Modeling method and device of discrete fracture network model and discrete fracture network model

Abstract

The invention relates to a discrete fracture network model modeling method and device and a discrete fracture network model. The method comprises the following steps: acquiring seismic anisotropy intensity data; obtaining analysis result data of imaging logging; taking the seismic anisotropy intensity data and the analysis result data as constraint conditions, and adopting a sequential Gaussian simulation algorithm to establish a fracture density three-dimensional geologic model; and generating a discrete fracture network model by adopting a random algorithm based on the fracture density three-dimensional geological model. According to the method, the seismic anisotropy intensity data and the analysis result of imaging logging are used as constraint conditions, the sequential Gaussian simulation algorithm is adopted, the natural fracture density three-dimensional geologic model is established, and finally the discrete fracture network model is generated by using the random algorithm, so that the discrete fracture network model can represent small-scale natural fractures with the fault displacement smaller than 10 m. Therefore, effective guidance can be provided for horizontal well guiding and fracturing design, and effective technical support is provided for oil and gas development.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical exploration and development, and in particular to a modeling method and device for a discrete fracture network model and a discrete fracture network model. Background Art

[0002] In the unconventional oil and gas development dominated by horizontal wells, natural fracture prediction is the key to effectively prevent drilling leakage, casing change, etc. In related technologies, seismic event axis misalignment identification and seismic attribute extraction can generally quantitatively characterize faults with a fault distance greater than 10m (medium- and large-scale fractures). For natural fractures with a fault distance less than 10m (small-scale fractures), although they can be identified through core or imaging logging, their planar distribution characteristics and quantitative characterization are difficult. Summary of the invention

[0003] In view of this, the present invention provides a modeling method of a discrete fracture network model capable of generating a small-scale discrete fracture network model.

[0004] Specifically, the present invention is achieved through the following technical solutions:

[0005] According to a first aspect of the present invention, a modeling method for a discrete fracture network model is provided, the method comprising: obtaining seismic anisotropic intensity data; obtaining analysis result data of imaging logging; using the seismic anisotropic intensity data and the analysis result data as constraints, and adopting a sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density; and generating a discrete fracture network model based on the three-dimensional geological model of fracture density using a random algorithm.

[0006] In some embodiments, the step of obtaining seismic anisotropic intensity data specifically includes: obtaining characteristic data of earthquakes; and generating seismic anisotropic intensity data based on the characteristic data.

[0007] In some embodiments, the characteristic data includes amplitude data of seismic waves, travel time data of seismic waves, attenuation gradient data of seismic waves, and propagation velocity data of seismic waves.

[0008] In some embodiments, the step of obtaining characteristic data of an earthquake specifically includes: collecting initial seismic data; offsetting the initial seismic data using OVT domain processing technology to generate a spiral gather; performing superposition based on the spiral gather using a preset azimuth and shot offset; and extracting characteristic data of the earthquake based on the superposition result.

[0009] In some embodiments, the step of acquiring initial seismic data specifically includes: acquiring initial seismic data in a wide frequency band, a wide azimuth, and a high density.

[0010] In some embodiments, the step of obtaining analysis result data of imaging logging specifically includes: obtaining fracture tendency data, fracture dip data, fracture strike data and fracture density data.

[0011] In some embodiments, the steps of using the seismic anisotropic intensity data and the analysis result data as constraints and using the sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density specifically include: performing ant body attribute calculations on the seismic anisotropic intensity data to generate ant body attributes; performing variogram analysis and normal function analysis on the analysis result data to generate variogram and normal functions; and using the sequential Gaussian simulation algorithm based on the ant body attributes, variogram and normal functions to establish a three-dimensional geological model of fracture density.

[0012] In some embodiments, the analysis result data is subjected to variogram analysis and normal function analysis to generate the steps of variogram and normal function, specifically including: coarsening the crack density curve using the arithmetic mean method according to the numerical simulation method; performing variogram analysis and normal function analysis based on the coarsened crack density curve to generate variogram and normal function.

