Nonlinear isochronous stratigraphic slice determination method and system

Abstract

The invention relates to the technical field of oil-gas exploration, in particular to a non-linear isochronous stratigraphic slice determination method and system. The method comprises the following steps: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; carrying out adaptive nonlinear filtering processing on the preprocessed seismic data to obtain adaptive nonlinear filtering data; performing nonlinear seismic attribute extraction on the adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; acquiring geological data and inversion target data; constructing an initial geological model according to the geological data to obtain the initial geological model; and performing residual calculation according to the inversion target data and the initial geologic model to obtain a residual calculation result. According to the method, the complexity of a nonlinear geological structure is better processed, the precision and reliability of isochronous stratigraphic slicing are improved, and more accurate geological information is provided for oil-gas exploration and development.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and in particular to a method and system for determining nonlinear isochronous stratum slices. Background Art

[0002] Isochronous stratigraphic slicing is an important geological interpretation tool in oil and gas exploration and development. It can reflect the sedimentary history and evolution process of the strata. The traditional method of determining isochronous stratigraphic slicing is mainly based on stratigraphic interpretation and linear interpolation, that is, after interpreting the key stratigraphic layers, linear interpolation is performed between the stratigraphic layers to generate isochronous stratigraphic slices. Early methods mainly relied on manual interpretation and linear interpolation, which had low accuracy and efficiency, were greatly affected by the subjective factors of the interpreters, and could not handle complex geological structures. Later, some automatic or semi-automatic stratigraphic slicing methods based on geostatistics and interpolation algorithms were developed, such as Kriging interpolation, nearest neighbor interpolation, bilinear interpolation, spline interpolation, etc. However, the existing methods are not good at handling the complexity of nonlinear geological structures. Its shortcomings are as follows: Fault influence: The existence of faults will cause stratum dislocation and velocity field changes. Existing methods are difficult to accurately deal with the influence of faults on isochronous stratigraphic slices, resulting in reduced accuracy of stratigraphic slices near the faults.

[0003] Heterogeneity: The heterogeneity of underground media can cause the bending of the seismic wave propagation path and changes in velocity. Existing methods are based on linear assumptions and it is difficult to accurately describe this nonlinear change, which affects the accuracy of isochronous stratigraphic slicing.

[0004] Complex structures: In areas with complex geological structures such as folds and fault blocks, the morphology and thickness of the strata vary dramatically. Existing methods are difficult to accurately depict such complex changes, resulting in distortion of isochronous stratigraphic slices. Summary of the invention

[0005] Based on this, it is necessary to provide a method and system for determining nonlinear isochronous stratigraphic slices to solve at least one of the above technical problems.

[0006] To achieve the above object, a method for determining nonlinear isochronous stratigraphic slices comprises the following steps: Step S1: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performing adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; performing nonlinear seismic attribute extraction on the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field; Step S2: obtaining geological data and inversion target data; constructing an initial geological model according to the geological data to obtain an initial geological model; performing residual calculation according to the inversion target data and the initial geological model to obtain a residual calculation result; performing model iterative inversion on the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model; Step S3: extract the fault influence zone of the nonlinear geological structure model to obtain the fault influence zone; construct a velocity model based on the nonlinear geological structure model and the fault influence zone to obtain the velocity model; perform seismic wave ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; construct a nonlinear time correction field based on the time-depth conversion data to obtain a nonlinear time correction field; use the nonlinear time correction field to perform seismic data time correction on the preprocessed seismic data, and perform key layer interpretation processing to obtain key layer interpretation data; determine nonlinear isochronous stratigraphic slices based on the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices.

[0007] The present invention effectively suppresses noise and highlights the nonlinear characteristics of the stratum through preprocessing, adaptive nonlinear filtering and nonlinear seismic attribute extraction, thereby obtaining a clearer and more accurate nonlinear seismic attribute field, providing a high-quality data basis for subsequent inversion and stratigraphic slice construction. By combining geological data, using inversion target data for iterative inversion, and gradually optimizing the initial geological model, a high-precision nonlinear geological structure model that matches the actual seismic data is finally obtained, providing an accurate structural framework for subsequent velocity modeling and stratigraphic slice construction. Through fault influence zone extraction, velocity model construction, ray tracing, time-depth conversion, nonlinear time difference correction, time correction, key horizon interpretation and nonlinear isochronous stratigraphic slice generation and adjustment, refined nonlinear isochronous stratigraphic slices are finally obtained, which can accurately reflect the morphology and evolution of underground geological structures, and effectively eliminate the influence of factors such as faults and velocity changes, improve the accuracy and reliability of stratigraphic slices, and provide more accurate geological information for oil and gas exploration and development. Therefore, the present invention provides a method for determining nonlinear isochronous stratigraphic slices, which can better handle the complexity of nonlinear geological structures, improve the accuracy and reliability of isochronous stratigraphic slices, and provide more accurate geological information for oil and gas exploration and development.

[0008] Preferably, step S1 comprises the following steps: Step S11: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; Step S12: performing multi-scale wavelet decomposition on the pre-processed seismic data to obtain multi-scale wavelet decomposition data; Step S13: performing adaptive nonlinear filtering on each decomposition layer in the multi-scale wavelet decomposition data to obtain adaptive nonlinear filtering data; Step S14: performing nonlinear feature enhancement processing on the adaptive nonlinear filtering data to obtain nonlinear feature enhanced data; Step S15: performing multi-scale reconstruction on the nonlinear feature enhanced data to obtain multi-scale reconstructed data; performing nonlinear seismic attribute extraction on the multi-scale reconstructed data to obtain a nonlinear seismic attribute field.

[0009] The present invention obtains and preprocesses the original seismic data. By obtaining and preprocessing the original seismic data, noise and interference in the data can be removed, the signal-to-noise ratio of the data can be improved, and a high-quality data basis can be provided for subsequent multi-scale wavelet decomposition and nonlinear filtering processing, thereby ensuring the accuracy and reliability of the nonlinear seismic attributes finally extracted. Multi-scale wavelet decomposition is performed on the pre-processed seismic data. Multi-scale wavelet decomposition can decompose seismic data into components of different scales and frequencies, separate noise and effective signals, so that subsequent nonlinear filtering and feature enhancement processing can perform targeted operations on information of different scales, thereby better retaining effective signals, removing noise, and improving the extraction accuracy of nonlinear features. Adaptive nonlinear filtering is performed on each decomposition layer in the multi-scale wavelet decomposition data. Adaptive nonlinear filtering can adaptively adjust filtering parameters according to local features of seismic data on different decomposition layers, while effectively removing noise, better retaining stratum edges and nonlinear features, avoiding the destruction of nonlinear features by traditional linear filtering methods, thereby improving the effect of subsequent nonlinear feature enhancement. Nonlinear feature enhancement processing is performed on the adaptive nonlinear filtering data. Nonlinear feature enhancement processing can amplify weak nonlinear features in seismic data, making them easier to identify and extract, while suppressing background noise and improving the signal-to-noise ratio of nonlinear features, thereby improving the reliability and resolution of the nonlinear seismic attributes finally extracted. Multiscale reconstruction is performed on nonlinear feature enhanced data and nonlinear seismic attributes are extracted. Multiscale reconstruction recombines the different scale components that have been processed by nonlinear feature enhancement into complete seismic data, and extracts nonlinear seismic attributes on this basis. Since the previous processing steps effectively remove noise and enhance nonlinear features, the extracted nonlinear seismic attributes can more accurately reflect the nonlinear characteristics of the formation, thereby improving the accuracy and reliability of isochronous formation slices.

[0010] Preferably, step S2 comprises the following steps: Step S21: Acquire geological data; construct an initial geological model based on the geological data to obtain an initial geological model; Step S22: setting rock physical parameters for the initial geological model and performing forward simulation to obtain forward simulation seismic data; Step S23: performing the same nonlinear seismic attribute extraction process as step S1 on the forward modeling seismic data to obtain a simulated nonlinear seismic attribute field; Step S24: acquiring inversion target data; performing residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field according to the inversion target data to obtain a residual calculation result; Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21, otherwise, execute step S26; Step S26: updating the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model.

[0011] The present invention obtains geological data and constructs an initial geological model. By collecting and arranging geological data and constructing an initial geological model, geological prior information can be integrated into the determination process of nonlinear isochronous stratigraphic slices, providing a reliable initial model for subsequent model iteration inversion, thereby improving the accuracy and reliability of the final geological model. The initial geological model is rock-physical parameter set and forward simulation is performed. By setting rock-physical parameters for the initial geological model and performing forward simulation, simulated seismic data can be generated, providing basic data for subsequent nonlinear seismic attribute extraction and residual calculation, and linking the geological model with the seismic data, thereby achieving effective matching of the geological model with the seismic data. The forward simulation seismic data is subjected to the same nonlinear seismic attribute extraction process as step S1. The forward simulation seismic data is subjected to the same nonlinear seismic attribute extraction process as the actual seismic data, ensuring the consistency and comparability between the simulated nonlinear seismic attribute field and the actual nonlinear seismic attribute field, and providing a reliable basis for subsequent residual calculation and model inversion. Inversion target data is obtained and residual calculation is performed. By obtaining the inversion target data (the nonlinear seismic attribute field actually measured) and performing residual calculation with the simulated nonlinear seismic attribute field, the difference between the simulation result and the actual data can be quantified, providing guidance for subsequent model updates, and driving the model to iterate in a direction that is more consistent with the actual data. The residual calculation result is judged and iterated. By comparing the residual calculation result with the residual threshold of the preset objective function, and deciding whether to return to step S21 to reconstruct the geological model based on the comparison result, the iterative inversion of the model can be achieved, and the difference between the simulation result and the actual data can be gradually reduced, thereby improving the accuracy of the final geological model. The initial geological model is updated according to the residual calculation result. By updating the initial geological model according to the residual calculation result, the information in the seismic data can be integrated into the geological model, so that the geological model is more consistent with the actual geological conditions, and finally a high-precision nonlinear geological structure model is obtained, thereby improving the accuracy and reliability of isochronous stratigraphic slices.

