A method and device for identifying seismic response regularity of a fracture zone

Abstract

The application discloses a method and device for identifying seismic response law of fracture zone, and relates to the technical field of oil and gas field exploration. The method comprises the following steps: acquiring seismic related data of a high-density seismic area; establishing fracture zone distribution models of different characteristic dimensions by using the seismic related data, and creating equivalent medium models by using the fracture zone distribution models of different characteristic dimensions; performing seismic wave field simulation based on the equivalent medium models to obtain simulation results; and performing characteristic analysis on the simulation results and actual seismic profiles to obtain seismic wave field response characteristics. The application fully utilizes the high resolution advantage of high-density seismic data, and combines the method of Gaussian random medium modeling to flexibly and conveniently describe the underground non-uniform medium. In addition, by determining the seismic wave field response characteristics of the random medium fracture zone in the work area, the identification accuracy of the fracture development zone is significantly improved, the fracture reservoir distribution of the target layer can be effectively predicted, and reliable basis is provided for high-quality exploration and development of the fractured oil and gas reservoir.

Description

Technical Field

[0001] This application relates to the field of oil and gas field exploration technology, and in particular to a method and apparatus for identifying the seismic response patterns of fracture zones. Background Technology

[0002] Carbonate reservoirs are generally classified into three types: fractured, fracture-vuggy, and pore-vuggy. These reservoirs, composed of pores, fractures, and cavities, exhibit random spatial distribution, and their occurrence and morphology change rapidly, making reservoir identification more difficult and simulation even more challenging. Unlike terrigenous clastic reservoirs, carbonate reservoirs have significant practical value in oil and gas exploration and development. However, these reservoirs are greatly influenced by sedimentary environments, diagenesis, and tectonic activity. Carbonate reservoirs exhibit diverse reservoir space types, significant secondary variations, and strong heterogeneity, leading to greater complexity and diversity during exploration.

[0003] To simulate carbonate reservoir spaces, previous research has yielded numerous studies, establishing geological and seismic reservoir models with some equivalence. However, within carbonate reservoirs, the rock physical parameters (such as velocity, density, porosity, permeability, and oil saturation) of the reservoir space composed of pores and fractures are discrete variables. Existing methods and models do not adequately understand the seismic response characteristics of different types of fracture zones or the seismic wavefield characteristics of small-scale fractured reservoirs from actual data, and their accuracy in identifying seismic fault zones is not high. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this application provides a method and apparatus for identifying the seismic response law of fracture zones.

[0005] According to one aspect of the embodiments of this application, a method for identifying the seismic response patterns of fracture zones is provided, comprising: acquiring seismic correlation data of a high-density seismic zone, wherein the seismic correlation data includes seismic data and geological data of the high-density seismic zone; establishing fracture zone distribution models with different feature dimensions using the seismic correlation data; creating an equivalent medium model using the fracture zone distribution models with different feature dimensions; performing seismic wavefield simulation based on the equivalent medium model to obtain simulation results; and performing feature analysis between the simulation results and actual seismic profiles to obtain seismic wavefield response characteristics.

[0006] Furthermore, the step of establishing a fracture zone distribution model with different feature dimensions using the earthquake-related data includes extracting a first dataset corresponding to high-angle fractures, a second dataset corresponding to network fractures, and a third dataset corresponding to horizontal fractures from the earthquake-related data; constructing a fracture zone distribution model corresponding to high-angle fractures using the first dataset; constructing a fracture zone distribution model corresponding to network fractures using the second dataset; and constructing a fracture zone distribution model corresponding to horizontal fractures using the third dataset.

[0007] Furthermore, the creation of an equivalent medium model using fracture zone distribution models with different feature dimensions includes: the fracture zones based on fracture zone distribution models with different feature dimensions are equivalent to randomly distributed fractures; the randomly distributed fractures are embedded into an isotropic medium to obtain multiple equal probability models; the multiple equal probability models are superimposed on the reservoir structure model to obtain a reservoir stochastic model; and the equivalent medium model is constructed using the reservoir stochastic model.

[0008] Furthermore, the seismic wavefield simulation based on the equivalent medium model to obtain simulation results includes performing forward modeling on the equivalent medium model using a two-dimensional non-uniform isotropic elastic wave equation and its staggered grid finite difference wave equation; during the forward modeling process, shot collection is performed using parameters such as the same trace spacing, minimum and maximum offset, sampling interval, and wavelet dominant frequency collected from seismic related data to obtain simulation results.

