A method and system for determining a nonlinear isochronous stratigraphic slice

Abstract

The present application relates to the technical field of oil and gas exploration, and particularly relates to a method and system for determining nonlinear isochronous stratigraphic slices. The method comprises the following steps: obtaining original seismic data; performing seismic data preprocessing on the original seismic data to obtain preprocessed seismic data; performing 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; obtaining 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 error calculation according to the inversion target data and the initial geological model to obtain a residual error calculation result. The present application can better handle nonlinear geological structure complexity, improve the accuracy and reliability of isochronous stratigraphic slices, and provide more accurate geological information for oil and gas exploration and development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method and system for determining nonlinear isochronous stratigraphic slices. Background Technology

[0002] Isochronous stratigraphic slices are important geological interpretation tools in oil and gas exploration and development, reflecting the sedimentary history and evolution of strata. Traditional methods for determining isochronous stratigraphic slices mainly rely on stratigraphic interpretation and linear interpolation; that is, after interpreting key stratigraphic horizons, linear interpolation is performed between horizons to generate isochronous stratigraphic slices. Early methods relied heavily on manual interpretation and linear interpolation, resulting in low accuracy, low efficiency, significant influence from the subjective factors of interpreters, and an inability to handle complex geological structures. Later, some automatic or semi-automatic stratigraphic slice methods based on geostatistics and interpolation algorithms were developed, such as Kriging interpolation, nearest neighbor interpolation, bilinear interpolation, and spline interpolation. However, existing methods are insufficient for handling the complexity of nonlinear geological structures. Their shortcomings are as follows:

[0003] Fault effects: The presence of faults can cause stratigraphic dislocation and changes in velocity fields. Existing methods are unable to accurately handle the impact of faults on isochronous stratigraphic slices, resulting in reduced accuracy of stratigraphic slices near faults.

[0004] Heterogeneity: The heterogeneity of the subsurface medium leads to the curvature of the seismic wave propagation path and changes in velocity. Existing methods are based on linear assumptions and cannot accurately describe this nonlinear change, which affects the accuracy of isochronous stratigraphic slices.

[0005] Complex structures: In regions with complex geological structures such as folds and fault blocks, the morphology and thickness of strata vary dramatically. Existing methods are unable to accurately depict such complex changes, resulting in distortion of isochronous stratigraphic slices. Summary of the Invention

[0006] Therefore, it is necessary to provide a method and system for determining nonlinear isochronous stratigraphic slices to solve at least one of the above-mentioned technical problems.

[0007] To achieve the above objective, a method for determining nonlinear isochronous stratigraphic slices includes the following steps:

[0008] Step S1: Acquire raw seismic data; perform seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; perform adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; extract nonlinear seismic attributes from the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field.

[0009] Step S2: Acquire geological data and inversion target data; construct an initial geological model based on the geological data to obtain the initial geological model; perform residual calculation based on the inversion target data and the initial geological model to obtain the residual calculation results; perform model iteration inversion on the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model;

[0010] Step S3: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model; perform seismic ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth transformation on the preprocessed seismic data to obtain time-depth transformed data; construct a nonlinear time correction field based on the time-depth transformed data to obtain the 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.

[0011] This invention effectively suppresses noise and highlights the nonlinear characteristics of the stratigraphy 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 foundation for subsequent inversion and stratigraphic slice construction. By combining geological data and using the target inversion data for iterative inversion, the initial geological model is gradually optimized, ultimately obtaining a high-precision nonlinear geological structure model that matches the actual seismic data, 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 stratigraphic interpretation, and nonlinear isochronous stratigraphic slice generation and adjustment, a refined nonlinear isochronous stratigraphic slice is finally obtained. This slice can accurately reflect the morphology and evolution of underground geological structures and effectively eliminate the influence of factors such as faults and velocity changes, improving the accuracy and reliability of stratigraphic slices and providing more accurate geological information for oil and gas exploration and development. Therefore, this 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.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Obtain raw seismic data; perform seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data;

[0014] Step S12: Perform multi-scale wavelet decomposition on the preprocessed seismic data to obtain multi-scale wavelet decomposed data;

[0015] Step S13: Perform adaptive nonlinear filtering on each decomposition layer in the multi-scale wavelet decomposition data to obtain adaptive nonlinear filtered data;

[0016] Step S14: Perform nonlinear feature enhancement processing on the adaptive nonlinear filter data to obtain nonlinear feature enhanced data;

[0017] Step S15: Perform multi-scale reconstruction on the nonlinear feature enhancement data to obtain multi-scale reconstructed data; extract nonlinear seismic attributes from the multi-scale reconstructed data to obtain a nonlinear seismic attribute field.

[0018] This invention acquires and preprocesses raw seismic data. Preprocessing removes noise and interference, improves the signal-to-noise ratio, and provides a high-quality data foundation for subsequent multi-scale wavelet decomposition and nonlinear filtering, ensuring the accuracy and reliability of the extracted nonlinear seismic attributes. Multi-scale wavelet decomposition is performed on the preprocessed seismic data. This decomposition separates seismic data into components of different scales and frequencies, separating noise from effective signals. This allows subsequent nonlinear filtering and feature enhancement to be targeted at different scales, better preserving effective signals, removing noise, and improving the accuracy of nonlinear feature extraction. Adaptive nonlinear filtering is applied to each decomposition layer in the multi-scale wavelet decomposition data. Adaptive nonlinear filtering adjusts filtering parameters adaptively based on the local characteristics of the seismic data at different decomposition layers. While effectively removing noise, it better preserves stratigraphic edges and nonlinear features, avoiding the destruction of nonlinear features by traditional linear filtering methods, thus improving the effect of subsequent nonlinear feature enhancement. Nonlinear feature enhancement is then performed on the adaptively nonlinear filtered data. Nonlinear feature enhancement can amplify weak nonlinear features in seismic data, making them easier to identify and extract. Simultaneously, it suppresses background noise and improves the signal-to-noise ratio of nonlinear features, thereby enhancing the reliability and resolution of the final extracted nonlinear seismic attributes. Multi-scale reconstruction is then performed on the enhanced nonlinear feature data to extract nonlinear seismic attributes. Multi-scale reconstruction recombines the different scale components from the enhanced nonlinear feature data into complete seismic data, and nonlinear seismic attributes are extracted based on this. Because the previous processing steps effectively removed noise and enhanced nonlinear features, the extracted nonlinear seismic attributes more accurately reflect the nonlinear characteristics of the strata, thus improving the accuracy and reliability of isochronous stratigraphic slices.

[0019] Preferably, step S2 includes the following steps:

[0020] Step S21: Obtain geological data; construct an initial geological model based on the geological data to obtain the initial geological model;

[0021] Step S22: Set the rock physical parameters of the initial geological model and perform forward modeling to obtain forward modeling seismic data;

[0022] Step S23: Perform the same nonlinear seismic attribute extraction process as in step S1 on the forward modeling seismic data to obtain the simulated nonlinear seismic attribute field;

[0023] Step S24: Obtain the inversion target data; perform residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field based on the inversion target data to obtain the residual calculation results;

[0024] Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21; otherwise, proceed to step S26.

[0025] Step S26: Update the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model.

[0026] This invention acquires geological data and constructs an initial geological model. By collecting and organizing geological data and using it to construct the initial geological model, prior geological information can be integrated into the determination process of nonlinear isochronous stratigraphic slices, providing a reliable initial model for subsequent model iteration and inversion, thereby improving the accuracy and reliability of the final geological model. Rock physical parameters are set for the initial geological model, and forward modeling is performed. By setting the rock physical parameters for the initial geological model and performing forward modeling, 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, thus achieving effective matching between the geological model and the seismic data. The same nonlinear seismic attribute extraction process as in step S1 is performed on the forward-modeled seismic data. Performing the same nonlinear seismic attribute extraction process as on the actual seismic data ensures the consistency and comparability between the simulated nonlinear seismic attribute field and the actual nonlinear seismic attribute field, providing a reliable basis for subsequent residual calculation and model inversion. The target inversion data is acquired, and residual calculation is performed. By acquiring the inversion target data (the actual measured nonlinear seismic attribute field) and performing residual calculations with the simulated nonlinear seismic attribute field, the difference between the simulation results and the actual data can be quantified. This provides guidance for subsequent model updates and drives the model to iterate towards a direction that better matches the actual data. The residual calculation results are evaluated and iterated. By comparing the residual calculation results with the residual threshold of the preset objective function and determining whether to return to step S21 to reconstruct the geological model based on the comparison results, iterative inversion of the model can be achieved, gradually reducing the difference between the simulation results and the actual data, thereby improving the accuracy of the final geological model. The initial geological model is updated based on the residual calculation results. By updating the initial geological model based on the residual calculation results, information from the seismic data can be integrated into the geological model, making the geological model more consistent with actual geological conditions, and ultimately obtaining a high-precision nonlinear geological structure model, thereby improving the accuracy and reliability of isochronous stratigraphic slices.

[0027] Preferably, step S24 includes the following steps:

[0028] Step S241: Extract simulated seismic data from the nonlinear seismic attribute field to be residual calculated, and obtain simulated seismic data; extract actual seismic data from the preprocessed seismic data based on the simulated seismic data, and obtain actual seismic data;

[0029] Step S242: Perform spatiotemporal registration of simulated earthquake data and actual earthquake data to obtain spatiotemporal registration earthquake data;

[0030] Step S243: Perform multi-attribute residual calculation on the seismic spatiotemporal registration data based on geological data and inverted target data to obtain multi-attribute residual data;

[0031] Step S244: Determine the residual weight coefficients based on statistical analysis of the multi-attribute residual data according to the inverted target data, and obtain the residual weight coefficients;

[0032] Step S245: Calculate the weighted residuals of the multi-attribute residuals using the residual weighting coefficients to obtain the weighted residuals; perform residual post-processing on the weighted residuals to obtain the post-processed residuals.

