Shale gas resource quantity calculation method and system in complex structure region

Abstract

The application discloses a kind of complex structure area shale gas resource quantity calculation method and system, belong to shale gas field, steps include: S1. collection complex structure area three-dimensional seismic data, well logging data and core analysis data, and carry out high frequency denoising and wave impedance inversion;S2. fracture distribution model is generated using fracture structure quantitative description algorithm, and three-dimensional geological structure model is established in combination with well logging lithology identification;S3. three-dimensional geological structure model is corrected to high-precision stratum thickness, and reservoir effective stress distribution is calculated using stress field numerical inversion;S4. reservoir effective porosity-permeability parameter distribution is calculated in combination with porosity-permeability nonlinear coupling equation;S5. obtain porosity structure parameter, and superimposed formation reservoir physical property comprehensive model;S6. to the model is carried out super-resolution volume modeling, generates three-dimensional reserve model;S7. based on three-dimensional reserve model, cell resource quantity is calculated and summed up in unit. Advantageous effect: improve the accuracy and stability of complex structure area shale gas resource quantity calculation.

Description

Methods and Systems for Calculating Shale Gas Resources in Complex Structural Zones Technical Field

[0001] This invention relates to the field of shale gas, and more specifically, to a method and system for calculating shale gas resources in complex tectonic zones. Background Technology

[0002] The development of methods and systems for calculating shale gas resources in complex structural areas initially relied on two-dimensional seismic interpretation and simple reservoir parameter estimation, which struggled to accurately reflect the true conditions of fracture-developed zones and heterogeneous reservoirs. With the introduction of three-dimensional seismic technology, refined well logging interpretation, and numerical simulation methods, the accuracy of geological structural characterization and reservoir evaluation capabilities have gradually improved, forming a comprehensive workflow centered on seismic interpretation, geological modeling, and reserve estimation. In this process, the improved signal-to-noise ratio of seismic data processing, the optimization of geological modeling algorithms, and the diversification of methods for obtaining physical property parameters have significantly enhanced the reliability of the calculation results, providing a more scientific basis for shale gas exploration and development.

[0003] However, existing technologies still have several shortcomings: the utilization rate of high-frequency seismic information is limited, resulting in insufficient characterization of fracture boundaries; the quantitative characterization of fracture structures and the integration with well logging lithology are not deep enough, limiting the realism of 3D geological models; the matching degree between grids and discontinuities in stress field simulation is not high, affecting the accuracy of mechanical field distribution; high-resolution stress clustering analysis is lacking in reservoir mechanical zoning; the porosity-permeability relationship model does not adequately consider the influence of effective stress; the fusion degree of multi-source experimental data in parameter calibration is low, making it easy to get trapped in local optima; there is a resolution loss in pore structure determination and physical property fusion; and there is still room for improvement in the ability of 3D reservoir models to express small-scale structures. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for calculating shale gas resources in complex structural areas, in order to solve the problems mentioned in the background: the limited utilization rate of high-frequency seismic information leads to insufficient characterization of fracture boundaries; the insufficient integration of quantitative characterization of fracture structures with well logging lithology limits the realism of three-dimensional geological models; the low matching degree between grids and discontinuities in stress field simulation affects the accuracy of mechanical field distribution; the lack of high-resolution stress clustering analysis in reservoir mechanical zoning; insufficient consideration of the influence of effective stress in porosity-permeability relationship models; low integration of multi-source experimental data in parameter calibration, easily getting trapped in local optima; resolution loss in pore structure determination and physical property integration; and room for improvement in the ability of three-dimensional reserve models to express small-scale structures.

[0005] Technical solution: The method for calculating shale gas resources in complex structural areas includes the following steps:

[0006] S1. Collect 3D seismic data, well logging data and core analysis data of complex structural areas, and perform high-frequency denoising and wave impedance inversion processing on the 3D seismic data, well logging data and core analysis data to obtain detailed structural interpretation results;

[0007] S2. Based on the detailed structural interpretation results, a fracture distribution model is generated using a fracture structure quantitative characterization algorithm, and a three-dimensional geological structure model is established by combining the well logging lithology identification results.

