Detection method and system for pore structure of shale gas reservoir

Abstract

The invention relates to the technical field of reservoir detection, in particular to a method and system for detecting a pore structure of a shale gas reservoir, and the method comprises the following steps: based on pore structure data of the shale gas reservoir, extracting and analyzing pore volume parameters, correcting initial parameters of pore permeability, extracting local flow characteristics, and generating a permeability distribution model. According to the method, through extraction of the pore volume parameter and the distribution density value, enhancement of correction of the reservoir permeability parameter, improvement of the accuracy of pore structure analysis, detailed analysis of regional characteristics by using the distribution gradient value of the pore morphology, and optimization of the application of pore connectivity and fluid response characteristics, the understanding of the dynamic relationship of the reservoir is improved; fluid dynamic data calculation is optimized through multi-point operation and dynamic relation correction, analysis of dynamic characteristics of a communication path is enhanced, decision-making efficiency of reservoir management is improved, fluid path analysis is optimized, high-permeability path recognition is enhanced, and economic benefits and development efficiency of a detection technology are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir detection, 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, 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 the existing technologies in reservoir detection include experimental testing technology, geological modeling and other means, common methods have limitations when dealing with the pore structure and fluid migration characteristics of unconventional oil and gas fields such as shale gas. The 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 of permeability assessment and resource development plans. The existing technologies rely on traditional well logging analysis and numerical simulation when analyzing reservoir permeability. When dealing with permeability changes in highly heterogeneous reservoirs, they are 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, there are often problems of insufficient understanding of reservoir characteristics and low development efficiency. 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] In order to achieve the above object, the present invention adopts the following technical scheme, a method for detecting the pore structure of a shale gas reservoir, comprising the following steps: 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; 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; S3: Based on the regional distribution structure of the pore morphology, the pore regional morphology change characteristics are calculated, the fluid dynamics data is calculated segmentedly in combination with the pore volume and flow rate data, the regional dynamic relationship is adjusted, and the pore dynamics behavior field is generated; S4: Based on the pore dynamics behavior field, extract the fluid dynamics distribution value in the region, classify the region 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 path; S5: Based on the dynamic distribution diagram of the connected path, 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 area is analyzed and the path area is marked in combination with the permeation resistance trend calculation inside and outside the path, so as to generate a connected path trend analysis matrix; 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.

[0007] As a further scheme of the present invention, 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 morphology change characteristic values, multi-point calculation results, and fluid dynamic calculations; 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.

[0008] 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 steps of generating the permeability distribution model are specifically as follows: S101: Based on the pore structure data of shale gas reservoirs, extract key parameters of pore structure, identify and record pore volume and distribution density, and obtain pore characteristic data set through data screening and aggregation; S102: identifying the adjustment requirements of pore permeability parameters, including pore size and connectivity, based on the pore characteristic data set, optimizing the pore permeability parameters by applying quantitative adjustment technology, and obtaining corrected permeability parameters; S103: Analyze the boundary data of the permeability field through the corrected permeability parameters, identify key permeability paths and local flow characteristics, integrate the data, and construct a permeability distribution model.

[0009] 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, the regional pore boundaries are recalibrated, and the steps of generating the pore morphology regional distribution structure are specifically as follows: S201: Based on the permeability distribution model, the gradient distribution of pore morphology in the region is analyzed, and differentiated pore characteristic regions are distinguished by quantitative analysis of gradient values ​​to obtain a pore characteristic partition map; S202: Based on the pore characteristic zoning mapping, 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 classification result of different regions; S203: Using the difference region classification results, calibrate the pore boundaries of each region, and obtain the pore morphology regional distribution structure through comparative analysis with real-time observation data.