[0033] Step S246: Analyze the magnitude and spatial distribution characteristics of the post-processed residual data to obtain the residual calculation results.

[0034] This invention extracts simulated and actual seismic data. By extracting simulated and actual seismic data relevant to the calculation of nonlinear seismic attribute fields, the effectiveness and relevance of subsequent residual calculations can be ensured, enabling the residuals to accurately reflect the simulation effect of the geological model at specific locations and times. Spatiotemporal registration of data is performed. By performing spatiotemporal registration of simulated and actual seismic data, spatiotemporal differences caused by acquisition and processing errors can be eliminated, ensuring consistency between simulated and actual data in time and space, thereby improving the accuracy of residual calculations. Multi-attribute residual calculation is performed. By performing residual calculations on multiple nonlinear seismic attributes, the simulation effect of the geological model can be evaluated more comprehensively, avoiding the bias caused by a single attribute, thus more accurately reflecting the difference between the model and actual geological conditions. Residual weighting coefficients are determined. By determining residual weighting coefficients based on statistical analysis methods, different weights can be assigned according to the contribution of different attributes to the inversion results, improving the stability and reliability of the inversion and highlighting the influence of important attributes on the inversion results. Weighted residual calculation and post-processing are then performed. By weighting and post-processing multi-attribute residual data, the impact of noise on the residual calculation results can be effectively reduced, the signal-to-noise ratio of the residuals can be improved, and the residual results can be made smoother and more stable, thus providing a more reliable basis for model updates. The residual data is analyzed to obtain the residual calculation results. By analyzing the size and spatial distribution characteristics of the post-processed residual data, a deeper understanding of the differences between the geological model and the actual geological conditions can be obtained, providing more detailed guidance for subsequent model updates and helping to identify parts of the model that need improvement.

[0035] Preferably, step S3 includes the following steps:

[0036] Step S31: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model;

[0037] Step S32: Perform seismic wave ray tracing on the velocity model to obtain ray tracing data;

[0038] Step S33: Use ray tracing data to perform time-depth transformation on the preprocessed seismic data to obtain time-depth transformed data;

[0039] Step S34: Perform nonlinear time difference correction based on the time depth conversion data to obtain nonlinear time difference correction data;

[0040] Step S35: Construct a nonlinear time correction field from the nonlinear time difference correction data to obtain the nonlinear time correction field;

[0041] Step S36: Perform seismic data time correction on the preprocessed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; perform key horizon interpretation processing on the time-corrected seismic data to obtain key horizon interpretation data.

[0042] Step S37: Calculate the relative geological time based on the interpretation data of key stratigraphic layers to obtain relative geological time data; generate isochronous stratigraphic slices based on the relative geological time data to obtain nonlinear isochronous stratigraphic slices;

[0043] Step S38: Use a nonlinear geological structure model to fine-tune the nonlinear isochronous stratigraphic slices to obtain refined nonlinear isochronous stratigraphic slices.

[0044] This invention extracts the fault-affected zone and constructs a velocity model. Extracting the fault-affected zone and constructing a velocity model based on it allows for a more detailed characterization of velocity changes near the fault, improving the accuracy of the velocity model in the vicinity of the fault, thus providing more accurate velocity information for subsequent ray tracing and time-depth conversion. Seismic wave ray tracing is then performed. By ray tracing the velocity model, the propagation path and travel time information of seismic waves in the subsurface medium can be obtained, providing necessary parameters for subsequent time-depth conversion and establishing a connection between time-domain seismic data and depth-domain geological models. Time-depth conversion is then performed. Using ray-traced data for time-depth conversion transforms seismic data from the time domain to the depth domain, eliminating depth distortion caused by velocity changes, enabling more accurate comparison and matching between seismic data and geological models. Nonlinear time difference correction is then performed. Nonlinear time difference correction eliminates time differences caused by factors such as stratigraphic tilt and tectonic deformation, allowing seismic data from different locations to reflect stratigraphic information from the same geological period, providing a basis for subsequent generation of isochronous stratigraphic slices. A nonlinear time correction field is then constructed. Constructing a nonlinear time correction field allows the results of nonlinear time difference correction to be applied to the entire seismic data volume, achieving overall time correction of the seismic data and thus improving the accuracy of subsequent interpretation of key stratigraphic layers. This involves performing seismic data time correction and key stratigraphic interpretation. Using a nonlinear time correction field to correct the seismic data improves its accuracy and resolution, making the interpretation of key stratigraphic layers more accurate and reliable, and providing accurate stratigraphic information for subsequent relative geological time calculations. Calculating relative geological time and generating isochronous stratigraphic slices converts seismic data into a geological time domain, visually showcasing the sedimentary history and evolution of strata, providing crucial information for geological analysis and oil and gas exploration. Fine-tuning the nonlinear isochronous stratigraphic slices using a nonlinear geological structural model integrates the structural information of the geological model into the slices, improving their accuracy and geological significance, and enabling them to more accurately reflect the morphology and evolution of subsurface geological structures.

[0045] Preferably, step S31 includes the following steps:

[0046] Step S311: Construct an initial velocity model based on geological data and a nonlinear geological structure model to obtain the initial velocity model;

[0047] Step S312: Extract fault attributes from the nonlinear geological structure model to obtain fault attribute data;

[0048] Step S313: Divide the fault-affected area according to the fault attribute data to obtain the fault-affected area;

[0049] Step S314: Perform velocity analysis of the fault-affected zone based on the preprocessed seismic data to obtain the velocity analysis results of the fault-affected zone;

[0050] Step S315: Based on the velocity analysis results of the fault-affected zone, perform velocity modeling of the fault-affected zone to obtain the velocity model of the fault-affected zone;

[0051] Step S316: Fusion of the velocity model in the fault-affected zone and the initial velocity model to obtain the velocity model.

[0052] This invention constructs an initial velocity model based on geological data and a nonlinear geological structural model. Utilizing geological data and a nonlinear geological structural model to construct the initial velocity model allows for the integration of prior geological information into the velocity model, providing a reliable initial model for subsequent velocity analysis and modeling of fault-affected areas, and improving the accuracy of the final velocity model. Fault attributes are extracted from the nonlinear geological structural model. Extracting fault attribute data provides information such as the fault's location, strike, dip angle, and displacement, providing necessary parameters for subsequent fault-affected area delineation and velocity analysis, thus enabling a more refined characterization of the structural features near the fault. Fault-affected areas are delineated based on the fault attribute data. Delineating fault-affected areas based on fault attribute data identifies regions significantly affected by the fault, providing spatial scope for subsequent specialized velocity analysis and modeling of these areas, thereby improving the accuracy of the velocity model near the fault. Velocity analysis of the fault-affected areas is then performed based on preprocessed seismic data. By conducting specialized velocity analysis on the fault-affected zone, more refined velocity information near the fault can be obtained, compensating for the insufficient accuracy of the initial velocity model near the fault and thus more accurately reflecting the fault's influence on velocity. Velocity modeling of the fault-affected zone is then performed based on the velocity analysis results. This modeling transforms the velocity analysis results into a continuous velocity field, providing a high-precision velocity model for subsequent velocity model fusion and making the velocity model more refined near the fault. The velocity model of the fault-affected zone and the initial velocity model are then fused. This fusion combines the advantages of both models to generate a complete velocity model that includes both regional velocity background information and refined velocity information near the fault, thereby improving the overall accuracy of the velocity model and its accuracy near the fault.

[0053] Preferably, step S31 specifically includes:

[0054] Based on the fault-affected area, the preprocessed seismic data is extracted to obtain the fault-affected area seismic data;

[0055] Seismic attributes are extracted from seismic data in the fault-affected area to obtain regional seismic attribute data;

[0056] Regional seismic attribute data is used to spatially register fault attribute data, resulting in registered fault attribute data.

[0057] Seismic attribute-fault attribute relationship analysis was performed on regional seismic attribute data and registered fault attribute data to obtain seismic attribute-fault attribute relationship data;

[0058] Based on the earthquake attribute-fault attribute relationship data, the velocity variation law in the fault-affected area is inferred, and the velocity variation law inference data is obtained.

[0059] Based on the velocity variation pattern, the velocity analysis results of the fault-affected area are generated by inferring the data.

[0060] This invention focuses the analysis on the vicinity of faults by extracting seismic data from fault-affected areas, eliminating interference from regions far from faults. This allows for a more effective study of the impact of faults on seismic wave propagation and the extraction of fault-related characteristic information. Extracting regional seismic attribute data quantifies the seismic wave characteristics of the fault-affected area, such as amplitude, frequency, and phase, providing a data foundation for subsequent analysis of the relationship between faults and velocities and aiding in the identification of fault-related anomalies. Spatial registration eliminates spatial discrepancies between seismic data and geological models, ensuring spatial consistency between fault attribute data and seismic attribute data, thereby improving the accuracy of subsequent analyses. Analyzing the relationship between seismic and fault attributes reveals the influence of faults on seismic wave propagation, such as the attenuation of amplitude and the scattering of frequency, providing a basis for inferring velocity variation patterns. Inferring velocity variation patterns based on seismic attribute-fault attribute relationship data allows linking changes in seismic attributes to changes in velocity; for example, inferring velocity reduction based on amplitude attenuation, thus predicting velocity distribution near faults. By inferring the velocity variation patterns from the data to generate velocity analysis results for the fault-affected zone, qualitative velocity variation patterns can be transformed into quantitative velocity values, providing more accurate constraints for subsequent velocity modeling and improving the accuracy of the velocity model near the fault.