[0013] According to a second aspect of the present invention, a modeling device for a discrete fracture network model is provided, the device comprising a first acquisition module for acquiring seismic anisotropic intensity data; a second acquisition module for acquiring analysis result data of imaging logging; a three-dimensional geological model construction module for using the seismic anisotropic intensity data and the analysis result data as constraints to establish a three-dimensional geological model of fracture density using a sequential Gaussian simulation algorithm; and a discrete fracture network model construction module for generating a discrete fracture network model using a random algorithm based on the three-dimensional geological model of fracture density.

[0014] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for modeling a discrete fracture network model in the first aspect or any possible implementation of the first aspect are implemented.

[0015] According to a fourth aspect of the present invention, there is provided a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for modeling a discrete fracture network model in the first aspect or any possible implementation of the first aspect.

[0016] According to a fifth aspect of the present invention, a discrete fracture network model is provided, which is constructed by using the modeling method of the discrete fracture network model in the first aspect or any possible implementation of the first aspect.

[0017] The technical solution provided by the present invention brings at least the following beneficial effects: the present application uses the analysis results of seismic anisotropic intensity data and imaging logging as constraints, adopts a sequential Gaussian simulation algorithm, establishes a three-dimensional geological model of natural fracture density, and finally uses a random algorithm to generate a discrete fracture network model, so that the discrete fracture network model can characterize small-scale natural fractures with a fault distance of less than 10m, thereby being able to effectively guide horizontal well guidance and fracturing design, and providing effective technical support for oil and gas development. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A schematic flow chart of a modeling method for a discrete fracture network model provided in a first embodiment of the present invention;

[0021] Figure 2 A schematic flow chart of a modeling method for a discrete fracture network model provided in a second embodiment of the present invention;

[0022] Figure 3 A schematic flow chart of a modeling method for a discrete fracture network model provided in a third embodiment of the present invention;

[0023] Figure 4 A block diagram of a modeling device for a discrete fracture network model provided by one embodiment of the present invention;

[0024] Figure 5 A block diagram of an electronic device provided by one embodiment of the present invention;

[0025] Figure 6 A plane diagram of anisotropic strength of a certain lithologic segment in a deep shale gas horizontal well development area in a certain region provided by one embodiment of the present invention;

[0026] Figure 7 A discrete fracture network model of a certain lithologic segment in a deep shale gas horizontal well development zone in a certain region is provided as an embodiment of the present invention.

[0027] in, Figure 4 and Figure 5 The corresponding relationship between the reference numerals and component names in the figure is:

[0028] 1 a modeling device for a discrete fracture network model, 10 a first acquisition module, 12 a second acquisition module, 14 a three-dimensional geological model building module, 16 a discrete fracture network model building module, 2 an electronic device, 20 a memory, and 22 a processor. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 The embodiment of the present invention provides a modeling method of a discrete fracture network model, which may include the following steps:

[0031] S101. Obtain seismic anisotropy intensity data.

[0032] S102: Obtain analysis result data of imaging logging.

[0033] S103. Using the seismic anisotropy intensity data and the analysis result data as constraints, a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of fracture density.

[0034] S104. Generate a discrete fracture network model using a random algorithm based on the fracture density three-dimensional geological model.

[0035] According to the modeling method of the discrete fracture network model provided by the present invention, the analysis results of seismic anisotropic intensity data and imaging logging are used as constraints, a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of natural fracture density, and finally a discrete fracture network model is generated using a random algorithm, so that the discrete fracture network model can characterize small-scale natural fractures with a fault distance of less than 10m, thereby effectively guiding the horizontal well guidance and fracturing design, and providing effective technical support for oil and gas development.

[0036] In the above embodiment, the step of obtaining seismic anisotropic intensity data specifically includes: obtaining characteristic data of earthquakes; and generating seismic anisotropic intensity data based on the characteristic data.

[0037] In the above embodiment, the characteristic data includes the amplitude data of the seismic wave, the travel time data of the seismic wave, the attenuation gradient data of the seismic wave and the propagation speed data of the seismic wave.