[0009] Furthermore, the isotropic elastic wave equation is:

[0010]

[0011] Where: σ xx =σ xx (x,z,t), σ zz =σ zz (x,z,t),τ xz =τ xz (x,z,t) is the stress tensor; ρ = ρ(x,z) is the density; U = U(x,z,t) and W = W(x,z,t) are the velocity components of the medium in the x and z directions; λ = λ(x,z) and μ = μ(x,z) are the Lamé coefficients. These are the particle velocity components U and W, respectively. These are the particle stress components σ xx σ zz and τ xz The discrete values ​​are: Δt represents the time step; k is the time domain discretization exponent; i represents discretization in the x-axis direction; j represents discretization in the z-axis direction; Δx and Δz represent the spatial step sizes in the x and z directions, respectively.

[0012] Furthermore, the step of performing feature analysis on the simulation results and the actual seismic profile to obtain seismic wavefield response characteristics includes performing feature analysis on the simulation results and the actual seismic profile according to dynamic characteristics and kinematic characteristics respectively to obtain seismic wavefield response characteristics. The kinematic characteristics include at least: waveform structure, external morphology, and internal structure, and the dynamic characteristics include at least: amplitude, frequency, and derived seismic attributes.

[0013] Furthermore, the simulation results and actual seismic profiles are analyzed according to their dynamic and kinematic characteristics to obtain seismic wavefield response characteristics, including:

[0014] According to another aspect of the embodiments of this application, a device for identifying the seismic response patterns of fracture zones is also provided, characterized in that it includes an acquisition module for acquiring seismic-related data of a high-density seismic zone, wherein the seismic-related data includes seismic data and geological data of the high-density seismic zone; an establishment module for establishing fracture zone distribution models with different feature dimensions using the seismic-related data, and creating an equivalent medium model using the fracture zone distribution models with different feature dimensions; a processing module for performing seismic wavefield simulation based on the equivalent medium model to obtain simulation results; and a comparison module for performing feature analysis between the simulation results and actual seismic profiles to obtain seismic wavefield response characteristics.

[0015] According to another aspect of the embodiments of this application, a storage medium is also provided, the storage medium including a stored program that executes the above steps when the program is run.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used to store computer programs; and the processor is used to execute the steps in the above method by running the programs stored in the memory.

[0017] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps in the above-described method.

[0018] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application fully utilizes the high resolution advantage of high-density seismic data, and the combination with Gaussian random medium modeling method makes the description of subsurface non-homogeneous media more flexible and convenient. In addition, by clarifying the seismic wavefield response characteristics of the random medium fracture zone in the work area, the identification accuracy of earthquakes in fracture development zones is significantly improved, and the distribution of fracture reservoirs in the target layer can be effectively predicted, providing a reliable basis for high-quality exploration and development of fractured oil and gas reservoirs. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for identifying the seismic response patterns of fracture zones, provided in an embodiment of this application.

[0022] Figure 2 The diagram shows equivalent media models of cracked zones with different densities provided in the embodiments of this application.

[0023] Figure 3 Seismic response characteristic maps of different types of fracture zones provided for embodiments of this application.

[0024] Figure 4 A block diagram of a device for identifying the seismic response pattern of a fracture zone, provided in an embodiment of this application.

[0025] Figure 5 This application provides a schematic diagram of the structure of an electronic device. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another similar entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] This application provides a method for identifying the seismic response patterns of fracture zones. The method provided in this invention can be applied to any electronic device as needed, such as servers, terminals, etc. For ease of description, it will be referred to as an electronic device below.

[0029] According to one aspect of the embodiments of this application, an embodiment of a method for identifying the seismic response patterns of fracture zones is provided. Figure 1 A flowchart illustrating a method for identifying the seismic response characteristics of fracture zones, as provided in this application embodiment, is shown below. Figure 1 As shown, the method includes the following steps:

[0030] Step S11: Obtain earthquake-related data for high-density seismic zones, including seismic data and geological data of high-density seismic zones.

