Shale gas content dynamic detection method based on impedance spectrum analysis

Abstract

The invention discloses a shale gas content dynamic detection method based on impedance spectrum analysis, which comprises the following steps: acquiring impedance spectrum data of a shale reservoir, acquiring resistivity parameters and phase angle characteristics according to the impedance spectrum data, and obtaining reservoir electrical response data; carrying out dimensionality reduction on the reservoir electrical response data through a principal component analysis method to obtain spectrum feature data; classifying the frequency spectrum characteristic data through a machine learning classification method to obtain a preliminary gas quantity judgment result; partitioning the preliminary gas content judgment result through a clustering method to obtain a gas content distribution partition map; performing three-dimensional interpolation according to the gas content distribution partition map to obtain three-dimensional gas content distribution data; the three-dimensional gas content distributed data is visualized, and visualized geological information is obtained; performing data fusion according to the visual geological information to obtain an optimized three-dimensional gas content distribution diagram; and refining according to the optimized three-dimensional gas content distribution diagram to obtain a high-resolution gas content distribution diagram.

Description

Technical Field

[0001] This invention belongs to the field of shale gas content detection technology, and particularly relates to a dynamic detection method for shale gas content based on impedance spectrum analysis. Background Technology

[0002] Shale gas, as a crucial component of clean energy, is vital for optimizing energy structure and protecting the environment through efficient exploration and development. Accurately assessing the gas content of shale reservoirs is key to achieving precise extraction, and detection methods based on electrical properties have attracted widespread attention due to their efficiency and non-invasiveness. However, existing methods often struggle to accurately capture the dynamic distribution characteristics of gas reservoirs under complex geological conditions, especially when facing the high heterogeneity of shale reservoirs and uneven gas distribution. The limitations of traditional technologies in data interpretation and result presentation restrict exploration efficiency.

[0003] Existing methods primarily rely on single electrical measurements or simple geological models, making it difficult to adapt to the complex electrical response characteristics of shale reservoirs under different geological environments. This often leads to discrepancies between gas-bearing capacity assessments and actual reservoir distribution during actual exploration. For example, in some highly heterogeneous reservoirs, traditional methods may fail to effectively distinguish between gas-bearing and non-gas-bearing areas, resulting in flawed development decisions. Furthermore, existing technologies lack the means to combine complex electrical information with intuitive geological features when processing dynamic data, limiting explorers' comprehensive understanding of the reservoir's internal structure.

[0004] The core challenge lies in extracting key features from the electrical response of shale reservoirs and transforming them into intuitive information on gas reservoir distribution. Impedance spectral analysis can capture the dynamic characteristics of reservoirs through electrical responses at different frequencies, but its data is complex and difficult to use directly for gas reservoir identification. Effective interpretation of complex electrical data relies on the extraction of key parameters, such as changes in resistivity and phase angle. However, the correlation between the dynamic changes of these parameters and reservoir gas distribution is not yet fully clarified, especially in highly heterogeneous reservoirs. Accurately mapping these parameters to gas reservoir distribution models remains a challenge. This further leads to the inability to intuitively present dynamic detection data as a three-dimensional gas reservoir distribution map in actual exploration, affecting decision-making efficiency. For example, in the exploration of a shale gas field, detection equipment acquired a large amount of spectral data, but due to the lack of effective parameter extraction and visualization methods, the exploration team found it difficult to quickly determine which areas had high gas content, missing the optimal extraction opportunity.

[0005] Therefore, how to extract key parameters from the complex electrical data of impedance spectrum and transform them into an intuitive three-dimensional gas reservoir distribution map has become a key issue in the integration of dynamic detection and visualization of shale gas content. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a dynamic detection method for shale gas content based on impedance spectrum analysis, thereby resolving the issues present in the prior art.

[0007] To achieve the above objectives, this invention provides a method for dynamic detection of shale gas content based on impedance spectrum analysis, comprising:

[0008] Impedance spectrum data of shale reservoirs are acquired, and resistivity parameters and phase angle characteristics are obtained from the impedance spectrum data to obtain reservoir electrical response data. Principal component analysis is used to reduce the dimensionality of the reservoir electrical response data to obtain spectral feature data. Machine learning classification methods are used to classify the spectral feature data to obtain preliminary gas content discrimination results. Clustering methods are used to partition the preliminary gas content discrimination results to obtain a gas content distribution partition map. Three-dimensional interpolation is performed on the gas content distribution partition map to obtain three-dimensional gas content distribution data. The three-dimensional gas content distribution data is visualized to obtain visualized geological information. Data fusion is performed based on the visualized geological information to obtain an optimized three-dimensional gas content distribution map. The optimized three-dimensional gas content distribution map is then refined to obtain a high-resolution gas content distribution map.

[0009] Optionally, the process of acquiring the reservoir electrical response data includes:

[0010] The shale reservoir is subjected to electrical signal feedback acquisition using a multi-band electrical measurement method to obtain raw impedance spectrum data. The raw impedance spectrum data is then subjected to Fourier transform to extract impedance amplitude and phase information at different frequencies, resulting in impedance spectrum data. Based on the impedance spectrum data, a set of resistivity parameters is obtained by least squares fitting. Phase angle features are extracted from the impedance spectrum data, and a set of phase angle features is obtained based on these features. Finally, the reservoir electrical response data is obtained by integrating the set of resistivity parameters and the set of phase angle features.

[0011] Optionally, the process of acquiring the spectral feature data includes:

[0012] The reservoir electrical response data is preprocessed, and the dimensionality of the preprocessed reservoir electrical response data is determined. Based on the dimensionality determination result, the preprocessed reservoir electrical response data is dimensionality reduced by principal component analysis to obtain dimensionality-reduced features. The variance contribution rate of the dimensionality-reduced features is obtained, and the variance contribution rate is determined. Based on the determination result, the principal component feature set is obtained. The principal component feature set is clustered to obtain feature grouping results. The spectral characteristics of the features are extracted based on the feature grouping results to obtain spectral feature data.

[0013] Optionally, the process of obtaining the preliminary gas volume determination results includes:

[0014] The spectral feature data is classified using a machine learning classification model, wherein the machine learning classification model adopts a support vector machine model. The spectral feature data is classified according to the classification hyperplane of the support vector machine model to obtain preliminary gas volume discrimination results.

