Fractured reservoir connectivity earthquake prediction method and device

Abstract

The invention discloses a fractured reservoir connectivity earthquake prediction method and device. The method comprises the following steps: obtaining an inclination angle attribute body, a curvature attribute body and a discontinuity attribute body based on post-stack seismic data; performing crack identification on the inclination angle attribute body, the curvature attribute body and the discontinuity attribute body by adopting unsupervised clustering, and determining a curvature threshold value of a large-scale crack according to an obtained crack identification result; obtaining an ant attribute body according to the curvature attribute body and the curvature threshold value; establishing a large-scale crack network based on the ant attribute body; and determining the reservoir connectivity according to the large-scale fracture network. The method is a deterministic fracture modeling method based on ant tracking, fracture distribution does not need to be assumed in advance, relative deterministic inter-well fracture information is obtained, and fractured reservoir connectivity is predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum geophysical exploration, and in particular to a method and device for seismic prediction of connectivity of fractured reservoirs. Background Art

[0002] Fractures are a crucial factor influencing the exploration and development of oil and gas reservoirs. They increase porosity and permeability, connect matrix pores, strengthen connectivity between different reservoir units within a reservoir, and control oil and gas injection pathways and the distribution of high-quality reservoirs. Only when fractures connect, forming an internally connected fracture network, can they serve as effective seepage pathways. However, fracture connectivity can also lead to problems such as loss of caprock integrity, drilling fluid loss, leakage from reservoirs (such as gas storage, CO2 storage, and waste disposal), and the induction of geological hazards. Therefore, the study of fracture connectivity is essential in many geological fields.

[0003] Extensive research has been conducted on the origin, formation mechanism, development characteristics, controlling factors, and distribution patterns of fractures, as well as on the effectiveness and influencing factors of fractures. However, due to multiple factors such as multi-stage tectonic movement and complex lithology, characterizing fracture connectivity is difficult, resulting in limited research. Public literature and patents on seismic prediction methods for fractured reservoir connectivity are even scarcer. While some theoretical research on fracture connectivity has been conducted abroad, it primarily focuses on hydrogeology and mathematical geology.

[0004] The patent "Method for evaluating fracture connectivity and optimizing fracture parameters based on complex network theory" (application number: CN202111442768.0) discloses a fracture connectivity evaluation method. This method obtains the distribution law and characteristic parameters of natural fractures in the formation based on the previous understanding of natural fractures in the formation and statistical analysis of core fractures, and establishes a discrete fracture network model of natural fractures based on this to evaluate the connectivity of the formation fracture network. The discrete fracture network modeling method adopted requires pre-assumptions on parameters such as the shape, size, and inclination of the fractures during modeling. However, the fracture properties themselves are extremely heterogeneous, and the pre-made assumptions are difficult to match the actual distribution, thereby affecting the reliability of the fracture model. Summary of the Invention

[0005] To enrich process routes and increase selection space, an embodiment of the present invention provides a method and device for seismic prediction of fracture-type reservoir connectivity. Based on ant tracking, a fracture model is established, which does not require pre-assumption of fracture distribution, obtains relatively deterministic inter-well fracture information, and predicts fracture-type reservoir connectivity.

[0006] In a first aspect, an embodiment of the present invention provides a method for seismic prediction of fracture reservoir connectivity, comprising:

[0007] Obtain dip attribute volume, curvature attribute volume and discontinuity attribute volume based on post-stack seismic data;

[0008] Unsupervised clustering is used to identify cracks on the inclination attribute body, curvature attribute body, and discontinuity attribute body, and a curvature threshold value of large-scale cracks is determined based on the obtained crack identification results;

[0009] Obtaining an ant attribute body from the curvature attribute body and the curvature threshold value;

[0010] Establishing a large-scale crack model based on the ant attribute body;

[0011] Reservoir connectivity is determined based on the large-scale fracture model.