[0038] In this embodiment, seismic anisotropy intensity data can be accurately generated by acquiring the amplitude data of seismic waves, the travel time data of seismic waves, the attenuation gradient data of seismic waves, and the propagation velocity data of seismic waves. It is known to those skilled in the art that, according to the theory of seismic wave propagation, in vertical fracture reservoirs (HTI media), due to the influence of fracture density and fluid in the fractures, the velocity, amplitude, travel time, frequency attenuation gradient and other parameter characteristics of seismic waves in the process of propagation in different directions are all different. Therefore, seismic anisotropy intensity data can be accurately generated by acquiring the amplitude data of seismic waves, the travel time data of seismic waves, the attenuation gradient data of seismic waves, and the propagation velocity data of seismic waves.

[0039] In the above embodiment, the steps of using the seismic anisotropic intensity data and the analysis result data as constraints and using the sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density specifically include: performing ant body attribute calculation on the seismic anisotropic intensity data to generate ant body attributes; performing variogram analysis and normal function analysis on the analysis result data to generate variogram and normal function; and based on the ant body attributes, variogram and normal function, using the sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density.

[0040] In this embodiment, the seismic anisotropic intensity data can be subjected to ant body attribute calculation to generate ant body attributes, and the analysis result data can be subjected to variogram analysis and normal function analysis to generate variogram and normal function, thereby establishing a three-dimensional geological model of fracture density based on the ant body attributes, variogram and normal function using a sequential Gaussian simulation algorithm. The ant body attribute calculation parameters should be selected to make the main trend characteristics of the ant body attributes consistent with those of the seismic coherent attributes.

[0041] like Figure 2 As shown, an embodiment of the present invention provides a modeling method for a discrete fracture network model, which may include the following steps:

[0042] S201. Obtain characteristic data of earthquakes.

[0043] S202. Generate seismic anisotropy intensity data based on the characteristic data.

[0044] S203, obtaining analysis result data of imaging logging.

[0045] S204, performing ant volume attribute calculation on the seismic anisotropic intensity data to generate ant volume attributes.

[0046] S205, performing variogram analysis and normal function analysis on the analysis result data to generate variogram and normal function.

[0047] S206. Based on ant body attributes, variation and normal function, a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of fracture density.

[0048] S207. Generate a discrete fracture network model using a random algorithm based on the fracture density three-dimensional geological model.

[0049] According to the modeling method of the discrete fracture network model provided by the present invention, the analysis results of seismic anisotropic intensity data and imaging logging are used as constraints, and a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of natural fracture density. Finally, a discrete fracture network model is generated using a random algorithm. The discrete fracture network model can characterize small-scale natural fractures with a fault distance of less than 10m, thereby being able to effectively guide the horizontal well guidance and fracturing design, and providing effective technical support for oil and gas development. Specifically, seismic anisotropic intensity data can be generated by earthquake characteristic data, so that the anisotropic intensity data can be more accurate. At the same time, the seismic anisotropic intensity data can be subjected to ant body attribute calculation to generate ant body attributes, and the analysis result data can be subjected to variogram analysis and normal function analysis to generate variogram and normal function, thereby based on ant body attributes, variogram and normal function, a three-dimensional geological model of fracture density is established using a sequential Gaussian simulation algorithm.

[0050] Furthermore, the selection of ant body attribute calculation parameters should make the ant body attributes consistent with the main trend characteristics of the seismic coherent attributes.

[0051] like Figure 3 As shown, an embodiment of the present invention provides a modeling method for a discrete fracture network model, which may include the following steps:

[0052] S301, collecting initial seismic data.

[0053] S302, the initial seismic data is migrated using the OVT domain processing technology to generate a spiral gather.

[0054] S303, performing stacking based on the spiral gather using a preset azimuth and offset.

[0055] S304. Extract characteristic data of earthquake based on the stacking result.

[0056] S305. Generate seismic anisotropy intensity data based on the characteristic data.

[0057] S306: Obtain analysis result data of imaging logging.

[0058] S307, performing ant volume attribute calculation on the seismic anisotropic intensity data to generate ant volume attributes.

[0059] S308, performing variogram analysis and normal function analysis on the analysis result data to generate variogram and normal function.

[0060] S309. Based on ant body attributes, variation and normal function, a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of fracture density.

[0061] S310, generating a discrete fracture network model using a random algorithm based on the fracture density three-dimensional geological model.