[0061] Preferably, step S33 includes the following steps:

[0062] Step S331: Perform seismic data gridding on the preprocessed seismic data to obtain a seismic data grid;

[0063] Step S332: Calculate the seismic ray path from the earthquake source to each grid point using ray tracing data, velocity model pairs, and seismic data grids to obtain the ray path;

[0064] Step S333: Based on the ray path, calculate the travel time of the seismic wave using the velocity model to obtain travel time data;

[0065] Step S334: Construct a time-depth mapping table based on travel time data and spatial location information of the velocity model to obtain the time-depth mapping table;

[0066] Step S335: Use the time-depth mapping table to perform time-depth conversion on both sides of the fault in the fault-affected area, and stitch the conversion results together to obtain the time-depth conversion data of the fault-affected area;

[0067] Step S336: Use 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;

[0068] Step S337: Perform 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.

[0069] This invention utilizes seismic data gridding to transform preprocessed seismic data. Seismic data gridding converts seismic trace data into regular grid data, aligning the spatial location of the seismic data with the velocity model, providing a unified spatial framework for subsequent ray path calculations and time-depth conversions. The seismic ray path from the earthquake source to each grid point is calculated using ray tracing data, the velocity model, and the seismic data grid. Calculating the ray path from the earthquake source to each grid point determines the propagation path of seismic waves in the subsurface medium, providing necessary path information for subsequent travel time calculations and time-depth conversions. Based on the ray path, the travel time of the seismic waves is calculated using the velocity model. Calculating the travel time based on the ray path and the velocity model determines the time required for the seismic wave to propagate from the source to each grid point, providing the foundational data for the time-depth correspondence in subsequent time-depth conversions. A time-depth mapping table is constructed based on the travel time data and the spatial location information of the velocity model. This time-depth mapping table establishes the correspondence between seismic wave travel time and depth, providing a lookup table for subsequent time-depth conversions, thereby improving conversion efficiency. Time-depth mapping tables were used to perform time-depth transformations on both sides of the fault in the fault-affected zone, and the transformation results were then stitched together. Performing time-depth transformations and stitching on both sides of the fault separately avoids abrupt depth changes caused by fault displacement and more accurately reflects the depth relationship of the strata on both sides of the fault. Time-depth transformations were also performed on the non-fault-affected zone using time-depth mapping tables. This transformation converts seismic data from the time domain to the depth domain, eliminating depth distortion caused by velocity variations and making the seismic data more spatially aligned with the geological model. Time-depth data fusion was then performed on the time-depth transformed data from the fault-affected zone and the non-fault-affected zone. This fusion yields a complete depth-domain seismic data volume, providing a unified data foundation for subsequent time difference correction and isochronous stratigraphic slice generation.

[0070] Preferably, step S34 includes the following steps:

[0071] Step S341: Select a reference layer for the time-depth transformation data to obtain a reference layer; extract the time-depth from the time-depth transformation data based on the reference layer to obtain the time-depth curve of the reference layer;

[0072] Step S342: Extract the target layer time-depth from the time-depth converted data to obtain the target layer time-depth curve;

[0073] Step S343: Calculate the time difference between the time-depth curve of the baseline layer and the time-depth curve of the target layer to obtain the time difference data; construct a time difference spatial distribution map based on the time difference data to obtain the time difference spatial distribution map;

[0074] Step S344: Based on the time difference spatial distribution map, perform time difference correction on both sides of the fault in the fault-affected area to obtain time difference correction data for the fault-affected area;

[0075] Step S345: Perform time difference correction for the non-fault-affected area based on the time difference spatial distribution map to obtain time difference correction data for the non-fault-affected area;

[0076] Step S346: Perform 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.

[0077] This invention selects a reference stratigraphic level and extracts its time-depth curve. Selecting a reference stratigraphic level and extracting its time-depth curve establishes a depth benchmark, providing a reference for subsequent calculations of the time difference between the target stratigraphic level and the reference stratigraphic level, and ensuring the accuracy of the time difference calculation. The time-depth of the target stratigraphic level is extracted from the time-depth converted data to obtain its time-depth curve. Extracting the target stratigraphic level's time-depth curve reveals its morphology in the depth domain, providing necessary data for subsequent time difference calculation and correction. Time difference calculations are performed on both the reference stratigraphic level's time-depth curve and the target stratigraphic level's time-depth curve, and a spatial distribution map of the time difference is constructed. Calculating the time difference and constructing the spatial distribution map visually displays the spatial trend of time difference changes, providing guidance for subsequent time difference correction and helping to identify anomalous time difference regions. Based on the spatial distribution map of the time difference, time difference correction is performed on both sides of the fault-affected zone. Performing time difference correction on both sides of the fault separately eliminates abrupt time difference changes caused by fault displacement, making the time difference changes on both sides of the fault smoother, thereby improving the accuracy of time difference correction. Time difference correction is performed in non-fault-affected areas based on the spatial distribution map of time difference. Time difference correction in non-fault-affected areas eliminates time differences caused by factors such as stratigraphic tilt and tectonic deformation, ensuring that seismic data from different locations reflect stratigraphic information from the same geological period. Nonlinear time difference correction data is then fused from fault-affected and non-fault-affected areas. This fusion yields a complete time difference correction data volume, providing a unified data foundation for subsequent construction of nonlinear time correction fields and ensuring the continuity of the correction results.

[0078] Preferably, the present invention also provides a system for determining nonlinear isochronous stratigraphic slices, used to perform the method for determining nonlinear isochronous stratigraphic slices as described above, the system comprising:

[0079] The nonlinear seismic attribute extraction module acquires raw seismic data; performs seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; performs adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; and extracts nonlinear seismic attributes from the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field.

[0080] The nonlinear attribute and geological model joint inversion module acquires geological data and inversion target data; constructs an initial geological model based on the geological data; calculates residuals based on the inversion target data and the initial geological model; and iterates and inverts the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model.

[0081] The nonlinear stratigraphic time correction module is used to extract fault-affected zones from a nonlinear geological structure model, obtaining the fault-affected zones; construct a velocity model based on the nonlinear geological structure model and the fault-affected zones, obtaining a velocity model; perform seismic ray tracing on the velocity model, obtaining ray tracing data; use the ray tracing data to perform time-depth transformation on the preprocessed seismic data, obtaining time-depth transformed data; construct a nonlinear time correction field based on the time-depth transformed data, obtaining 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 stratigraphic interpretation processing, obtaining key stratigraphic interpretation data; and determine nonlinear isochronous stratigraphic slices based on the key stratigraphic interpretation data and the nonlinear geological structure model, obtaining refined nonlinear isochronous stratigraphic slices.

[0082] This invention effectively suppresses noise and highlights the nonlinear characteristics of the stratigraphy 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 foundation for subsequent inversion and stratigraphic slice construction. By combining geological data and using the target inversion data for iterative inversion, the initial geological model is gradually optimized, ultimately obtaining a high-precision nonlinear geological structure model that matches the actual seismic data, 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 stratigraphic interpretation, and nonlinear isochronous stratigraphic slice generation and adjustment, a refined nonlinear isochronous stratigraphic slice is finally obtained. This slice can accurately reflect the morphology and evolution of underground geological structures and effectively eliminate the influence of factors such as faults and velocity changes, improving the accuracy and reliability of stratigraphic slices and providing more accurate geological information for oil and gas exploration and development. Therefore, this 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. Attached Figure Description

[0083] Figure 1 A schematic diagram illustrating the steps of a method for determining nonlinear isochronous stratigraphic slices;

[0084] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0085] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0086] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0087] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0088] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0089] 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 merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0090] To achieve the above objectives, please refer to Figures 1 to 3 A method for determining nonlinear isochronous stratigraphic slices includes the following steps:

[0091] Step S1: Acquire raw seismic data; perform seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; perform adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; extract nonlinear seismic attributes from the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field.

[0092] Step S2: Acquire geological data and inversion target data; construct an initial geological model based on the geological data to obtain the initial geological model; perform residual calculation based on the inversion target data and the initial geological model to obtain the residual calculation results; perform model iteration inversion on the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model;

[0093] Step S3: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model; perform seismic ray tracing on the velocity model to obtain ray tracing data; use the ray tracing data to perform time-depth transformation on the preprocessed seismic data to obtain time-depth transformed data; construct a nonlinear time correction field based on the time-depth transformed data to obtain the 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] In this embodiment of the invention, reference is made to Figure 1 The above is a schematic flowchart of the method for determining nonlinear isochronous stratigraphic slices according to the present invention. In this example, the method for determining nonlinear isochronous stratigraphic slices includes the following steps:

[0095] Step S1: Acquire raw seismic data; perform seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; perform adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; extract nonlinear seismic attributes from the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field.

[0096] In this embodiment of the invention, raw seismic data in SEG-Y format is acquired and preprocessed, including median filtering to remove outliers, spherical divergence compensation to recover energy, and 8-60Hz bandpass filtering to remove noise. Then, an adaptive nonlinear filtering process is performed using an anisotropic diffusion filtering method based on 5x5 window local variance. Finally, nonlinear seismic attributes such as the Lyapunov exponent and fractal dimension are extracted to construct a nonlinear seismic attribute field.