[0010] As a further solution of the present invention, based on the regional distribution structure of pore morphology, the pore regional morphology change characteristics are calculated, the fluid dynamics data is calculated segmentedly in combination with the pore volume and flow rate data, and the regional dynamic relationship is adjusted to generate the pore dynamics behavior field. The specific steps are: S301: Based on the pore morphology regional distribution structure, analyzing the pore morphology change characteristic value of each region, using the characteristic value to perform comparative analysis of the permeability between pore regions, and obtaining pore region permeability characteristic data by quantitatively processing the permeability data; S302: using the pore area permeability characteristic data, combined with the pore volume and fluid velocity parameters of each area, performing segmented operation of the data, analyzing the fluid dynamic characteristics, and obtaining regional fluid dynamic characteristics results; 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.

[0011] As a further solution of the present invention, the formula using 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 chemical composition concentration of the fluid, represents the weight coefficient obtained by data fitting, Represents the density of the fluid.

[0012] As a further solution of the present invention, based on the pore dynamics behavior field, the fluid dynamics distribution value in the region is extracted, the pressure gradient and flow velocity difference value are used to classify the region, the fluid path distribution change trend is refined, and the steps of generating a dynamic distribution map of the connected path are specifically as follows: S401: extracting the fluid dynamics distribution value in 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 the regional fluid dynamics characteristic classification result; S402: Based on the regional fluid dynamic characteristics classification result, a distribution change trend analysis of the fluid path is performed, flow velocity and pressure data between differentiated paths are compared, and dynamic changes of fluid behavior are plotted to obtain a fluid behavior dynamic change diagram; S403: using the fluid behavior dynamic change graph, analyzing the boundary area characteristics of the connection path, and generating a connection path dynamic distribution graph by comparing and analyzing the boundary data.

[0013] As a further solution of the present invention, based on the dynamic distribution diagram of the connected path, 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 area is analyzed and the path area is marked in combination with the permeation resistance trend calculation inside and outside the path. The steps of generating the connected path trend analysis matrix are specifically as follows: S501: Based on the dynamic distribution diagram of the connecting path, analyzing the resistance distribution values ​​of the high flow velocity path and the low flow velocity path, extracting the fluid dynamic characteristics according to the distribution values, and classifying the fluid dynamic characteristics of the region by analyzing the resistance difference to obtain the fluid dynamic characteristics region division; S502: using the fluid dynamic characteristic area division, combined with the penetration resistance data inside and outside the path, performing a trend analysis of the fluid dynamic characteristics, and analyzing the fluid dynamics in the low resistance area through data comparison to obtain a low resistance dynamic analysis result; S503: Using the low-resistance dynamic analysis results, marking the fluid dynamic behavior of the corresponding path area, refining and analyzing the dynamic characteristics of the marked area, and generating a connected path trend analysis matrix through integration and comparison of the characteristics.

[0014] 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 low resistance and high permeability path distribution results, and generating a key pore structure detection plan are as follows: S601: extracting the characteristic values ​​of the low resistance distribution area of ​​the pore area based on the connection path trend analysis matrix, performing a sorting operation according to the range of the regional characteristic values, and correcting the sorting priority in combination with the fluid dynamic characteristic data, and generating a regional priority sorting result by analyzing the low resistance area; S602: using the regional priority ranking result, analyzing the dynamic characteristic index of each distribution area, combining the permeability resistance value and dynamic flow rate data of the path, classifying the path distribution by segment, and generating low resistance and high permeability path classification results by comparing the internal and external characteristics of each area; S603: Using the low resistance and high permeability path classification results, re-analyze the path distribution characteristics according to the regional sorting information, mark the key path areas through the resistance and permeability differences between the regions, and generate a key detection plan for the pore structure.