[0031] In this embodiment, the seismic-related data for the high-density seismic zone includes: high-density seismic zone data and seismic data within the target study area. The high-density seismic zone data and seismic data include comprehensive seismic data as well as imaging logging, drilling, testing, and core data within the work area, which can be accurately completed in the subsequent modeling process.

[0032] Step S12: Establish fracture zone distribution models with different feature dimensions using earthquake-related data, and create equivalent medium models using fracture zone distribution models with different feature dimensions.

[0033] In this embodiment of the application, a fracture zone distribution model with different feature dimensions is established using earthquake-related data, including:

[0034] The first dataset corresponding to high-angle cracks, the second dataset corresponding to network cracks, and the third dataset corresponding to horizontal cracks are extracted from earthquake-related data. The first dataset is used to construct a crack zone distribution model corresponding to high-angle cracks, the second dataset is used to construct a crack zone distribution model corresponding to network cracks, and the third dataset is used to construct a crack zone distribution model corresponding to horizontal cracks.

[0035] It should be noted that different types of single fracture zone distribution models can be established based on the statistical characteristics of parameters such as fracture length, width, and angle from earthquake-related data of the study area. That is, a first dataset corresponding to high-angle fractures, a second dataset corresponding to network fractures, and a third dataset corresponding to horizontal fractures can be extracted from earthquake-related data. Then, different types of single fracture zone distribution models can be constructed using the first, second, and third datasets respectively.

[0036] For example, the main reservoir in the study area is a fractured reservoir with well-developed fractures, low porosity, and severe filling. Through core observation and logging data analysis of typical wells in the study area, it can be seen that the study area mainly develops high-angle fractures, network fractures, and horizontal fractures, with dip angles mostly between 45° and 90°. Among them, high-angle fractures are the most effective fracture type in this area and have the widest distribution. The subsequent embodiments of this application mainly focus on the design of high-angle fractures using the equivalent medium model of fracture zones.

[0037] In this embodiment, the process of creating an equivalent medium model using fracture zone distribution models with different feature dimensions is as follows: Fracture zones in a fracture-type seismic geological model can be equivalent to randomly distributed fractures, where the length and diameter of each individual fracture, as well as the spatial arrangement and distribution of the fractures, are random. The fractures in the fracture-type seismic geological model can be simulated through the following process: By changing r (roughness factor) in the autocorrelation function of the mixed random medium, making r = 0, a Gaussian autocorrelation function can be obtained.

[0038] x and y represent the displacements in the x and y directions, r is the roughness factor, and a and b are the autocorrelation lengths in the x and y directions, respectively. This elliptic autocorrelation function is used to describe random media, and the autocorrelation lengths in the x and z directions are used to represent the scale of the medium's inhomogeneity in these two directions. It can describe isotropic inhomogeneity, inhomogeneity with extended directions, and even layered inhomogeneity of the medium.

[0039] Secondly, the center point (x0, y0) of the crack distribution is determined using a threshold truncation method. Where n is the number of crack zones. Then, at the center point, a Gaussian simulation method is selected to simulate the crack based on the dip angle, length, and width of the crack zone.

[0040] The fracture-type seismic geological model can be equivalent to an isotropic medium by the Gassmann equation, that is, it is composed of multi-scale random fractures embedded in the isotropic medium. By changing the simulation parameters, multiple equal probability models can be established and superimposed on the reservoir structure model to obtain the equivalent medium model of superimposed fractures, that is, the fine reservoir stochastic model.

[0041] The equivalent medium model for superimposed cracks is based on a multi-scale stochastic medium model, and its form is as follows:

[0042]

[0043] in It can be a Gaussian autocorrelation function or an exponential autocorrelation function, x, a i These represent the displacement and autocorrelation length of the autocorrelation function, respectively, ω. i Weights are assigned to random perturbations at different scales. The equivalent medium model for superimposed fracture zones is as follows: Figure 2 As shown.

[0044] Step S13: Perform seismic wave field simulation based on the equivalent medium model to obtain simulation results.