[0097] Step S2: Acquire geological data and inversion target data; construct an initial geological model based on the geological data to obtain the initial geological model; perform residual calculation based on the inversion target data and the initial geological model to obtain the residual calculation results; perform model iteration inversion on the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model;

[0098] In this embodiment of the invention, geological data such as drilling, logging, stratigraphic sequence, and fault interpretation are collected. An initial geological model, including stratigraphic interfaces and fault models, is constructed using geological modeling software (such as Petrel). The initial geological model is iteratively updated (e.g., adjusting stratigraphic interface depths and fault locations) by calculating the residuals between the model and the actual measured nonlinear seismic attribute field (using root mean square error) until the residuals are less than or equal to a preset threshold (e.g., 0.1). Finally, the model is iteratively inverted using the gradient descent method to obtain a nonlinear geological structural model.

[0099] Step S3: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model; perform seismic ray tracing on the velocity model to obtain ray tracing data; perform time-depth transformation on the preprocessed seismic data using the ray tracing data to obtain time-depth transformed data; construct a nonlinear time correction field based on the time-depth transformed data to obtain the 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.

[0100] In this embodiment of the invention, the fault-affected zone (e.g., a 50-meter radius on both sides of the fault plane) is extracted from a nonlinear geological structural model. A velocity model is constructed by combining the geological model and the fault-affected zone, and velocity adjustments are considered within the fault-affected zone. Ray tracing is performed using the fast-tracing method, and the preprocessed seismic data is converted to time and depth using the ray tracing data. A nonlinear time-corrected field is constructed based on the time-depth converted data and applied to the preprocessed seismic data for time correction. Key stratigraphic interpretation is performed on the time-corrected data (e.g., manual picking or automatic tracing). Finally, relative geological time is calculated based on the key stratigraphic interpretation data, and nonlinear isochronous stratigraphic slices are generated and finely adjusted using the nonlinear geological structural model, for example, by local adjustments based on fault location and morphology.

[0101] Preferably, step S1 includes the following steps:

[0102] Step S11: Obtain raw seismic data; perform seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data;

[0103] Step S12: Perform multi-scale wavelet decomposition on the preprocessed seismic data to obtain multi-scale wavelet decomposed data;

[0104] Step S13: Perform adaptive nonlinear filtering on each decomposition layer in the multi-scale wavelet decomposition data to obtain adaptive nonlinear filtered data;

[0105] Step S14: Perform nonlinear feature enhancement processing on the adaptive nonlinear filter data to obtain nonlinear feature enhanced data;

[0106] Step S15: Perform multi-scale reconstruction on the nonlinear feature enhancement data to obtain multi-scale reconstructed data; extract nonlinear seismic attributes from the multi-scale reconstructed data to obtain a nonlinear seismic attribute field.

[0107] In this embodiment of the invention, raw seismic data is acquired and preprocessed. First, raw seismic data is read from the seismic data acquisition system. This data is typically stored in SEGY format and contains information such as time, space, and amplitude. Then, the raw seismic data is preprocessed, including outlier removal, amplitude compensation, and noise reduction filtering. Specifically, median filtering is used to remove outliers. This method replaces the data at the center of the sequence with the median of an odd number of adjacent data points, effectively removing impulse noise. Next, spherical diffusion compensation is applied to recover seismic wave energy, compensating for the amplitude attenuation as the propagation distance increases. Finally, bandpass filtering is used to remove noise outside a specific frequency range, retaining the effective seismic signal. 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.

[0108] Multi-scale wavelet decomposition was performed on the preprocessed seismic data. Discrete wavelet transform was used to decompose the preprocessed seismic data into wavelet coefficients at different scales. The db4 wavelet basis function was selected for a three-level decomposition. The decomposition yielded low-frequency approximation coefficients and high-frequency detail coefficients, representing seismic wave information at different scales. The low-frequency coefficients reflect the overall trend of the seismic waves, while the high-frequency coefficients contain detailed features of the strata. For example, the first-level decomposition decomposed the seismic data into a low-frequency approximation coefficient A1 and a high-frequency detail coefficient D1; the second-level decomposition decomposed A1 into A2 and D2; and the third-level decomposition decomposed A2 into A3 and D3. Finally, a low-frequency approximation coefficient A3 and three high-frequency detail coefficients D1, D2, and D3 were obtained, constituting the multi-scale wavelet decomposed data.

[0109] Adaptive nonlinear filtering is performed on each decomposition layer in the multi-scale wavelet decomposition data. Adaptive nonlinear filtering is applied to each decomposition layer (A3, D1, D2, D3) obtained in step S12. A nonlinear diffusion filtering method based on local variance is used. This method adaptively adjusts the filtering intensity according to the local variance, smoothing noise while preserving important information such as stratigraphic edges. For example, for the detail coefficient D1, the variance within a 5x5 window around each data point is first calculated; then, the diffusion coefficient is determined based on the variance magnitude—the larger the variance, the smaller the diffusion coefficient, thus weakening the filtering intensity in the edge region; finally, nonlinear diffusion filtering is performed using the calculated diffusion coefficient. The same processing is performed on each decomposition layer to obtain adaptive nonlinear filtered data.

[0110] Nonlinear feature enhancement processing is performed on the adaptive nonlinear filtered data. The adaptive nonlinear filtered data obtained in step S13 is then subjected to nonlinear feature enhancement processing using a sigmoid function-based nonlinear enhancement method. This method can enhance weak signals such as formation boundaries while suppressing background noise. For example, for the filtered detail coefficients D1, their mean and standard deviation are first calculated; then, each data point of D1 is substituted into the sigmoid function for transformation, the parameters of which 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.

[0111] Multi-scale reconstruction and extraction of nonlinear seismic attributes are performed on the nonlinear feature-enhanced data. The nonlinear feature-enhanced data obtained in step S14 is reconstructed at multiple scales. The low-frequency approximation coefficients A3 and high-frequency detail coefficients D1, D2, and D3, after nonlinear feature enhancement, are reconstructed back into the original data domain using inverse discrete wavelet transform. The reconstructed data contains enhanced stratigraphic information and suppressed noise. Finally, nonlinear seismic attributes are extracted from the reconstructed data. For example, attributes such as the Lyapunov exponent and fractal dimension based on chaos theory are calculated to characterize the nonlinear characteristics of the stratigraphy, forming a nonlinear seismic attribute field. These attributes can more sensitively reflect subtle changes in stratigraphic lithology, physical properties, and tectonic structures.

[0112] Preferably, step S2 includes the following steps:

[0113] Step S21: Obtain geological data; construct an initial geological model based on the geological data to obtain the initial geological model;

[0114] Step S22: Set the rock physical parameters of the initial geological model and perform forward modeling to obtain forward modeling seismic data;

[0115] Step S23: Perform the same nonlinear seismic attribute extraction process as in step S1 on the forward modeling seismic data to obtain the simulated nonlinear seismic attribute field;

[0116] Step S24: Obtain the inversion target data; perform residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field based on the inversion target data to obtain the residual calculation results;

[0117] Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21; otherwise, proceed to step S26.

[0118] Step S26: Update the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model.

[0119] As an example of the present invention, reference is made to... Figure 2 As shown, in this example, step S2 includes:

[0120] Step S21: Obtain geological data; construct an initial geological model based on the geological data to obtain the initial geological model;

[0121] In this embodiment of the invention, geological data is acquired and an initial geological model is constructed. Geological data for the target area is collected from existing geological databases, drilling data, well logging data, and outcrop information. This data includes stratigraphic sequence, fault distribution, lithological information, etc. Based on the collected geological data, an initial geological model is constructed using geological modeling software (e.g., Petrel). This model represents the subsurface geological structure in the form of a three-dimensional grid, with each grid cell assigned a specific lithological value. For example, the location and depth of key strata are determined based on drilling data, and then the drilling data is extended to the entire study area using the Kriging interpolation method to generate an initial stratigraphic interface model. Simultaneously, fault information is incorporated into the model to construct an initial fault model. The initial geological model provides the foundation for subsequent forward and inverse simulations.

[0122] Step S22: Set the rock physical parameters of the initial geological model and perform forward modeling to obtain forward modeling seismic data;

[0123] In this embodiment of the invention, rock physical parameters are set and forward modeling is performed. Based on existing well logging data, core analysis data, and regional empirical formulas, corresponding rock physical parameters, such as P-wave velocity, S-wave velocity, and density, are assigned to different lithologies in the initial geological model. Then, forward modeling is performed using the finite difference numerical simulation method of the acoustic equation. The constructed initial geological model is used as input to simulate the propagation process of seismic waves in the subsurface medium, generating synthetic seismic records. During the simulation, a Ricker wavelet with a dominant frequency of 30Hz is used as the seismic source, and the time sampling interval of the simulation record is 2ms. The forward modeling seismic data is used for subsequent nonlinear seismic attribute extraction and residual calculation.

[0124] Step S23: Perform the same nonlinear seismic attribute extraction process as in step S1 on the forward modeling seismic data to obtain the simulated nonlinear seismic attribute field;

[0125] In this embodiment of the invention, nonlinear seismic attributes are extracted from simulated seismic data. The same nonlinear seismic attribute extraction process as in step S1 is performed on the forward-modeled simulated seismic data generated in step S22. Specifically, the forward-modeled simulated seismic data undergoes multi-scale wavelet decomposition, adaptive nonlinear filtering, nonlinear feature enhancement, and multi-scale reconstruction. Then, nonlinear seismic attributes, such as the Lyapunov exponent and fractal dimension, are extracted from the reconstructed data to finally obtain the simulated nonlinear seismic attribute field. This is consistent with the nonlinear attribute extraction process for actual seismic data, ensuring the effectiveness of subsequent residual calculations.