[0015] 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 comprises: The pore parameter extraction module measures the pore volume and distribution density based on the pore structure data of the shale gas reservoir, corrects 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 according to the pore morphology regional distribution structure, performs multi-point calculations on the permeability change characteristics, combines the pore volume and fluid velocity data, calculates the fluid dynamics data in the region in sections, adjusts the dynamic relationship between regions, and constructs the pore dynamics 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 connected path trend analysis module analyzes the fluid dynamics and permeation resistance data in the path based on the dynamic distribution diagram of the connected path, distinguishes the resistance characteristics of high and low flow rate paths, marks the low resistance area paths, and obtains the key detection plan of the pore structure.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: 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, the parameter application of pore connectivity and fluid response characteristics can be more accurate, and the understanding of reservoir dynamic relationships can be further improved. This multi-point operation and correction of dynamic relationships between regions optimize the calculation of fluid dynamic data, enhance the ability to analyze the dynamic characteristics of connected paths, and provide more efficient decision support for reservoir management. By extracting fluid dynamic characteristics and characteristic values ​​of low resistance distribution areas, the analysis of fluid migration paths is effectively optimized, and the ability to identify high permeability paths is enhanced, thereby significantly improving the economic benefits and development efficiency of reservoir detection technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0019] 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 indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] 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: S1: Based on the pore structure data of shale gas reservoirs, extract the 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; S2: Based on the permeability distribution model, the distribution gradient value of pore morphology is used to analyze the regional distribution characteristics, call the pore connectivity parameters and fluid response characteristic parameters, perform range screening and classification merging of characteristic differences, recalibrate the pore boundary range of each region, and generate the regional distribution structure of pore morphology; S3: Based on the regional distribution structure of pore morphology, the morphological change characteristic value of the pore area is used to perform multi-point calculations on the permeability change characteristics. The fluid dynamic data in the area is calculated in sections by combining the pore volume parameters and the fluid velocity data. The dynamic distribution trend value is called, the dynamic relationship between regions is corrected, and the pore dynamic behavior field is generated. S4: Based on the pore dynamics behavior field, extract the fluid dynamic distribution value in the region, use the fluid pressure gradient value and flow velocity difference value 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 path, and generate the dynamic distribution map of the connected path; S5: Based on the dynamic distribution diagram of the connected path, the resistance distribution values ​​of the high flow velocity path and the low flow velocity path are analyzed, the fluid dynamic characteristics of the path are extracted, the trend calculation is performed in combination with the penetration resistance data inside and outside the path, the low resistance distribution area is analyzed and the corresponding path area is marked, and the connected path trend analysis matrix is ​​generated; 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 the low resistance path and high permeability path, and generate a key detection plan for the pore structure.

[0021] 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 parameters, and fluid response characteristics. The pore dynamics behavior field includes morphology change characteristic values, multi-point calculation results, and fluid dynamic calculations. The dynamic distribution diagram of the connected path includes pressure gradient values, flow velocity differences, and dynamic distribution values. The connected path trend analysis matrix includes resistance distribution values, fluid dynamic characteristics, and low resistance area markings. The key detection plan for pore structure includes low resistance area characteristic values, priority sorting rules, and high permeability path identification.

[0022] Specifically, if Figure 2 As shown in FIG, 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 steps of generating the permeability distribution model are as follows:

[0023] S101: Based on the pore structure data of shale gas reservoirs, extract key parameters of pore structure, identify and record pore volume and distribution density, and obtain pore characteristic data set through data screening and aggregation; When extracting key parameters of pore structure, image analysis-based methods can be used to resolve and segment SEM data, and the pore morphology can be divided into three types: micropores, mesopores and macropores according to the volume range. The total volume proportion of different types of pores and the average volume of a single pore can be calculated. The pore distribution density can be identified by analyzing the spatial distribution of pores of different volumes in the reservoir, constructing a three-dimensional distribution map of pore density, and using this data to evaluate the storage capacity of different regions. 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.

[0024] S102: Based on the pore characteristic data set, identify the adjustment requirements of the pore permeability parameters, including pore size and connectivity, apply quantitative adjustment technology to optimize the pore permeability parameters, and obtain the corrected permeability parameters; When the need for permeability adjustment is identified, 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, the 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 parameters.