[0045] In this embodiment of the application, seismic wavefield simulation is performed based on an equivalent medium model to obtain simulation results, including: forward modeling of the equivalent medium model using a two-dimensional non-uniform isotropic elastic wave equation and its staggered grid finite difference wave equation. During the forward modeling process, shot collections are performed using parameters such as the same trace spacing, minimum and maximum offset, sampling interval, and wavelet dominant frequency from seismic correlation data to obtain simulation results. The isotropic elastic wave equation is:

[0046]

[0047] Where: σ xx =σ xx (x,z,t), σ zz =σ zz (x,z,t),τ xz =τ xz (x,z,t) is the stress tensor, ρ=ρ(x,z) is the density, U=U(x,z,t), W=W(x,z,t) are the velocity components of the medium in the x and z directions, λ=λ(x,z), and μ=μ(x,z) are the Lamé coefficients. These are the particle velocity components U and W, respectively. These are the particle stress components σ xx σ zz and τ xzThe discrete values ​​are given by Δt, where Δt represents the time step, k is the time domain discretization exponent, i represents discretization in the x-axis direction, j represents discretization in the z-axis direction, and Δx and Δz represent the spatial step sizes in the x and z directions, respectively.

[0048] It should be noted that, in order to make the simulation process closer to the actual earthquake acquisition, wave propagation, and processing processes, the same parameters as actual seismic data acquisition, such as trace spacing, minimum and maximum offset, sampling interval, and wavelet dominant frequency, were used for forward modeling of shot gather records. Furthermore, to reduce the impact of processing errors on the migration results, pre-stack depth migration was performed directly using model velocity and converted to the time domain; then, the data was processed and analyzed using software such as Focus and GeoDepth.

[0049] Step S14: Perform feature analysis on the simulation results and the actual seismic profile to obtain the seismic wavefield response characteristics. This includes performing feature analysis on the simulation results and the actual seismic profile according to dynamic and kinematic characteristics respectively to obtain the seismic wavefield response characteristics.

[0050] In this embodiment of the application, the simulation results and the actual seismic profile are subjected to feature analysis to obtain seismic wavefield response characteristics. This includes: performing feature analysis on the simulation results and the actual seismic profile according to dynamic characteristics and kinematic characteristics respectively to obtain seismic wavefield response characteristics. The kinematic characteristics include at least: waveform structure, external morphology, and internal structure. The dynamic characteristics include at least: amplitude, frequency, and derived seismic attributes.

[0051] Specifically, based on dynamic and kinematic characteristics, the simulation results and actual seismic profiles are analyzed to obtain the seismic wavefield response characteristics. For example, referencing... Figure 3 The intensity of seismic response in fracture zones is positively correlated with the length, width, and density of the fracture zone, and negatively correlated with the spatial development angle of the fracture zone and the difference in velocity between the fracture zone and the surrounding rock. When the length, width, and density of the fracture zone increase, the chaotic reflection energy of the seismic profile increases and the distortion of the in-phase axis intensifies. When the spatial development angle of the fracture zone increases, the anomalous reflection energy on the seismic profile weakens significantly, and the larger the angle, the lower the energy. When the difference between the filling velocity of the fracture zone and the velocity of the surrounding rock increases, the chaotic reflection energy of the seismic profile increases, and the overall characteristics are short-axis strong amplitude reflection.

[0052] The method provided in this application embodiment performs a combined comparative analysis of model migration results and actual seismic profiles, and combines seismic attribute analysis to summarize the seismic wavefield response characteristics of fractured reservoirs from the perspectives of kinematics (waveform structure, external morphology, internal structure) and dynamics (amplitude, frequency and their derived seismic attributes), thereby ensuring the accuracy of identifying the seismic response law of fracture zones.

[0053] The method provided in this application fully leverages the high resolution of high-density seismic data, and combines it with Gaussian stochastic medium modeling methods to describe subsurface heterogeneous media more flexibly and conveniently. Furthermore, by clearly defining the seismic wavefield response characteristics of fracture zones in the work area, the method significantly improves the identification accuracy of earthquakes in fracture-developed zones, effectively predicting the distribution of fractured reservoirs in the target stratigraphic interval, and providing a reliable basis for high-quality exploration and development of fractured oil and gas reservoirs.

[0054] Figure 4 This is a block diagram of a device for identifying the seismic response pattern of a fracture zone, provided as an embodiment of this application. This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. Figure 4 As shown, this device for identifying the seismic response pattern of a fracture zone includes:

[0055] The acquisition module 41 is used to acquire earthquake-related data of high-density seismic zones, including earthquake data and geological data of high-density seismic zones.