[0012] Preferably, step S24 comprises the following steps: Step S241: extracting simulated seismic data to be calculated for the nonlinear seismic attribute field to obtain simulated seismic data; extracting actual seismic data from the preprocessed seismic data according to the simulated seismic data to obtain actual seismic data; Step S242: performing time-space registration of simulated earthquake data and actual earthquake data to obtain earthquake time-space registration data; Step S243: performing multi-attribute residual calculation on the seismic time-space registration data according to the geological data and the inversion target data to obtain multi-attribute residual data; Step S244: determining the residual weight coefficient based on statistical analysis of the multi-attribute residual data according to the inversion target data to obtain the residual weight coefficient; Step S245: performing weighted residual calculation on the multi-attribute residual data using the residual weight coefficient to obtain weighted residual data; performing residual data post-processing on the weighted residual data to obtain post-processed residual data; Step S246: Analyze the size and spatial distribution characteristics of the post-processed residual data to obtain a residual calculation result.

[0013] The present invention extracts simulated seismic data and actual seismic data. By extracting simulated seismic data and actual seismic data related to nonlinear seismic attribute field calculation, the effectiveness and pertinence of subsequent residual calculation can be ensured, so that the residual can accurately reflect the simulation effect of the geological model at a specific position and time. Perform data time-space registration. By performing time-space registration on simulated seismic data and actual seismic data, the time-space difference caused by factors such as acquisition error and processing error can be eliminated, and the consistency of simulated data and actual data in time and space can be ensured, thereby improving the accuracy of residual calculation. Perform multi-attribute residual calculation. By performing residual calculation on multiple nonlinear seismic attributes, the simulation effect of the geological model can be more comprehensively evaluated, and the one-sidedness caused by a single attribute can be avoided, thereby more accurately reflecting the difference between the model and the actual geological situation. Determine the residual weight coefficient. By determining the residual weight coefficient based on a statistical analysis method, different weights can be given according to the contribution degree of different attributes to the inversion result, the stability and reliability of the inversion can be improved, and the influence of important attributes on the inversion result can be highlighted. Perform weighted residual calculation and post-processing. By weighted calculation and post-processing of multi-attribute residual data, the impact of noise on residual calculation results can be effectively reduced, the signal-to-noise ratio of residuals can be improved, and residual results can be made smoother and more stable, thus providing a more reliable basis for model updates. Analyze residual data and obtain residual calculation results. By analyzing the size and spatial distribution characteristics of post-processed residual data, we can gain a deeper understanding of the differences between geological models and actual geological conditions, provide more detailed guidance information for subsequent model updates, and help identify parts of the model that need improvement.

[0014] Preferably, step S3 comprises the following steps: Step S31: extracting the fault influence area of ​​the nonlinear geological structure model to obtain the fault influence area; constructing a velocity model according to the nonlinear geological structure model and the fault influence area to obtain a velocity model; Step S32: performing seismic wave ray tracing on the velocity model to obtain ray tracing data; Step S33: using ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; Step S34: performing nonlinear time difference correction according to the time-depth conversion data to obtain nonlinear time difference correction data; Step S35: constructing a nonlinear time correction field for the nonlinear time difference correction data to obtain a nonlinear time correction field; Step S36: performing time correction on the pre-processed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; performing key-layer interpretation processing on the time-corrected seismic data to obtain key-layer interpretation data; Step S37: performing relative geological time calculation according to the key layer interpretation data to obtain relative geological time data; performing isochronous stratigraphic slice generation according to the relative geological time data to obtain nonlinear isochronous stratigraphic slices; Step S38: Using the nonlinear geological structure model, fine-tune the nonlinear isochronous stratigraphic slices to obtain refined nonlinear isochronous stratigraphic slices.

[0015] The present invention extracts the fault influence zone and constructs a velocity model. Extracting the fault influence zone and constructing the velocity model based on it can more finely characterize the velocity change near the fault, improve the accuracy of the velocity model near the fault, and provide more accurate velocity information for subsequent ray tracing and time-depth conversion. Perform seismic wave ray tracing. By performing seismic wave ray tracing on the velocity model, the propagation path and travel time information of the seismic wave in the underground medium can be obtained, providing necessary parameters for subsequent time-depth conversion, and establishing the connection between the time-domain seismic data and the depth-domain geological model. Perform time-depth conversion. Using ray tracing data to perform time-depth conversion, seismic data can be converted from the time domain to the depth domain, eliminating the depth distortion caused by velocity changes, so that the seismic data can be more accurately compared and matched with the geological model. Perform nonlinear time difference correction. Performing nonlinear time difference correction can eliminate the time difference caused by factors such as stratum tilt and structural deformation, so that seismic data at different locations can reflect the stratum information of the same geological period, providing a basis for the subsequent generation of isochronous stratum slices. Construct a nonlinear time correction field. Constructing a nonlinear time correction field can apply the results of nonlinear time difference correction to the entire seismic data volume, realize the overall time correction of seismic data, and thus improve the accuracy of subsequent key horizon interpretation. Perform seismic data time correction and key horizon interpretation. Using a nonlinear time correction field to time correct seismic data can improve the accuracy and resolution of seismic data, thereby making the interpretation of key horizons more accurate and reliable, and providing accurate horizon information for subsequent relative geological time calculations. Calculate relative geological time and generate isochronous stratigraphic slices. By calculating relative geological time and generating isochronous stratigraphic slices, seismic data can be converted into the geological time domain, intuitively showing the sedimentary history and evolution process of the strata, and providing important information for geological analysis and oil and gas exploration. Use a nonlinear geological structural model to fine-tune the nonlinear isochronous stratigraphic slices. Using a nonlinear geological structural model to fine-tune the isochronous stratigraphic slices can integrate the structural information of the geological model into the slices, improve the accuracy and geological significance of the slices, and make them more accurately reflect the morphology and evolution of underground geological structures.

[0016] Preferably, step S31 includes the following steps: Step S311: constructing an initial velocity model according to geological data and a nonlinear geological structure model to obtain an initial velocity model; Step S312: extracting fault attributes from the nonlinear geological structure model to obtain fault attribute data; Step S313: dividing the fault influence area according to the fault attribute data to obtain the fault influence area; Step S314: performing fault influence zone velocity analysis on the fault influence zone according to the pre-processed seismic data to obtain the fault influence zone velocity analysis result; Step S315: performing fault influence zone velocity modeling on the fault influence zone according to the fault influence zone velocity analysis result to obtain a fault influence zone velocity model; Step S316: performing velocity model fusion on the velocity model of the fault-affected zone and the initial velocity model to obtain a velocity model.

[0017] The present invention constructs an initial velocity model according to geological data and a nonlinear geological structure model. By using geological data and a nonlinear geological structure model to construct an initial velocity model, geological prior information can be integrated into the velocity model, providing a reliable initial model for subsequent fault impact zone velocity analysis and modeling, and improving the accuracy of the final velocity model. Fault attributes are extracted from the nonlinear geological structure model. Extracting fault attribute data can obtain information such as the position, strike, dip, and fault distance of the fault, providing necessary parameters for subsequent fault impact zone division and velocity analysis, so that the structural characteristics near the fault can be more finely characterized. The fault impact zone is divided according to the fault attribute data. By dividing the fault impact zone according to the fault attribute data, the area near the fault that is greatly affected by the fault can be identified, providing a spatial range for subsequent special velocity analysis and modeling for the fault impact zone, thereby improving the accuracy of the velocity model near the fault. The fault impact zone velocity analysis is performed on the fault impact zone according to the preprocessed seismic data. By performing a special velocity analysis on the fault-affected zone, more detailed velocity information near the fault can be obtained, which makes up for the shortcomings of the initial velocity model in the vicinity of the fault, thereby more accurately reflecting the impact of the fault on the velocity. The fault-affected zone velocity modeling is performed on the fault-affected zone according to the velocity analysis results of the fault-affected zone. Velocity modeling based on the velocity analysis results of the fault-affected zone can convert the velocity analysis results into a continuous velocity field, provide a high-precision velocity model of the fault-affected zone for subsequent velocity model fusion, and make the velocity model more refined near the fault. Velocity model fusion is performed on the velocity model of the fault-affected zone and the initial velocity model. By fusing the velocity model of the fault-affected zone and the initial velocity model, the advantages of both can be combined to generate a complete velocity model that contains both regional velocity background information and fine velocity information near the fault, thereby improving the overall accuracy of the velocity model and the accuracy near the fault.

[0018] Preferably, step S31 is specifically: Extracting seismic data of the fault-affected area from the preprocessed seismic data according to the fault-affected area to obtain seismic data of the fault-affected area; Extract seismic attributes from seismic data in the fault-affected area to obtain regional seismic attribute data; Using regional earthquake attribute data to perform fault attribute data spatial registration, to obtain registered fault attribute data; Perform seismic attribute-fault attribute relationship analysis on regional seismic attribute data and aligned fault attribute data to obtain seismic attribute-fault attribute relationship data; Infer the velocity variation law of the fault-affected area based on the earthquake attribute-fault attribute relationship data to obtain velocity variation law inference data; The velocity analysis results of the fault-affected area are generated by inferring the data according to the law of velocity change, and the velocity analysis results of the fault-affected area are obtained.