[0015] Optionally, the process of obtaining the gas content distribution zoning map includes:

[0016] The preliminary gas volume determination results are divided into regions using a clustering method to obtain preliminary partitioning results. Based on the preliminary partitioning results, the spatial distribution characteristics of each region are calculated to obtain a first distribution feature set. The regions of the first distribution feature set are optimized using a density clustering algorithm to obtain a second partitioning result. Based on the second partitioning result, the geological characteristics of the gas-bearing regions are extracted to obtain a geological feature set. The geological feature set and the spatial distribution characteristics are fused to generate a gas volume distribution partitioning map.

[0017] Optionally, the process of acquiring the three-dimensional gas content distribution data includes:

[0018] The gas content distribution zoning map is divided into three-dimensional spatial grids to obtain a three-dimensional spatial grid. The spectral feature data is matched and mapped with the three-dimensional spatial grid to obtain preliminary three-dimensional gas content data. Geological model parameters are obtained, and the preliminary three-dimensional gas content data is constrained and adjusted according to the geological model parameters to obtain adjusted three-dimensional gas content data. The adjusted three-dimensional gas content data is smoothed to obtain smoothed three-dimensional gas content data. The smoothed three-dimensional content data is verified to obtain three-dimensional gas content distribution data.

[0019] Optionally, the process of acquiring visualized geological information includes:

[0020] The spatial distribution characteristics of three-dimensional gas content distribution data are obtained. Based on the spatial distribution characteristics, the three-dimensional gas content distribution data is processed using a stereomicroscope rendering method to obtain a preliminary gas content image. The preliminary gas content image is preprocessed to obtain a clear gas content image. The dynamic distribution characteristics of the clear gas content image are extracted to generate a dynamic gas content distribution sequence. The dynamic gas content distribution sequence is reconstructed in three dimensions using a stereomicroscope rendering method to obtain a dynamic gas content distribution map. The dynamic gas content distribution map is completed to obtain a complete dynamic distribution map. Geological information is annotated on the complete dynamic distribution map to obtain visualized geological information.

[0021] Optional, optimized processes for obtaining three-dimensional gas content distribution maps include:

[0022] Gas content distribution parameters are extracted from visualized geological information to obtain initial three-dimensional gas content distribution data. The initial three-dimensional gas content distribution data is then optimized using the gradient descent method to improve the view accuracy, resulting in an optimized three-dimensional gas content distribution view.

[0023] Optionally, the process of obtaining a high-resolution gas content distribution map includes:

[0024] The resolution of the grid cells in the optimized 3D gas content distribution map is determined. Based on the determination result, an interpolation algorithm is used to subdivide the grid cells in the optimized 3D gas content distribution map to obtain high-resolution grid cells. Based on the high-resolution grid cells, the gas content of each cell is calculated to obtain a gas content distribution dataset. Based on the gas content distribution dataset, a view generation technique is used to generate a high-resolution gas content distribution map.

[0025] This invention provides a dynamic detection system for shale gas content based on impedance spectrum analysis, used to perform the above-described method.

[0026] Compared with the prior art, the present invention has the following advantages and technical effects:

[0027] This invention discloses a refined three-dimensional distribution analysis method for gas content in shale reservoirs based on multi-band electrical sensing technology and data fusion. Addressing the challenge of accurately characterizing gas content distribution in shale reservoirs, this method acquires reservoir impedance spectrum data using multi-band electrical sensing technology, extracting resistivity and phase angle features to form an electrical response dataset. Principal component analysis is used to extract key feature vectors, a support vector machine is constructed to classify gas-bearing regions, and k-means clustering is applied for partitioning. This data is then mapped to a reservoir geological model using a three-dimensional interpolation algorithm to generate three-dimensional gas content distribution data. Stereomicroscopy rendering technology is used to generate a dynamic distribution map, and a data fusion algorithm integrates the dynamic detection data with the reservoir model to optimize the three-dimensional view. Finally, a grid partitioning algorithm is used to achieve a high-resolution gas content distribution map. This invention, through the fusion of multiple technologies, realizes a complete process from electrical response data extraction to refined three-dimensional distribution, significantly improving the characterization accuracy of gas content distribution in shale reservoirs and providing a reliable basis for reservoir development. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0029] Figure 1 This is a flowchart of a dynamic detection method for shale gas content based on impedance spectrum analysis, according to an embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram of a dynamic detection system for shale gas content based on impedance spectrum analysis, according to an embodiment of the present invention. Detailed Implementation

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0033] like Figure 1 As shown in this embodiment, a dynamic detection method and system for shale gas content based on impedance spectrum analysis may specifically include:

[0034] Step S101: Obtain impedance spectrum data of shale reservoir at different frequencies, and use multi-band electrical measurement technology to extract resistivity parameters and phase angle characteristics to obtain reservoir electrical response dataset.

[0035] Multi-band electrical sensing technology was employed, applying electrical signals of different frequencies to the shale reservoir using electrical sensing equipment to acquire raw impedance spectrum data. Signal processing was then performed on the raw impedance spectrum data, using a Fast Fourier Transform (FFT) algorithm to extract impedance amplitude and phase information at different frequencies, generating a processed impedance spectrum dataset. Resistivity parameters were extracted from the processed impedance spectrum dataset, and a resistivity model was fitted using the least squares method to obtain a resistivity parameter set. Phase angle features were extracted from the processed impedance spectrum dataset; if the phase angle variation trend exceeded a preset threshold, the phase angle feature value was calculated using the arctangent function, generating a phase angle feature set. Based on the resistivity parameter set and the phase angle feature set, a reservoir electrical response dataset was constructed.

[0036] For example, when employing multi-band electrical testing technology, electrical signals with a frequency range of 0.1 Hz to 100 kHz can be applied to shale reservoirs using specialized electrical testing equipment to obtain raw impedance spectrum data. The equipment typically includes a signal generator and an impedance analyzer. The signal generator produces sine waves of different frequencies, and the impedance analyzer records the reservoir response, yielding spectrum data containing amplitude and phase. This method effectively captures the electrical characteristics of the reservoir at different frequencies, reflecting its pore structure and fluid distribution, and helps in the accurate assessment of reservoir properties.

[0037] In one possible implementation, when processing the raw impedance spectrum data, the Fast Fourier Transform algorithm is often used to convert the time-domain signal into a frequency-domain signal.

[0038] For example, assuming impedance data is collected, including responses at 1Hz, 10Hz, and 100Hz, Fast Fourier Transform (FFT) can extract the impedance amplitude and phase angle at each frequency; for instance, at 1Hz, the amplitude is 50Ω and the phase angle is -30°. The processed dataset more intuitively displays the reservoir's electrical response characteristics, providing a reliable foundation for subsequent analysis. This processing effectively filters out noise and improves data accuracy.