[0012] In a second aspect, an embodiment of the present invention provides a fracture reservoir connectivity seismic prediction device, comprising:

[0013] An attribute body acquisition module is used to obtain a dip attribute body, a curvature attribute body and a discontinuity attribute body based on post-stack seismic data;

[0014] A clustering crack identification module is used to identify cracks using unsupervised clustering on the inclination attribute body, curvature attribute body and discontinuity attribute body, and determine a curvature threshold value of large-scale cracks based on the obtained crack identification results;

[0015] an ant attribute body establishing module, configured to obtain an ant attribute body from the curvature attribute body and the curvature threshold value;

[0016] A large-scale crack model establishment module, used to establish a large-scale crack model based on the ant attribute body;

[0017] A reservoir connectivity analysis module is used to determine reservoir connectivity based on the large-scale fracture model.

[0018] In a third aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned fracture-type reservoir connectivity seismic prediction method is implemented.

[0019] In a fourth aspect, an embodiment of the present disclosure provides a server comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for seismic prediction of fracture-type reservoir connectivity when executing the program.

[0020] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0021] (1) The seismic prediction method for fracture-type reservoir connectivity provided by an embodiment of the present invention obtains a dip attribute body, a curvature attribute body, and a discontinuity attribute body based on post-stack seismic data; uses unsupervised clustering to identify fractures in the dip attribute body, curvature attribute body, and discontinuity attribute body, and determines a curvature threshold value for large-scale fractures based on the obtained fracture identification results; obtains an ant attribute body from the curvature attribute body and the curvature threshold value; establishes a large-scale fracture network model based on the ant attribute body; and determines reservoir connectivity based on the large-scale fracture network model. This method is a deterministic fracture modeling method that does not require a prior assumption of fracture distribution, obtains relatively deterministic interwell fracture information, and predicts fracture-type reservoir connectivity.

[0022] (2) The fracture reservoir connectivity seismic prediction method provided by the embodiment of the present invention limits the distribution range of large-scale fractures, further limits the range of curvature attributes, and provides control and constraints for fracture modeling.

[0023] (3) The seismic prediction method for connectivity of fractured reservoirs provided in an embodiment of the present invention obtains post-stack seismic data of fracture enhancement based on an all-round local angle domain imaging method, and performs filtering processing based on dip-guided filtering and anisotropic diffusion equation on the post-stack seismic data in turn. Without changing the structural morphology, the signal-to-noise ratio of the seismic data is greatly improved, the continuity of seismic reflection is enhanced, the phase axis is smoother, and the vertical fracture characteristics are clearer. The seismic geometric attributes extracted on this basis can better describe the fractures.

[0024] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0025] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0027] Figure 1 Flowchart of a method for seismic prediction of fracture reservoir connectivity according to an embodiment of the present invention;

[0028] Figure 2 for Figure 1 Flowchart of the specific implementation of the method for obtaining the discontinuity attribute body;

[0029] Figure 3This is a large-scale fracture network distribution diagram of the target layer in an embodiment of the present invention;

[0030] Figure 4 A comparison diagram of the fracture model statistical results and the wellbore fracture statistical results in an embodiment of the present invention;

[0031] Figure 5 This is a production curve diagram of Well C-1 and Well C1-1 in an embodiment of the present invention;

[0032] Figure 6 This is a prediction diagram of the connectivity and connection path between Well C-1 and Well C1-1 in an embodiment of the present invention;

[0033] Figure 7 This is a production performance curve diagram of wells C1-1 and C1-13 in an embodiment of the present invention;

[0034] Figure 8 This is a prediction diagram of the connectivity and connection path between Well C-1 and Well C1-13 in an embodiment of the present invention;

[0035] Figure 9 Schematic diagram of the structure of the seismic prediction device for fracture-type reservoir connectivity in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0037] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0038] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0039] Example

[0040] The embodiment of the present invention provides a method for seismic prediction of fracture reservoir connectivity, the process of which is as follows Figure 1 As shown, the following steps are included:

[0041] Step S11: Obtaining a dip attribute volume, a curvature attribute volume, and a discontinuity attribute volume based on post-stack seismic data.

[0042] For details, see Figure 2 As shown in FIG, obtaining a discontinuity attribute volume based on post-stack seismic data may include the following steps:

[0043] Step S111: performing spectral decomposition on the post-stack seismic data to obtain an amplitude spectrum data volume.

[0044] Preferably, the post-stack seismic data is subjected to spectral decomposition based on generalized S-transformation to obtain an amplitude spectrum data volume.

