis of permeability between pore regions. By quantitatively processing the permeability data, the permeability characteristic data of the pore region is obtained; Using the pore area permeability characteristic data, combined with the pore volume and fluid velocity parameters of each area, performing segmented operations on the data, analyzing the fluid dynamic characteristics, and obtaining regional fluid dynamic characteristics results; Using the regional fluid dynamic characteristics results, a multivariate regression analysis algorithm is used to call the dynamic distribution trend value, correct the dynamic relationship between regions, and obtain the pore dynamic behavior field; Based on the pore dynamics behavior field, the fluid dynamics distribution value in the region is extracted, the pressure gradient and flow velocity difference values are used to classify the regions, the fluid path distribution change trend is refined, and a dynamic distribution map of the connected paths is generated; Based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high and low flow velocity paths are analyzed, the dynamic characteristics of the fluid are extracted, and the low resistance areas are analyzed and marked in combination with the permeation resistance trend calculation inside and outside the paths to generate a connected path trend analysis matrix; Based on the connectivity path trend analysis matrix, regional priority sorting rules are extracted, dynamic characteristic indicators are analyzed, low resistance and high permeability path distribution results are classified, and a key pore structure detection plan is generated; The steps of the pore structure key detection scheme are specifically as follows: Based on the connectivity path trend analysis matrix, the low resistance distribution area characteristic values of the pore area are extracted, sorting is performed according to the range of the regional characteristic values, and the sorting priority is corrected in combination with the fluid dynamic characteristic data. By analyzing the low resistance area, a regional priority sorting result is generated; Using the regional priority ranking results, the dynamic characteristic indicators of each distribution area are analyzed, and the path distribution is segmented and classified by combining the permeability resistance value and dynamic flow rate data of the path. By comparing the internal and external characteristics of each area, the low resistance and high permeability path classification results are generated; Using the low resistance and high permeability path classification results, the path distribution characteristics are reanalyzed according to the regional sorting information, and the key path areas are marked based on the resistance and permeability differences between regions to generate a key pore structure detection plan.

2. The method for detecting the pore structure of a shale gas reservoir according to claim 1, characterized in that: The permeability distribution model includes boundary data range, local flow feature extraction, and pore distribution density; the pore morphology area distribution structure includes distribution gradient value, connectivity parameter, and fluid response characteristics; the pore dynamics behavior field includes morphology change characteristic value, multi-point operation results, and fluid dynamic calculation; the connected path dynamic distribution diagram includes pressure gradient value, flow velocity difference, and dynamic distribution value; the connected path trend analysis matrix includes resistance distribution value, fluid dynamic characteristics, and low resistance area mark; the pore structure key detection scheme includes low resistance area characteristic value, priority sorting rules, and high permeability path identification.

3. The method for detecting the pore structure of a shale gas reservoir according to claim 1, wherein: Based on the pore structure data of shale gas reservoirs, the pore volume parameters are extracted and analyzed, the initial pore permeability parameters are corrected, the local flow characteristics are extracted, and the permeability distribution model is generated as follows: Based on the pore structure data of shale gas reservoirs, key pore structure parameters are extracted, pore volume and distribution density are identified and recorded, and a pore characteristic data set is obtained through data screening and aggregation; Based on the pore characteristic data set, identifying the need for adjustment of pore permeability parameters, including pore size and connectivity, and optimizing the pore permeability parameters using quantitative adjustment techniques to obtain corrected permeability parameters; The corrected permeability parameters are used to analyze the boundary data of the permeability field, identify key permeability paths and local flow characteristics, and perform data integration to construct a permeability distribution model.

4. The method for detecting the pore structure of a shale gas reservoir according to claim 1, characterized in that: Based on the permeability distribution model, the pore morphology distribution characteristics are analyzed, the pore connectivity and fluid response characteristic parameters are called, the classification characteristic differences are screened, the regional pore boundaries are recalibrated, and the steps of generating the pore morphology regional distribution structure are as follows: Based on the permeability distribution model, the gradient distribution of pore morphology in the region is analyzed, and by quantitative analysis of the gradient values, differentiated pore characteristic regions are distinguished to obtain a pore characteristic zoning map; Based on the pore characteristic zoning mapping, combined with the pore connectivity parameter and the fluid response characteristic parameter, a differentiated screening of regional pore characteristics is performed, and regions with different characteristics are screened through parameter comparison and metrological classification to obtain a differential region classification result; The pore boundaries of each region are corrected using the difference region classification results, and the regional distribution structure of the pore morphology is obtained through comparative analysis with real-time observation data.