[0062] According to the modeling method of the discrete fracture network model provided by the present invention, the analysis results of seismic anisotropic intensity data and imaging logging are used as constraints, and a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of natural fracture density. Finally, a random algorithm is used to generate a discrete fracture network model. The discrete fracture network model can characterize small-scale natural fractures with a fault distance of less than 10m, so that it can effectively guide the horizontal well guidance and fracturing design, and provide effective technical support for oil and gas development. Specifically, the present application uses the spiral gather (OVG) after the "two widths and one height" seismic data (initial seismic data) is offset by the OVT domain, and a reasonable azimuth-shot distance stacking parameter is adopted to extract the characteristic data of the earthquake, thereby generating seismic anisotropic intensity data. At the same time, the seismic anisotropic intensity data can be subjected to ant body attribute calculation to generate ant body attributes, and the analysis result data can be subjected to variogram analysis and normal function analysis to generate variogram and normal function, so as to establish a three-dimensional geological model of fracture density based on ant body attributes, variogram and normal function, and sequential Gaussian simulation algorithm.

[0063] In the above embodiment, the step of collecting initial seismic data specifically includes: collecting initial seismic data with wide frequency band, wide azimuth and high density.

[0064] In this embodiment, the initial seismic data may be collected in a "two wide and one high" manner, that is, the initial seismic data may be collected in a wide frequency band, wide azimuth and high density, wherein the initial seismic data may be original single shot data.

[0065] In any of the above embodiments, the step of obtaining the analysis result data of the imaging logging specifically includes: obtaining fracture tendency data, fracture dip angle data, fracture strike data and fracture density data.

[0066] In these embodiments, the variance and normal functions may be generated by acquiring fracture tendency data, fracture dip data, fracture strike data, and fracture density data.

[0067] In any of the above embodiments, the analysis result data is subjected to variogram analysis and normal function analysis to generate the steps of variogram and normal function, specifically including: coarsening the crack density curve using the arithmetic mean method according to the numerical simulation method; performing variogram analysis and normal function analysis based on the coarsened crack density curve to generate variogram and normal function.

[0068] In these embodiments, the crack density curve may be coarsened using an arithmetic mean method according to a numerical simulation method, and then a variogram analysis and a normal function analysis may be performed based on the coarsened crack density curve to generate variogram and normal functions.

[0069] Based on the same inventive concept, Figure 4 As shown, an embodiment of the present invention further provides a modeling device 1 for a discrete fracture network model, the device comprising: a first acquisition module 10, for acquiring seismic anisotropic intensity data; a second acquisition module 12, for acquiring analysis result data of imaging logging; a three-dimensional geological model building module 14, for using the seismic anisotropic intensity data and the analysis result data as constraints, and adopting a sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density; a discrete fracture network model building module 16, for generating a discrete fracture network model based on the three-dimensional geological model of fracture density using a random algorithm.

[0070] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0071] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0072] Based on the same inventive concept, see Figure 5The embodiment of the present invention also provides an electronic device 2, including a memory 20 (such as a non-volatile memory), a processor 22, and a computer program stored in the memory 20 and executable on the processor 22. When the processor 22 executes the program, the steps of the modeling method of the discrete fracture network model in any possible implementation manner described above are implemented, which can be equivalent to the modeling device of the discrete fracture network model described above. Of course, the processor can also be used to process other data or operations. The electronic device can be a PC, a server, a terminal, and other devices.

[0073] The electronic device 2 may also generally include: a memory, a network interface, and an internal bus. In addition to these components, other hardware may also be included, which will not be described in detail.

[0074] It should be pointed out that the electronic device 2 can be implemented by software. As a device in a logical sense, it is formed by the processor of the electronic device in which it is located reading the computer program instructions stored in the non-volatile memory into the internal memory and running them.

[0075] Based on the same inventive concept, an embodiment of the present invention further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the modeling method of the discrete fracture network model in any possible implementation manner described above are implemented.

[0076] Alternatively, the storage medium may be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0077] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the modeling method of a discrete fracture network model in any possible implementation manner described above.

[0078] Based on the same inventive concept, an embodiment of the present invention further provides a discrete fracture network model, which is constructed by using the modeling method of the discrete fracture network model in the first aspect or any possible implementation of the first aspect.