[0045] Step S112: Based on the amplitude spectrum data volume, determine the temporal variation pattern of the main frequency of the target layer segment, and obtain amplitude volumes with different main frequencies in different time periods.

[0046] According to the temporal variation law of the main frequency of the target layer, the target layer is divided into multiple time periods. The main frequency in each time period is basically the same, and amplitude bodies with different main frequencies in different time periods are obtained.

[0047] Step S113: performing coherent calculations on amplitude bodies with different main frequencies in different time periods to obtain discontinuous attribute bodies.

[0048] The final discontinuous attribute volume is the discontinuous attribute volume of the entire target layer segment.

[0049] Step S12: using unsupervised clustering to identify cracks on the inclination attribute body, the curvature attribute body and the discontinuity attribute body, and determining the curvature threshold value of large-scale cracks based on the obtained crack identification results.

[0050] Preferably, an unsupervised clustering algorithm based on a Bayesian probability model may be used to identify cracks.

[0051] Fractures of different scales play different roles in oil reservoirs. Large-scale fractures, which appear in small clusters, serve as primary seepage pathways, resulting in highly heterogeneous seepage. Small-scale fractures, however, are the primary reservoir space. When studying fracture connectivity, only large-scale fractures are typically considered. Therefore, fracture classification is necessary to further identify large-scale fractures from fracture identification results.

[0052] The rules for determining large-scale fractures can be flexibly set according to research needs. For example, the extension length threshold of large-scale fractures can be determined based on the distribution density of the well network in the study area, and fractures with lengths greater than the threshold can be determined as large-scale fractures.

[0053] The intersection analysis of attribute space is used as a quality control method to determine the threshold value of curvature attributes within the range of large-scale cracks.

[0054] The fracture classification method uses seismic data to classify and refine fractures, limiting the distribution range and characteristics of large-scale fractures, and further limiting the range of curvature properties, providing control and constraints for fracture modeling.

[0055] Step S13: Obtain an ant attribute body from the curvature attribute body and the curvature threshold value.

[0056] Step S14: establishing a large-scale crack model based on the ant attribute body.

[0057] Automatically track cracks by slicing them layer by layer on the ant attribute body. By calculating the morphological parameters and topological index of the cracks, grid the tracked large-scale cracks and establish a large-scale crack model. Figure 3 shown.

[0058] Furthermore, the length and direction information of the fractures in the fracture model can be extracted and compared with the imaging logging results to verify the reliability of the model.

[0059] See also Figure 4 As shown in the figure, a and b are the large-scale fracture length statistical histogram and direction statistical histogram obtained by the fracture model statistics, respectively, and c is the rose diagram of fracture length and direction statistics on the well. It can be seen that the large-scale fracture model established in this embodiment has a relatively high degree of consistency with the well.

[0060] Step S15: Determine reservoir connectivity based on the large-scale fracture model.

[0061] The reservoir connectivity here mainly refers to the connectivity of the reservoir between two wells. If there are large-scale fractures that run through the two wells, it is determined that the two wells are connected, that is, the reservoir between the two wells is connected.

[0062] The seismic prediction method for fractured reservoir connectivity provided by an embodiment of the present invention obtains dip, curvature, and discontinuity attributes based on post-stack seismic data. Unsupervised clustering is used to identify fractures in the dip, curvature, and discontinuity attributes. A curvature threshold for large-scale fractures is determined based on the fracture identification results. An ant attribute is derived from the curvature attribute and the curvature threshold. A large-scale fracture network model is established based on the ant attribute. Reservoir connectivity is determined based on the large-scale fracture network model. This method is a deterministic fracture modeling method that does not require pre-assumptions about fracture distribution. It obtains relatively deterministic interwell fracture information and predicts the connectivity of fractured reservoirs.

[0063] Preferably, before the above steps, post-stack seismic data of fault enhancement can be obtained based on the omnidirectional local angle domain imaging method; the maximum dip angle of the post-stack seismic data within the target layer segment is determined; and according to the maximum dip angle, the post-stack seismic data is sequentially subjected to dip-guided filtering and filtering based on the anisotropic diffusion equation.