[0008] S3. Perform high-precision formation thickness correction on the three-dimensional geological structure model, and use the stress field numerical inversion method to calculate the effective stress distribution of the reservoir;

[0009] S4. Based on the effective stress distribution of the reservoir, calculate the effective porosity-permeability parameter distribution of the reservoir using the porosity-permeability nonlinear coupling equation;

[0010] S5. Using a multi-scale shale organic matter pore distribution measurement method, pore structure parameters are obtained and superimposed on the effective porosity and permeability parameter distribution of the reservoir to form a comprehensive reservoir property model;

[0011] S6. Perform super-resolution volume modeling on the reservoir property integrated model to generate a three-dimensional reservoir model for complex structural zones;

[0012] S7. Based on the three-dimensional reserve model of the complex tectonic zone, calculate the resource quantity of each geological unit grid by grid, and sum the total resource quantity of the region.

[0013] Preferably, the S1 high-frequency noise reduction process further includes the following steps:

[0014] S1-1. After dividing the three-dimensional seismic data into blocks according to trace gathers, the wavelet packet decomposition method with adaptive threshold selection is used to separate the frequency components above 80Hz.

[0015] S1-2. Perform singular value decomposition on the frequency components above 80Hz and remove feature vectors with a contribution of less than 0.05 to reconstruct low-noise seismic data blocks;

[0016] S1-3. Perform inter-block phase consistency correction on the reconstructed seismic data blocks to obtain the denoised 3D seismic data volume.

[0017] Preferably, the wavelet packet decomposition method uses complex Morlet mother wavelets to construct basis functions and terminates the decomposition when the number of decomposition layers reaches 8. The adaptive threshold selection adopts the layer-by-layer energy ratio threshold criterion.

[0018] Preferably, step S1-1 further includes the following step:

[0019] S1-1-1. In the scale selection of the complex Morlet mother wavelet, the scale parameter is discretized into an even sequence and the local energy spectrum is calculated for each scale;

[0020] S1-1-2. Based on the local energy spectrum, a dual-threshold strategy is adopted to determine the interlayer threshold, with the lower threshold being 0.01 of the maximum value of the local energy spectrum and the upper threshold being 0.10 of the maximum value of the local energy spectrum;

[0021] S1-1-3. After threshold removal, edge-preserving reconstruction is performed on the remaining coefficients, and the reconstruction window length is dynamically adjusted according to the peak width of the seismic gather.

[0022] Preferably, the S3 stress field numerical inversion method uses a linear elastic constitutive model to simulate the mechanical response of the elements. Before the finite element solution, the three-dimensional geological structure model automatically generates a mesh according to the geometric and physical property discontinuities. The mesh is composed of tetrahedral elements with a side length not exceeding 5 meters. An in-situ stress gradient field with a linear distribution according to depth is applied in the boundary condition settings.

[0023] Preferably, step S3 further includes the following step:

[0024] S3-1. After the finite element solution is completed, calculate the principal stress value for each tetrahedral element and perform tension-compression discrimination;

[0025] S3-2. Input the elements identified as compressive stress into the clustering module, and use the K-means clustering algorithm to divide the stress partitions according to the similarity of the principal stress vectors. The number of clusters is determined by the elbow method.

[0026] S3-3. The stress zoning results are superimposed onto the three-dimensional geological structure model to form a zoning stress distribution map, which is used for index mapping of subsequent reservoir effective porosity and permeability parameter allocation.

[0027] Preferably, the porosity-permeability nonlinear coupling equation of S4 is expressed as:

[0028]

[0029] Wherein, k is the effective penetration rate, and the For effective porosity, the For effective stress, the The above The n and m are measured calibration parameters; the porosity-permeability nonlinear coupling equation is used as the numerical description of S4 in the calculation process.

[0030] Preferably, the parameter calibration of S4 adopts the joint inversion of in-situ steady-state gas seepage experimental data and nuclear magnetic resonance borehole measurement experimental data. The joint inversion uses the Tikhonov regularized inversion operator as the parameter estimation framework and uses multi-starting-point least squares search to determine the globally optimal calibration parameter set.