[0079] like Figure 6 As shown, it is a plane diagram of the anisotropic strength of a certain lithologic section in a deep shale gas horizontal well development area in a certain region.

[0080] like Figure 7 As shown in FIG. 1 , it is a discrete fracture network model of a certain lithologic segment in a deep shale gas horizontal well development area in a certain region.

[0081] The embodiments of the subject matter and functional operations described in this specification may be implemented in the following: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules in computer program instructions encoded on a tangible non-temporary program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver device for execution by a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0082] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuits, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits), and the apparatus can also be implemented as special purpose logic circuits.

[0083] Computers suitable for executing computer programs include, for example, general and / or special microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more large-capacity storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to this large-capacity storage device to receive data from it or to transmit data to it, or both. However, the computer does not necessarily have such a device. In addition, the computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0084] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0085] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of the specific embodiments of specific inventions. Certain features described in multiple embodiments in this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claim protection, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of a sub-combination.

[0086] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or requiring that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0087] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0088] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0089] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A modeling method for a discrete fracture network model, characterized in that: The method comprises: Obtain seismic anisotropy intensity data; Obtain analysis result data of imaging logging; Using the seismic anisotropy intensity data and the analysis result data as constraint conditions, a sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of fracture density; A discrete fracture network model is generated by adopting a random algorithm based on the fracture density three-dimensional geological model.

2. The method according to claim 1, characterized in that The step of obtaining seismic anisotropy intensity data specifically includes: Obtain characteristic data of earthquakes; The seismic anisotropy intensity data is generated based on the characteristic data.

3. The method according to claim 2, characterized in that The characteristic data include amplitude data of seismic waves, travel time data of seismic waves, attenuation gradient data of seismic waves and propagation speed data of seismic waves.

4. The method according to claim 2, characterized in that: The step of obtaining characteristic data of earthquakes specifically includes: Collect initial seismic data; The initial seismic data are migrated using an OVT domain processing technique to generate a spiral gather; Based on the spiral gather, stacking is performed using a preset azimuth and offset; Extract earthquake characteristic data based on the stacking results.

5. The method according to claim 4, characterized in that The step of collecting initial seismic data specifically includes: The initial seismic data are collected in a wide frequency band, wide azimuth and high density.

6. The method according to claim 1, characterized in that The step of obtaining the analysis result data of the imaging logging specifically includes: Obtain fracture tendency data, fracture dip data, fracture strike data and fracture density data.

7. The method according to claim 1, characterized in that The step of using the seismic anisotropy intensity data and the analysis result data as constraint conditions and using a sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density specifically includes: Calculate the ant volume attributes of the seismic anisotropic intensity data to generate ant volume attributes; Performing variogram analysis and normal function analysis on the analysis result data to generate variogram and normal function; Based on the ant body attributes, the variation and the normal function, the sequential Gaussian simulation algorithm is used to establish a three-dimensional geological model of fracture density.

8. The method according to claim 7, characterized in that The step of performing variogram analysis and normal function analysis on the analysis result data to generate variogram and normal function specifically includes: According to the numerical simulation method, the crack density curve is coarsened by using the arithmetic average method; A variogram analysis and a normal function analysis are performed based on the roughened crack density curve to generate the variogram and the normal function.

9. A modeling device (1) for a discrete fracture network model, characterized in that: include: A first acquisition module (10) is used to acquire seismic anisotropy intensity data; A second acquisition module (12) is used to acquire analysis result data of imaging logging; A three-dimensional geological model building module (14) is used to use the seismic anisotropy intensity data and the analysis result data as constraint conditions and use a sequential Gaussian simulation algorithm to establish a three-dimensional geological model of fracture density; A discrete fracture network model building module (16) is used to generate a discrete fracture network model using a random algorithm based on the fracture density three-dimensional geological model.

10. An electronic device (2), comprising a memory (20), a processor (22), and a computer program stored in the memory (20) and executable on the processor (22), characterized in that: When the processor (22) executes the program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A discrete fracture network model, characterized in that: The discrete fracture network model is constructed using the modeling method of any one of claims 1 to 8.

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