[0064] After two filtering steps, without changing the structural morphology, the signal-to-noise ratio of the seismic data is greatly improved, the continuity of seismic reflections is enhanced, the event axes are smoother, and the vertical fracture characteristics are clearer. The seismic geometric attributes extracted on this basis can better describe the fractures.

[0065] Based on the data of the omnidirectional local angle domain depth migration processing results, the embodiment of the present invention establishes a deterministic fracture model based on ant tracking under the control of fracture classification, obtains information such as the length and direction of the fracture, and predicts the fracture connectivity and connection path. Taking the xx fracture type oil reservoir as an example, the accuracy of the method of this embodiment is verified by the production data of C1-1 well and C-1 well: Figure 5 As shown in the figure, before the C1-1 well was put into production, the formation pressure dropped by 1.28 MPa for every 1 MMSTB of crude oil produced by the C1-1 well. After the C1-1 well was put into production, the formation pressure dropped by 4.49 MPa for every 1 MMSTB of crude oil produced by the C1-1 well. The formation pressure dropped by 2.86 MPa for every 1 MMSTB of crude oil produced by the C1-1 well. This indicates that there is pressure interference between the two wells and the reservoirs are connected. Establish a fracture network model of the C1-1 well and the C-1 well ( Figure 6 ). The light gray fractures in the figure are shared fractures, i.e., the inter-well communication paths. The prediction results of the single-well fracture network model indicate that the two wells are connected, which is consistent with the production dynamics and predicts the communication path. Before and after the commissioning of Well C1-13, the production of Well C1-1 changed due to the change in the working system, but the rate of decline of the bottomhole flowing pressure of Well C1-1 did not change significantly due to the commissioning of Well C1-13. Figure 7 ), C1-1 and C1-13 wells may not be connected. Establish a large-scale fracture model between C1-1 and C1-13 wells, such as Figure 8As shown in Figure 2, the prediction results show that there are no common fractures between the two wells, that is, the wells are not connected, which is consistent with the production data of the wells.

[0066] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a fracture reservoir connectivity seismic prediction device, the structure of which is as follows: Figure 9 Shown, including:

[0067] An attribute body acquisition module 91 is used to obtain a dip attribute body, a curvature attribute body and a discontinuity attribute body based on post-stack seismic data;

[0068] A clustering crack identification module 92 is configured to identify cracks using unsupervised clustering of the inclination attribute body, curvature attribute body, and discontinuity attribute body, and determine a curvature threshold value of large-scale cracks based on the obtained crack identification results;

[0069] an ant attribute body establishing module 93, configured to obtain an ant attribute body from the curvature attribute body and the curvature threshold value;

[0070] A large-scale crack model building module 94 is used to build a large-scale crack model based on the ant attribute body;

[0071] The reservoir connectivity analysis module 95 is configured to determine reservoir connectivity based on the large-scale fracture model.

[0072] In some embodiments, the attribute volume acquisition module 91 obtains a discontinuity attribute volume based on post-stack seismic data, and is used to:

[0073] The post-stack seismic data is spectrally decomposed to obtain an amplitude spectrum data volume; based on the amplitude spectrum data volume, the temporal variation pattern of the main frequency of the target layer segment is determined to obtain amplitude volumes with different main frequencies in different time periods; and coherence calculations are performed on the amplitude volumes with different main frequencies in different time periods to obtain discontinuous attribute volumes.

[0074] In some embodiments, the attribute body acquisition module 91 performs spectral decomposition on the post-stack seismic data to:

[0075] The post-stack seismic data are subjected to spectral decomposition based on generalized S transform.

[0076] In some embodiments, the clustering crack identification module 92 uses unsupervised clustering to identify cracks on the dip attribute body, curvature attribute body, and discontinuity attribute body, for:

[0077] For the dip attribute body, curvature attribute body and discontinuity attribute body, unsupervised clustering based on the Bayesian probability model is used to identify cracks.

[0078] In some embodiments, the large-scale crack model building module 94 builds a large-scale crack model based on the ant attribute body, and is used to:

[0079] The cracks are automatically tracked by slicing them layer by layer on the ant attribute body. The morphological parameters and topological indexes of the cracks are calculated, and the tracked large-scale cracks are gridded to establish a large-scale crack model.