[0031] Preferably, step S4 further includes the following step:

[0032] S4-1. In the in-situ steady-state gas seepage experiment, the steady-state flow rate was measured according to three different average pressure difference levels, the steady-state pressure distribution was recorded and the single-point permeability was calculated.

[0033] S4-2. In the nuclear magnetic resonance porosimetry experiment, T2 spectra were obtained at the same pore location of the sample and pore size was converted. Porosity distribution vectors were obtained according to pore size ranges.

[0034] S4-3. Input the steady-state flow rate-derived single-point permeability and the porosity allocation vector into the Tikhonov regularized inversion operator, and solve the parameters through parallel multi-starting-point solution.

[0035] Preferably, the shale gas resource calculation system for complex structural areas relates to the shale gas resource calculation method for complex structural areas described in claims 1-9; characterized in that the shale gas resource calculation system for complex structural areas is designed based on the shale gas resource calculation method for complex structural areas.

[0036] Compared with the prior art, the advantages of this invention are:

[0037] (1) In the process of seismic data processing in complex tectonic areas, a combination of high-frequency denoising and wave impedance inversion is introduced, and the high-frequency signal fidelity is significantly improved by combining wavelet packet decomposition and singular value decomposition.

[0038] (2) In the characterization of fracture structures, a quantitative characterization algorithm for fracture structures is used to generate a fracture distribution model, which is then combined with the results of well logging lithology identification to establish a more accurate three-dimensional geological structure model.

[0039] (3) In the calculation of reservoir stress field, a linear elastic constitutive model is introduced in conjunction with automatic mesh generation of finite element elements to ensure the true simulation accuracy of geometric and physical property discontinuities.

[0040] (4) The stress zoning process adds principal stress tension-compression discrimination and K-means clustering based on principal stress vector similarity to realize the spatial zoning of reservoir mechanical properties.

[0041] (5) The porosity-permeability relationship adopts a nonlinear coupling equation and introduces an effective stress index term to better reflect the actual response characteristics of shale gas reservoirs.

[0042] (6) During the calibration of porosity-permeability parameters, the data from in-situ steady-state gas flow experiments and nuclear magnetic resonance porosity measurements are combined and inverted. Tikhonov regularization and multi-starting-point search are introduced to ensure the global optimal solution.

[0043] (7) Use the multi-scale shale organic matter pore distribution measurement method in the pore structure parameter determination, and integrate it with the effective pore permeability parameter distribution to form a comprehensive reservoir property model.

[0044] (8) Introduce super-resolution volume modeling technology in the three-dimensional reserve modeling stage to improve the small-scale structural expression ability of the reserve model in complex tectonic areas. Attached Figure Description

[0045] Figure 1 is a schematic diagram of the overall process of the method for calculating shale gas resources in complex structural areas according to the present invention. Detailed Implementation

[0046] Example: Please refer to Figure 1. 1. Method for calculating shale gas resources in complex structural areas, characterized in that the method for calculating shale gas resources in complex structural areas includes the following steps:

[0047] S1. Collect 3D seismic data, well logging data and core analysis data of complex structural areas, and perform high-frequency denoising and wave impedance inversion processing on the 3D seismic data, well logging data and core analysis data to obtain detailed structural interpretation results;

[0048] S2. Based on the results of the detailed structural interpretation, a fracture distribution model is generated using a quantitative fracture structure characterization algorithm, and a three-dimensional geological structure model is established by combining the well logging lithology identification results.

[0049] S3. Perform high-precision formation thickness correction on the three-dimensional geological structure model, and use the stress field numerical inversion method to calculate the effective stress distribution of the reservoir;

[0050] S4. Based on the effective stress distribution of the reservoir, the distribution of effective porosity and permeability parameters of the reservoir is calculated by combining the nonlinear coupling equation of porosity and permeability.

[0051] S5. Using a multi-scale shale organic matter pore distribution measurement method, pore structure parameters are obtained and superimposed on the reservoir effective porosity and permeability parameter distribution to form a comprehensive reservoir property model;

[0052] S6. Perform super-resolution volumetric modeling on the integrated reservoir property model to generate a three-dimensional reservoir model for complex structural zones;

[0053] S7. Based on the three-dimensional reserve model of complex tectonic zones, calculate the resource quantity of each geological unit grid by grid, and sum the total resource quantity of the region.