[0019] The present invention can concentrate the analysis scope near the fault by extracting the seismic data of the fault-affected area, eliminate the interference far from the fault area, so as to more effectively study the influence of the fault on the propagation of seismic waves, and extract the characteristic information related to the fault. Extracting regional seismic attribute data can quantify the seismic wave characteristics of the fault-affected area, such as amplitude, frequency, phase, etc., provide a data basis for the subsequent analysis of the relationship between the fault and the velocity, and help identify anomalies related to the fault. Spatial registration can eliminate the spatial position deviation between the seismic data and the geological model, ensure the consistency of the fault attribute data and the seismic attribute data in space, thereby improving the accuracy of subsequent analysis. Analyzing the relationship between seismic attributes and fault attributes can reveal the influence of faults on the propagation of seismic waves, such as the attenuation effect of faults on amplitude, the scattering effect on frequency, etc., and provide a basis for inferring the law of velocity change. According to the seismic attribute-fault attribute relationship data, the law of velocity change can be inferred, and the change of seismic attributes can be linked to the change of velocity, for example, the reduction of velocity can be inferred according to the attenuation of amplitude, thereby predicting the velocity distribution near the fault. The velocity analysis results of the fault-affected zone are generated by inferring data based on the velocity variation law, which can transform the qualitative velocity variation law into quantitative velocity value, provide more accurate constraint conditions for subsequent velocity modeling, and improve the accuracy of the velocity model near the fault.

[0020] Preferably, step S33 includes the following steps: Step S331: gridding the pre-processed seismic data to obtain a seismic data grid; Step S332: Calculate the seismic ray path from the earthquake source to each grid point using the ray tracing data, the velocity model pair, and the seismic data grid to obtain the ray path; Step S333: Calculate the travel time of the seismic wave using the velocity model according to the ray path to obtain travel time data; Step S334: constructing a time-depth mapping table according to the travel time data and the spatial position information of the velocity model to obtain a time-depth mapping table; Step S335: using the time-depth mapping table to perform time-depth conversion on both sides of the fault in the fault-affected area respectively, and splicing the conversion results to obtain time-depth conversion data of the fault-affected area; Step S336: using the time-depth mapping table to perform time-depth conversion of the non-fault-affected area to obtain time-depth conversion data of the non-fault-affected area; Step S337: performing time-depth data fusion on the time-depth conversion data of the fault-affected area and the time-depth conversion data of the non-fault-affected area to obtain time-depth conversion data.

[0021] The present invention performs seismic data gridding on pre-processed seismic data. Seismic data gridding converts seismic trace data into regular grid data, so that the spatial position of seismic data is consistent with that of velocity model, and provides a unified spatial framework for subsequent ray path calculation and time-depth conversion. The seismic ray path calculation from the seismic source to each grid point is performed using ray tracing data, velocity model and seismic data grid. By calculating the ray path from the seismic source to each grid point, the propagation path of the seismic wave in the underground medium can be determined, and necessary path information can be provided for subsequent travel time calculation and time-depth conversion. According to the ray path, the travel time of the seismic wave is calculated using the velocity model. According to the travel time calculated by the ray path and the velocity model, the time required for the seismic wave to propagate from the source to each grid point can be determined, and the basic data of the time-depth correspondence relationship is provided for the subsequent time-depth conversion. The time-depth mapping table is constructed according to the travel time data and the spatial position information of the velocity model. The construction of the time-depth mapping table establishes the correspondence between the travel time and depth of the seismic wave, and provides a lookup table for the subsequent time-depth conversion, thereby improving the conversion efficiency. The time-depth mapping table is used to perform time-depth conversion on both sides of the fault in the fault-affected area, and the conversion results are spliced. By performing time-depth conversion and splicing on both sides of the fault, the depth mutation caused by fault dislocation can be avoided, and the depth relationship of the strata on both sides of the fault can be more accurately reflected. The time-depth mapping table is used to perform time-depth conversion in the non-fault-affected area. By performing time-depth conversion in the non-fault-affected area, the seismic data can be converted from the time domain to the depth domain, the depth distortion caused by velocity changes can be eliminated, and the seismic data can be more spatially matched with the geological model. The time-depth data of the time-depth conversion data of the fault-affected area and the time-depth conversion data of the non-fault-affected area are fused. By fusing the time-depth conversion data of the fault-affected area and the non-fault-affected area, a complete depth-domain seismic data volume can be obtained, which provides a unified data basis for the subsequent time difference correction and isochronous stratigraphic slice generation.

[0022] Preferably, step S34 includes the following steps: Step S341: selecting a reference layer for the time-depth conversion data to obtain a reference layer; extracting the time-depth of the time-depth conversion data according to the reference layer to obtain a reference layer time-depth curve; Step S342: extracting the target layer time-depth from the time-depth conversion data to obtain a target layer time-depth curve; Step S343: performing time difference calculation on the reference layer time-depth curve and the target layer time-depth curve to obtain time difference data; constructing a time difference spatial distribution map according to the time difference data to obtain a time difference spatial distribution map; Step S344: performing fault influence zone time difference correction on both sides of the fault in the fault influence zone according to the time difference spatial distribution map to obtain fault influence zone time difference correction data; Step S345: performing time difference correction in the non-fault affected area according to the time difference spatial distribution map to obtain time difference correction data in the non-fault affected area; Step S346: performing nonlinear time difference correction data fusion on the time difference correction data of the fault-affected area and the time difference correction data of the non-fault-affected area to obtain nonlinear time difference correction data.

[0023] The present invention selects a reference layer and extracts a time-depth curve. By selecting a reference layer and extracting its time-depth curve, a depth reference can be established to provide a reference for the subsequent calculation of the time difference between the target layer and the reference layer, and to ensure the accuracy of the time difference calculation. The time-depth of the target layer is extracted from the time-depth conversion data to obtain the time-depth curve of the target layer. By extracting the time-depth curve of the target layer, the morphology of the target layer in the depth domain can be obtained, and necessary data can be provided for subsequent time difference calculation and correction. The time difference is calculated for the reference layer time-depth curve and the target layer time-depth curve, and a time difference spatial distribution map is constructed. By calculating the time difference and constructing the time difference spatial distribution map, the spatial variation trend of the time difference can be intuitively displayed, which provides guidance for the subsequent time difference correction and helps to identify the time difference abnormal area. According to the time difference spatial distribution map, the time difference correction of the fault influence area is respectively performed on both sides of the fault in the fault influence area. By performing time difference correction on both sides of the fault respectively, the time difference mutation caused by the fault dislocation can be eliminated, and the time difference change on both sides of the fault can be smoother, thereby improving the accuracy of the time difference correction. Time difference correction is performed in the non-fault affected area according to the time difference spatial distribution map. Time difference correction in the non-fault affected area can eliminate the time difference caused by factors such as stratum tilt and structural deformation, so that seismic data at different locations can reflect the stratigraphic information of the same geological period. Nonlinear time difference correction data fusion is performed on the time difference correction data in the fault affected area and the time difference correction data in the non-fault affected area. By fusing the time difference correction data in the fault affected area and the non-fault affected area, a complete time difference correction data body can be obtained, which provides a unified data basis for the subsequent nonlinear time correction field construction and ensures the continuity of the correction results.

[0024] Preferably, the present invention further provides a system for determining nonlinear isochronous stratum slices, which is used to execute the method for determining nonlinear isochronous stratum slices as described above. The system for determining nonlinear isochronous stratum slices comprises: The nonlinear seismic attribute extraction module obtains original seismic data; performs seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performs adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtering data; performs nonlinear seismic attribute extraction on the adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; The nonlinear attribute and geological model joint inversion module obtains geological data and inversion target data; constructs an initial geological model based on the geological data to obtain an initial geological model; performs residual calculation based on the inversion target data and the initial geological model to obtain a residual calculation result; performs model iterative inversion on the initial geological model based on the residual calculation result to obtain a nonlinear geological structure model; The nonlinear stratigraphic time correction module is used to extract the fault influence zone of the nonlinear geological structure model to obtain the fault influence zone; construct a velocity model based on the nonlinear geological structure model and the fault influence zone to obtain the velocity model; perform seismic wave ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; construct a nonlinear time correction field based on the time-depth conversion data to obtain a nonlinear time correction field; use the nonlinear time correction field to perform seismic data time correction on the preprocessed seismic data, and perform key layer interpretation processing to obtain key layer interpretation data; determine nonlinear isochronous stratigraphic slices based on the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices.

[0025] The present invention effectively suppresses noise and highlights the nonlinear characteristics of the stratum through preprocessing, adaptive nonlinear filtering and nonlinear seismic attribute extraction, thereby obtaining a clearer and more accurate nonlinear seismic attribute field, providing a high-quality data basis for subsequent inversion and stratigraphic slice construction. By combining geological data, using inversion target data for iterative inversion, and gradually optimizing the initial geological model, a high-precision nonlinear geological structure model that matches the actual seismic data is finally obtained, providing an accurate structural framework for subsequent velocity modeling and stratigraphic slice construction. Through fault influence zone extraction, velocity model construction, ray tracing, time-depth conversion, nonlinear time difference correction, time correction, key horizon interpretation and nonlinear isochronous stratigraphic slice generation and adjustment, refined nonlinear isochronous stratigraphic slices are finally obtained, which can accurately reflect the morphology and evolution of underground geological structures, and effectively eliminate the influence of factors such as faults and velocity changes, improve the accuracy and reliability of stratigraphic slices, and provide more accurate geological information for oil and gas exploration and development. Therefore, the present invention provides a method for determining nonlinear isochronous stratigraphic slices, which can better handle the complexity of nonlinear geological structures, improve the accuracy and reliability of isochronous stratigraphic slices, and provide more accurate geological information for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic flow chart of the steps of a method for determining a nonlinear isochronous stratigraphic slice; Figure 2 for Figure 1Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0027] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0028] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0030] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0031] To achieve this, please refer to Figures 1 to 3 , a method for determining nonlinear isochronous stratigraphic slices, comprising the following steps: Step S1: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performing adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; performing nonlinear seismic attribute extraction on the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field; Step S2: obtaining geological data and inversion target data; constructing an initial geological model according to the geological data to obtain an initial geological model; performing residual calculation according to the inversion target data and the initial geological model to obtain a residual calculation result; performing model iterative inversion on the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model; Step S3: extract the fault influence zone of the nonlinear geological structure model to obtain the fault influence zone; construct a velocity model based on the nonlinear geological structure model and the fault influence zone to obtain the velocity model; perform seismic wave ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; construct a nonlinear time correction field based on the time-depth conversion data to obtain a nonlinear time correction field; use the nonlinear time correction field to perform seismic data time correction on the preprocessed seismic data, and perform key layer interpretation processing to obtain key layer interpretation data; determine nonlinear isochronous stratigraphic slices based on the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices.

