A method, system, medium and processor for analyzing formation mechanism of low signal-to-noise ratio seismic data in karst areas

Abstract

The application discloses a karst area low signal-to-noise ratio seismic data formation mechanism analysis method and system, medium and processor, and belongs to the technical field of signal-to-noise ratio seismic data analysis, and comprises the following steps: S1, modeling and describing the medium characteristics of a complex karst area based on a generalized multi-scale karst random medium; S2, performing forward modeling by using a variable-encryption staggered grid finite difference wave equation numerical simulation method for the near-surface conditions of the complex karst area, and outputting seismic records and wave field snapshots; and S3, judging the seismic wave field propagation law and low signal-to-noise ratio data formation mechanism of the complex karst area by using a wave field snapshot and seismic record dynamic synchronization construction method. The application can process low signal-to-noise ratio seismic data, and provides accurate data for comprehensive geophysical prospecting prediction of shale gas reservoirs.

Description

Technical Field

[0001] This invention relates to the field of signal-to-noise ratio seismic data analysis technology, and in particular to a method, system, medium, and processor for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions. Background Technology

[0002] At present, there is very little research on geophysical prediction methods and technologies for shale gas reservoirs in complex karst areas in my country. It is still in its initial stage and there are many thorny problems: (1) The signal-to-noise ratio of seismic data in complex karst areas is low and its formation mechanism is unclear, making it difficult to formulate targeted seismic data processing and interpretation schemes; (2) Multiple strata are exposed on the surface, with large lateral variations in lithology and large topographic relief, posing a huge challenge to static correction and surface consistency processing of seismic data; (3) The signal-to-noise ratio of the original seismic data is very low. It is difficult to improve the signal-to-noise ratio by eliminating false data while maintaining the authenticity; (4) The internal reflection energy of the target layer is weak and the resolution is low. How to accurately image the target layer is a difficult point; (5) Under the condition that it is difficult to obtain good data processing results, the corresponding seismic data interpretation and reservoir prediction face problems such as poor continuity of stratigraphic tracking, unclear fault imaging, and limited reliability of reservoir prediction.

[0003] Therefore, there is a need for a method, system, medium, and processor for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method, system, medium, and processor for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas. This method can process low signal-to-noise ratio seismic data, providing accurate data for comprehensive geophysical prediction of shale gas reservoirs. The specific technical solution is as follows: A method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions, including: S1. Describing the characteristics of complex karst media based on generalized multi-scale karst random media modeling; S2. Forward modeling is performed using a numerical simulation method of finite difference wave equation with variable density staggered grid for complex near-surface conditions in karst areas, and seismic records and wavefield snapshots are output. S3. The method of dynamically synchronizing wavefield snapshots and seismic records was used to determine the propagation law of seismic wavefields in complex karst areas and the formation mechanism of low signal-to-noise ratio data.

[0005] Preferably, it also includes: establishing a model of a composite multi-scale random medium based on random medium modeling by combining random perturbations.

[0006] Preferably, the forward modeling using a variable-density staggered-grid finite-difference wave equation numerical simulation method for near-surface conditions in complex karst regions, outputting seismic records and wavefield snapshots, includes: Import the model file of the composite multi-scale random medium, which includes velocity files and terrain elevation files; The grid is divided according to the surface undulation and medium velocity distribution, with a fine grid used for low-velocity areas and a coarse grid used for high-velocity areas. Set the source function; Based on the mesh, select appropriate finite difference schemes for mesh regions of different sizes; Calculate the pressure values ​​at each grid point in the zone; Output seismic records and wavefield snapshots.

[0007] Preferably, the method of using wavefield snapshots and dynamic synchronous construction of seismic records to determine the propagation law of seismic wavefields and the formation mechanism of low signal-to-noise ratio data in complex karst areas includes: By comparing seismic records and wavefield snapshots under different near-surface medium conditions, we can analyze the characteristics of scattered wavefields of seismic waves in heterogeneous materials of different types and properties, and obtain the formation mechanism of low signal-to-noise ratio of seismic data in complex karst areas.

[0008] Preferably, prior to step S3, a method for processing the seismic data is required, including: Intelligent first-arrival tomography static correction constrained by multi-source information in karst areas; Pre-stack distributed multi-domain high-fidelity denoising is performed using pre-stack anomalous amplitude attenuation, high-energy surface wave attenuation, and pre-stack linear noise attenuation methods. The residual correction amount is corrected by using the multi-iteration super-track coupling residual correction domain velocity analysis method.