[0054] Specifically, when acquiring 3D seismic data using S1, a sampling interval of 1ms and a source frequency of 30Hz should be used. Well logging curves should include sonic transit time, density, gamma rays, and resistivity. Core analysis data should include measured values ​​of mineral composition, porosity, and permeability. High-frequency denoising should employ wavelet packet decomposition combined with singular value decomposition, with a decomposition layer count of 6 and a singular value threshold of 95% of the total energy. After denoising, inter-channel phase consistency correction is required to ensure waveform continuity across the entire signal domain. Impedance inversion should use a model-constrained least-squares inversion method, achieving a volumetric impedance resolution 1.5 times that of the original seismic body. Quantitative characterization of fracture structures should combine curvature attribute analysis with an ant-tracking algorithm, and be interactively corrected with well logging lithology identification results. The 3D geological structure model should use tetrahedral elements with an average element side length not exceeding 2m. Stress field calculations should be based on a linear elastic constitutive model, with boundary conditions of a free top surface, a fixed bottom surface, and horizontal displacement constraints around the perimeter. Automatic mesh generation must ensure a node matching accuracy of 0.01m for discontinuous surfaces. The porosity-permeability equation is adopted Among them, effective stress The pore pressure was calculated from the measured difference between pore pressure and geostress. During parameter calibration, the pressure difference for the in-situ steady-state gas flow experiment was set to 0.5 MPa, 1 MPa, and 1.5 MPa; the echo interval for the nuclear magnetic resonance (NMR) porosimetry experiment was 0.2 ms, the Tikhonov regularization coefficient was 0.01, and the number of searches at multiple starting points was no less than 100. Multi-scale pore distribution measurements were performed at the nanometer, micrometer, and millimeter scales, using nitrogen adsorption, NMR, and X-ray CT scanning respectively, and then fused into a unified reservoir property model through voxel registration. Super-resolution volumetric modeling, based on a deep convolutional neural network, doubled the resolution of the original model. The resource quantity per unit was calculated by comprehensively considering the pore volume, saturation, and gas compressibility factor of each unit, and finally summed to obtain the resource quantity.

[0055] S1 high-frequency noise reduction processing also includes the following steps:

[0056] S1-1. After dividing the 3D seismic data into blocks according to gathers, the wavelet packet decomposition method with adaptive threshold selection is used to separate the frequency components above 80Hz.

[0057] S1-2. Perform singular value decomposition on frequency components above 80Hz and remove eigenvectors with a contribution of less than 0.05 to reconstruct low-noise seismic data blocks;

[0058] S1-3. Perform inter-block phase consistency correction on the reconstructed seismic data blocks to obtain the denoised 3D seismic data volume.

[0059] Specifically, the gather division should follow the CMP gather pattern, with each gather containing 100 seismic traces; wavelet packet decomposition uses complex Morlet mother wavelets, with a decomposition layer of 6 and a bandwidth parameter of 0.8; singular value decomposition needs to be truncated according to the matrix energy accumulation ratio of 95%, retaining the main signal components; inter-block phase consistency correction is achieved by adjusting the phase spectrum of the reference trace, which is the trace with the highest energy.

[0060] The wavelet packet decomposition method uses complex Morlet mother wavelets to construct basis functions and terminates the decomposition when the number of decomposition layers reaches 8. The adaptive threshold selection adopts the energy ratio threshold criterion layer by layer.

[0061] Specifically, the center frequency of the complex Morlet mother wavelet is 0.8125, and the bandwidth parameter is 0.5; the decomposition termination condition is that the energy of the current layer is less than 5% of that of the previous layer; the adaptive threshold formula is... ,in Where is the noise standard deviation, N is the number of sample points, and the layer-by-layer energy ratio criterion is calculated based on the proportion of energy in each layer to the total energy.