[0032] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for determining a nonlinear isochronous stratigraphic slice according to the present invention. In this example, the method for determining a nonlinear isochronous stratigraphic slice includes the following steps: Step S1: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performing adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; performing nonlinear seismic attribute extraction on the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field; In the embodiment of the present invention, the original seismic data in SEG-Y format is obtained and preprocessed, including using median filtering to remove outliers, spherical divergence compensation to restore energy, and 8-60Hz bandpass filtering to remove noise. Then, an anisotropic diffusion filtering method based on 5x5 window local variance is used for adaptive nonlinear filtering. Finally, nonlinear seismic attributes such as Lyapunov exponents and fractal dimensions are extracted to form a nonlinear seismic attribute field.

[0033] Step S2: obtaining geological data and inversion target data; constructing an initial geological model according to the geological data to obtain an initial geological model; performing residual calculation according to the inversion target data and the initial geological model to obtain a residual calculation result; performing model iterative inversion on the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model; In an embodiment of the present invention, geological data such as drilling, logging, stratigraphic sequence and fault interpretation are collected, and an initial geological model including stratigraphic interface and fault models is constructed using geological modeling software (such as Petrel). The initial geological model is iteratively updated (for example, adjusting the stratigraphic interface depth and fault position) by calculating the residual with the actual measured nonlinear seismic attribute field (using the root mean square error) until the residual is less than or equal to a preset threshold (for example, 0.1). Finally, the model is iteratively inverted using the gradient descent method to obtain a nonlinear geological structure model.

[0034] Step S3: extracting the fault influence zone of the nonlinear geological structure model to obtain the fault influence zone; constructing a velocity model according to the nonlinear geological structure model and the fault influence zone to obtain the velocity model; performing seismic wave ray tracing on the velocity model to obtain ray tracing data; performing time-depth conversion on the preprocessed seismic data using the ray tracing data to obtain time-depth conversion data; constructing a nonlinear time correction field according to the time-depth conversion data to obtain a nonlinear time correction field; performing seismic data time correction on the preprocessed seismic data using the nonlinear time correction field, and performing key layer interpretation processing to obtain key layer interpretation data; performing nonlinear isochronous stratigraphic slice determination according to the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices; In an embodiment of the present invention, a fault influence zone (e.g., a range of 50 meters on both sides of the fault plane) is extracted from a nonlinear geological structure model. A velocity model is constructed in combination with the geological model and the fault influence zone, and velocity adjustment is considered in the fault influence zone. Ray tracing is performed using the fast marching method, and the preprocessed seismic data is converted to time depth using the ray tracing data. A nonlinear time correction field is constructed based on the time-depth conversion data, and is applied to the preprocessed seismic data for time correction. The time-corrected data is interpreted for key horizons (e.g., manually picked or automatically tracked). Finally, the relative geological time is calculated based on the key horizon interpretation data, and the nonlinear isochronous stratigraphic slices are generated and finely adjusted using the nonlinear geological structure model, for example, local adjustments are made based on the fault position and morphology.

[0035] Preferably, step S1 comprises the following steps: Step S11: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; Step S12: performing multi-scale wavelet decomposition on the pre-processed seismic data to obtain multi-scale wavelet decomposition data; Step S13: performing adaptive nonlinear filtering on each decomposition layer in the multi-scale wavelet decomposition data to obtain adaptive nonlinear filtering data; Step S14: performing nonlinear feature enhancement processing on the adaptive nonlinear filtering data to obtain nonlinear feature enhanced data; Step S15: performing multi-scale reconstruction on the nonlinear feature enhanced data to obtain multi-scale reconstructed data; performing nonlinear seismic attribute extraction on the multi-scale reconstructed data to obtain a nonlinear seismic attribute field.

[0036] In an embodiment of the present invention, raw seismic data is obtained and preprocessed. First, the raw seismic data is read from the seismic data acquisition system. These data are usually stored in SEGY format and contain information such as time, space, and amplitude. Then, the raw seismic data is preprocessed, including steps such as removing wild values, amplitude compensation, and filtering and denoising. Specifically, a median filter is used to remove wild values. This method replaces the data at the center of the sequence with the median of adjacent odd-numbered data in the data sequence, effectively removing pulse noise; then, spherical diffusion compensation is applied to restore the seismic wave energy and compensate for the amplitude that attenuates with increasing propagation distance; finally, a bandpass filter is used to remove noise outside a specific frequency range and retain valid seismic signals. For example, an 8-60Hz bandpass filter is used to remove low-frequency noise and high-frequency interference. The signal-to-noise ratio of the preprocessed seismic data is improved, laying the foundation for subsequent processing.

[0037] Perform multi-scale wavelet decomposition on the pre-processed seismic data. Use discrete wavelet transform to decompose the pre-processed seismic data into wavelet coefficients of different scales. Select the db4 wavelet basis function and perform three-level decomposition. After decomposition, low-frequency approximate coefficients and high-frequency detail coefficients are obtained, which represent seismic wave information of different scales. The low-frequency coefficients reflect the overall trend of the seismic wave, and the high-frequency coefficients contain the detailed characteristics of the stratum. For example, the first-level decomposition decomposes the seismic data into low-frequency approximate coefficients A1 and high-frequency detail coefficients D1, the second-level decomposition decomposes A1 into A2 and D2, and the third-level decomposition decomposes A2 into A3 and D3. Finally, a low-frequency approximate coefficient A3 and three high-frequency detail coefficients D1, D2, and D3 are obtained, which constitute the multi-scale wavelet decomposition data.

[0038] Adaptive nonlinear filtering is performed on each decomposition layer in the multiscale wavelet decomposition data. Adaptive nonlinear filtering is performed on each decomposition layer (A3, D1, D2, D3) obtained in step S12. A nonlinear diffusion filtering method based on local variance is adopted. This method adaptively adjusts the filtering strength according to the local variance, and retains important information such as the edge of the formation while smoothing the noise. For example, for the detail coefficient D1, the variance in the 5x5 window around each data point is first calculated; then, the diffusion coefficient is determined according to the variance. The larger the variance, the smaller the diffusion coefficient, thereby weakening the filtering strength in the edge area; finally, the calculated diffusion coefficient is used for nonlinear diffusion filtering. The same processing is performed on each decomposition layer to obtain adaptive nonlinear filtering data.

[0039] The adaptive nonlinear filtering data is subjected to nonlinear feature enhancement processing. The adaptive nonlinear filtering data obtained in step S13 is subjected to nonlinear feature enhancement processing. A nonlinear enhancement method based on the sigmoid function is adopted. This method can enhance weak signals such as stratum boundaries while suppressing background noise. For example, for the filtered detail coefficient D1, its mean and standard deviation are first calculated; then, each data point of D1 is substituted into the sigmoid function for transformation, and the parameters of the sigmoid function are determined by the mean and standard deviation; finally, the transformed data is normalized. The same processing is performed on each decomposition layer to obtain nonlinear feature enhanced data.

[0040] The nonlinear feature enhancement data is reconstructed at multiple scales and nonlinear seismic attributes are extracted. The nonlinear feature enhancement data obtained in step S14 is reconstructed at multiple scales. The low-frequency approximation coefficient A3 and high-frequency detail coefficients D1, D2, and D3 that have been processed with nonlinear feature enhancement are reconstructed back to the original data domain using an inverse discrete wavelet transform. The reconstructed data contains enhanced formation information and suppressed noise. Finally, nonlinear seismic attributes are extracted from the reconstructed data, for example, the Lyapunov exponent, fractal dimension and other attributes based on chaos theory are calculated to characterize the nonlinear characteristics of the formation and form a nonlinear seismic attribute field. These attributes can more sensitively reflect subtle changes in formation lithology, physical properties and structure.

[0041] Preferably, step S2 comprises the following steps: Step S21: Acquire geological data; construct an initial geological model based on the geological data to obtain an initial geological model; Step S22: setting rock physical parameters for the initial geological model and performing forward simulation to obtain forward simulation seismic data; Step S23: performing the same nonlinear seismic attribute extraction process as step S1 on the forward modeling seismic data to obtain a simulated nonlinear seismic attribute field; Step S24: acquiring inversion target data; performing residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field according to the inversion target data to obtain a residual calculation result; Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21, otherwise, execute step S26; Step S26: updating the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model.

[0042] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Acquire geological data; construct an initial geological model based on the geological data to obtain an initial geological model; In an embodiment of the present invention, geological data is obtained and an initial geological model is constructed. Geological data of the target area are collected from existing geological databases, drilling data, logging data, and outcrop information. These data include stratigraphic sequences, fault distribution, lithology information, etc. Based on the collected geological data, an initial geological model is constructed using geological modeling software (such as Petrel). The model represents the underground geological structure in the form of a three-dimensional grid, and each grid unit is assigned a specific lithology value. For example, the location and depth of the key layer are determined based on the drilling data, and then the Kriging interpolation method is used to expand the drilling data to the entire study area to generate an initial stratigraphic interface model. At the same time, the fault information is integrated into the model to construct an initial fault model. The initial geological model provides a basis for subsequent forward simulation and inversion.

[0043] Step S22: setting rock physical parameters for the initial geological model and performing forward simulation to obtain forward simulation seismic data; In an embodiment of the present invention, rock physical parameters are set and forward simulation is performed. Based on existing logging data, core analysis data and regional empirical formulas, corresponding rock physical parameters are assigned to different lithologies in the initial geological model, such as longitudinal wave velocity, shear wave velocity, density, etc. Then, forward simulation is performed using the finite difference numerical simulation method of the acoustic wave equation. The constructed initial geological model is used as input to simulate the propagation process of seismic waves in the underground medium and generate synthetic seismic records. During the simulation process, a Ricker wavelet with a main frequency of 30 Hz is used as the source, and the time sampling interval of the simulated record is 2 ms. The forward simulated seismic data is used for subsequent nonlinear seismic attribute extraction and residual calculation.