[0056] Module 42 is established to create fracture zone distribution models with different feature dimensions using earthquake-related data, and to create equivalent medium models using fracture zone distribution models with different feature dimensions.

[0057] Processing module 43 is used to perform seismic wave field simulation based on the equivalent medium model and obtain simulation results.

[0058] The comparison module 44 is used to perform feature analysis on the simulation results and the actual seismic profile to obtain the seismic wavefield response characteristics.

[0059] In this embodiment of the application, module 42 is used to extract a first dataset corresponding to high-angle cracks, a second dataset corresponding to network cracks, and a third dataset corresponding to horizontal cracks from earthquake-related data; construct a crack zone distribution model corresponding to high-angle cracks using the first dataset; construct a crack zone distribution model corresponding to network cracks using the second dataset; and construct a crack zone distribution model corresponding to horizontal cracks using the third dataset.

[0060] In this embodiment of the application, module 42 is used to establish that the fracture zone distribution model based on different feature dimensions is equivalent to a randomly distributed fracture zone, and to embed the randomly distributed fracture zone into an isotropic medium to obtain multiple equal probability models; the multiple equal probability models are superimposed on the reservoir structure model to obtain a reservoir random model, and the reservoir random model is used to construct an equivalent medium model.

[0061] In this embodiment of the application, the processing module 43 is used to perform forward modeling of the equivalent medium model using the two-dimensional non-uniform isotropic elastic wave equation and its staggered grid finite difference wave equation; during the forward modeling process, the same parameters such as trace spacing, minimum and maximum offset, sampling interval, and wavelet dominant frequency are collected from the seismic related data to record the shot collection and obtain the simulation results.

[0062] In this embodiment of the application, the isotropic elastic wave equation is:

[0063]

[0064] Where: σ xx =σ xx (x,z,t), σ zz =σ zz (x,z,t),τ xz =τ xz (x,z,t) is the stress tensor; ρ = ρ(x,z) is the density; U = U(x,z,t) and W = W(x,z,t) are the velocity components of the medium in the x and z directions; λ = λ(x,z) and μ = μ(x,z) are the Lamé coefficients. These are the particle velocity components U and W, respectively. These are the particle stress components σ xx σ zz and τ xz The discrete values; Δt represents the time step; k is the time domain discretization exponent; i represents discretization in the x-axis direction; j represents discretization in the z-axis direction; Δx and Δ z These represent the spatial step sizes in the x and z directions, respectively.

[0065] In this embodiment of the application, the comparison module 44 is used to perform feature analysis on the simulation results and the actual seismic profile according to the dynamic features and kinematic features respectively, so as to obtain the seismic wavefield response features. The kinematic features include at least: waveform structure, external morphology and internal structure, and the dynamic features include at least: amplitude, frequency and derived seismic attributes.

[0066] In this embodiment, the simulation results and actual seismic profiles are analyzed according to dynamic and kinematic characteristics to obtain seismic wavefield response characteristics, including...

[0067] This application also provides an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 1501, a communication interface 1502, a memory 1503, and a communication bus 1504, wherein the processor 1501, the communication interface 1502, and the memory 1503 communicate with each other through the communication bus 1504.

[0068] Memory 1503 is used to store computer programs.

[0069] When the processor 1501 executes the computer program stored in the memory 1503, it implements the steps of the above embodiments.

[0070] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0071] The communication interface is used for communication between the aforementioned terminal and other devices.

[0072] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0073] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc., or digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0074] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments.

[0075] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the methods described in the above embodiments.

[0076] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk).

[0077] The above are merely preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application are included within the scope of protection of this application.

[0078] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. 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 this application. Therefore, this application 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 claimed herein.