[0126] Step S24: Obtain the inversion target data; perform residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field based on the inversion target data to obtain the residual calculation results;

[0127] In this embodiment of the invention, inversion target data is acquired and residuals are calculated. The inversion target data is typically an actual measured nonlinear seismic attribute field, for example, a nonlinear seismic attribute obtained by processing actual seismic data using the same procedure as in step S1. Residuals are calculated between the actual measured nonlinear seismic attribute field and the simulated nonlinear seismic attribute field obtained in step S23. The residual calculation uses the root mean square error formula, which is the square root of the average of the sum of squares of the differences between the corresponding positional values ​​of the two attribute fields. The residual value reflects the difference between the simulation results and the actual measured data.

[0128] Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21; otherwise, proceed to step S26.

[0129] In this embodiment of the invention, it is determined whether the residual meets a preset threshold and iterative steps are taken. The residual calculation result obtained in step S24 is compared with the preset objective function residual threshold. If the residual value is greater than the preset threshold, it indicates that there is a significant difference between the simulation result and the actual data, and the geological model needs to be adjusted. At this time, the process returns to step S21, and the parameters of the initial geological model are adjusted according to the residual calculation result. For example, the depth of the stratigraphic interface, the location of the fault, etc., are modified, the initial geological model is reconstructed, and steps S22 to S24 are repeated. 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 level of accuracy, and step S26 is executed.

[0130] Step S26: Update the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model;

[0131] In this embodiment of the invention, a final nonlinear geological structure model is obtained based on the residual update model. According to the residual calculation results obtained in step S24, the initial geological model is updated using optimization algorithms such as gradient descent. 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 the maximum number of iterations is reached. The final model obtained is the nonlinear geological structure model, which can better reflect the complexity of underground geological structures.

[0132] Preferably, step S24 includes the following steps:

[0133] Step S241: Extract simulated seismic data from the nonlinear seismic attribute field to be residual calculated, and obtain simulated seismic data; extract actual seismic data from the preprocessed seismic data based on the simulated seismic data, and obtain actual seismic data;

[0134] Step S242: Perform spatiotemporal registration of simulated earthquake data and actual earthquake data to obtain spatiotemporal registration earthquake data;

[0135] Step S243: Perform multi-attribute residual calculation on the seismic spatiotemporal registration data based on geological data and inverted target data to obtain multi-attribute residual data;

[0136] Step S244: Determine the residual weight coefficients based on statistical analysis of the multi-attribute residual data according to the inverted target data, and obtain the residual weight coefficients;

[0137] Step S245: Calculate the weighted residuals of the multi-attribute residuals using the residual weighting coefficients to obtain the weighted residuals; perform residual post-processing on the weighted residuals to obtain the post-processed residuals.

[0138] Step S246: Analyze the magnitude and spatial distribution characteristics of the post-processed residual data to obtain the residual calculation results.

[0139] In this embodiment of the invention, simulated seismic data and actual seismic data are extracted. First, based on the spatial location information of the nonlinear seismic attribute field, simulated seismic data corresponding to the location is extracted from the forward modeling simulated seismic data. For example, if the nonlinear seismic attribute field calculates the Lyapunov index, then the simulated seismic data used in calculating the Lyapunov index is extracted. Then, based on the correspondence between the simulated seismic data and the actual seismic data, the actual seismic data is extracted. This correspondence can be determined based on the spatial location and temporal information of the simulated and actual seismic data. For example, the correspondence can be determined by searching and matching key peaks or troughs in the simulated seismic data within the actual seismic data. The extracted simulated and actual seismic data will be used for subsequent spatiotemporal registration and residual calculation.

[0140] Spatiotemporal registration of the data is performed. The simulated seismic data and actual seismic data extracted in step S241 are spatiotemporally registered. A registration method based on Dynamic Time Warping (DTW) is adopted. The DTW algorithm can calculate the optimal matching path between two time series of different lengths, thereby achieving temporal alignment. First, the simulated seismic data and actual seismic data are used as inputs to the DTW algorithm, respectively; then, the distance matrix between the two time series is calculated; next, a dynamic programming algorithm is used to search for the optimal matching path in the distance matrix; finally, the actual seismic data is temporally stretched or compressed according to the optimal matching path to align it with the simulated seismic data in time. The registered data is called seismic spatiotemporal registration data.

[0141] Multi-attribute residual calculation is performed. Based on geological data and inversion target data, multi-attribute residual calculation is performed on the seismic spatiotemporal registration data obtained in step S242. Geological data includes stratigraphic sequence, lithological information, etc., used to determine the weights of different stratigraphic or lithological units. Inversion target data includes multiple nonlinear seismic attributes, such as Lyapunov exponent and fractal dimension. The residual between the simulated data and the actual data is calculated for each attribute separately, for example, using root mean square error. The residuals of each attribute are combined to form multi-attribute residual data.

[0142] Determine the residual weight coefficients. Based on the inversion target data, determine the residual weight coefficients of the multi-attribute residual data obtained in step S243 using statistical analysis. Principal component analysis (PCA) is used to determine the weight coefficients of each attribute. PCA transforms multiple correlated variables into a few uncorrelated variables, called principal components. First, the multi-attribute residual data is standardized; then, the covariance matrix of the residual data is calculated; next, eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues ​​and eigenvectors; finally, the eigenvectors with the largest eigenvalues ​​are selected as principal components, and the corresponding eigenvalues ​​are used as weight coefficients. These weight coefficients reflect the contribution of each attribute to the residuals.

[0143] Weighted residual calculation and post-processing are performed. Using the residual weight coefficients obtained in step S244, weighted residual calculation is performed on the multi-attribute residual data obtained in step S243. The residual of each attribute is multiplied by its corresponding weight coefficient, and then the weighted residuals of all attributes are summed to obtain the weighted residual data. Post-processing, such as median filtering, is then applied to the weighted residual data to remove outliers and smooth the residual data, resulting in post-processed residual data.

[0144] Analyze the residual data and obtain the residual calculation results. Perform a magnitude and spatial distribution analysis on the post-processed residual data obtained in step S245. Analyze the residual data's mean, standard deviation, maximum, minimum, and other statistical measures, as well as its spatial distribution. For example, a spatial distribution map of the residual data can be drawn to visually display the magnitude of the residuals at different locations. Based on these analysis results, the final residual calculation results are obtained for subsequent geological model updates.

[0145] Preferably, step S3 includes the following steps:

[0146] Step S31: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model;

[0147] Step S32: Perform seismic wave ray tracing on the velocity model to obtain ray tracing data;

[0148] Step S33: Use ray tracing data to perform time-depth transformation on the preprocessed seismic data to obtain time-depth transformed data;

[0149] Step S34: Perform nonlinear time difference correction based on the time depth conversion data to obtain nonlinear time difference correction data;

[0150] Step S35: Construct a nonlinear time correction field from the nonlinear time difference correction data to obtain the nonlinear time correction field;

[0151] Step S36: Perform seismic data time correction on the preprocessed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; perform key horizon interpretation processing on the time-corrected seismic data to obtain key horizon interpretation data.

[0152] Step S37: Calculate the relative geological time based on the interpretation data of key stratigraphic layers to obtain relative geological time data; generate isochronous stratigraphic slices based on the relative geological time data to obtain nonlinear isochronous stratigraphic slices;

[0153] Step S38: Use a nonlinear geological structure model to fine-tune the nonlinear isochronous stratigraphic slices to obtain refined nonlinear isochronous stratigraphic slices.

[0154] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes:

[0155] Step S31: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model;

[0156] In this embodiment of the invention, the fault-affected zone is extracted and a velocity model is constructed. First, the fault-affected zone is identified and extracted based on a nonlinear geological structure model. The fault-affected zone is defined using the normal vector of the fault plane and the changes in the attitude of the strata on both sides of the fault. For example, the area within a certain distance (e.g., 50 meters) from the fault plane on both sides of the fault is defined as the fault-affected zone. Then, a velocity model is constructed based on the nonlinear geological structure model and the fault-affected zone. In non-fault-affected zones, the P-wave velocity values ​​of each grid cell in the nonlinear geological structure model are directly used; in fault-affected zones, the influence of the fault on the velocity is considered. For example, the velocity is adjusted according to the dip angle of the fault plane and the differences 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-velocity characteristics of the fault fracture zone, and finally, a complete velocity model is obtained.

[0157] Step S32: Perform seismic wave ray tracing on the velocity model to obtain ray tracing data;

[0158] In this embodiment of the invention, seismic wave ray tracing is performed. The velocity model constructed in step S31 is ray-traced using a seismic wave ray tracing algorithm. The Fast Marching Method is employed for ray tracing. First, the velocity model is discretized into a grid; then, the source location is set, and the travel time of each grid point is initialized; next, the Fast Marching Method is used to calculate the shortest travel time and ray path of the seismic wave from the source to each grid point; finally, the calculated ray path information is saved as ray tracing data, including the coordinates of each grid point traversed by the ray and its travel time.