[0039] Specifically, when extracting resistivity parameters, the real and imaginary parts can be separated from the processed impedance spectrum dataset, and the resistivity can be calculated in combination with the reservoir geometry.

[0040] For example, the real impedance of a reservoir sample is 40Ω at 10Hz, and the sample dimensions are 10cm in length and 2cm² in cross-sectional area. 2 The resistivity can be calculated as 80 Ω·m using the resistivity formula. When fitting the resistivity model using the least squares method, it can be assumed that the reservoir conforms to the classic Cole-Cole model, and the fitted result will be a set of resistivity parameters, such as DC resistivity and frequency dependence coefficient. This method can accurately describe the electrical behavior of the reservoir and provide a quantitative basis for reservoir evaluation.

[0041] For example, when extracting phase angle features, if the phase angle varies by more than a preset threshold of 10° within the range of 0.1Hz to 1kHz, the feature value is calculated using the arctangent function.

[0042] For example, at a certain frequency, the real part is 30Ω, the imaginary part is 10Ω, and the phase angle is approximately 18.4°. Values ​​exceeding a threshold are recorded as characteristic values, generating a phase angle feature set. This feature reflects the dielectric response characteristics of the reservoir and is closely related to fluid type and saturation, helping to identify the distribution of oil, gas, or water in the reservoir.

[0043] In one possible implementation, a reservoir electrical response dataset is constructed based on a set of resistivity parameters and a set of phase angle features.

[0044] For example, a shale reservoir dataset includes parameters such as resistivity of 80 Ω·m, frequency dependence coefficient of 0.2, and phase angle of 18.4°. This data can be used to construct an electrical response model and, combined with geological information, infer reservoir porosity and permeability. Such datasets provide multi-dimensional information for reservoir evaluation, supporting reservoir stratification and production capacity prediction in oil and gas exploration.

[0045] It should be noted that the advantage of the above method lies in its comprehensive capture of reservoir electrical characteristics through multi-band electrical measurement and signal processing. Combined with fitting models and feature extraction, it enhances the accuracy of data interpretation, ultimately generating an electrical response dataset that provides a reliable basis for reservoir property analysis. This technology can significantly improve reservoir evaluation efficiency and optimize development strategies in shale gas development.

[0046] Step S102: For the reservoir electrical response dataset, the principal component analysis algorithm is used to extract key feature vectors from the spectral data to obtain the spectral feature set.

[0047] Resistivity parameters and phase angle features are obtained from the raw reservoir electrical response data through preprocessing. A standardization method is then used to normalize these parameters and features, resulting in a standardized spectral dataset. If the dimensionality of the standardized spectral dataset exceeds a preset threshold, principal component analysis (PCA) is used to reduce its dimensionality, extracting key feature vectors and generating a dimensionality-reduced feature set. Based on the dimensionality-reduced feature set, the variance contribution rate of each feature vector is calculated, and the cumulative contribution rate of the feature vectors is determined to meet a preset ratio, thus obtaining the principal component feature set. For the principal component feature set, a clustering algorithm is used to group the feature vectors, obtaining the feature grouping results. Based on the feature grouping results, the spectral characteristics of each group of feature vectors are extracted, generating a spectral feature set.

[0048] For example, in processing shale reservoir electrical response data, preprocessing is a crucial step in obtaining resistivity parameters and phase angle characteristics. Preprocessing typically involves filtering and denoising the raw electrical measurement signals to ensure the accuracy of subsequent analysis. Suppose a set of raw data, containing impedance values ​​at multiple frequencies, is acquired using multi-band electrical measurement equipment, which may contain high-frequency noise interference. Noise above a certain frequency threshold can be removed using a low-pass filter, for example, setting the cutoff frequency to 10kHz, while retaining the main components of the reservoir electrical response signal. Next, when extracting resistivity parameters, the resistivity can be calculated from the real part of the impedance; for example, at 1kHz, the real part of the impedance is 50Ω, which, combined with reservoir geometry parameters, yields a resistivity of approximately 100Ω·m. Phase angle characteristics are calculated using the ratio of the imaginary to the real part of the impedance; for example, a phase angle of 30° reflects the electrical properties of the reservoir medium.

[0049] In one possible implementation, standardization, which normalizes the resistivity parameters and phase angle characteristics, is a crucial step in ensuring data comparability. Assuming the resistivity parameters range from 50 to 200 Ω·m and the phase angle ranges from 10° to 45°, linear normalization maps the data to the 0-1 interval.

[0050] For example, the resistivity of 100 Ω·m is normalized to 0.33, and the phase angle of 30° is normalized to 0.56. This eliminates dimensional differences and facilitates subsequent analysis. The standardized spectrum dataset contains data from multiple frequency points. If the dimensionality is too high, such as a data vector containing 100 frequency points, dimensionality reduction is required.

[0051] Specifically, principal component analysis (PCA) can be used for dimensionality reduction. Assume a standardized spectral dataset contains resistivity and phase angle data at 10 frequency points, forming a 20-dimensional vector. Through PCA, eigenvalues ​​and eigenvectors are calculated, and the first three principal components are retained, assuming their cumulative variance contribution rate reaches 85%, satisfying the preset ratio of 80%.

[0052] For example, the first principal component may primarily reflect the resistivity variation trend, contributing 50%; the second principal component reflects the phase angle variation, contributing 25%. The key feature vectors generated after dimensionality reduction retain the core information of the reservoir's electrical response.

[0053] For example, cluster analysis can be used to group dimensionality-reduced feature sets. Suppose that after dimensionality reduction, a set of feature vectors is obtained, containing three principal components, with each vector representing a sample point. Using the k-means clustering algorithm, with a cluster size of 3, the sample points are divided into groups such as high resistivity / low phase angle and low resistivity / high phase angle. The grouping results reflect the electrical heterogeneity of the reservoir; for example, the high resistivity group may correspond to tight shale, while the low resistivity group may correspond to aquifers.

[0054] In one possible implementation, when extracting spectral characteristics, spectral features are calculated for each set of feature vectors.

[0055] For example, the high resistivity group has an average resistivity of 150 Ω·m and an average phase angle of 20° at 1 kHz, while the low resistivity group has a resistivity of 80 Ω·m and a phase angle of 40°. These characteristics can be used to characterize the electrical behavior of the reservoir, generate a spectral feature set, and provide data support for subsequent reservoir evaluation.