Claims

1. A method for identifying the seismic response characteristics of fracture zones, characterized in that, include: Acquire earthquake-related data of high-density seismic zones, wherein the earthquake-related data includes: earthquake data of the high-density seismic zones and geological data of the high-density seismic zones; Using the earthquake-related data, we establish fracture zone distribution models with different feature dimensions, and use these fracture zone distribution models with different feature dimensions to create equivalent medium models. Seismic wavefield simulation was performed based on the aforementioned equivalent medium model, and simulation results were obtained. The simulation results are compared with actual seismic profiles to obtain the seismic wavefield response characteristics. The step of establishing a fracture zone distribution model with different feature dimensions using the earthquake-related data includes: extracting a first dataset corresponding to high-angle fractures, a second dataset corresponding to network fractures, and a third dataset corresponding to horizontal fractures from the earthquake-related data; constructing a fracture zone distribution model corresponding to high-angle fractures using the first dataset; constructing a fracture zone distribution model corresponding to network fractures using the second dataset; and constructing a fracture zone distribution model corresponding to horizontal fractures using the third dataset. The method of creating an equivalent medium model using fracture zone distribution models with different feature dimensions includes: the fracture zones based on fracture zone distribution models with different feature dimensions are equivalent to randomly distributed fractures, and the randomly distributed fractures are embedded into an isotropic medium to obtain multiple equal probability models; the multiple equal probability models are superimposed on the reservoir structure model to obtain a reservoir stochastic model, and the equivalent medium model is constructed using the reservoir stochastic model. The seismic wavefield simulation based on the equivalent medium model, and the resulting simulation, includes: performing forward modeling on the equivalent medium model using a two-dimensional non-uniform isotropic elastic wave equation and its staggered grid finite difference wave equation; during the forward modeling process, shot collections are performed using the same trace spacing, minimum and maximum offset, sampling interval, and wavelet dominant frequency parameters from seismic related data to obtain the simulation results.

2. The method for identifying the seismic response pattern of a fracture zone according to claim 1, characterized in that, The isotropic elastic wave equation is as follows: in: , , It is the stress tensor; It is density; , It is the medium in Velocity components in two directions; , It is the Lamé coefficient. These are the particle velocity components U and W, respectively. These are the particle stress components. , and discrete values; This represents the time step; k is the time domain discretization exponent. This indicates discretization along the x-axis. This represents the discreteness along the z-axis. and These represent the spatial step sizes in the x and z directions, respectively.

3. The method for identifying the seismic response pattern of a fracture zone according to claim 1, characterized in that, The step of performing feature analysis on the simulation results and the actual seismic profile to obtain the seismic wavefield response characteristics includes: The simulation results and actual seismic profiles are analyzed according to dynamic and kinematic characteristics to obtain seismic wavefield response characteristics. The kinematic characteristics include at least waveform structure, external morphology, and internal structure, and the dynamic characteristics include at least amplitude, frequency, and derived seismic properties.

4. A device for identifying the seismic response pattern of fracture zones, characterized in that, include: The acquisition module is used to acquire earthquake-related data of high-density seismic zones, wherein the earthquake-related data includes: earthquake data of the high-density seismic zones and geological data of the high-density seismic zones; A module is established to create fracture zone distribution models with different feature dimensions using the earthquake-related data, and to create an equivalent medium model using the fracture zone distribution models with different feature dimensions. The processing module is used to perform seismic wavefield simulation based on the equivalent medium model and obtain simulation results; The comparison module is used to perform feature analysis on the simulation results and the actual seismic profile to obtain the seismic wavefield response characteristics; The establishment module is used to extract a first dataset corresponding to high-angle cracks, a second dataset corresponding to network cracks, and a third dataset corresponding to horizontal cracks from the earthquake-related data; construct a crack zone distribution model corresponding to the high-angle cracks using the first dataset; construct a crack zone distribution model corresponding to the network cracks using the second dataset; and construct a crack zone distribution model corresponding to the horizontal cracks using the third dataset. The establishment module is used to transform the fracture zone distribution model based on different feature dimensions into a randomly distributed fracture zone, and to embed the randomly distributed fracture zone into an isotropic medium to obtain multiple equal probability models; the multiple equal probability models are superimposed on the reservoir structure model to obtain a reservoir random model, and the equivalent medium model is constructed using the reservoir random model. The processing module is used to perform forward modeling of the equivalent medium model using a two-dimensional non-uniform isotropic elastic wave equation and its staggered grid finite difference wave equation. During the forward modeling process, shot collections are performed using the same trace spacing, minimum and maximum offset, sampling interval, and wavelet dominant frequency parameters collected from seismic related data to obtain the simulation results.

5. A storage medium, characterized in that, The storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 3 when it is run.

6. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other through the communication bus; wherein: Memory, used to store computer programs; A processor for performing the method of any one of claims 1 to 3 by running a program stored in memory.

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