[0159] Step S33: Use ray tracing data to perform time-depth transformation on the preprocessed seismic data to obtain time-depth transformed data;

[0160] In this embodiment of the invention, a time-depth conversion is performed. Using the ray tracing data obtained in step S32, the preprocessed seismic data is subjected to a time-depth conversion. Each sampling point in the seismic data is mapped to the velocity model along its corresponding ray path. The depth value corresponding to each sampling point is calculated based on the velocity and travel time of each grid point along the ray path. The time of all sampling points is converted to depth to obtain time-depth converted data, which represents seismic reflection information in the depth domain, eliminating the influence of velocity variations in the time domain.

[0161] Step S34: Perform nonlinear time difference correction based on the time depth conversion data to obtain nonlinear time difference correction data;

[0162] In this embodiment of the invention, nonlinear time difference correction is performed. Nonlinear time difference correction is performed based on time-depth conversion data. First, a reference layer is selected, and its time-depth curve is extracted. Then, a target layer is selected, and its time-depth curve is extracted. Next, the time difference between the target layer and the reference layer is calculated, i.e., the depth difference between the two layers at the same location. Finally, nonlinear fitting of the time difference data is performed using methods such as polynomial fitting to obtain a nonlinear time difference correction function. This function is applied to the time-depth conversion data to obtain nonlinear time difference corrected data.

[0163] Step S35: Construct a nonlinear time correction field from the nonlinear time difference correction data to obtain the nonlinear time correction field;

[0164] In this embodiment of the invention, a nonlinear time correction field is constructed. The nonlinear time difference correction data obtained in step S34 is used to construct the nonlinear time correction field. The nonlinear time difference correction data is converted into the time domain to obtain the nonlinear time correction field. This correction field represents the time correction amount at different locations and is used to eliminate time distortion in seismic data caused by velocity variations and tectonic deformation.

[0165] Step S36: Perform seismic data time correction on the preprocessed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; perform key horizon interpretation processing on the time-corrected seismic data to obtain key horizon interpretation data.

[0166] In this embodiment of the invention, seismic data undergoes time correction and key horizon interpretation. The preprocessed 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 corresponding location's correction value to obtain time-corrected seismic data. Key horizon interpretation is then performed on the time-corrected seismic data, for example, by manually picking or automatically tracing key horizons, resulting in key horizon interpretation data, including the spatial location and time of the key horizons.

[0167] Step S37: Calculate the relative geological time based on the interpretation data of key stratigraphic layers to obtain relative geological time data; generate isochronous stratigraphic slices based on the relative geological time data to obtain nonlinear isochronous stratigraphic slices;

[0168] In this embodiment of the invention, relative geological time is calculated and isochronous stratigraphic slices are generated. Based on the interpretation data of key stratigraphic layers obtained in step S36, relative geological time is calculated. The time of each key stratigraphic layer is subtracted from the time of the reference stratigraphic layer 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 strata in different geological periods.

[0169] Step S38: Use a nonlinear geological structure model to fine-tune the nonlinear isochronous stratigraphic slices to obtain refined nonlinear isochronous stratigraphic slices;

[0170] In this embodiment of the invention, the isochronous stratigraphic slices are finely adjusted. A nonlinear geological structural model is used to finely adjust the nonlinear isochronous stratigraphic slices generated in step S37. For example, based on the location and morphology of faults in the geological model, the slices are locally adjusted to better match the geological structure, ultimately resulting in refined nonlinear isochronous stratigraphic slices.

[0171] Preferably, step S31 includes the following steps:

[0172] Step S311: Construct an initial velocity model based on geological data and a nonlinear geological structure model to obtain the initial velocity model;

[0173] Step S312: Extract fault attributes from the nonlinear geological structure model to obtain fault attribute data;

[0174] Step S313: Divide the fault-affected area according to the fault attribute data to obtain the fault-affected area;

[0175] Step S314: Perform velocity analysis of the fault-affected zone based on the preprocessed seismic data to obtain the velocity analysis results of the fault-affected zone;

[0176] Step S315: Based on the velocity analysis results of the fault-affected zone, perform velocity modeling of the fault-affected zone to obtain the velocity model of the fault-affected zone;

[0177] Step S316: Fusion of the velocity model in the fault-affected zone and the initial velocity model to obtain the velocity model.

[0178] In this embodiment of the invention, an initial velocity model is constructed. This model combines geological data (e.g., drilling and logging data) with a nonlinear geological structural model. The geological data provides velocity information for known locations, while the nonlinear geological structural model provides stratigraphic structure information. Using kriging interpolation, the velocity information from the drilling and logging data is extended to the entire three-dimensional space to generate an initial velocity model. This model reflects the overall distribution trend of regional velocities, but its accuracy is low near faults. For example, using geostatistical software, setting the variogram parameters, and performing three-dimensional kriging interpolation, an initial velocity model with a resolution of 10 meters × 10 meters × 2 meters can be generated.

[0179] Extract fault attribute data. Fault attribute data is extracted from the nonlinear geological structure model. These attributes include fault location, strike, dip angle, and displacement. Faults are automatically or manually identified using fault interpretation software, and information such as coordinates, strike, dip angle, and displacement of each point on the fault plane is extracted. For example, using the fault interpretation module in Petrel software, attribute information at points every 10 meters on the fault plane is extracted and stored as fault attribute data.

[0180] Delineate the fault influence zone. Based on the fault attribute data extracted in step S312, delineate the fault influence zone. Using the fault plane as the center, define an influence range; for example, the area within 50 meters on both sides of the fault is designated as the fault influence zone. The size of the influence range can be adjusted according to the scale of the fault and geological conditions. Using the fault location, strike, and dip information from the fault attribute data, calculate the coordinates of each grid point within the fault influence zone and mark it as part of the fault influence zone.

[0181] Velocity analysis is performed in the fault-affected zone. Based on the preprocessed seismic data, velocity analysis is conducted in the fault-affected zone delineated in step S313. Within the fault-affected zone, seismic gather data is extracted, and merge analysis or velocity spectrum analysis is performed to obtain the velocity analysis results for the fault-affected zone. For example, using SeisWorks software, a seismic gather is extracted every 20 meters within the fault-affected zone, merge analysis is performed, merge values ​​are calculated at different depths and velocities, and the velocity with the highest merge value is extracted as the velocity at that location.

[0182] Velocity modeling is performed in the fault-affected zone. Based on the velocity analysis results obtained in step S314, a velocity model is constructed for the fault-affected zone. Using methods such as kriging interpolation or inverse distance weighted interpolation, the velocity analysis results are interpolated to each grid point within the fault-affected zone to build the velocity model. For example, using geostatistical software, the variogram parameters are set, and three-dimensional kriging interpolation is performed on the velocity analysis results within the fault-affected zone to generate a velocity model of the fault-affected zone with the same resolution as the initial velocity model (10 m × 10 m × 2 m).

[0183] Velocity model fusion is performed. The velocity model of the fault-affected zone obtained in step S315 is fused with the initial velocity model obtained in step S311 to obtain the final velocity model. Within the fault-affected zone, the velocity values ​​from the fault-affected zone velocity model are used; within the non-fault-affected zone, the velocity values ​​from the initial velocity model are used. The fusion process can employ a weighted average method, weighting the two velocity models according to their distance from the fault plane; the closer to the fault plane, the greater the weight of the fault-affected zone velocity model. The final result is a complete velocity model that reflects both the overall distribution trend of regional velocities and the influence of the fault on velocities.

[0184] Preferably, step S31 specifically includes:

[0185] Based on the fault-affected area, the preprocessed seismic data is extracted to obtain the fault-affected area seismic data;

[0186] Seismic attributes are extracted from seismic data in the fault-affected area to obtain regional seismic attribute data;

[0187] Regional seismic attribute data is used to spatially register fault attribute data, resulting in registered fault attribute data.

[0188] Seismic attribute-fault attribute relationship analysis was performed on regional seismic attribute data and registered fault attribute data to obtain seismic attribute-fault attribute relationship data;

[0189] Based on the earthquake attribute-fault attribute relationship data, the velocity variation law in the fault-affected area is inferred, and the velocity variation law inference data is obtained.

[0190] Based on the velocity variation pattern, the velocity analysis results of the fault-affected area are generated by inferring the data.

[0191] In this embodiment of the invention, based on the fault influence zone range determined in the previous steps, the corresponding seismic data within that region is extracted from the preprocessed seismic data volume. The extraction process is based on the polygonal boundary of the fault influence zone, extracting the seismic trace data located within the boundary to form the fault influence zone seismic data. For example, using seismic interpretation software, based on the spatial range of the fault influence zone, the amplitude data of all seismic traces within that region are extracted, and their spatial location information (e.g., CDP number and inline / crossline number) is retained.

[0192] Seismic attributes are extracted from seismic data in the fault-affected area. Various seismic attributes are calculated, such as root-mean-square amplitude, coherence volume attributes, and dip / azimuth attributes. These attributes reflect the amplitude, phase, and frequency characteristics of seismic waves within the fault-affected area, providing fundamental data for subsequent analysis of the relationship between faults and velocities. For example, seismic attribute analysis software is used to calculate the root-mean-square amplitude of seismic data in the fault-affected area within a 10ms time window and generate the root-mean-square amplitude attribute volume.

[0193] Spatial registration of fault attribute data is performed using regional seismic attribute data. Due to differences in spatial coordinates between seismic data and geological models, fault attribute data needs to be registered to the spatial coordinate system of the seismic data. For example, an image-based registration algorithm can be used to register fault attribute data with regional seismic attribute data, ensuring that the fault location matches the spatial location of the seismic attributes. The registered fault attribute data includes information such as the fault's position, strike, dip angle, and displacement in a unified coordinate system.