[0062] S1-1 also includes the following steps:

[0063] S1-1-1. In the scale selection of complex Morlet mother wavelet, the scale parameter is discretized into an even sequence and the local energy spectrum is calculated for each scale;

[0064] S1-1-2. Based on the local energy spectrum, a two-end threshold strategy is adopted to determine the interlayer threshold. The lower threshold is 0.01 of the maximum value of the local energy spectrum, and the upper threshold is 0.10 of the maximum value of the local energy spectrum.

[0065] S1-1-3. After threshold removal, edge-preserving reconstruction is performed on the remaining coefficients, and the reconstruction window length is dynamically adjusted according to the peak width of the seismic gather.

[0066] Specifically, the decomposition scale parameter takes a logarithmic sequence from 2 to 64, with an interval of 2; the local energy spectrum is calculated using a sliding window length of 256 points and an overlap rate of 50%; the lower threshold of the dual-threshold strategy is 1.5 times the noise standard deviation, and the upper threshold is 0.7 times the root mean square amplitude of the signal; the edge-preserving reconstruction adopts a weighted average fusion method of adjacent window reconstruction results.

[0067] The S3 stress field numerical inversion method uses a linear elastic constitutive model to simulate the mechanical response of the elements. Before the finite element solution, the three-dimensional geological structure model automatically generates a mesh according to the geometric and physical property discontinuities. The mesh consists of tetrahedral elements with a side length of no more than 5 meters. In the boundary condition settings, an in-situ stress gradient field with a linear distribution according to depth is applied.

[0068] Specifically, the formula for the linear elastic constitutive model is as follows: Where D is the elastic matrix, and the parameters are obtained from triaxial compression experiments of the core. The automatic identification of discontinuities is achieved through seismic attribute volume edge detection and voxel connectivity analysis. The tetrahedral element mesh is generated by Delaunay meshing, and the element quality control index is a shape ratio greater than 0.3 and a node matching error less than 0.01m. The in-situ stress gradient increases linearly with depth, and the gradient value is obtained by fitting the measured data.

[0069] S3 also includes the following steps:

[0070] S3-1. After the finite element solution is completed, calculate the principal stress value for each tetrahedral element and perform tension-compression discrimination;

[0071] S3-2. Input the elements identified as compressive stress into the clustering module, and use the K-means clustering algorithm to divide the stress partitions according to the similarity of the principal stress vectors. The number of clusters is determined by the elbow method.

[0072] S3-3. The stress zoning results are superimposed onto the three-dimensional geological structure model to form a zoning stress distribution map. The zoning stress distribution map is used for index mapping of subsequent reservoir effective porosity and permeability parameter allocation.

[0073] Specifically, the tensile-compressive nature of the principal stresses is classified according to the comparison between the direction of the maximum principal stress and the perpendicular stress. If the maximum principal stress is greater than the perpendicular stress, it is classified as tensile; otherwise, it is classified as compressive. The cosine similarity formula is used for the similarity of the principal stress vectors. The similarity threshold was set to 0.95; the K value of K-means clustering was determined based on the Calinski-Harabasz index, and the initial cluster centers were selected by the maximum-minimum distance method; when the clustering results were mapped to the three-dimensional geological structure model, a voxel labeling method with one-to-one correspondence between grid nodes was adopted, and each voxel recorded the tensile-compressive category and stress amplitude.

[0074] The nonlinear coupling equation of porosity-permeability for S4 is expressed as:

[0075]

[0076] Where k is the effective penetration rate, For effective porosity, For effective stress, , , n, and m are measured calibration parameters; the nonlinear coupling equation of porosity-permeability is used as the numerical description of S4 in the calculation process.

[0077] Specifically, The average value was taken from the core sample porosity test in the laboratory. The permeability was taken from the average value of the corresponding porosity sample; the n value was obtained by fitting the measured data using the least squares method, and the m value was obtained by fitting the permeability changes under different confining pressures in the gas flow experiment; effective stress During the calculation, pore pressure was measured by multi-point pressure gauges, and geostress was determined by a combination of sonic transit time inversion and measured wellbore stress tests. All parameters were dimensionless before calculation to ensure model stability under different unit systems.