[0009] Preferably, it also includes: a dual correction method combining local static correction and time-shifting extension method before the multi-source information-constrained intelligent first-arrival tomography static correction of karst areas, specifically including: Local static correction is used to correct the local elevation to a smooth surface; By approximating the seismic wavefield of complex surface backward extension using Rayleigh II integral and combining it with the Green's function based on Gaussian beam representation, the wavefield backward extension formula based on Gaussian beam representation is derived. By combining the deconvolution imaging conditions and simplifying the two-dimensional complex integral in the derivation process using the steepest descent method, the amplitude-preserving Gaussian beam migration formula is obtained.

[0010] Preferably, the seismic data processing method also includes a pre-stack time migration method, specifically including: determining the pre-stack time migration aperture; determining the maximum imaging dip angle of the pre-stack time migration; migration velocity analysis and velocity model optimization; and summation calculation of the imaging results.

[0011] A system for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions, applied to the above-mentioned method, includes a random medium model establishment module, a seismic data processing module, and a data analysis module; The random medium model building module is used to create random medium models and models of composite multi-scale random media. The earthquake data processing module is used to correct earthquake data; The data analysis module is used to analyze and determine the formation mechanism of low signal-to-noise seismic data.

[0012] A computer-readable storage medium comprising a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

[0013] A processor for running a program, wherein the program, when running, performs the method described in any one of claims 1 to 7.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: In this invention, by performing multi-composite and multi-scale precise modeling of karst topography and geomorphology, more accurate data can be obtained. At the same time, by performing forward modeling of seismic data and correcting low signal-to-noise seismic data, the impact of the non-homogeneity of complex near-surface media on seismic wave propagation and low signal-to-noise ratio data of complex karst areas can be accurately obtained. This helps to carry out corresponding seismic data interpretation and reservoir prediction to address the problems of continuous stratigraphic tracking, unclear fault imaging, and limited reliability of reservoir prediction. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is an actual structural diagram of the karst landform in this invention; Figure 3 This is a background image showing the non-uniform karst landform structure in this invention; Figure 4 Add cave images to the karst landform background image in this invention; Figure 5 Flowchart for numerical simulation implementation; Figure 6 Flowchart of a multivariate information-constrained intelligent tomography static correction method. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0019] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] Example 1: like Figure 1 As shown, this invention discloses a method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions, comprising the following steps: S1. Based on generalized multi-scale karst stochastic medium modeling, the characteristics of complex karst mediums are described as follows: First, a continuous stochastic medium model is constructed based on conventional stochastic medium theory. v ( x , y ); ② To describe the irregular distribution of karst caves, the length of the region boundary is set. R Its function is to divide the entire study area into sections with sides of length [missing information]. R A small square area, therefore R =2i , i The value range is 1 to n Integers. Simultaneously, a density parameter is set to describe the percentage of caves in the entire model. p Its value range is limited to between 0 and 1; ③ Use the set region boundary length R For the continuous stochastic medium model constructed in the first step v ( x , y ) Divide the grid into sub-divisions; ④ For each sub-division, determine the number of all grid nodes within it. K ⑤ Sort all nodes within the small region in descending order of their values, and store the sorting results in a one-dimensional array. A ⑥ According to the set porosity p From array A Before being selected p × K nodes, calculate their values ​​in the stochastic medium model. v ( x , y The coordinates in the array are stored in another one-dimensional array. W Middle; ⑦ Based on array W The coordinate information in the random medium is used to designate the medium at the corresponding position in the random medium as the cave, and the rest as the background medium; ⑧ The corresponding velocity values ​​are assigned to the cave and the background medium respectively to obtain the final random cave model. S2. A forward modeling method using a variable-density staggered-grid finite-difference wave equation numerical simulation method for near-surface conditions in complex karst regions is employed to output seismic records and wavefield snapshots, such as... Figure 4 As shown, the details are as follows: Import the model file of the complex multi-scale random medium, which includes velocity files and terrain elevation files; perform grid generation based on surface undulation and medium velocity distribution, using a fine mesh for low-velocity zones and a coarse mesh for high-velocity zones; set the source function; select an appropriate finite difference scheme for different grid sizes based on the generated grid; calculate the pressure values ​​at each grid point; output the seismic record and wavefield snapshot.

[0022] S3. The method of dynamically synchronizing wavefield snapshots and seismic records was used to determine the propagation patterns of seismic wavefields in complex karst areas and the formation mechanism of low signal-to-noise ratio data, as detailed below: Numerical simulation of seismic waves is an important tool for understanding the seismic response characteristics of reservoirs. To investigate the impact of the inhomogeneity of complex near-surface media on seismic wave propagation and the formation mechanism of low signal-to-noise ratio (SNR) data in complex karst regions, this study, based on the construction of various types of complex karst near-surface models containing inhomogeneities and the development of numerical simulation methods for seismic wave fields under undulating surface conditions, analyzes the scattered wavefield characteristics of seismic waves in inhomogeneities of different types and properties by comparing seismic records and wavefield snapshots under different near-surface media conditions. This yields the formation mechanism of low SNR in seismic data from complex karst regions. Specifically, wavefield snapshots are used to analyze the propagation patterns of seismic waves, seismic records are used to analyze the wavefield structure and characteristics, and a synchronous dynamic display method of wavefield snapshots and seismic records is used to analyze the formation mechanism of wavefield structure and special features.