[0025] S103: Analyze the boundary data of the permeability field through the corrected permeability parameters, identify key permeability paths and local flow characteristics, and integrate the data to construct a permeability distribution model; The reservoir area is divided into several grid units using the corrected permeability parameters. The corresponding permeability value is calculated for each grid unit, and the boundary range of the permeability field is identified based on the differences in permeability in different regions. In the identification of the key permeability path, the permeability maximum value is gradually tracked, 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 inside the reservoir are quantified through the statistics of local flow characteristics. After integrating the boundary conditions and flow characteristics of the permeability of each region, the spatial interpolation method 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.

[0026] 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 of generating the regional distribution structure of the pore morphology are as follows:

[0027] S201: Based on the permeability distribution model, the gradient distribution of pore morphology in the region is analyzed, and the differentiated pore characteristic regions are distinguished through the quantitative analysis of the gradient values ​​to obtain the pore characteristic partition mapping; 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. A 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 mapping that can reflect the pore gradient characteristics in the area.

[0028] S202: Based on the pore characteristic zoning mapping, combined with the pore connectivity parameter and the fluid response characteristic parameter, perform differential screening of regional pore characteristics, screen the regions with different characteristics through parameter comparison and metrological classification, and obtain the classification results of the difference regions; Through multiple calculations to analyze the differentiated pore characteristics within the region, according to the formula: ; 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 in each partition is: Represents the number of partitions; Obtain pore connectivity data and derive connectivity factors based on the relationship between flow velocity and pressure difference through permeability experiments, in units of m²; 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; The pore connectivity and fluid response characteristic parameters in each partition are linearly weighted to calculate the comprehensive score. When selecting the weight coefficient, the variance normalized value of each indicator is used as the basis. 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: ; ; 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 need further optimization and screening.

[0029] S203: using the difference region classification results, correcting the pore boundary of each region, and obtaining the regional distribution structure of pore morphology through comparative analysis with real-time observation data; Firstly, 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 and the obviously discrete noise points are eliminated. The deviation value of each point from the ideal boundary of the region is calculated through a statistically-based boundary difference model. The correction model is constructed using a kernel function centered on the mean deviation value. The points whose deviation values ​​exceed a certain threshold are recalculated to belong to the region. 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 diagram of the pore morphology.

[0030] 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 segmentedly 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:

[0031] 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 compare and analyze the permeability between pore regions, and the permeability characteristic data of the pore region is obtained by quantitatively processing the permeability data; 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 permeability characteristics of differentiated regions are determined by fitting the permeability change curve based on the correlation between permeability and pore morphology characteristics. A regional permeability comparison analysis table is constructed using the permeability eigenvalue data. The permeability change trends of each region are compared to obtain the permeability characteristic data of the pore region.

[0032] S302: using the pore area permeability characteristic data, combined with the pore volume and fluid velocity parameters of each area, performing segmented operation of the data, analyzing the fluid dynamic characteristics, and obtaining regional fluid dynamic characteristics results; 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.

[0033] S303: using the regional fluid dynamic characteristics 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; 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 chemical composition concentration of the fluid, represents the weight coefficient obtained by data fitting, represents the density of the fluid; This formula takes into account multiple important physical and chemical parameters. By adjusting the weight coefficients and introducing square root and absolute value operations, the flexibility of the model and the prediction accuracy are improved. This method can not only more accurately describe the fluid dynamic characteristics in complex geological environments, but is also applicable to similar engineering and scientific research occasions. Detailed explanation of the formula and the process of formula calculation and derivation: The average value of the fluid velocity ( ): By arranging flow velocity sensors in different areas, the fluid velocity is continuously monitored, and the average value is calculated after recording the data. The unit is meters per second (m / s). Depending on the fluid type and pipeline conditions, the average velocity obtained by monitoring is set to 2.5m / s between 0.1 and 5m / s; 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; Fluid chemical composition concentration ( ): Collect fluid samples and use chemical analysis methods to determine the concentration of specific components in milligrams per liter (mg / L), ranging from 0 to 1000 mg / L depending on the purpose of the fluid, and set the concentration obtained by analysis to 200 mg / L; Fluid density ( ): Directly measured by a density meter, or by consulting a standard density table based on fluid composition and temperature, in kilograms per cubic meter (kg / m3), between 800 and 1200 kg / m3 depending on the fluid type, setting the measured density to 1000 kg / m3; Weight coefficient ( ): Through historical data and expert experience, determine the influence of each parameter on the pore dynamics behavior field. According to different working conditions and goals, the weight coefficient can be adjusted and set , , .