[0044] Step S23: performing the same nonlinear seismic attribute extraction process as step S1 on the forward modeling seismic data to obtain a simulated nonlinear seismic attribute field; In an embodiment of the present invention, nonlinear seismic attributes of simulated seismic data are extracted. The same nonlinear seismic attribute extraction process as step S1 is performed on the forward simulated seismic data generated in step S22. Specifically, multi-scale wavelet decomposition, adaptive nonlinear filtering, nonlinear feature enhancement and multi-scale reconstruction are performed on the forward simulated seismic data. Then, nonlinear seismic attributes such as Lyapunov exponents and fractal dimensions are extracted from the reconstructed data, and finally a simulated nonlinear seismic attribute field is obtained. This is consistent with the nonlinear attribute extraction process of actual seismic data, which ensures the effectiveness of subsequent residual calculations.

[0045] Step S24: acquiring inversion target data; performing residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field according to the inversion target data to obtain a residual calculation result; In an embodiment of the present invention, inversion target data is obtained and residuals are calculated. The inversion target data is usually a nonlinear seismic attribute field actually measured, for example, a nonlinear seismic attribute obtained by subjecting the actual seismic data to the same processing flow as step S1. Residuals are calculated between the nonlinear seismic attribute field actually measured and the simulated nonlinear seismic attribute field obtained in step S23. The residual calculation adopts the root mean square error formula, that is, the square root of the average value of the sum of the squares of the differences in the corresponding position values ​​between the two attribute fields is calculated. The residual value reflects the difference between the simulation result and the actual measured data.

[0046] Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21, otherwise, execute step S26; In the embodiment of the present invention, it is determined whether the residual satisfies a preset threshold and iterates. The residual calculation result calculated in step S24 is compared with the preset residual threshold of the objective function. If the residual value is greater than the preset threshold, it means that there is a large difference between the simulation result and the actual data, and the geological model needs to be adjusted. At this time, return to step S21, adjust the parameters of the initial geological model according to the residual calculation result, for example, modify the depth of the stratum interface, the position of the fault, etc., rebuild the initial geological model and repeat steps S22 to S24. If the residual value is less than or equal to the preset threshold, it is considered that the simulation result and the actual data have reached an acceptable accuracy, and step S26 is executed.

[0047] Step S26: updating the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model; In the embodiment of the present invention, the model is updated according to the residual to obtain the final nonlinear geological structure model. According to the residual calculation result obtained in step S24, the initial geological model is updated using an optimization algorithm such as the gradient descent method. Specifically, the gradient of the residual with respect to the geological model parameters is calculated, and then the model parameters are adjusted along the gradient descent direction to reduce the residual value. The geological model is iteratively updated until the residual reaches a preset threshold or reaches the maximum number of iterations. The final model is a nonlinear geological structure model, which can better reflect the complexity of the underground geological structure.

[0048] Preferably, step S24 comprises the following steps: Step S241: extracting simulated seismic data to be calculated for the nonlinear seismic attribute field to obtain simulated seismic data; extracting actual seismic data from the preprocessed seismic data according to the simulated seismic data to obtain actual seismic data; Step S242: performing time-space registration of simulated earthquake data and actual earthquake data to obtain earthquake time-space registration data; Step S243: performing multi-attribute residual calculation on the seismic time-space registration data according to the geological data and the inversion target data to obtain multi-attribute residual data; Step S244: determining the residual weight coefficient based on statistical analysis of the multi-attribute residual data according to the inversion target data to obtain the residual weight coefficient; Step S245: performing weighted residual calculation on the multi-attribute residual data using the residual weight coefficient to obtain weighted residual data; performing residual data post-processing on the weighted residual data to obtain post-processed residual data; Step S246: Analyze the size and spatial distribution characteristics of the post-processed residual data to obtain a residual calculation result.

[0049] In an embodiment of the present invention, simulated seismic data and actual seismic data are extracted. First, according to the spatial position information of the nonlinear seismic attribute field, the simulated seismic data of the corresponding position are extracted from the forward simulation seismic data. For example, if the nonlinear seismic attribute field calculates the Lyapunov index, the simulated seismic data used to calculate the Lyapunov index is extracted. Then, according to the corresponding relationship between the simulated seismic data in the actual seismic data, the actual seismic data is extracted. This corresponding relationship can be determined based on the spatial position and time information of the simulated seismic data and the actual seismic data. For example, the key peaks or troughs in the simulated seismic data can be searched and matched in the actual seismic data to determine the corresponding relationship between the two. The extracted simulated seismic data and the actual seismic data will be used for subsequent spatiotemporal registration and residual calculation.

[0050] Perform spatiotemporal registration of data. Perform spatiotemporal registration of the simulated seismic data extracted in step S241 and the actual seismic data. A registration method based on dynamic time warping (DTW) is used. The DTW algorithm can calculate the best matching path between two time series of different lengths, thereby achieving temporal alignment. First, the simulated seismic data and the actual seismic data are respectively used as inputs of the DTW algorithm; then, the distance matrix between the two time series is calculated; then, the dynamic programming algorithm is used to search for the best matching path in the distance matrix; finally, the actual seismic data is temporally stretched or compressed according to the best matching path to align it with the simulated seismic data in time. The registered data is called seismic spatiotemporal registration data.

[0051] Perform multi-attribute residual calculation. Perform multi-attribute residual calculation on the seismic spatiotemporal registration data obtained in step S242 according to the geological data and the inversion target data. The geological data includes stratigraphic sequence, lithological information, etc., which are used to determine the weights of different stratigraphic or lithological units. The inversion target data includes multiple nonlinear seismic attributes, such as Lyapunov exponents, fractal dimensions, etc. The residual between the simulated data and the actual data is calculated for each attribute, for example, using the root mean square error. The residuals of each attribute are combined to form multi-attribute residual data.

[0052] Determine the residual weight coefficient. According to the inversion target data, the residual weight coefficient of the multi-attribute residual data obtained in step S243 is determined based on statistical analysis. The principal component analysis (PCA) method is used to determine the weight coefficient of each attribute. The PCA method can convert multiple related variables into a few unrelated variables, called principal components. First, the multi-attribute residual data is standardized; then, the covariance matrix of the residual data is calculated; then, the covariance matrix is ​​eigenvalue decomposed to obtain eigenvalues ​​and eigenvectors; finally, the eigenvectors with the largest eigenvalues ​​are used as principal components, and the corresponding eigenvalues ​​are used as weight coefficients. These weight coefficients reflect the contribution of each attribute to the residual.

[0053] Perform weighted residual calculation and post-processing. Use the residual weight coefficient obtained in step S244 to perform weighted residual calculation on the multi-attribute residual data obtained in step S243. Multiply the residual of each attribute by the corresponding weight coefficient, and then add the weighted residuals of all attributes to obtain weighted residual data. Perform post-processing on the weighted residual data, such as median filtering, to remove outliers and smooth the residual data to obtain post-processed residual data.

[0054] Analyze the residual data and obtain the residual calculation results. Perform the residual data size and spatial distribution characteristics analysis on the post-processed residual data obtained in step S245. Analyze the statistical quantities such as the mean, standard deviation, maximum value, minimum value, and the spatial distribution of the residual data. For example, a residual data spatial distribution map can be drawn to intuitively display the size of the residual at different locations. Based on these analysis results, the final residual calculation results are obtained for subsequent geological model updates.

[0055] Preferably, step S3 comprises the following steps: Step S31: extracting the fault influence area of ​​the nonlinear geological structure model to obtain the fault influence area; constructing a velocity model according to the nonlinear geological structure model and the fault influence area to obtain a velocity model; Step S32: performing seismic wave ray tracing on the velocity model to obtain ray tracing data; Step S33: using ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; Step S34: performing nonlinear time difference correction according to the time-depth conversion data to obtain nonlinear time difference correction data; Step S35: constructing a nonlinear time correction field for the nonlinear time difference correction data to obtain a nonlinear time correction field; Step S36: performing time correction on the pre-processed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; performing key-layer interpretation processing on the time-corrected seismic data to obtain key-layer interpretation data; Step S37: performing relative geological time calculation according to the key layer interpretation data to obtain relative geological time data; performing isochronous stratigraphic slice generation according to the relative geological time data to obtain nonlinear isochronous stratigraphic slices; Step S38: Using the nonlinear geological structure model, fine-tune the nonlinear isochronous stratigraphic slices to obtain refined nonlinear isochronous stratigraphic slices.

[0056] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: extracting the fault influence area of ​​the nonlinear geological structure model to obtain the fault influence area; constructing a velocity model according to the nonlinear geological structure model and the fault influence area to obtain a velocity model; In an embodiment of the present invention, the fault influence zone is extracted and a velocity model is constructed. First, based on the nonlinear geological structure model, the fault influence zone is identified and extracted. The fault influence zone is defined by using the normal vector of the fault plane and the change in the dip of the strata on both sides of the fault. For example, the area within a certain distance (for example, 50 meters) from the fault plane on both sides of the fault is defined as the fault influence zone. Then, a velocity model is constructed based on the nonlinear geological structure model and the fault influence zone. In the non-fault influence zone, the longitudinal wave velocity value of each grid cell in the nonlinear geological structure model is directly used; in the fault influence zone, the influence of the fault on the velocity is considered. For example, the velocity is adjusted according to the inclination of the fault plane and the difference in the physical properties of the strata on both sides of the fault. For example, the velocity value is reduced in the fault fracture zone to simulate the low-speed characteristics of the fault fracture zone, and finally a complete velocity model is obtained.

[0057] Step S32: performing seismic wave ray tracing on the velocity model to obtain ray tracing data; In the embodiment of the present invention, seismic wave ray tracing is performed. The velocity model constructed in step S31 is ray traced using a seismic wave ray tracing algorithm. Ray tracing is performed using the Fast Marching Method. First, the velocity model is discretized into a grid; then, the source position is set, and the travel time of each grid point is initialized; then, the shortest travel time and ray path of the seismic wave from the source to each grid point are calculated using the Fast Marching Method; finally, the calculated ray path information is saved as ray tracing data, including the grid point coordinates and travel time of each ray passing through.