[0056] Step S103: Based on the spectral feature set, construct a support vector machine classification algorithm. If the resistivity parameter of the feature vector is greater than a preset threshold, it is judged to be a gas-bearing region, and a preliminary gas volume discrimination result is obtained.

[0057] Based on the spectral feature set data, a support vector machine (SVM) algorithm is used to train a classification model, resulting in a trained SVM model. The classification hyperplane parameters are then obtained from the trained SVM model, and classification calculations are performed on the input feature vectors to obtain the classification results. If the resistivity parameter of the feature vector in the classification result is greater than a preset threshold, it is determined to be a gas-bearing region, thus obtaining a preliminary gas distribution area.

[0058] In one possible implementation, when training the model using a support vector machine (SVM) algorithm based on spectral feature set data, the spectral feature set first needs to be partitioned, for example, into a training set and a test set, with a ratio of 8:2. The spectral feature set typically includes resistivity parameters and phase angle features, which reflect the physical characteristics of the reservoir's electrical response. The SVM then identifies the optimal classification hyperplane, classifying the feature vectors into different categories, such as gas-bearing and non-gas-bearing regions. During training, a kernel function, such as a radial basis function kernel, can be selected, and parameters such as the penalty coefficient C and kernel parameter γ can be adjusted to optimize model performance.

[0059] For example, C is set to 1.0 and γ to 0.1 to balance the model's generalization ability and classification accuracy. After training, the model can calculate the distance between the input feature vector and the classification hyperplane, and then determine the category.

[0060] For example, when obtaining the parameters of the classification hyperplane, the hyperplane is defined by support vectors and weight vectors. The weight vectors reflect the importance of each feature to the classification. Assuming the spectral feature set includes resistivity parameters, phase angles, and frequency-related features, the weight vectors might show that resistivity parameters have a greater impact on the classification results. By analyzing the weight vectors, it can be determined which features play a dominant role in the classification.

[0061] For example, the resistivity parameter has a weight of 0.75, while the phase angle has a weight of 0.25, indicating that resistivity is more critical in distinguishing gas-bearing regions. This helps in subsequent feature selection optimization and reduces the impact of redundant features on the model.

[0062] In one embodiment, when performing classification calculations on the input feature vector, the model calculates the distance from the feature vector to the hyperplane based on the hyperplane parameters.

[0063] For example, given a set of feature vectors with a resistivity of 100 Ω·m and a phase angle of 30°, the model will output a positive value, indicating that the vector belongs to a gas-bearing region. If the resistivity is below 50 Ω·m, the output may be negative, indicating a non-gas-bearing region. The classification results can intuitively reflect the geological characteristics of the reservoir and help identify potential natural gas distribution areas.

[0064] For example, when identifying gas-bearing areas, a preset resistivity threshold can be set to 80 Ω·m based on geological experience. Suppose a region's feature vector shows a resistivity of 120 Ω·m, far exceeding the threshold, the model identifies it as a gas-bearing area. Combining this with phase angle features, such as a phase angle greater than 25°, further confirms the reliability of the gas distribution in that region. This method, through comprehensive analysis of multiple features, improves the accuracy of gas-bearing area identification. The preliminary gas distribution area can be visualized, for example, by drawing a two-dimensional distribution map, marking the location and extent of the gas-bearing area.

[0065] In one possible implementation, the application of the classification results is not limited to the determination of a single region, but can be extended to the analysis of gas volume distribution in multiple regions.

[0066] For example, based on the spectral feature sets of multiple well locations, the model can generate a gas distribution map of the entire block, identifying the concentrated distribution trend of high resistivity areas. This extended scheme ensures the reliability and consistency of the classification results through multi-point data validation.

[0067] It should be noted that the training and application of the classification model need to be combined with the actual geological background, such as considering the influence of reservoir porosity and fluid saturation on resistivity, in order to optimize the practicality of the judgment results.

[0068] Step S104: Obtain preliminary gas volume determination results, and use the k-means clustering algorithm to partition the gas-bearing region and the non-gas-bearing region to obtain a gas volume distribution partition map.

[0069] Based on the preliminary gas distribution area, k-means clustering is used to partition the region, identifying gas-bearing and non-gas-bearing areas, resulting in preliminary partitioning results. For the preliminary partitioning results, the spatial distribution characteristics of each region are calculated, generating a first distribution feature set. If the regional boundaries of the first distribution feature set are ambiguous, density clustering is used to optimize the partitioning, resulting in a second partitioning result. Based on the second partitioning result, geological features of the gas-bearing areas are extracted, generating a geological feature set. By fusing the geological feature set with the spatial distribution characteristics, a gas-bearing distribution partitioning map is generated.

[0070] Specifically, based on the preliminary gas distribution area, the k-means clustering algorithm is used to partition the area, determine the gas-bearing area and the non-gas-bearing area, and obtain the preliminary partitioning results.

[0071] For example, in the exploration of an oil and gas field, the preliminary gas distribution area generated based on seismic and well logging data contains multiple grid points, each with characteristics such as resistivity, porosity, and sonic transit time. The k-means clustering algorithm sets an initial cluster center (e.g., k=2, representing gas-bearing and non-gas-bearing areas respectively), iteratively calculates the distance from each grid point to the center, and classifies it into the nearest cluster to generate preliminary partitioning results.

[0072] Preferably, the initial cluster centers can be selected based on prior geological knowledge, such as choosing high resistivity regions as the initial centers of gas-bearing regions. Based on the preliminary zoning results, the spatial distribution characteristics of each region are calculated to generate the first distribution feature set.

[0073] Specifically, spatial distribution characteristics include the connectivity, area, and boundary curvature of the region.

[0074] For example, in the aforementioned oil and gas field, when calculating the connectivity of the gas-bearing area, the connectivity status of each grid point with its neighboring points can be statistically analyzed to obtain a connectivity index, such as the proportion of connected grid points in the gas-bearing area being 70%. If the regional boundaries of the first distribution feature set are ambiguous, a density clustering algorithm is used to optimize the partitioning to obtain the second partitioning result.

[0075] In one embodiment, if the resistivity values ​​of boundary grid points are between 3 and 5 ohm-meters, making clear classification difficult, a density clustering algorithm (such as DBSCAN) identifies high-density areas and eliminates isolated points by setting a radius (e.g., 0.5 km) and a minimum number of points (e.g., 5 points), generating a clearer second partitioning result. Based on the second partitioning result, the geological features of the gas-bearing areas are extracted to generate a geological feature set.