[0078] The parameter calibration of S4 adopts the joint inversion of in-situ steady-state gas seepage experimental data and nuclear magnetic resonance borehole measurement experimental data. The joint inversion uses the Tikhonov regularized inversion operator as the parameter estimation framework, and uses multi-starting-point least squares search to determine the globally optimal calibration parameter set.

[0079] Specifically, the in-situ steady-state gas percolation experiment used nitrogen as the medium, with an experimental temperature of 25℃. The percolation direction was parallel to the sample bedding direction, and the pressure difference was set to three levels: 0.5MPa, 1MPa, and 1.5MPa. The nuclear magnetic resonance porosity measurement experiment used a Carr-Purcell-Meiboom-Gill sequence with an echo interval of 0.2ms and a T2 spectral resolution of 128 channels. The Tikhonov regularization coefficient was fixed at 0.01, and 100 uniformly distributed initial parameters were used for the multi-starting point search, with each parameter covering ±50% of the measured value. The inversion algorithm adopted the conjugate gradient method, and the convergence criterion was that the rate of change of the objective function was less than 100%. .

[0080] S4 also includes the following steps:

[0081] S4-1. In the in-situ steady-state gas seepage experiment, the steady-state flow rate was measured according to three different average pressure difference levels, the steady-state pressure distribution was recorded and the single-point permeability was calculated.

[0082] S4-2. In the nuclear magnetic resonance porosimetry experiment, T2 spectra were obtained at the same pore location of the sample and pore size was converted. The porosity distribution vector was obtained according to the pore size range.

[0083] S4-3. Input the steady-state flow rate - derived single-point permeability and porosity allocation vector into the Tikhonov regularized inversion operator, and realize the parameters through parallel multi-starting-point solution.

[0084] Specifically, the initial parameter space has 100 uniform sampling sets, and the parameter vector includes four components: , , n, and m. Each component's sampling range is ±50% of the measured mean. The residual calculation formula is... ,in These are measured values. The calculated value is N, where N is the number of sample points. In the validation process, all experimental data are randomly divided into a training set (70%) and a validation set (30%), and this division is repeated 5 times. The mean and variance of the residuals are calculated. If the variance is less than... Then it is determined to be a stable solution.

[0085] A shale gas resource calculation system for complex structural areas, relating to the shale gas resource calculation methods for complex structural areas as described in claims 1-9; characterized in that the shale gas resource calculation system for complex structural areas is designed based on the shale gas resource calculation methods for complex structural areas.

[0086] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating shale gas resources in complex structural zones, characterized in that, The method for calculating shale gas resources in complex structural areas includes the following steps: S1. Acquiring 3D seismic data, well logging data, and core analysis data of the complex structural area, and performing high-frequency denoising and impedance inversion processing on the 3D seismic data, well logging data, and core analysis data to obtain detailed structural interpretation results; S2. Based on the detailed structural interpretation results, generating a fracture distribution model using a quantitative fracture structure characterization algorithm, and establishing a 3D geological structure model in conjunction with well logging lithology identification results; S3. Performing high-precision formation thickness correction on the 3D geological structure model, and employing a stress field numerical inversion method. S4. Based on the effective stress distribution of the reservoir, calculate the effective porosity-permeability parameter distribution of the reservoir using the nonlinear coupling equation of porosity-permeability; S5. Obtain pore structure parameters using a multi-scale shale organic matter pore distribution measurement method, and superimpose them onto the effective porosity-permeability parameter distribution of the reservoir to form a comprehensive reservoir property model; S6. Perform super-resolution volumetric modeling on the comprehensive reservoir property model to generate a three-dimensional reserve model for the complex structural area; S7. Based on the three-dimensional reserve model of the complex structural area, calculate the unit resource quantity grid by grid according to geological units, and perform a total regional resource quantity summation calculation.

2. The method for calculating shale gas resources in complex tectonic zones according to claim 1, characterized in that, The S1 high-frequency denoising process further includes the following steps: S1-1. After dividing the three-dimensional seismic data into blocks according to trace gathers, the frequency components above 80Hz are separated using an adaptive threshold selection wavelet packet decomposition method; S1-2. The frequency components above 80Hz are subjected to singular value decomposition and feature vectors with a contribution of less than 0.05 are removed to reconstruct low-noise seismic data blocks; S1-3. The reconstructed seismic data blocks are subjected to inter-block phase consistency correction to obtain the denoised three-dimensional seismic data volume.