[0034] Compute linear combinations: ; ; Take the absolute value and square root: ; Compute the square root of the density: ; Calculate the final result: ; 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.

[0035] 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 value are used to classify the region, the fluid path distribution change trend is refined, and the steps of generating the dynamic distribution map of the connected path are as follows:

[0036] 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, and obtain the classification result of the regional fluid dynamic characteristics; The fluid pressure and velocity data at each time point are obtained through dynamic monitoring technology, and 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, the 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 regional fluid dynamic characteristics classification results are formed through statistical analysis of the differences between categories.

[0037] S402: Based on the regional fluid dynamic characteristics classification results, the distribution change trend of the fluid path is analyzed, the flow velocity and pressure data between the differentiated paths are compared, the dynamic change of the fluid behavior is plotted, and a dynamic change diagram of the fluid behavior is obtained; When analyzing the distribution change trend of fluid paths, we first calibrate the path distribution in the regional classification results in sections, calculate the average flow velocity and pressure data of each section of the path, express the change trend of the path flow velocity over time as a time series, and fit the flow velocity change curve of the path 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, and compare and analyze the flow velocity change data between paths. By constructing a dynamic change matrix, the time correlation characteristics between paths are obtained. Finally, the flow velocity change trend and pressure distribution are combined to generate a dynamic change map of fluid behavior.

[0038] 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 by comparing and analyzing the boundary data; According to the formula: ; Generate a dynamic distribution graph of connected paths, where represents the dynamic feature strength 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 in meters per second, is the number of path segments; The pressure difference was monitored by a manometer and calculated by recording the pressure values ​​at the starting and end points of each path, in Pascals; The path length is measured directly in the three-dimensional distribution map of the path, in meters; The flow velocity was obtained by tracer experiments to obtain the average velocity of each path, in meters per second; 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: ; Calculation steps: ; 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.

[0039] Specifically, if Figure 6 As shown in the figure, based on the dynamic distribution diagram of the connected path, 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 area is analyzed and the path area is 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:

[0040] 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 execution area is performed by analyzing the resistance difference to obtain the fluid dynamic characteristics area division; 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 areas of resistance, and finally the regional division of fluid dynamic characteristics is formed.

[0041] S502: using the fluid dynamic characteristics area division, combined with the penetration resistance data inside and outside the path, performing the trend analysis of the fluid dynamic characteristics, and analyzing the fluid dynamics in the low resistance area through data comparison to obtain the low resistance dynamic analysis result; 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 dynamic behavior of the fluid dynamics in the low resistance area is obtained, and the data is presented in a concrete way to form a low resistance dynamic analysis result.

[0042] S503: using the low-resistance dynamic analysis results, marking the fluid dynamic behavior of the corresponding path area, refining the dynamic characteristics of the marked area, and generating a connected path trend analysis matrix through integration and comparison of the characteristics; According to the formula: ; Generate a connectivity path trend analysis matrix, where represents the path calibration value of the trend analysis matrix, is the flow velocity at the start 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; The flow velocity data at the start and end of the path were obtained experimentally, in meters per second; Pressure is the average pressure value of the path monitored by the sensor, in Pascals; The permeation resistance is calculated by the resistance formula of the path, combining the path fluid parameters and experimentally measured data; Set the flow rate at the starting point of the path to 2 meters per second, the flow rate at the end point to 1.5 meters per second, the average pressure of the path to 1000 Pascals, and the penetration resistance to 5 Pascals per cubic meter. Substitute into the formula: ; Calculation steps: ; 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 definite basis for the generation of the trend analysis matrix of the connected path.