[0076] For example, a geological feature set may include characteristics such as lithology, porosity, and permeability. In a certain block, the gas-bearing area is mainly composed of sandstone, with an average porosity of 15% and a permeability between 50 and 100 millidarcy. These features are extracted through well logging data and core analysis to ensure the accuracy of the feature set.

[0077] Preferably, the geological feature set is integrated with the spatial distribution features to generate a gas content distribution zoning map.

[0078] In one possible implementation, the fusion process can be achieved through weighted superposition, such as assigning 60% weight to geological features and 40% weight to spatial distribution features to generate a zoning map that clearly shows the distribution areas with high and low gas content.

[0079] It should be noted that each step of the above method is closely linked. k-means clustering provides the initial partitions, density clustering optimizes the boundary ambiguity problem, and geological feature extraction and feature fusion further refine the gas content distribution, together forming a logically rigorous analysis process.

[0080] For example, in the development of a gas field, this method can effectively distinguish between gas-bearing and non-gas-bearing areas, guide the selection of drilling locations, and improve exploration efficiency.

[0081] Step S105: Based on the gas content distribution zoning map and the reservoir geological model, a three-dimensional interpolation algorithm is used to map the spectral feature set to the reservoir model to obtain three-dimensional gas content distribution data.

[0082] For the gas content distribution zoning map, a three-dimensional spatial grid division method is used to spatially discretize the reservoir geological model, resulting in a three-dimensional spatial grid. If the spectral feature set data matches the points on the three-dimensional spatial grid, a three-dimensional interpolation algorithm is used to map the spectral feature set to the three-dimensional spatial grid, obtaining preliminary three-dimensional gas content data. Based on the geological model parameters, the preliminary three-dimensional gas content data is constrained and adjusted to obtain adjusted three-dimensional gas content data. A volume parallax algorithm is used to smooth the adjusted three-dimensional gas content data, resulting in smoothed three-dimensional gas content data. If the smoothed three-dimensional gas content data matches a preset threshold range, a three-dimensional gas content distribution data volume is generated, resulting in the final three-dimensional gas content distribution data volume.

[0083] Specifically, regarding the generation process of the gas content distribution zoning map, a three-dimensional spatial grid division method is used to spatially discretize the reservoir geological model to obtain a three-dimensional spatial grid. For example...

[0084] In one possible implementation, assuming the reservoir model covers an area 1000 meters long, 800 meters wide, and 200 meters high, a 100×80×20 three-dimensional grid structure can be generated by uniformly dividing each dimension into 10-meter units, with each grid point representing a spatial location. This method effectively captures the geometric features within the reservoir, facilitating subsequent data mapping.

[0085] It should be noted that the accuracy of grid division needs to be adjusted according to the complexity of the reservoir. If the reservoir has many faults or fractures, the grid can be appropriately densified to improve resolution. When matching the spectral feature set data with the three-dimensional spatial grid points, a three-dimensional interpolation algorithm is used to map the spectral feature set to the grid points.

[0086] For example, inverse distance weighted interpolation can be used. Assuming there are four known spectral feature points near a grid point, with recorded gas content values ​​of 0.2, 0.3, 0.4, and 0.5 respectively, and distances of 5 meters, 8 meters, 10 meters, and 12 meters from the grid point, the gas content at that grid point can be estimated through weighted calculation. This interpolation method can better preserve the spatial continuity of spectral features, and the generated preliminary three-dimensional gas content data can reflect the gas distribution trend within the reservoir. The preliminary three-dimensional gas content data is then constrained and adjusted based on geological model parameters. For example...

[0087] In one embodiment, the geological model may indicate the presence of high-permeability sandstone in a certain area, with a gas content upper limit of 0.6. However, some grid points in the initial data exceed this threshold. By introducing geological constraints, the data values ​​of these points are adjusted to below 0.6, and smoothing is performed by combining data from neighboring grid points to ensure the data conforms to geological reality. This adjustment method effectively improves the geological validity of the data. The adjusted three-dimensional gas content data is then smoothed using a volume parallax algorithm.

[0088] For example, Gaussian smoothing, a method within the volume parallax algorithm, can be used. The smoothing radius is set to 3 grid cells. For each grid point, a weighted average is calculated based on the values ​​of its surrounding 27 grid points to generate smoothed 3D gas content data. This method reduces noise in the data, making the gas content distribution more continuous and facilitating subsequent analysis. When the smoothed 3D gas content data matches a preset threshold range, the final 3D gas content distribution data volume is generated.

[0089] For example, assuming a preset gas content threshold range of 0.1 to 0.8, if the values ​​of all grid points fall within this range, the data volume can be directly output as a three-dimensional gas content distribution data volume. This data volume can intuitively reflect the spatial distribution characteristics of gas content in the reservoir, providing a basis for subsequent development decisions.

[0090] It should be noted that if some grid point values ​​exceed the threshold range, the smoothing parameters can be adjusted iteratively or the values ​​can be re-interpolated until the requirements are met. This method ensures the reliability and consistency of the data volume.

[0091] Specifically, the generation process of the three-dimensional gas content distribution data volume, from mesh generation to final output, is progressive and interconnected.

[0092] For example, gridding provides a spatial framework, interpolation mapping enables data filling, and geological constraints and volumetric parallax smoothing further optimize the accuracy and continuity of the data. These steps support each other and work together to ensure the accuracy and usability of the final data volume.

[0093] Step S106: Based on the three-dimensional gas content distribution data, a dynamic gas content distribution map is generated using stereomicroscopy rendering technology to obtain visualized geological information.

[0094] Three-dimensional gas content distribution data was acquired to determine its spatial distribution characteristics. Stereomicroscopy rendering technology was used to process the three-dimensional gas content distribution data, generating a preliminary gas content image. If noise existed in the preliminary gas content image, a Gaussian filtering algorithm was used to denoise the image, resulting in a clear gas content image. Based on the clear gas content image, dynamic distribution features were extracted to generate a dynamic gas content distribution sequence. The dynamic gas content distribution sequence was then reconstructed in three dimensions using stereomicroscopy rendering technology to obtain a dynamic gas content distribution map. If the geological information in the dynamic gas content distribution map was incomplete, interpolation algorithms were used to supplement the missing data, resulting in a complete dynamic distribution map. Geological information annotations were overlaid on the complete dynamic distribution map to generate the final visualized geological information.

[0095] Specifically, to address the business requirement of acquiring three-dimensional gas content distribution data and determining spatial distribution characteristics, this paper employs technologies such as stereomicroscopy rendering, Gaussian filtering algorithm, dynamic distribution feature extraction, interpolation algorithm to supplement missing data, and geological information annotation. Combined with the domain background of reservoir geological models, the following analysis provides examples from principles to specific implementations, ensuring that the content closely revolves around the single scenario of gas content distribution, with rigorous logic and mutual support.