5. The method for detecting the pore structure of a shale gas reservoir according to claim 1, wherein: The formula for the multivariate regression analysis algorithm is as follows: ； in, represents the predicted value of the pore dynamics behavior field, represents the average value of the fluid velocity, represents the average temperature of the fluid, represents the concentration of chemical components of the fluid, represents the weight coefficient obtained by data fitting, Represents the density of the fluid.

6. The method for detecting the pore structure of a shale gas reservoir according to claim 1, characterized in that: Based on the pore dynamics behavior field, the steps of extracting the fluid dynamics distribution value in the region, classifying the regions using the pressure gradient and flow velocity difference values, refining the fluid path distribution change trend, and generating a dynamic distribution map of the connected paths are as follows: Based on the pore dynamics behavior field, the fluid dynamics distribution value in the region is extracted, and the fluid pressure gradient value and flow velocity difference are analyzed by data to divide and classify the region, thereby obtaining the regional fluid dynamic characteristics classification result; Based on the classification results of the regional fluid dynamic characteristics, the distribution change trend of the fluid path is analyzed, the flow velocity and pressure data between the differentiated paths are compared, and the dynamic changes of the fluid behavior are plotted to obtain a dynamic change diagram of the fluid behavior; The fluid behavior dynamic change diagram is used to analyze the boundary area characteristics of the communication path, and a dynamic distribution diagram of the communication path is generated through comparison and analysis of the boundary data.

7. The method for detecting the pore structure of a shale gas reservoir according to claim 1, characterized in that: Based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high and low flow rate paths are analyzed, the dynamic characteristics of the fluid are extracted, and the low resistance areas are analyzed and marked in combination with the permeation resistance trend calculation inside and outside the paths. The specific steps for generating the connected path trend analysis matrix are as follows: Based on the dynamic distribution diagram of the connected paths, the resistance distribution values of the high-flow-velocity paths and the low-flow-velocity paths are analyzed, the fluid dynamic characteristics are extracted according to the distribution values, and the fluid dynamic characteristics classification of the regions is performed by analyzing the resistance differences to obtain the fluid dynamic characteristics regional division; Using the fluid dynamic characteristics regional division, combined with the penetration resistance data inside and outside the path, a trend analysis of the fluid dynamic characteristics is performed, and the fluid dynamics of the low resistance area is analyzed by data comparison to obtain a low resistance dynamic analysis result; The low-resistance dynamic analysis results are used to mark the fluid dynamic behavior of the corresponding path area, and the dynamic characteristics of the marked area are analyzed in detail. Through the integration and comparison of the characteristics, a connection path trend analysis matrix is generated.

8. A shale gas reservoir pore structure detection system, characterized in that: The system is used to implement the method for detecting the pore structure of a shale gas reservoir according to any one of claims 1 to 7, and the system comprises: The pore parameter extraction module measures the pore volume and distribution density based on the pore structure data of the shale gas reservoir, calibrates the initial parameters of the pore permeability, analyzes the permeability field boundary data, compares the local flow characteristics of the pore path, and obtains the permeability distribution model; The permeability distribution analysis module uses the permeability distribution model to analyze the distribution gradient of pore morphology, calls pore connectivity parameters and fluid response characteristic parameters, performs screening and classification merging of characteristic differences, recalibrates the pore boundary range, and obtains the regional distribution structure of pore morphology; The pore morphology regional analysis module analyzes the morphology change characteristic values of the pore region based on the pore morphology regional distribution structure, performs multi-point calculations on the permeability change characteristics, combines the pore volume and fluid flow rate data, calculates the fluid dynamic data in the region in sections, adjusts the dynamic relationship between regions, and constructs the pore dynamic behavior field; The pore dynamics modeling module extracts the fluid dynamics distribution value from the pore dynamics behavior field, uses the fluid pressure gradient and flow velocity difference to analyze the distribution change trend of the fluid path, analyzes the boundary area characteristics of the connected path, and obtains the dynamic distribution map of the connected path; The connectivity path trend analysis module analyzes the fluid dynamics and permeation resistance data within the path based on the connectivity path dynamic distribution diagram, distinguishes the resistance characteristics of high and low flow rate paths, marks low resistance area paths, and obtains a key detection plan for the pore structure.

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