[0043] 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:

[0044] S601: based on the connectivity path trend analysis matrix, extract the characteristic values ​​of the low resistance distribution area of ​​the pore area, perform sorting operations according to the range of the regional characteristic values, and correct the sorting priority in combination with the fluid dynamic characteristic data, and generate the regional priority sorting result by analyzing the low resistance area; The average, maximum and minimum values ​​of the resistance in each area 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 order from small to large in the eigenvalue range. Through comparative analysis with the fluid dynamic characteristics data, the priority deviation caused by sudden changes in flow velocity or abnormal regional data in the sorting process is corrected, and the priority sorting results are updated using matrix operations to generate regional priority sorting results.

[0045] S602: using the regional priority ranking results, analyzing the dynamic characteristic index of each distribution area, combining the permeability resistance value and dynamic flow rate data of the path, classifying the path distribution by segment, and generating low resistance and high permeability path classification results by comparing the internal and external characteristics of each area; 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 difference in resistance inside and outside the path, its change characteristics on the time axis are analyzed, and 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.

[0046] S603: Using the classification results of low resistance and high permeability paths, reanalyze the path distribution characteristics according to the regional sorting information, mark the key path areas through the resistance and permeability differences between regions, and generate a key detection plan for the pore structure; According to the formula: ; 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 start of the path, in Pascals, is the path velocity in meters per second, is the cross-sectional area of ​​the path, in square meters; The pressure at the start and end of the path is collected in real time by the installed pressure sensor, and the initial and final pressure values ​​of each path are recorded in Pascals; 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; The path cross-sectional area is obtained by the 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, and the unit is square meters; Set the pressure at the end of the path to 800 Pascals, the pressure at the starting point to 1200 Pascals, the flow rate of the path to 2 meters per second, and the cross-sectional area to 0.05 square meters. Substitute into the formula: ; Calculation steps: ; The calculation results show that -16000 is the critical path calibration value, indicating that there are large pressure gradient changes and significant dynamic flow velocity characteristics on this path. 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.

[0047] like Figure 8As shown, a shale gas reservoir pore structure detection system includes: The pore parameter extraction module measures the pore volume and distribution density based on the pore structure data of the shale gas reservoir, corrects 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, call the pore connectivity parameters and fluid response characteristic parameters, perform the screening and classification of characteristic differences, recalibrate the pore boundary range, and obtain the regional distribution structure of pore morphology; The pore morphology regional analysis module analyzes the morphology change characteristic values ​​of the pore area according to the pore morphology regional distribution structure, performs multi-point calculations on the permeability change characteristics, combines the pore volume and fluid velocity data, calculates the fluid dynamics data in the region in sections, adjusts the dynamic relationship between regions, and constructs the pore dynamics 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 permeability 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 key pore structure detection plans.

[0048] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope 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 segmentedly in combination with the pore volume and flow velocity data, the regional dynamic relationship is adjusted, and the pore dynamics behavior field is generated; Based on the pore dynamics behavior field, the fluid dynamics distribution value in the region is extracted, the pressure gradient and flow velocity difference value are used to classify the region, the fluid path distribution change trend is refined, and a dynamic distribution map of the connected path is generated; Based on the dynamic distribution diagram of the connected path, 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 the path areas are marked in combination with the permeation resistance trend calculation inside and outside the path, so as 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.

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 regional distribution structure includes distribution gradient value, connectivity parameter, and fluid response characteristics; the pore dynamics behavior field includes morphology change characteristic values, multi-point operation results, and fluid dynamic calculations; 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 plan includes low resistance area characteristic value, priority sorting rules, and high permeability path identification.