[0096] For example, stereomicroscopy rendering technology is used to generate preliminary gas content images. The principle is to use the stereomicroscope's stereo imaging function to transform the three-dimensional gas content data volume into a visual image, preserving spatial structure information.

[0097] In one possible implementation, assuming the reservoir model covers an area of ​​1000×1000×500 meters, gas content data is mapped onto a 3D mesh using stereomicroscopy rendering to generate a preliminary image with a resolution of 1 meter per pixel. During rendering, pseudo-color mapping can be selected, mapping gas content levels to red-to-blue color gradations for easy identification of high-gas-content areas. This method clearly demonstrates the gas distribution pattern within the reservoir.

[0098] Specifically, if the preliminary gas content image contains noise, it may be caused by sensor errors or uneven grid division during data acquisition. Gaussian filtering can be used for noise reduction. Its principle is to use a Gaussian function to weighted smooth the image pixels, reducing the impact of noise.

[0099] In one embodiment, a Gaussian kernel with a standard deviation of 2 is used to convolve noise points in the gas content image to generate a clear gas content image.

[0100] For example, the gas content in a certain area fluctuates between 10% and 15% due to noise, but after noise removal, it stabilizes at around 12%. This processing can effectively improve image quality and facilitate subsequent feature extraction.

[0101] For example, when extracting dynamic distribution characteristics, it is necessary to analyze the changing trend of gas content over time or geological conditions. The principle is to identify the dynamic patterns of gas content distribution through time series analysis or spatial gradient calculation.

[0102] In one possible implementation, for gas content data over 30 consecutive days, the distribution characteristics of each 5-day time node are extracted to generate a dynamic distribution sequence.

[0103] For example, if the gas content in a reservoir gradually decreases from 15% to 10%, it indicates a possible gas leak. This dynamic sequence provides time-dimensional data support for reservoir management.

[0104] In one embodiment, stereomicroscopy rendering technology can also be used for the three-dimensional reconstruction of dynamic gas content distribution sequences. The principle is to reconstruct time-series gas content data into a continuous three-dimensional dynamic graph using a volume rendering algorithm.

[0105] For example, based on the aforementioned 30 days of data, a dynamic distribution map refreshed every second is generated, visually displaying the gas migration paths. This method effectively presents the dynamic changes within the reservoir, facilitating the analysis of gas flow trends.

[0106] It should be noted that incomplete geological information in the dynamic gas content distribution map may be due to insufficient data acquisition coverage. Interpolation algorithms can be used to supplement missing data. The principle is to extrapolate the gas content value of the missing area using the gas content values ​​of neighboring grid points.

[0107] For example, if a region is missing 10% of its grid data due to equipment limitations, Kriging interpolation can be used to estimate the missing data to be approximately 13% based on the gas content values ​​of surrounding grids (e.g., 12% and 14%). This method ensures the integrity of the distribution map and improves the reliability of the analysis.

[0108] For example, overlaying geological information annotations on a complete dynamic distribution map can further enrich the image information. The principle is to overlay reservoir geological parameters (such as porosity and permeability) onto the distribution map in the form of labels.

[0109] In one possible implementation, for areas with high gas content, geological information with a porosity of 20% and a permeability of 50 millidarcy is labeled to create a labeled, visualized distribution map. This approach facilitates comprehensive analysis of the correlation between gas content and geological conditions, providing a reference for reservoir development.

[0110] It is understandable that the above embodiments, from core solutions to extended solutions, cover the entire process from data processing to visualization, mutually supporting each other to form a complete gas content distribution analysis system. The implementation methods of each technical topic revolve around reservoir geological models to ensure the singularity of business scenarios, while enhancing the operability of the solutions through specific numerical values ​​and analysis processes.

[0111] Step S107: For the visualized geological information, a data fusion algorithm is used to integrate the dynamic detection data and the reservoir model to obtain an optimized three-dimensional gas content distribution view.

[0112] Gas content distribution parameters are extracted from visualized geological information to generate initial 3D gas content distribution data. The initial 3D gas content distribution data is then optimized using a gradient descent algorithm, and parameters are adjusted to improve view accuracy, resulting in an optimized 3D gas content distribution view.

[0113] Specifically, extracting gas content distribution parameters from visualized geological information, generating initial 3D gas content distribution data, and optimizing view accuracy are key steps in geological information analysis. The following analysis and examples focus on four aspects: parameter extraction, initial data generation, algorithm optimization, and view accuracy improvement. These are closely integrated with the field of geological information visualization, ensuring logical coherence and mutual support.

[0114] For example, when extracting gas content distribution parameters from visualized geological information, the spatial distribution characteristics of gas content can be identified by analyzing image data generated by a geological stereomicroscope.

[0115] Specifically, geological information images typically contain color-coded gas content intensities, with red areas indicating high gas content and blue areas indicating low gas content. Image segmentation techniques can be used to extract the gas content value corresponding to each pixel, generating a parameter set.

[0116] For example, in an image of an oil and gas field block, the gas content value of pixel A might be 0.8, while that of pixel B might be 0.2. These parameters reflect the gas distribution patterns within the geological body, providing fundamental data for subsequent modeling.

[0117] It should be noted that parameter extraction must ensure the spatial correspondence of the data, that is, each parameter corresponds one-to-one with the three-dimensional coordinates of the geological body, in order to avoid data mismatch.

[0118] In one embodiment, the initial three-dimensional gas content distribution data can be generated based on extracted parameters combined with a spatial grid model of the geological body.

[0119] Specifically, the geological body can be divided into a 10×10×10 grid, with each grid cell corresponding to a gas content parameter value.

[0120] For example, the gas content value of a certain grid cell might be 0.5, reflecting the degree of gas enrichment in that area. Using stereomicroscopy, these parameters are mapped into three-dimensional space to form initial three-dimensional gas content distribution data. This method can visually present the gas distribution trend within a geological body, facilitating subsequent optimization and analysis.

[0121] Preferably, the generation of initial data also needs to take into account the boundary conditions of the geological body to ensure that the data covers the entire target area.

[0122] For example, when using the gradient descent algorithm to optimize the initial three-dimensional gas content distribution data, the deviation between the data and the actual geological features can be reduced by iteratively adjusting the parameters.