[0194] A seismic attribute-fault attribute relationship analysis was performed on the regional seismic attribute data and the fault attribute data registered in step three. The correlation between different seismic attributes and fault attributes was analyzed; for example, the relationship between root-mean-square amplitude and fault distance, or the relationship between coherence volume attributes and fault dip angle. 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, the average value of the root-mean-square amplitude attribute at different distances on both sides of the fault was calculated, and the variation law of root-mean-square amplitude with fault distance was analyzed.

[0195] Based on the relationship data between earthquake and fault attributes, velocity variation patterns in the fault-affected zone can be inferred. For example, if the analysis reveals a significant decrease in root-mean-square amplitude near the fault, it can be inferred that the velocity near the fault also decreases, as fault fracture zones typically exhibit low velocities. Based on the relationships between different earthquake and fault attributes, the variation patterns of velocity within the fault-affected zone with attributes such as fault distance and fault dip angle can be inferred. For instance, an empirical formula relating root-mean-square amplitude and velocity can be established to predict velocity distribution within the fault-affected zone.

[0196] Based on the velocity variation patterns, velocity analysis results for the fault-affected zone are generated. These patterns are applied to the fault-affected zone to calculate the velocity value at each location within it. For example, using the empirical formula between root-mean-square amplitude and velocity, the velocity value at each grid point within the fault-affected zone is calculated, generating the velocity analysis results. These results can serve as input data for velocity modeling in the fault-affected zone.

[0197] Preferably, step S33 includes the following steps:

[0198] Step S331: Perform seismic data gridding on the preprocessed seismic data to obtain a seismic data grid;

[0199] Step S332: Calculate the seismic ray path from the earthquake source to each grid point using ray tracing data, velocity model pairs, and seismic data grids to obtain the ray path;

[0200] Step S333: Based on the ray path, calculate the travel time of the seismic wave using the velocity model to obtain travel time data;

[0201] Step S334: Construct a time-depth mapping table based on travel time data and spatial location information of the velocity model to obtain the time-depth mapping table;

[0202] Step S335: Use the time-depth mapping table to perform time-depth conversion on both sides of the fault in the fault-affected area, and stitch the conversion results together to obtain the time-depth conversion data of the fault-affected area;

[0203] Step S336: Use 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;

[0204] Step S337: Perform 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.

[0205] In this embodiment of the invention, preprocessed seismic data is gridded to establish a seismic data grid. Seismic data is typically stored in the form of seismic traces, where each trace represents the change in seismic wave amplitude at a specific underground location over time. The gridding process converts the seismic trace data into regular grid data, where each grid point represents the seismic wave amplitude at a specific spatial location underground. For example, based on the spatial sampling interval of the seismic data (e.g., CDP interval and inline / crossline interval), the seismic data is interpolated onto a regular rectangular grid, with a grid cell size of 10 meters × 10 meters.

[0206] Using ray tracing data and a velocity model, seismic ray paths from the earthquake source to each seismic data grid point are calculated. The ray tracing data contains ray path information from the source to each grid point in the velocity model. Based on the location of the seismic data grid point in the velocity model, the nearest ray path is found and used as the ray path for that grid point. For example, the nearest neighbor interpolation method is used to interpolate the ray path information from the velocity model grid onto the seismic data grid.

[0207] Based on the ray path, the travel time of the seismic wave from the source to each seismic data grid point is calculated using the velocity model. Along the ray path, the travel time increment of each grid cell (equal to the grid cell length divided by the velocity) is summed to obtain the total travel time from the source to that 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 that grid cell, and then all results are summed to obtain the travel time of that grid point.

[0208] Based on travel time data and spatial location information from the velocity model, a time-depth mapping table is constructed. This table stores the correspondence between time and depth for each seismic data grid point. For example, storing the travel time and corresponding depth value of each seismic data grid point in a table forms the time-depth mapping table.

[0209] Using a time-depth mapping table, time-depth transformations are performed separately on both sides of the fault in the fault-affected zone, and the transformation results are then stitched together. Due to the presence of the fault, there is displacement of the strata on both sides, necessitating separate time-depth transformations. For example, for each seismic data grid point in the fault-affected zone, its time value is converted to a depth value according to the time-depth mapping table. Then, the transformed depth data from both sides of the fault are stitched together to obtain the time-depth transformed data for the fault-affected zone.

[0210] A time-depth mapping table is used to perform time-depth conversion on non-fault-affected areas. For example, for each seismic data grid point in a non-fault-affected area, its time value is converted to a depth value according to the time-depth mapping table to obtain time-depth converted data for the non-fault-affected area.

[0211] The time-depth transformation data of the fault-affected area and the non-fault-affected area are fused to obtain the final time-depth transformation data. The fusion process combines the time-depth transformation data of the fault-affected area and the non-fault-affected area into a single complete data volume. For example, the time-depth transformation data of the fault-affected area and the non-fault-affected area are placed into separate 3D data volumes, and then the two data volumes are merged into a single complete data volume to obtain the final time-depth transformation data.

[0212] Preferably, step S34 includes the following steps:

[0213] Step S341: Select a reference layer for the time-depth transformation data to obtain a reference layer; extract the time-depth from the time-depth transformation data based on the reference layer to obtain the time-depth curve of the reference layer;

[0214] Step S342: Extract the target layer time-depth from the time-depth converted data to obtain the target layer time-depth curve;

[0215] Step S343: Calculate the time difference between the time-depth curve of the baseline layer and the time-depth curve of the target layer to obtain the time difference data; construct a time difference spatial distribution map based on the time difference data to obtain the time difference spatial distribution map;

[0216] Step S344: Based on the time difference spatial distribution map, perform time difference correction on both sides of the fault in the fault-affected area to obtain time difference correction data for the fault-affected area;

[0217] Step S345: Perform time difference correction for the non-fault-affected area based on the time difference spatial distribution map to obtain time difference correction data for the non-fault-affected area;

[0218] Step S346: Perform 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.

[0219] In this embodiment of the invention, a stable and easily identifiable layer is selected as the reference layer from the time-depth conversion data. The reference layer is typically chosen for its good regional continuity, strong reflection energy, and ease of tracking. For example, a regional unconformity or a marker layer can be selected as the reference layer. Then, based on the location 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 the reference layer's time-depth curve.

[0220] Time-depth curves are extracted for the target strata requiring time-difference correction. The target strata are typically those associated with hydrocarbon reservoirs, such as reservoir top and bottom interfaces. Based on the location of the target strata on the seismic profile, the time-depth curves are extracted from the time-depth converted data. The time and depth values ​​of the target strata on each seismic trace are recorded to form the target strata's time-depth curves.

[0221] Using the time-depth curves of the reference layer and the target layer, the time difference between the two layers is calculated. The time difference is defined as the time at the target layer minus the time at the reference layer. At each seismic trace location, the difference between the time at the target layer and the time at the reference layer is calculated to obtain the time difference value at that location. The time difference values ​​of all seismic traces are plotted into a spatial distribution map, which can visually display the spatial trend of time difference variation. For example, colors or contour lines can be used to represent the magnitude of the time difference at different locations.

[0222] Based on the spatial distribution map of time differences, time difference corrections are performed on both sides of the fault in the fault-affected area. Due to the presence of the fault, there are differences in time differences on both sides. Therefore, time difference corrections need to be performed separately on both sides of the fault. For example, within the fault-affected area, based on the spatial distribution map of time differences, the time differences on both sides of the fault are interpolated or smoothed to eliminate abrupt changes in time differences caused by the fault.

[0223] Based on the spatial distribution map of time differences, time difference correction is performed in the non-fault-affected area. In the non-fault-affected area, the time difference changes relatively smoothly, and interpolation or fitting methods can be used to correct the time difference. For example, using the Kriging interpolation method, the time difference values ​​in the spatial distribution map are interpolated to each grid point in the non-fault-affected area to obtain the time difference correction data for the non-fault-affected area.

[0224] The time difference correction data from the fault-affected area and 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 from both areas into a single, complete data volume. For example, the time difference correction data from both areas can be placed into separate three-dimensional data volumes, and then the two volumes can be merged to obtain the final nonlinear time difference correction data.

[0225] Preferably, the present invention also provides a system for determining nonlinear isochronous stratigraphic slices, used to perform the method for determining nonlinear isochronous stratigraphic slices as described above, the system comprising:

[0226] The nonlinear seismic attribute extraction module acquires raw seismic data; performs seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; performs adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; and extracts nonlinear seismic attributes from the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field.

[0227] The nonlinear attribute and geological model joint inversion module acquires geological data and inversion target data; constructs an initial geological model based on the geological data; calculates residuals based on the inversion target data and the initial geological model; and iterates and inverts the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model.

[0228] The nonlinear stratigraphic time correction module is used to extract fault-affected zones from a nonlinear geological structure model, obtaining the fault-affected zones; construct a velocity model based on the nonlinear geological structure model and the fault-affected zones, obtaining a velocity model; perform seismic ray tracing on the velocity model, obtaining ray tracing data; use the ray tracing data to perform time-depth transformation on the preprocessed seismic data, obtaining time-depth transformed data; construct a nonlinear time correction field based on the time-depth transformed data, obtaining 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 stratigraphic interpretation processing, obtaining key stratigraphic interpretation data; and determine nonlinear isochronous stratigraphic slices based on the key stratigraphic interpretation data and the nonlinear geological structure model, obtaining refined nonlinear isochronous stratigraphic slices.