3. The method for detecting pore structure of shale gas reservoir according to claim 1, characterized in that: 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 parameters of pore structure are extracted, pore volume and distribution density are identified and recorded, and pore characteristic data sets are obtained through data screening and aggregation; According to the pore characteristic data set, identifying the adjustment requirements of pore permeability parameters, including pore size and connectivity, optimizing the pore permeability parameters by applying quantitative adjustment technology, and obtaining 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 pore structure of shale gas reservoir according to claim 1, characterized in that: Based on the permeability distribution model, the steps of analyzing the pore morphology distribution characteristics, calling the pore connectivity and fluid response characteristic parameters, screening the classification characteristic differences, recalibrating the regional pore boundaries, and 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 partition 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 classification result of the difference regions; 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 pore structure of shale gas reservoir according to claim 1, characterized in that: Based on the regional distribution structure of pore morphology, the pore regional morphology change characteristics are calculated, the fluid dynamics data is calculated segmentedly in combination with the pore volume and flow rate data, and the regional dynamic relationship is adjusted to generate the pore dynamics behavior field. The specific steps are: 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 compare and analyze the permeability between pore regions, and the permeability characteristic data of the pore region is obtained by quantitatively processing the permeability data; 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 adopted to call the dynamic distribution trend value, correct the dynamic relationship between regions, and obtain the pore dynamic behavior field.

6. The method for detecting pore structure of shale gas reservoir according to claim 5, characterized in that: 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 chemical composition concentration of the fluid, represents the weight coefficient obtained by data fitting, Represents the density of the fluid.

7. The method for detecting pore structure of 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 region by using the pressure gradient and flow velocity difference value, refining the fluid path distribution change trend, and generating the dynamic distribution map of the connected path 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 classification result of the regional fluid dynamic characteristics; 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, the dynamic changes of the fluid behavior are plotted, and a dynamic change diagram of the fluid behavior is obtained; The fluid behavior dynamic change diagram is used to analyze the boundary area characteristics of the connected path, and a dynamic distribution diagram of the connected path is generated by comparing and analyzing the boundary data.

8. The method for detecting pore structure of shale gas reservoir according to claim 1, characterized in that: Based on the dynamic distribution diagram of the connected path, 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 area is analyzed and the path area is marked in combination with the permeation resistance trend calculation inside and outside the path. The steps of generating the connected path trend analysis matrix are specifically 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 execution area is performed by analyzing the resistance difference to obtain the fluid dynamic characteristics area division; The fluid dynamic characteristics area division is adopted, and the penetration resistance data inside and outside the path are combined to perform the trend analysis of the fluid dynamic characteristics, and the fluid dynamics in the low resistance area is analyzed by data comparison to obtain the 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 connected path trend analysis matrix is ​​generated.

9. The shale gas reservoir pore structure detection system according to claim 1, characterized in that: Based on the connectivity path trend analysis matrix, the steps of extracting regional priority sorting rules, analyzing dynamic characteristic indicators, classifying low resistance and high permeability path distribution results, and generating a key pore structure detection plan are as follows: Based on the connectivity path trend analysis matrix, the characteristic values ​​of the low resistance distribution area 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, and the regional priority sorting result is generated by analyzing the low resistance area; The regional priority ranking result is used to analyze the dynamic characteristic index of each distribution area, 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 classification results of low resistance and high permeability paths 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 through the resistance and permeability differences between regions to generate a key detection plan for the pore structure.

10. A shale gas reservoir pore structure detection system, characterized in that: According to the method for detecting the pore structure of a shale gas reservoir according to any one of claims 1 to 9, 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, corrects 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 according to the pore morphology regional distribution structure, performs multi-point calculations on the permeability change characteristics, combines the pore volume and fluid velocity data, calculates the fluid dynamics data in the region in sections, adjusts the dynamic relationship between regions, and constructs the pore dynamics 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 connected path trend analysis module analyzes the fluid dynamics and permeation resistance data in the path based on the dynamic distribution diagram of the connected path, distinguishes the resistance characteristics of high and low flow rate paths, marks the low resistance area paths, and obtains the key detection plan of the pore structure.

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