[0023] Furthermore, conventional stochastic medium modeling methods are mainly applicable to the theoretical study of stationary stochastic media, where stationarity refers to the assumption that the statistical properties of the medium remain unchanged during the modeling process. However, in real media, the existence of non-homogeneous states is ubiquitous and complex. Due to the diversity of geological structures and the differences in sedimentary processes, the distribution and characteristics of non-homogeneity in the medium often exhibit significant variations. Therefore, it is difficult to expect that the non-homogeneous state will remain completely consistent throughout the entire study area. To more accurately describe the non-homogeneity in real complex media, it is necessary to develop a generalized multi-scale stochastic medium modeling theory. Therefore, a model of composite multi-scale stochastic media is established based on stochastic medium modeling by incorporating stochastic perturbations, as follows: Multiscale composite perturbations involve adding several random perturbations of different scales within the same study area. These random perturbations can be superimposed or mixed with one type of random perturbation. Figure 2 For a specific karst landform, this region features highly heterogeneous caves, but the background strata are also heterogeneous and exhibit abundant small-scale disturbances. The composite multi-scale stochastic medium modeling method can accurately model this type of geological structure. The modeling steps for composite multi-scale stochastic media are as follows: First, determine the overall framework structure of the modeling based on the distribution range of the actual karst medium, such as... Figure 2 As shown; then, based on the actual geological structure analysis of the large-scale uniform velocity of the strata and the scale of the karst medium, a suitable autocorrelation length is selected to establish a non-uniform background, such as... Figure 3 As shown; finally, by adding caverns to the non-uniform background, this non-uniform structure can be described, as shown. Figure 4 As shown. In addition, more non-uniform structures such as cracks and weathering media can be added according to actual geological conditions to form a more complex non-uniform model.

[0024] Furthermore, a multivariate information-constrained intelligent tomographic static correction method for karst areas, such as... Figure 5 As shown, the specific steps are as follows: a. Read in multivariate data and prior information, such as actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data, and micrologging data, for a research area in a karst region; b. A complex near-surface initial velocity model for karst areas is constructed using a multi-data fusion partitioning and multi-scale stochastic modeling method, and the velocity model is further subdivided using a near-surface variable grid. c. In the variable grid of step b, calculate the ray length of each grid cell in the inversion model space and the theoretical travel time of each receiving point using the ray tracing method; d. Using a combination of intelligent automatic and manual quality inspection, the first arrival travel time values ​​of actual seismic data in a certain study area of ​​karst region are picked up and subtracted from the travel time values ​​theoretically calculated in step c to obtain the travel time residuals; e. Using the travel time residuals from step d as the objective function and the other data from step a (excluding seismic data) as constraints, the inversion velocity model is iteratively updated using the multivariate information constrained tomography method. f. Calculate the static correction amount based on the velocity model obtained in step e, perform preliminary static correction on the seismic data, and then jump back to step d for secondary first arrival picking and tomography to obtain the final velocity model. g. Based on the velocity model obtained in step f, calculate the final static correction and output the final result.

[0025] Furthermore, before applying the multi-source information-constrained intelligent first-arrival tomography static correction for karst areas, a dual correction method combining local static correction and time-shifting extension is employed, specifically including: Specifically, when surface elevation changes drastically, direct static correction can still cause distortion in the wavefield, negatively impacting subsequent migration imaging, especially near-surface imaging. A dual-correction method combining local static correction and time-shift continuation is employed: First, local static correction corrects the local elevation to a smooth surface. Second, Rayleigh II integrals are used to approximate the back-continuation seismic wavefield of complex surfaces, and combined with Green's function based on Gaussian beam representation, a back-continuation formula based on Gaussian beam representation is derived. Then, by incorporating deconvolution imaging conditions and simplifying the two-dimensional complex-valued integral in the derivation process using the steepest descent method, an amplitude-preserving Gaussian beam migration formula is obtained. Compared to the conventional single local static correction method, dual correction, by considering the propagation characteristics of plane waves on undulating surfaces, directly decomposes local plane waves on the undulating surface, resulting in higher imaging accuracy (especially in the near-surface region). Furthermore, it eliminates the influence of seismic wave geometric diffusion on imaging amplitude, thus obtaining amplitude-preserving imaging results, which is beneficial for subsequent AVO and lithological analysis. Compared with the time-shift continuation method alone, the dual-correction method is better able to adapt to the problem of large variations in surface elevation in karst areas.