3. The method for calculating shale gas resources in complex tectonic zones according to claim 2, characterized in that, The wavelet packet decomposition method uses complex Morlet mother wavelets to construct basis functions and terminates the decomposition when the number of decomposition layers reaches 8. The adaptive threshold selection adopts the layer-by-layer energy ratio threshold criterion.

4. The method for calculating shale gas resources in complex tectonic zones according to claim 3, characterized in that, S1-1 further includes the following steps: S1-1-1. In the scale selection of the complex Morlet mother wavelet, the scale parameter is discretized into an even sequence and the local energy spectrum is calculated for each scale; S1-1-2. Based on the local energy spectrum, an inter-layer threshold is determined using a two-end thresholding strategy, with the lower threshold being 0.01 of the maximum value of the local energy spectrum and the upper threshold being 0.10 of the maximum value of the local energy spectrum; S1-1-3. After threshold removal, edge-preserving reconstruction is performed on the remaining coefficients, and the reconstruction window length is dynamically adjusted according to the peak width of the seismic gather.

5. The method for calculating shale gas resources in complex tectonic zones according to claim 1, characterized in that, The S3 stress field numerical inversion method uses a linear elastic constitutive model to simulate the mechanical response of the elements. Before the finite element solution, the three-dimensional geological structure model automatically generates a mesh according to the geometric and physical property discontinuities. The mesh is composed of tetrahedral elements with a side length not exceeding 5 meters. In the boundary condition settings, an in-situ stress gradient field linearly distributed according to depth is applied.

6. The method for calculating shale gas resources in complex tectonic zones according to claim 5, characterized in that, S3 further includes the following steps: S3-1. After the finite element solution is completed, the principal stress value is calculated for each tetrahedral element and tension-compression discrimination is performed; S3-2. The elements identified as compressive stress are input into the clustering module, and the stress partition is divided according to the similarity of the principal stress vector using the K-means clustering algorithm. The number of clusters is determined by the elbow method; S3-3. The stress partitioning results are superimposed on the three-dimensional geological structure model to form a partitioned stress distribution map. The partitioned stress distribution map is used for index mapping of subsequent reservoir effective porosity and permeability parameter allocation.

7. The method for calculating shale gas resources in complex tectonic zones according to claim 1, characterized in that, The porosity-permeability nonlinear coupling equation for S4 is expressed as follows: Wherein, k is the effective penetration rate, and the For effective porosity, the For effective stress, the The above The n and m are measured calibration parameters; the porosity-permeability nonlinear coupling equation is used as the numerical description of S4 in the calculation process.

8. The method for calculating shale gas resources in complex tectonic zones according to claim 7, characterized in that, The parameter calibration of S4 adopts the joint inversion of in-situ steady-state gas seepage experimental data and nuclear magnetic resonance borehole measurement experimental data. The joint inversion uses the Tikhonov regularized inversion operator as the parameter estimation framework and uses multi-starting-point least squares search to determine the globally optimal calibration parameter set.

9. The method for calculating shale gas resources in complex tectonic zones according to claim 8, characterized in that, S4 further includes the following steps: S4-1. In the in-situ steady-state gas seepage experiment, the steady-state flow rate is measured at three different average pressure difference levels, the steady-state pressure distribution is recorded, and the single-point permeability is calculated; S4-2. In the nuclear magnetic resonance pore measurement experiment, the T2 spectrum is obtained at the same pore location of the sample and the pore size is converted, and the porosity allocation vector is obtained according to the pore size range; S4-3. The steady-state flow rate-derived single-point permeability and the porosity allocation vector are input into the Tikhonov regularized inversion operator, and the parameters are realized through parallel multi-starting-point solution.

10. A shale gas resource calculation system for complex structural areas, relating to the shale gas resource calculation methods for complex structural areas as described in claims 1-9; characterized in that, The shale gas resource calculation system for complex structural areas is designed based on the shale gas resource calculation method for complex structural areas.

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