[0080] In some embodiments, the apparatus further includes a seismic data preprocessing module 96 , wherein the post-stack seismic data is post-stack seismic data of fracture enhancement obtained based on an omnidirectional local angle domain imaging method, and the seismic data preprocessing module 96 is configured to:

[0081] The maximum dip angle of the post-stack seismic data within the target layer segment is determined; and according to the maximum dip angle, the post-stack seismic data is sequentially subjected to dip-guided filtering and anisotropic diffusion equation-based filtering.

[0082] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0083] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned fracture-type reservoir connectivity seismic prediction method is implemented.

[0084] Based on the inventive concept of the present invention, an embodiment of the present invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor implements the above-mentioned fracture-type reservoir connectivity seismic prediction method when executing the program.

[0085] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0086] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0087] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0088] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0089] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0090] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0091] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A method for seismic prediction of fracture reservoir connectivity, characterized in that: include: Obtain dip attribute volume, curvature attribute volume and discontinuity attribute volume based on post-stack seismic data; Unsupervised clustering is used to identify cracks on the inclination attribute body, curvature attribute body, and discontinuity attribute body, and a curvature threshold value of large-scale cracks is determined based on the obtained crack identification results; Obtaining an ant attribute body from the curvature attribute body and the curvature threshold value; Establishing a large-scale crack model based on the ant attribute body; Reservoir connectivity is determined based on the large-scale fracture model.

2. The method according to claim 1, wherein Discontinuity attributes are obtained based on post-stack seismic data, including: Perform spectral decomposition on the post-stack seismic data to obtain the amplitude spectrum data volume; Based on the amplitude spectrum data body, determining the temporal variation of the main frequency of the target layer segment, and obtaining amplitude bodies with different main frequencies in different time periods; The coherence calculation is performed on the amplitude bodies with different main frequencies in different time periods to obtain discontinuous attribute bodies.

3. The method according to claim 2, wherein The performing spectral decomposition on the post-stack seismic data comprises: The post-stack seismic data are subjected to spectral decomposition based on generalized S transform.

4. The method according to claim 1, wherein The method of identifying cracks by using unsupervised clustering on the inclination attribute body, the curvature attribute body and the discontinuity attribute body comprises: For the dip attribute body, curvature attribute body and discontinuity attribute body, unsupervised clustering based on the Bayesian probability model is used to identify cracks.

5. The method according to claim 1, wherein The establishing of a large-scale crack model based on the ant attribute body includes: The cracks are automatically tracked by slicing them layer by layer on the ant attribute body. The morphological parameters and topological indexes of the cracks are calculated, and the tracked large-scale cracks are gridded to establish a large-scale crack model.

6. The method according to any one of claims 1 to 5, wherein: The post-stack seismic data is post-stack seismic data of fault enhancement obtained based on an all-around local angle domain imaging method.

7. The method according to claim 6, wherein Before obtaining the dip attribute body, the curvature attribute body and the discontinuity attribute body based on the post-stack seismic data, the method further includes: Determine the maximum dip angle of post-stack seismic data within the target interval; According to the maximum dip angle, the post-stack seismic data is sequentially subjected to dip-guided filtering and anisotropic diffusion equation-based filtering.

8. A fracture reservoir connectivity seismic prediction device, characterized in that: include: An attribute body acquisition module is used to obtain a dip attribute body, a curvature attribute body and a discontinuity attribute body based on post-stack seismic data; A clustering crack identification module is used to identify cracks using unsupervised clustering on the inclination attribute body, curvature attribute body and discontinuity attribute body, and determine a curvature threshold value of large-scale cracks based on the obtained crack identification results; an ant attribute body establishing module, configured to obtain an ant attribute body from the curvature attribute body and the curvature threshold value; A large-scale crack model establishment module, used to establish a large-scale crack model based on the ant attribute body; A reservoir connectivity analysis module is used to determine reservoir connectivity based on the large-scale fracture model.

9. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which, when executed by a processor, implement the method for seismic prediction of fracture reservoir connectivity according to any one of claims 1 to 7.

10. A server, characterized in that: include: 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 method for seismic prediction of fracture-type reservoir connectivity according to any one of claims 1 to 7 is implemented.

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