[0058] Step S33: using ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; In the embodiment of the present invention, time-depth conversion is performed. The ray tracing data obtained in step S32 is used to perform time-depth conversion on the preprocessed seismic data. Each sampling point in the seismic data is mapped to the velocity model along the corresponding ray path. The depth value corresponding to the sampling point is calculated according to the velocity and travel time of each grid point on the ray path. The time of all sampling points is converted into depth to obtain time-depth conversion data, which represents the seismic reflection information in the form of depth domain, eliminating the influence caused by velocity changes in the time domain.

[0059] Step S34: performing nonlinear time difference correction according to the time-depth conversion data to obtain nonlinear time difference correction data; In the embodiment of the present invention, nonlinear time difference correction is performed. Nonlinear time difference correction is performed according to the time-depth conversion data. First, a reference layer is selected and the time-depth curve of the layer is extracted; then, a target layer is selected and the time-depth curve of the layer is extracted; then, the time difference of the target layer relative to the reference layer is calculated, that is, the depth difference between the two layers at the same position; finally, the time difference data is nonlinearly fitted using a polynomial fitting method to obtain a nonlinear time difference correction function. The function is applied to the time-depth conversion data to obtain nonlinear time difference correction data.

[0060] Step S35: constructing a nonlinear time correction field for the nonlinear time difference correction data to obtain a nonlinear time correction field; In the embodiment of the present invention, a nonlinear time correction field is constructed. The nonlinear time difference correction data obtained in step S34 is subjected to a nonlinear time correction field construction. The nonlinear time difference correction data is converted into a time domain to obtain a nonlinear time correction field. The correction field represents the time correction amount at different positions and is used to eliminate the time distortion caused by velocity changes and structural deformation in the seismic data.

[0061] Step S36: performing time correction on the pre-processed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; performing key-layer interpretation processing on the time-corrected seismic data to obtain key-layer interpretation data; In the embodiment of the present invention, time correction of seismic data and interpretation of key horizons are performed. The pre-processed seismic data is time corrected using the nonlinear time correction field constructed in step S35. The time of each sampling point in the seismic data is added to the correction amount of the corresponding position to obtain the time-corrected seismic data. The time-corrected seismic data is interpreted for key horizons, for example, by manually picking or automatically tracking key horizons to obtain key horizon interpretation data, including the spatial position and time of the key horizons.

[0062] Step S37: performing relative geological time calculation according to the key layer interpretation data to obtain relative geological time data; performing isochronous stratigraphic slice generation according to the relative geological time data to obtain nonlinear isochronous stratigraphic slices; In the embodiment of the present invention, relative geological time is calculated and isochronous stratigraphic slices are generated. Relative geological time is calculated based on the key horizon interpretation data obtained in step S36. The time of each key horizon is subtracted from the time of the reference horizon to obtain relative geological time data. Isochronous stratigraphic slices are generated based on the relative geological time data. Data points with the same relative geological time are connected to form isochronous stratigraphic slices, which represent the sedimentary morphology of the strata in different geological periods.

[0063] Step S38: fine-tuning the nonlinear isochronous stratigraphic slices using the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices; In the embodiment of the present invention, the isochronous stratigraphic slices are finely adjusted. The nonlinear isochronous stratigraphic slices generated in step S37 are finely adjusted using the nonlinear geological structure model. For example, according to the position and shape of the fault in the geological model, the slices are locally adjusted to make them more consistent with the geological structure, and finally a refined nonlinear isochronous stratigraphic slice is obtained.

[0064] Preferably, step S31 includes the following steps: Step S311: constructing an initial velocity model according to geological data and a nonlinear geological structure model to obtain an initial velocity model; Step S312: extracting fault attributes from the nonlinear geological structure model to obtain fault attribute data; Step S313: dividing the fault influence area according to the fault attribute data to obtain the fault influence area; Step S314: performing fault influence zone velocity analysis on the fault influence zone according to the pre-processed seismic data to obtain the fault influence zone velocity analysis result; Step S315: performing fault influence zone velocity modeling on the fault influence zone according to the fault influence zone velocity analysis result to obtain a fault influence zone velocity model; Step S316: performing velocity model fusion on the velocity model of the fault-affected zone and the initial velocity model to obtain a velocity model.

[0065] In an embodiment of the present invention, an initial velocity model is constructed. The initial velocity model is constructed by combining geological data (such as drilling and logging data) and a nonlinear geological structure model. The geological data provides velocity information at known locations, while the nonlinear geological structure model provides stratigraphic structure information. The velocity information in the drilling and logging data is extended to the entire three-dimensional space using the Kriging interpolation method to generate an initial velocity model. The model reflects the overall distribution trend of regional velocity, but has low accuracy near faults. For example, using geostatistical software, setting the variogram parameters, performing three-dimensional Kriging interpolation, and generating an initial velocity model with a resolution of 10 meters × 10 meters × 2 meters.

[0066] Extract fault attribute data. Extract fault attribute data from nonlinear geological structure models. These attributes include fault location, strike, dip, and throw. Use fault interpretation software to automatically or manually identify faults and extract information such as the coordinates, strike, dip, and throw of each point on the fault plane. For example, use the fault interpretation module in Petrel software to extract attribute information for every 10-meter point on the fault plane and store it as fault attribute data.

[0067] Divide the fault influence zone. Divide the fault influence zone according to the fault attribute data extracted in step S312. Set an influence range with the fault plane as the center. For example, the area within 50 meters on both sides of the fault is divided into the fault influence zone. The size of the influence range can be adjusted according to the scale and geological conditions of the fault. Using the fault position, strike and dip information in the fault attribute data, calculate the coordinates of each grid point in the fault influence zone and mark it as the fault influence zone.

[0068] Perform velocity analysis on the fault-affected zone. Perform velocity analysis on the fault-affected zone divided in step S313 based on the preprocessed seismic data. Extract seismic gather data within the fault-affected zone, and perform semblance analysis or velocity spectrum analysis to obtain velocity analysis results for the fault-affected zone. For example, using SeisWorks software, extract a seismic gather every 20 meters within the fault-affected zone, perform semblance analysis, calculate semblance values ​​at different depths and velocities, and extract the velocity with the largest semblance value as the velocity at that location.

[0069] Perform velocity modeling of the fault-affected area. According to the velocity analysis results of the fault-affected area obtained in step S314, velocity modeling of the fault-affected area is performed. The velocity analysis results are interpolated to each grid point in the fault-affected area using methods such as Kriging interpolation or inverse distance weighted interpolation to construct a velocity model of the fault-affected area. For example, using geostatistical software, setting the variogram parameters, performing three-dimensional Kriging interpolation on the velocity analysis results in the fault-affected area, and generating a velocity model of the fault-affected area with the same resolution as the initial velocity model (10 meters × 10 meters × 2 meters).

[0070] Perform velocity model fusion. Fuse the velocity model of the fault-affected zone obtained in step S315 with the initial velocity model obtained in step S311 to obtain the final velocity model. In the fault-affected zone, the velocity values ​​in the velocity model of the fault-affected zone are used; in the non-fault-affected zone, the velocity values ​​in the initial velocity model are used. The fusion process can adopt a weighted average method to weight the two velocity models according to the distance from the fault plane. The closer the distance to the fault plane, the greater the weight of the velocity model of the fault-affected zone. Finally, a complete velocity model is obtained, which not only reflects the overall distribution trend of the regional velocity, but also takes into account the impact of the fault on the velocity.

[0071] Preferably, step S31 is specifically: Extracting seismic data of the fault-affected area from the preprocessed seismic data according to the fault-affected area to obtain seismic data of the fault-affected area; Extract seismic attributes from seismic data in the fault-affected area to obtain regional seismic attribute data; Using regional earthquake attribute data to perform fault attribute data spatial registration, to obtain registered fault attribute data; Perform seismic attribute-fault attribute relationship analysis on regional seismic attribute data and aligned fault attribute data to obtain seismic attribute-fault attribute relationship data; Infer the velocity variation law of the fault-affected area based on the earthquake attribute-fault attribute relationship data to obtain velocity variation law inference data; The velocity analysis results of the fault-affected area are generated by inferring the data according to the law of velocity change, and the velocity analysis results of the fault-affected area are obtained.

[0072] In the embodiment of the present invention, according to the range of the fault-affected area determined in the previous step, the corresponding seismic data in the area is extracted from the preprocessed seismic data volume. The extraction process is based on the polygonal boundary of the fault-affected area, and the seismic trace data located within the boundary is extracted to form the seismic data of the fault-affected area. For example, using seismic interpretation software, according to the spatial range of the fault-affected area, the amplitude data of all seismic traces in the area are extracted, and their spatial position information (such as CDP number and inline / crossline number) is retained.

[0073] Extract seismic attributes from seismic data in the fault-affected area. Calculate various seismic attributes, such as root mean square amplitude, coherence volume attributes, dip / azimuth attributes, etc. These attributes can reflect the amplitude, phase, frequency and other characteristics of seismic waves in the fault-affected area, and provide basic data for subsequent analysis of the relationship between faults and velocities. For example, use seismic attribute analysis software to calculate the root mean square amplitude of seismic data in the fault-affected area within a 10ms time window and generate a root mean square amplitude attribute volume.

[0074] The fault attribute data is spatially registered using regional seismic attribute data. Due to the difference in spatial coordinates between seismic data and geological models, the fault attribute data needs to be registered to the spatial coordinate system of the seismic data. For example, an algorithm based on image registration is used to register the fault attribute data with the regional seismic attribute data so that the position of the fault is consistent with the spatial position of the seismic attribute. The registered fault attribute data contains information such as the position, strike, dip, and fault distance of the fault in a unified coordinate system.