[0123] Specifically, the gradient descent algorithm gradually adjusts the parameter values ​​of each grid cell based on the error function of the gas content distribution.

[0124] For example, the initial data might show an excessively high gas content value for a certain area, which doesn't match the actual exploration data. The algorithm will iterate multiple times to reduce this value until the error is minimized. This optimization process improves the accuracy of the data, making the three-dimensional distribution more closely resemble the actual geological conditions.

[0125] It should be noted that a reasonable learning rate needs to be set during the optimization process, such as 0.01, to balance convergence speed and accuracy.

[0126] In one embodiment, adjusting parameters to improve view accuracy can be achieved by comparing the 3D distribution views before and after optimization.

[0127] For example, an optimized view might show that the gas content in a certain area has been adjusted from 0.6 to 0.4, which better matches the actual drilling data. This adjustment makes the boundaries of high gas content areas in the view clearer and highlights the details of the geological structure.

[0128] Preferably, the detail rendering of the view can be further improved by increasing the rendering resolution, for example, from 512×512 to 1024×1024. This high-precision view can provide a more reliable basis for geological analysis and facilitate the identification of potential oil and gas reservoir areas. Through the above method, each step from parameter extraction to view optimization is closely integrated with the business needs of geological information visualization, logically progressive and mutually supportive. Parameter extraction lays the foundation for initial data generation, algorithm optimization improves data accuracy, and high-precision views provide intuitive support for geological analysis. This solution can effectively address the needs of gas content distribution analysis in complex geological environments.

[0129] Step S108: Based on the optimized three-dimensional gas content distribution view, the reservoir is finely partitioned using a grid partitioning algorithm to obtain a high-resolution gas content distribution map.

[0130] If the resolution of the grid cells in the optimized 3D gas content distribution view is lower than a preset threshold, an interpolation algorithm is used to subdivide the grid cells to obtain high-resolution grid cells. For each high-resolution grid cell, the gas content is calculated to obtain a gas content distribution dataset. Based on the gas content distribution dataset, a view generation technique is applied to generate a high-resolution gas content distribution map. The high-resolution gas content distribution map is validated using a partitioning accuracy enhancement technique to determine if outliers exist. If outliers are found, the outlier regions are re-gridded and their gas content recalculated to obtain the final high-resolution gas content distribution map. Key partitioning features are extracted from the final high-resolution gas content distribution map to generate fine-grained reservoir partitioning results.

[0131] For example, if the grid cell resolution in an optimized 3D gas content distribution view is lower than a preset threshold, an interpolation algorithm can be used to improve the grid resolution. The core of the interpolation algorithm is to refine the low-resolution grid into denser grid cells to capture subtle changes in gas content within the reservoir.

[0132] In one possible implementation, a bilinear interpolation method is used to calculate the interpolation points of new grid cells based on the gas content values ​​of existing grid cells.

[0133] For example, a low-resolution grid cell with gas content values ​​of 5%, 6%, 4%, and 7% can be refined into four high-resolution grid cells using bilinear interpolation, with gas content values ​​potentially ranging from 5.2% to 5.8%. This method allows for a smooth transition in gas content distribution, ensuring richer view details.

[0134] In one embodiment, when calculating gas content for a high-resolution grid cell, the reservoir properties of each cell can be analyzed using geostatistical methods.

[0135] For example, gas content can be estimated by combining porosity and permeability data. Assuming a grid cell has a porosity of 20% and a permeability of 100 millidarcy, the gas content can be estimated to be approximately 6% using an empirical model. This process requires analyzing each cell individually to ensure the dataset fully reflects the reservoir characteristics.

[0136] For example, when generating high-resolution gas content distribution maps, the view generation technology can employ stereoscopic techniques to map the gas content data into a three-dimensional color-coded view. Areas with high gas content are represented by red, low areas by blue, and intermediate values ​​are represented by gradient colors.

[0137] For example, a region with a gas content ranging from 3% to 8% can be displayed as a gradient from blue to red, visually illustrating the reservoir distribution characteristics. This view facilitates geologists' analysis of reservoir potential.

[0138] In one possible implementation, partitioning accuracy enhancement techniques can be used to verify the distribution map through statistical anomaly detection methods.

[0139] For example, a threshold of ±2 standard deviations can be set for gas content anomalies. If the gas content in a certain area is 10%, while the average value of neighboring areas is 5%, it is marked as an anomaly. The anomaly area is then re-grid, and the gas content is recalculated using finer grid cells.

[0140] For example, dividing the abnormal area into smaller 0.1m x 0.1m grids and re-estimating the gas content may reveal that local anomalies are caused by data noise, leading to more accurate results after correction.

[0141] For example, when extracting key zoning features, reservoir zones can be divided based on gas content distribution maps. Suppose a reservoir has high gas content concentrated in the north, with an average gas content of 7%, which can be defined as a high-potential zone; the southern region, with a gas content below 4%, can be defined as a low-potential zone. Through feature extraction, refined zoning results are generated, clarifying reservoir development priorities. These zoning results provide a basis for subsequent development decisions.

[0142] In one embodiment, the iterative process of re-gridding and gas content calculation can be combined with geological constraints.

[0143] For example, considering the distribution of reservoir fractures, the grid is fined in areas with dense fractures to capture changes in gas content. Assuming significant fluctuations in gas content within fractured regions, recalculation reveals that the gas content can be adjusted from 5% to 6.5%, improving the accuracy of the distribution map. This method ensures accurate characterization of reservoir features.

[0144] Understandably, the aforementioned technical topics are closely interconnected, forming a complete workflow from grid subdivision to feature extraction, ensuring the generation and optimization of high-resolution gas content distribution maps. This method systematically improves the precision of reservoir analysis and provides reliable support for geological information visualization.

[0145] like Figure 2 As shown, this invention provides a dynamic detection system for shale gas content based on impedance spectrum analysis, mainly comprising:

[0146] The electrical data acquisition module is used to acquire impedance spectrum data of shale reservoirs at different frequencies. It uses multi-band electrical measurement technology to extract resistivity parameters and phase angle characteristics to obtain reservoir electrical response datasets.

[0147] The feature extraction module is used to extract key feature vectors from the spectral data of the reservoir electrical response dataset using the principal component analysis algorithm to obtain the spectral feature set.

[0148] The gas volume discrimination module is used to construct a support vector machine classification algorithm based on the spectral feature set. If the resistivity parameter of the feature vector is greater than a preset threshold, it is judged to be a gas-bearing area, and a preliminary gas volume discrimination result is obtained.