[0229] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0230] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for determining nonlinear isochronous stratigraphic slices, characterized in that, Includes the following steps: Step S1: Obtain raw seismic data; The raw seismic data is preprocessed to obtain preprocessed seismic data; The preprocessed seismic data is subjected to adaptive nonlinear filtering to obtain adaptive nonlinear filtered data; Nonlinear seismic attributes are extracted from adaptive nonlinear filtering data to obtain a nonlinear seismic attribute field; Step S2: Acquire geological data and inversion target data; construct an initial geological model based on the geological data to obtain the initial geological model; perform residual calculation based on the inversion target data and the initial geological model to obtain the residual calculation results; perform model iteration inversion on the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model; Step S3: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model; The velocity model is subjected to seismic ray tracing to obtain ray tracing data. The preprocessed seismic data is then subjected to time-depth transformation using the ray tracing data to obtain time-depth transformed data. A nonlinear time correction field is constructed based on the time-depth transformed data to obtain a nonlinear time correction field. The preprocessed seismic data is then subjected to seismic data time correction using the nonlinear time correction field, and key stratigraphic interpretation is performed to obtain key stratigraphic interpretation data. Based on the key stratigraphic interpretation data and the nonlinear geological structure model, nonlinear isochronous stratigraphic slices are determined 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: Obtain raw seismic data; perform seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; Step S12: Perform multi-scale wavelet decomposition on the preprocessed seismic data to obtain multi-scale wavelet decomposed data; Step S13: Perform adaptive nonlinear filtering on each decomposition layer in the multi-scale wavelet decomposition data to obtain adaptive nonlinear filtered data; Step S14: Perform nonlinear feature enhancement processing on the adaptive nonlinear filter data to obtain nonlinear feature enhanced data; Step S15: Perform multi-scale reconstruction on the nonlinear feature enhancement data to obtain multi-scale reconstructed data; extract nonlinear seismic attributes from 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: Obtain geological data; construct an initial geological model based on the geological data to obtain the initial geological model; Step S22: Set the rock physical parameters of the initial geological model and perform forward modeling to obtain forward modeling seismic data; Step S23: Perform the same nonlinear seismic attribute extraction process as in step S1 on the forward modeling seismic data to obtain the simulated nonlinear seismic attribute field; Step S24: Obtain the inversion target data; perform residual calculation on the nonlinear seismic attribute field and the simulated nonlinear seismic attribute field based on the inversion target data to obtain the residual calculation results; Step S25: If the residual calculation result is not equal to the residual threshold of the preset objective function, return to step S21; otherwise, proceed to step S26. Step S26: Update the initial geological model based on the residual calculation results 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: Extract simulated seismic data from the nonlinear seismic attribute field to be residual calculated, and obtain simulated seismic data; extract actual seismic data from the preprocessed seismic data based on the simulated seismic data, and obtain actual seismic data; Step S242: Perform spatiotemporal registration of simulated earthquake data and actual earthquake data to obtain spatiotemporal registration earthquake data; Step S243: Perform multi-attribute residual calculation on the seismic spatiotemporal registration data based on geological data and inverted target data to obtain multi-attribute residual data; Step S244: Determine the residual weight coefficients based on statistical analysis of the multi-attribute residual data according to the inverted target data, and obtain the residual weight coefficients; Step S245: Calculate the weighted residuals of the multi-attribute residuals using the residual weighting coefficients to obtain the weighted residuals; perform residual post-processing on the weighted residuals to obtain the post-processed residuals. Step S246: Analyze the magnitude and spatial distribution characteristics of the post-processed residual data to obtain the residual calculation results.

5. The method for determining nonlinear isochronous stratigraphic slices according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Extract the fault-affected zone from the nonlinear geological structure model to obtain the fault-affected zone; construct a velocity model based on the nonlinear geological structure model and the fault-affected zone to obtain the velocity model; Step S32: Perform seismic wave ray tracing on the velocity model to obtain ray tracing data; Step S33: Use ray tracing data to perform time-depth transformation on the preprocessed seismic data to obtain time-depth transformed data; Step S34: Perform nonlinear time difference correction based on the time depth conversion data to obtain nonlinear time difference correction data; Step S35: Construct a nonlinear time correction field from the nonlinear time difference correction data to obtain the nonlinear time correction field; Step S36: Perform seismic data time correction on the preprocessed seismic data using a nonlinear time correction field to obtain time-corrected seismic data; perform key horizon interpretation processing on the time-corrected seismic data to obtain key horizon interpretation data. Step S37: Calculate the relative geological time based on the interpretation data of key stratigraphic layers to obtain relative geological time data; generate isochronous stratigraphic slices based on the relative geological time data to obtain nonlinear isochronous stratigraphic slices; Step S38: Use a nonlinear geological structure model to 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: Construct an initial velocity model based on geological data and a nonlinear geological structure model to obtain the initial velocity model; Step S312: Extract fault attributes from the nonlinear geological structure model to obtain fault attribute data; Step S313: Divide the fault-affected area according to the fault attribute data to obtain the fault-affected area; Step S314: Perform velocity analysis of the fault-affected zone based on the preprocessed seismic data to obtain the velocity analysis results of the fault-affected zone; Step S315: Based on the velocity analysis results of the fault-affected zone, perform velocity modeling of the fault-affected zone to obtain the velocity model of the fault-affected zone; Step S316: Fusion of the velocity model in the fault-affected zone and the initial velocity model to obtain the velocity model.

7. The method for determining nonlinear isochronous stratigraphic slices according to claim 6, characterized in that, Step S314 is as follows: Based on the fault-affected area, the preprocessed seismic data is extracted to obtain the fault-affected area seismic data; Seismic attributes are extracted from seismic data in the fault-affected area to obtain regional seismic attribute data; Regional seismic attribute data is used to spatially register fault attribute data, resulting in registered fault attribute data. Seismic attribute-fault attribute relationship analysis was performed on regional seismic attribute data and registered fault attribute data to obtain seismic attribute-fault attribute relationship data; Based on the earthquake attribute-fault attribute relationship data, the velocity variation law in the fault-affected area is inferred, and the velocity variation law inference data is obtained. Based on the velocity variation pattern, the velocity analysis results of the fault-affected area are generated by inferring the data.

8. The method for determining nonlinear isochronous stratigraphic slices according to claim 5, characterized in that, Step S33 includes the following steps: Step S331: Perform seismic data gridding on the preprocessed seismic data to obtain a seismic data grid; Step S332: Calculate the seismic ray path from the earthquake source to each grid point using ray tracing data, velocity model pairs, and seismic data grids to obtain the ray path; Step S333: Based on the ray path, calculate the travel time of the seismic wave using the velocity model to obtain travel time data; Step S334: Construct a time-depth mapping table based on travel time data and spatial location information of the velocity model to obtain the time-depth mapping table; Step S335: Use the time-depth mapping table to perform time-depth conversion on both sides of the fault in the fault-affected area, and stitch the conversion results together to obtain the time-depth conversion data of the fault-affected area; Step S336: Use 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: Perform 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: Select a reference layer for the time-depth transformation data to obtain a reference layer; extract the time-depth from the time-depth transformation data based on the reference layer to obtain the time-depth curve of the reference layer; Step S342: Extract the target layer time-depth from the time-depth transformed data to obtain the target layer time-depth curve; Step S343: Calculate the time difference between the time-depth curve of the baseline layer and the time-depth curve of the target layer to obtain the time difference data; construct a time difference spatial distribution map based on the time difference data to obtain the time difference spatial distribution map; Step S344: Based on the time difference spatial distribution map, perform time difference correction on both sides of the fault in the fault-affected area to obtain time difference correction data for the fault-affected area; Step S345: Perform time difference correction for the non-fault-affected area based on the time difference spatial distribution map to obtain time difference correction data for the non-fault-affected area; Step S346: Perform 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.

10. A system for determining nonlinear isochronous stratigraphic slices, characterized in that, For performing the method for determining nonlinear isochronous stratigraphic slices as described in claim 1, the system for determining nonlinear isochronous stratigraphic slices includes: The nonlinear seismic attribute extraction module acquires raw seismic data; performs seismic data preprocessing on the raw seismic data to obtain preprocessed seismic data; performs adaptive nonlinear filtering on the preprocessed seismic data to obtain adaptive nonlinear filtered data; and extracts nonlinear seismic attributes from the adaptive nonlinear filtered data to obtain a nonlinear seismic attribute field. The nonlinear attribute and geological model joint inversion module acquires geological data and inversion target data; constructs an initial geological model based on the geological data; calculates residuals based on the inversion target data and the initial geological model; and iterates and inverts the initial geological model based on the residual calculation results to obtain a nonlinear geological structure model. The nonlinear stratigraphic time correction module is used to extract fault-affected zones from a nonlinear geological structure model, obtaining the fault-affected zones; construct a velocity model based on the nonlinear geological structure model and the fault-affected zones, obtaining a velocity model; perform seismic ray tracing on the velocity model, obtaining ray tracing data; use the ray tracing data to perform time-depth transformation on the preprocessed seismic data, obtaining time-depth transformed data; construct a nonlinear time correction field based on the time-depth transformed data, obtaining 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 stratigraphic interpretation processing, obtaining key stratigraphic interpretation data; and determine nonlinear isochronous stratigraphic slices based on the key stratigraphic interpretation data and the nonlinear geological structure model, obtaining refined nonlinear isochronous stratigraphic slices.

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