[0075] Perform seismic attribute-fault attribute relationship analysis on the regional seismic attribute data and the fault attribute data after registration in step 3. Analyze the correlation between different seismic attributes and fault attributes, for example, analyze the relationship between the root mean square amplitude and the fault distance, or the relationship between the coherence volume attribute and the fault dip. Statistical analysis methods, such as correlation coefficient analysis and regression analysis, can be used to quantify the relationship between seismic attributes and fault attributes. For example, calculate the average value of the root mean square amplitude attribute at different distances on both sides of the fault, and analyze the change pattern of the root mean square amplitude with the fault distance.

[0076] Based on the seismic attribute-fault attribute relationship data, the velocity change law of the fault-affected area is inferred. For example, if the analysis finds that the root mean square amplitude decreases significantly near the fault, it can be inferred that the velocity near the fault is also reduced, because the fault fracture zone usually has a lower velocity. Based on the relationship between different seismic attributes and fault attributes, the change law of velocity in the fault-affected area with fault distance, fault dip and other attributes is inferred. For example, an empirical formula between the root mean square amplitude and velocity is established to predict the velocity distribution in the fault-affected area.

[0077] According to the law of velocity variation, the velocity analysis results of the fault-affected area are generated. The velocity variation law is applied to the fault-affected area, and the velocity value of each position in the fault-affected area is calculated. For example, according to the empirical formula between the root mean square amplitude and velocity, the velocity value of each grid point in the fault-affected area is calculated, and the velocity analysis results of the fault-affected area are generated. This result can be used as input data for velocity modeling of the fault-affected area.

[0078] Preferably, step S33 includes the following steps: Step S331: gridding the pre-processed seismic data to obtain a seismic data grid; Step S332: Calculate the seismic ray path from the earthquake source to each grid point using the ray tracing data, the velocity model pair, and the seismic data grid to obtain the ray path; Step S333: Calculate the travel time of the seismic wave using the velocity model according to the ray path to obtain travel time data; Step S334: constructing a time-depth mapping table according to the travel time data and the spatial position information of the velocity model to obtain a time-depth mapping table; Step S335: using the time-depth mapping table to perform time-depth conversion on both sides of the fault in the fault-affected area respectively, and splicing the conversion results to obtain time-depth conversion data of the fault-affected area; Step S336: using the time-depth mapping table to perform time-depth conversion of the non-fault-affected area to obtain time-depth conversion data of the non-fault-affected area; Step S337: performing time-depth data fusion on the time-depth conversion data of the fault-affected area and the time-depth conversion data of the non-fault-affected area to obtain time-depth conversion data.

[0079] In an embodiment of the present invention, the pre-processed seismic data is gridded to establish a seismic data grid. Seismic data is usually stored in the form of seismic traces, and each seismic trace represents the change of seismic wave amplitude at a certain underground location over time. The gridding process converts the seismic trace data into regular grid data, and each grid point represents the seismic wave amplitude at a certain underground spatial location. For example, based on the spatial sampling interval of the seismic data (such as the CDP interval and the inline / crossline interval), the seismic data is interpolated onto a regular rectangular grid, and the grid unit size is 10 meters × 10 meters.

[0080] Using ray tracing data and velocity model, the seismic ray path from the earthquake source to each seismic data grid point is calculated. The ray tracing data contains the ray path information from the earthquake source to each grid point in the velocity model. According to the position of the seismic data grid point in the velocity model, the closest ray path is found and used as the ray path of the grid point. For example, the ray path information on the velocity model grid is interpolated to the seismic data grid using the nearest neighbor interpolation method.

[0081] The velocity model is used to calculate the travel time of seismic waves from the source to each seismic data grid point based on the ray path. The travel time increment (equal to the length of the grid cell divided by the velocity) of each grid cell is accumulated along the ray path to obtain the total travel time from the source to the grid point. For example, for each seismic data grid point, along its ray path, the length of the corresponding grid cell in the velocity model is divided by the velocity of the grid cell, and then all the results are accumulated to obtain the travel time of the grid point.

[0082] A time-depth mapping table is constructed based on the travel time data and the spatial position information of the velocity model. The mapping table stores the correspondence between the time and depth of each seismic data grid point. For example, the travel time and the corresponding depth value of each seismic data grid point are stored in a table to form a time-depth mapping table.

[0083] Using the time-depth mapping table, time-depth conversion is performed on both sides of the fault in the fault-affected area, and the conversion results are spliced ​​together. Due to the existence of the fault, the strata on both sides of the fault are displaced, so time-depth conversion needs to be performed separately. For example, for each seismic data grid point in the fault-affected area, its time value is converted to a depth value according to the time-depth mapping table. Then, the converted depth data on both sides of the fault are spliced ​​together to obtain the time-depth conversion data of the fault-affected area.

[0084] The time-depth mapping table is used to perform time-depth conversion on the non-fault affected area. For example, for each seismic data grid point in the non-fault affected area, its time value is converted into a depth value according to the time-depth mapping table to obtain the time-depth conversion data of the non-fault affected area.

[0085] The time-depth conversion data of the fault-affected area and the time-depth conversion data of the non-fault-affected area are fused to obtain the final time-depth conversion data. The fusion process combines the time-depth conversion data of the fault-affected area and the non-fault-affected area into a complete data volume. For example, the time-depth conversion data of the fault-affected area and the non-fault-affected area are respectively placed in a three-dimensional data volume, and then the two data volumes are merged into a complete data volume to obtain the final time-depth conversion data.

[0086] Preferably, step S34 includes the following steps: Step S341: selecting a reference layer for the time-depth conversion data to obtain a reference layer; extracting the time-depth of the time-depth conversion data according to the reference layer to obtain a reference layer time-depth curve; Step S342: extracting the target layer time-depth from the time-depth conversion data to obtain a target layer time-depth curve; Step S343: performing time difference calculation on the reference layer time-depth curve and the target layer time-depth curve to obtain time difference data; constructing a time difference spatial distribution map according to the time difference data to obtain a time difference spatial distribution map; Step S344: performing fault influence zone time difference correction on both sides of the fault in the fault influence zone according to the time difference spatial distribution map to obtain fault influence zone time difference correction data; Step S345: performing time difference correction in the non-fault affected area according to the time difference spatial distribution map to obtain time difference correction data in the non-fault affected area; Step S346: performing nonlinear time difference correction data fusion on the time difference correction data of the fault-affected area and the time difference correction data of the non-fault-affected area to obtain nonlinear time difference correction data.

[0087] In an embodiment of the present invention, a stable and easily identifiable layer is selected from the time-depth conversion data as a reference layer. The reference layer is usually selected from a layer with good regional continuity, strong reflection energy and easy tracking. For example, a regional unconformity surface or a marker layer is selected as a reference layer. Then, according to the position of the reference layer on the seismic profile, the time-depth curve of the reference layer is extracted from the time-depth conversion data. During the extraction process, the time and depth values ​​of the reference layer on each seismic trace are recorded to form a reference layer time-depth curve.

[0088] Extract the time-depth curve of the target layer that needs time difference correction. The target layer is usually a layer related to oil and gas reservoirs, such as the top and bottom interfaces of the reservoir. According to the position of the target layer on the seismic profile, extract the time-depth curve of the target layer from the time-depth conversion data. Record the time and depth values ​​of the target layer on each seismic trace to form the time-depth curve of the target layer.

[0089] The time difference between the two layers is calculated using the time-depth curve of the reference layer and the time-depth curve of the target layer. The time difference is defined as the target layer time minus the reference layer time. At each seismic trace position, the difference between the target layer time and the reference layer time is calculated to obtain the time difference value at that position. The time difference values ​​of all seismic traces are plotted into a time difference spatial distribution map, which can intuitively show the trend of time difference in space. For example, use colors or contour lines to represent the time difference size at different locations.

[0090] According to the time difference spatial distribution map, the time difference correction is performed on both sides of the fault in the fault-affected area. Due to the existence of the fault, there is a difference in the time difference on both sides of the fault. Therefore, it is necessary to perform time difference correction on both sides of the fault. For example, in the fault-affected area, according to the time difference spatial distribution map, the time difference on both sides of the fault is interpolated or smoothed to eliminate the sudden change of time difference caused by the fault.

[0091] According to the time difference spatial distribution map, the time difference correction is performed for the non-fault affected area. In the non-fault affected area, the time difference changes relatively slowly, and the time difference can be corrected using interpolation or fitting methods. For example, using the Kriging interpolation method, the time difference value in the time difference spatial distribution map is interpolated to each grid point in the non-fault affected area to obtain the time difference correction data for the non-fault affected area.

[0092] The time difference correction data of the fault-affected area and the time difference correction data of the non-fault-affected area are fused to obtain the final nonlinear time difference correction data. The fusion process combines the time difference correction data of the fault-affected area and the non-fault-affected area into a complete data volume. For example, the time difference correction data of the fault-affected area and the non-fault-affected area are respectively placed in a three-dimensional data volume, and then the two data volumes are merged to obtain the final nonlinear time difference correction data.

[0093] Preferably, the present invention further provides a system for determining nonlinear isochronous stratum slices, which is used to execute the method for determining nonlinear isochronous stratum slices as described above. The system for determining nonlinear isochronous stratum slices comprises: The nonlinear seismic attribute extraction module obtains original seismic data; performs seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performs adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtering data; performs nonlinear seismic attribute extraction on the adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; The nonlinear attribute and geological model joint inversion module obtains geological data and inversion target data; constructs an initial geological model based on the geological data to obtain an initial geological model; performs residual calculation based on the inversion target data and the initial geological model to obtain a residual calculation result; performs model iterative inversion on the initial geological model based on the residual calculation result to obtain a nonlinear geological structure model; The nonlinear stratigraphic time correction module is used to extract the fault influence zone of the nonlinear geological structure model to obtain the fault influence zone; construct a velocity model based on the nonlinear geological structure model and the fault influence zone to obtain the velocity model; perform seismic wave ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; construct a nonlinear time correction field based on the time-depth conversion data to obtain a nonlinear time correction field; use the nonlinear time correction field to perform seismic data time correction on the preprocessed seismic data, and perform key layer interpretation processing to obtain key layer interpretation data; determine nonlinear isochronous stratigraphic slices based on the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices.