[0149] The region clustering module is used to obtain preliminary gas volume discrimination results. It uses the k-means clustering algorithm to partition the gas-bearing region and the non-gas-bearing region to obtain a gas volume distribution partition map.

[0150] The 3D mapping module is used to map the spectral feature set to the reservoir model based on the gas content distribution zoning map and the reservoir geological model, and to obtain 3D gas content distribution data.

[0151] The visualization rendering module is used to generate dynamic gas content distribution maps based on three-dimensional gas content distribution data using stereomicroscopy rendering technology, thereby obtaining visualized geological information.

[0152] The data fusion module is used to integrate dynamic detection data and reservoir models with visualized geological information using data fusion algorithms to obtain an optimized three-dimensional gas content distribution view.

[0153] The grid partitioning module is used to refine the reservoir partitioning based on the optimized three-dimensional gas content distribution view using a grid partitioning algorithm, thereby obtaining a high-resolution gas content distribution map.

[0154] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dynamic detection of shale gas content based on impedance spectrum analysis, characterized in that, include: Impedance spectrum data of shale reservoirs are obtained, and resistivity parameters and phase angle characteristics are obtained from the impedance spectrum data to obtain reservoir electrical response data. Principal component analysis is used to reduce the dimensionality of the reservoir electrical response data to obtain spectral feature data. The spectral feature data is classified using a machine learning classification method to obtain preliminary gas volume discrimination results; the preliminary gas volume discrimination results are then partitioned using a clustering method to obtain a gas volume distribution partition map; and three-dimensional interpolation is performed based on the gas volume distribution partition map to obtain three-dimensional gas volume distribution data. The three-dimensional gas content distributed data is visualized to obtain visualized geological information; data fusion is performed based on the visualized geological information to obtain an optimized three-dimensional gas content distribution map; the optimized three-dimensional gas content distribution map is refined to obtain a high-resolution gas content distribution map.

2. The method according to claim 1, characterized in that, The process of acquiring the reservoir electrical response data includes: The shale reservoir is subjected to electrical signal feedback acquisition using a multi-band electrical measurement method to obtain raw impedance spectrum data. The raw impedance spectrum data is then subjected to Fourier transform to extract impedance amplitude and phase information at different frequencies, resulting in impedance spectrum data. Based on the impedance spectrum data, a set of resistivity parameters is obtained by least squares fitting. Phase angle features are extracted from the impedance spectrum data, and a set of phase angle features is obtained based on these features. Finally, the reservoir electrical response data is obtained by integrating the set of resistivity parameters and the set of phase angle features.

3. The method according to claim 1, characterized in that, The process of acquiring the spectral feature data includes: The reservoir electrical response data is preprocessed, and the dimensionality of the preprocessed reservoir electrical response data is determined. Based on the dimensionality determination result, the preprocessed reservoir electrical response data is dimensionality reduced by principal component analysis to obtain dimensionality-reduced features. The variance contribution rate of the dimensionality-reduced features is obtained, and the variance contribution rate is determined. Based on the determination result, the principal component feature set is obtained. The principal component feature set is clustered to obtain feature grouping results. The spectral characteristics of the features are extracted based on the feature grouping results to obtain spectral feature data.

4. The method according to claim 1, characterized in that, The process of obtaining the preliminary gas volume determination results includes: The spectral feature data is classified using a machine learning classification model, wherein the machine learning classification model adopts a support vector machine model. The spectral feature data is classified according to the classification hyperplane of the support vector machine model to obtain preliminary gas volume discrimination results.

5. The method according to claim 1, characterized in that, The process of obtaining the gas content distribution zoning map includes: The preliminary gas volume determination results are divided into regions using a clustering method to obtain preliminary partitioning results. Based on the preliminary partitioning results, the spatial distribution characteristics of each region are calculated to obtain a first distribution feature set. The regions of the first distribution feature set are optimized using a density clustering algorithm to obtain a second partitioning result. Based on the second partitioning result, the geological characteristics of the gas-bearing regions are extracted to obtain a geological feature set. The geological feature set and the spatial distribution characteristics are fused to generate a gas volume distribution partitioning map.

6. The method according to claim 1, characterized in that, The process of acquiring the three-dimensional gas content distribution data includes: The gas content distribution zoning map is divided into three-dimensional spatial grids to obtain a three-dimensional spatial grid. The spectral feature data is matched and mapped with the three-dimensional spatial grid to obtain preliminary three-dimensional gas content data. Geological model parameters are obtained, and the preliminary three-dimensional gas content data is constrained and adjusted according to the geological model parameters to obtain adjusted three-dimensional gas content data. The adjusted three-dimensional gas content data is smoothed to obtain smoothed three-dimensional gas content data. The smoothed three-dimensional content data is verified to obtain three-dimensional gas content distribution data.

7. The method according to claim 1, characterized in that, The process of acquiring visualized geological information includes: The spatial distribution characteristics of three-dimensional gas content distribution data are obtained. Based on the spatial distribution characteristics, the three-dimensional gas content distribution data is processed using a stereomicroscope rendering method to obtain a preliminary gas content image. The preliminary gas content image is preprocessed to obtain a clear gas content image. The dynamic distribution characteristics of the clear gas content image are extracted to generate a dynamic gas content distribution sequence. The dynamic gas content distribution sequence is reconstructed in three dimensions using a stereomicroscope rendering method to obtain a dynamic gas content distribution map. The dynamic gas content distribution map is completed to obtain a complete dynamic distribution map. Geological information is annotated on the complete dynamic distribution map to obtain visualized geological information.

8. The method according to claim 1, characterized in that, The process of obtaining the optimized three-dimensional gas content distribution map includes: Gas content distribution parameters are extracted from visualized geological information to obtain initial three-dimensional gas content distribution data. The initial three-dimensional gas content distribution data is then optimized using the gradient descent method to improve the view accuracy, resulting in an optimized three-dimensional gas content distribution view.

9. The method according to claim 1, characterized in that, The process of obtaining high-resolution gas content distribution maps includes: The resolution of the grid cells in the optimized 3D gas content distribution map is determined. Based on the determination result, an interpolation algorithm is used to subdivide the grid cells in the optimized 3D gas content distribution map to obtain high-resolution grid cells. Based on the high-resolution grid cells, the gas content of each cell is calculated to obtain a gas content distribution dataset. Based on the gas content distribution dataset, a view generation technique is used to generate a high-resolution gas content distribution map.

10. A dynamic detection system for shale gas content based on impedance spectrum analysis, characterized in that, Used to perform the method according to any one of claims 1-9.

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