[0026] Furthermore, the seismic data processing methods also include pre-stack time migration methods, specifically including: determining the pre-stack time migration aperture; determining the maximum imaging dip angle of the pre-stack time migration; migration velocity analysis and velocity model optimization; and summation calculation of imaging results.

[0027] Example 2: A system for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions, applied to the above-mentioned method, includes a random medium model establishment module, a seismic data processing module, and a data analysis module; The random medium model building module is used to create random medium models and models of composite multi-scale random media. The earthquake data processing module is used to correct earthquake data; The data analysis module is used to analyze and determine the formation mechanism of low signal-to-noise seismic data.

[0028] Example 3: A computer-readable storage medium comprising a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described above.

[0029] Example 4: A processor for running a program, wherein the program, when running, performs the method described in any one of claims 1 to 7.

[0030] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0031] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0032] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0033] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions, characterized in that, include: S1. Describing the characteristics of complex karst media based on generalized multi-scale karst random media modeling; S2. Forward modeling is performed using a numerical simulation method of finite difference wave equation with variable density staggered grid for complex near-surface conditions in karst areas, and seismic records and wavefield snapshots are output. S3. The method of dynamically synchronizing wavefield snapshots and seismic records was used to determine the propagation law of seismic wavefields in complex karst areas and the formation mechanism of low signal-to-noise ratio data.

2. The method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas according to claim 1, characterized in that, Also includes: A model of a complex multi-scale random medium is established based on the modeling of random medium by combining random perturbations.

3. The method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas according to claim 2, characterized in that, The method employs a numerical simulation approach using a variable-density staggered grid finite-difference wave equation for near-surface conditions in complex karst regions. Forward modeling is performed, outputting seismic records and wavefield snapshots, including: Import the model file of the composite multi-scale random medium, which includes velocity file and terrain elevation file; The grid is divided according to the surface undulation and medium velocity distribution, with a fine grid used for low-velocity areas and a coarse grid used for high-velocity areas. Set the source function; Based on the mesh, select appropriate finite difference schemes for mesh regions of different sizes; Calculate the pressure values ​​at each grid point in the zone; Output seismic records and wavefield snapshots.

4. The method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas according to claim 3, characterized in that, The method of using wavefield snapshots and dynamic synchronous construction of seismic records to determine the propagation patterns of seismic wavefields in complex karst areas and the formation mechanism of low signal-to-noise ratio data includes: By comparing seismic records and wavefield snapshots under different near-surface medium conditions, we can analyze the characteristics of scattered wavefields of seismic waves in heterogeneous materials of different types and properties, and obtain the formation mechanism of low signal-to-noise ratio of seismic data in complex karst areas.

5. The method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas according to claim 2, characterized in that, Prior to step S3, the seismic data processing method using the multivariate information-constrained intelligent tomography static correction method for karst regions includes: Intelligent first-arrival tomography static correction constrained by multi-source information in karst areas; Pre-stack distributed multi-domain high-fidelity denoising is performed using pre-stack anomalous amplitude attenuation, high-energy surface wave attenuation, and pre-stack linear noise attenuation methods. The residual correction amount is corrected by using the multi-iteration super-track coupling residual correction domain velocity analysis method.

6. The method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas according to claim 5, characterized in that, Also includes: Before applying multi-source information-constrained intelligent first-arrival tomography static correction to karst areas, a dual correction method combining local static correction and time-shifting continuation is employed, specifically including: Local static correction is used to correct the local elevation to a smooth surface; By approximating the seismic wavefield of complex surface backward extension using Rayleigh II integral and combining it with the Green's function based on Gaussian beam representation, the wavefield backward extension formula based on Gaussian beam representation is derived. By combining the deconvolution imaging conditions and simplifying the two-dimensional complex integral in the derivation process using the steepest descent method, the amplitude-preserving Gaussian beam migration formula is obtained.

7. The method for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst areas according to claim 2, characterized in that, Seismic data processing methods also include pre-stack time migration methods, specifically including: Determine the pre-stack time offset aperture; Determine the maximum imaging tilt angle of the pre-stack time migration; Offset velocity analysis and velocity model optimization; Summation calculation of imaging results.

8. A system for analyzing the formation mechanism of low signal-to-noise ratio seismic data in karst regions, characterized in that, The method applied to any one of claims 1 to 7 includes a random medium model establishment module, a seismic data processing module, and a data analysis module; The random medium model building module is used to create random medium models and models of composite multi-scale random media. The earthquake data processing module is used to correct earthquake data; The data analysis module is used to analyze and determine the formation mechanism of low signal-to-noise seismic data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 7 when it runs.

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