[0094] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0095] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for determining nonlinear isochronous stratigraphic slices, characterized in that: The following steps are involved: Step S1: obtaining original seismic data; Performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; Performing adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtering data; Extract nonlinear seismic attributes from the adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; Step S2: obtaining geological data and inversion target data; constructing an initial geological model according to the geological data to obtain an initial geological model; performing residual calculation according to the inversion target data and the initial geological model to obtain a residual calculation result; performing model iterative inversion on the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model; Step S3: extracting the fault influence area of ​​the nonlinear geological structure model to obtain the fault influence area; constructing a velocity model according to the nonlinear geological structure model and the fault influence area to obtain a velocity model; Perform seismic wave ray tracing on the velocity model to obtain ray tracing data; perform time-depth conversion on the preprocessed seismic data using the ray tracing data to obtain time-depth conversion data; construct a nonlinear time correction field based on the time-depth conversion data to obtain a nonlinear time correction field; perform seismic data time correction on the preprocessed seismic data using the nonlinear time correction field, and perform key layer interpretation processing to obtain key layer interpretation data; determine nonlinear isochronous stratigraphic slices based on the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices.

2. The method for determining nonlinear isochronous stratigraphic slices according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: acquiring original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; Step S12: performing multi-scale wavelet decomposition on the pre-processed seismic data to obtain multi-scale wavelet decomposition data; Step S13: performing adaptive nonlinear filtering on each decomposition layer in the multi-scale wavelet decomposition data to obtain adaptive nonlinear filtering data; Step S14: performing nonlinear feature enhancement processing on the adaptive nonlinear filtering data to obtain nonlinear feature enhanced data; Step S15: performing multi-scale reconstruction on the nonlinear feature enhanced data to obtain multi-scale reconstructed data; performing nonlinear seismic attribute extraction on the multi-scale reconstructed data to obtain a nonlinear seismic attribute field.

3. The method for determining nonlinear isochronous stratigraphic slices according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire geological data; construct an initial geological model based on the geological data to obtain an initial geological model; Step S22: setting rock physical parameters for the initial geological model and performing forward simulation to obtain forward simulation seismic data; Step S23: performing the same nonlinear seismic attribute extraction process as step S1 on the forward modeling seismic data to obtain a simulated nonlinear seismic attribute field; Step S24: acquiring inversion target data; performing residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field according to the inversion target data to obtain a residual calculation result; Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21, otherwise, execute step S26; Step S26: updating the initial geological model according to the residual calculation result to obtain a nonlinear geological structure model.

4. The method for determining nonlinear isochronous stratigraphic slices according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: extracting simulated seismic data to be calculated for the nonlinear seismic attribute field to obtain simulated seismic data; extracting actual seismic data from the preprocessed seismic data according to the simulated seismic data to obtain actual seismic data; Step S242: performing time-space registration of simulated earthquake data and actual earthquake data to obtain earthquake time-space registration data; Step S243: performing multi-attribute residual calculation on the seismic time-space registration data according to the geological data and the inversion target data to obtain multi-attribute residual data; Step S244: determining the residual weight coefficient based on statistical analysis of the multi-attribute residual data according to the inversion target data to obtain the residual weight coefficient; Step S245: performing weighted residual calculation on the multi-attribute residual data using the residual weight coefficient to obtain weighted residual data; performing residual data post-processing on the weighted residual data to obtain post-processed residual data; Step S246: Analyze the size and spatial distribution characteristics of the post-processed residual data to obtain a residual calculation result.

5. The method for determining nonlinear isochronous stratigraphic slices according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting the fault influence area of ​​the nonlinear geological structure model to obtain the fault influence area; constructing a velocity model according to the nonlinear geological structure model and the fault influence area to obtain a velocity model; Step S32: performing seismic wave ray tracing on the velocity model to obtain ray tracing data; Step S33: using ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; Step S34: performing nonlinear time difference correction according to the time-depth conversion data to obtain nonlinear time difference correction data; Step S35: constructing a nonlinear time correction field for the nonlinear time difference correction data to obtain a nonlinear time correction field; Step S36: performing time correction on the pre-processed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; performing key-layer interpretation processing on the time-corrected seismic data to obtain key-layer interpretation data; Step S37: performing relative geological time calculation according to the key layer interpretation data to obtain relative geological time data; performing isochronous stratigraphic slice generation according to the relative geological time data to obtain nonlinear isochronous stratigraphic slices; Step S38: Using the nonlinear geological structure model, fine-tune the nonlinear isochronous stratigraphic slices to obtain refined nonlinear isochronous stratigraphic slices.

6. The method for determining nonlinear isochronous stratigraphic slices according to claim 5, characterized in that: Step S31 includes the following steps: Step S311: constructing an initial velocity model according to geological data and a nonlinear geological structure model to obtain an initial velocity model; Step S312: extracting fault attributes from the nonlinear geological structure model to obtain fault attribute data; Step S313: dividing the fault influence area according to the fault attribute data to obtain the fault influence area; Step S314: performing fault influence zone velocity analysis on the fault influence zone according to the pre-processed seismic data to obtain the fault influence zone velocity analysis result; Step S315: performing fault influence zone velocity modeling on the fault influence zone according to the fault influence zone velocity analysis result to obtain a fault influence zone velocity model; Step S316: performing velocity model fusion on the velocity model of the fault-affected zone and the initial velocity model to obtain a velocity model.

7. The method for determining nonlinear isochronous stratigraphic slices according to claim 6, characterized in that: Step S314 is specifically as follows: Extracting seismic data of the fault-affected area from the preprocessed seismic data according to the fault-affected area to obtain seismic data of the fault-affected area; Extract seismic attributes from seismic data in the fault-affected area to obtain regional seismic attribute data; Using regional earthquake attribute data to perform fault attribute data spatial registration, to obtain registered fault attribute data; Perform seismic attribute-fault attribute relationship analysis on regional seismic attribute data and aligned fault attribute data to obtain seismic attribute-fault attribute relationship data; Infer the velocity variation law of the fault-affected area based on the earthquake attribute-fault attribute relationship data to obtain velocity variation law inference data; The velocity analysis results of the fault-affected area are generated by inferring the data according to the law of velocity change, and the velocity analysis results of the fault-affected area are obtained.

8. The method for determining nonlinear isochronous stratigraphic slices according to claim 5, characterized in that: Step S33 includes the following steps: Step S331: gridding the pre-processed seismic data to obtain a seismic data grid; Step S332: Calculate the seismic ray path from the earthquake source to each grid point using the ray tracing data, the velocity model pair, and the seismic data grid to obtain the ray path; Step S333: Calculate the travel time of the seismic wave using the velocity model according to the ray path to obtain travel time data; Step S334: constructing a time-depth mapping table according to the travel time data and the spatial position information of the velocity model to obtain a time-depth mapping table; Step S335: using the time-depth mapping table to perform time-depth conversion on both sides of the fault in the fault-affected area respectively, and splicing the conversion results to obtain time-depth conversion data of the fault-affected area; Step S336: using the time-depth mapping table to perform time-depth conversion of the non-fault-affected area to obtain time-depth conversion data of the non-fault-affected area; Step S337: performing time-depth data fusion on the time-depth conversion data of the fault-affected area and the time-depth conversion data of the non-fault-affected area to obtain time-depth conversion data.

9. The method for determining nonlinear isochronous stratigraphic slices according to claim 5, characterized in that: Step S34 includes the following steps: Step S341: selecting a reference layer for the time-depth conversion data to obtain a reference layer; extracting the time-depth of the time-depth conversion data according to the reference layer to obtain a reference layer time-depth curve; Step S342: extracting the target layer time-depth from the time-depth conversion data to obtain a target layer time-depth curve; Step S343: performing time difference calculation on the reference layer time-depth curve and the target layer time-depth curve to obtain time difference data; constructing a time difference spatial distribution map according to the time difference data to obtain a time difference spatial distribution map; Step S344: performing fault influence zone time difference correction on both sides of the fault in the fault influence zone according to the time difference spatial distribution map to obtain fault influence zone time difference correction data; Step S345: performing time difference correction in the non-fault affected area according to the time difference spatial distribution map to obtain time difference correction data in the non-fault affected area; Step S346: performing nonlinear time difference correction data fusion on the time difference correction data of the fault influence area and the time difference correction data of the non-fault influence area to obtain nonlinear time difference correction data.

10. A system for determining nonlinear isochronous stratigraphic slices, characterized in that: The method for determining the nonlinear isochronous stratigraphic slice according to claim 1 is used to execute the method, wherein the system for determining the nonlinear isochronous stratigraphic slice comprises: The nonlinear seismic attribute extraction module obtains original seismic data; performs seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performs adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtering data; performs nonlinear seismic attribute extraction on the adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; The nonlinear attribute and geological model joint inversion module obtains geological data and inversion target data; constructs an initial geological model based on the geological data to obtain an initial geological model; performs residual calculation based on the inversion target data and the initial geological model to obtain a residual calculation result; performs model iterative inversion on the initial geological model based on the residual calculation result to obtain a nonlinear geological structure model; The nonlinear stratigraphic time correction module is used to extract the fault influence zone of the nonlinear geological structure model to obtain the fault influence zone; construct a velocity model based on the nonlinear geological structure model and the fault influence zone to obtain the velocity model; perform seismic wave ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth conversion on the preprocessed seismic data to obtain time-depth conversion data; construct a nonlinear time correction field based on the time-depth conversion data to obtain a nonlinear time correction field; use the nonlinear time correction field to perform seismic data time correction on the preprocessed seismic data, and perform key layer interpretation processing to obtain key layer interpretation data; determine nonlinear isochronous stratigraphic slices based on the key layer interpretation data and the nonlinear geological structure model to obtain refined nonlinear isochronous stratigraphic slices.

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