Multi-azimuth seismic attribute tensor fusion method, device, equipment and medium

Abstract

The embodiment of the invention provides a multidirectional seismic attribute tensor fusion method and device, equipment and a medium, and relates to the technical field of oil-gas exploration and development. The method comprises the following steps: extracting seismic attribute data volumes in a plurality of observation orientations from five-dimensional seismic data, and respectively calculating a seismic attribute tensor corresponding to the seismic attribute data volume in each observation orientation, each seismic attribute tensor represents the change rate of the gradient of the seismic attribute in the corresponding observation orientation along three orthogonal directions of a three-dimensional space; adding the values of the elements with the same element positions in each seismic attribute tensor to obtain a multi-azimuth seismic attribute tensor fusion operator; and calculating the sum of main diagonal elements in the multidirectional seismic attribute tensor fusion operator to obtain a multidirectional seismic attribute tensor fusion result. Thus, according to the obtained fusion result, the influence of the included angle between the observation coordinate system and the constitutive coordinate system is eliminated, and effective fusion of multi-azimuth seismic attribute information is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration and development, and in particular to a multi-directional seismic attribute tensor fusion method, device, equipment and medium. Background Art

[0002] At present, in the field of oil and gas exploration and development, the use of seismic data to identify and predict fault systems is the key to describing the characteristics of oil and gas reservoirs. Seismic attributes can enhance the abnormal information of target reservoirs and geological structures by mining various types of information in seismic data. Seismic attributes are an important tool for effectively identifying and predicting fault systems.

[0003] In the prior art, single-azimuth post-stack three-dimensional seismic data is usually used to identify and predict fault systems, or five-dimensional seismic data is directly used to identify and predict fault systems.

[0004] However, when using single-azimuth three-dimensional post-stack seismic data to identify and predict fault systems, the narrow angle of the observation system leads to incomplete characterization of the fault system, making it difficult to meet the accuracy requirements for oil and gas identification in highly heterogeneous and anisotropic reservoirs. When using five-dimensional seismic data to identify and predict fault systems, the constitutive coordinate system of the underground medium (i.e., the natural orthogonal coordinate system with the underground medium as the research object) is usually inconsistent with the actual observation coordinate system. In other words, the observation orientation information in the five-dimensional data is not the true orientation of the underground medium, resulting in poor identification and prediction of underground reservoir fracture systems. Summary of the Invention

[0005] The embodiments of the present application provide a multi-directional seismic attribute tensor fusion method, apparatus, device and medium for achieving effective fusion of multi-directional seismic attributes, thereby improving the accuracy of fault system identification and prediction results.

[0006] In a first aspect, an embodiment of the present application provides a multi-azimuth seismic attribute tensor fusion method, the method comprising: Extract seismic attribute data volumes at multiple observation locations from five-dimensional seismic data, and calculate the seismic attribute tensor corresponding to each seismic attribute data volume at each observation location. Each seismic attribute tensor represents: the rate of change of the seismic attribute gradient at the corresponding observation location along three orthogonal directions in three-dimensional space; The values ​​of the elements with the same position in each seismic attribute tensor are added together to obtain a multi-directional seismic attribute tensor fusion operator; The sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator is calculated to obtain the fusion result of the multi-azimuth seismic attribute tensor.

[0007] In an optional embodiment, calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation orientation includes: Calculate the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, wherein the elements in each directional derivative vector represent: the first-order directional derivative of the seismic attribute at the corresponding observation azimuth along the three-dimensional space; Each directional derivative vector is decomposed by orthogonal projection to obtain each seismic attribute tensor.

[0008] In an optional embodiment, calculating the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth includes: The seismic attribute data volume at each observation direction is decomposed in the direction to obtain each directional derivative vector.

[0009] In an optional embodiment, the upper left corner element in each seismic attribute tensor represents: edge anomaly information of the geological body in the north-south direction in the geographic coordinate system; The central element in each seismic attribute tensor represents: the edge anomaly information of the geological body in the east-west direction in the geographic coordinate system; The lower right corner element of each seismic attribute tensor represents the change information of the stratum in the geographic coordinate system.

[0010] In an optional embodiment, the elements in each seismic attribute tensor are: the second-order partial differential of the seismic attribute at the corresponding observation orientation along the three-dimensional space coordinate axis; Then perform orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor, including: The finite difference method is used to approximately solve the second-order partial differentials and obtain the numerical values ​​of the elements in each seismic attribute tensor.

[0011] In an optional embodiment, after calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor, the method further includes: The fusion results are used to predict the fracture system.

[0012] In a second aspect, an embodiment of the present application further provides a multi-directional seismic attribute tensor fusion device, the device comprising: A processing module is used to extract seismic attribute data volumes at multiple observation positions from the five-dimensional seismic data, and calculate the seismic attribute tensor corresponding to the seismic attribute data volume at each observation position, wherein each seismic attribute tensor represents: the rate of change of the seismic attribute gradient at the corresponding observation position along three orthogonal directions in the three-dimensional space; The first fusion module is used to add the values ​​of the elements with the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator; The second fusion module is used to calculate the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor.

[0013] In an optional embodiment, when respectively calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation orientation, the processing module is further configured to: Calculate the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, wherein the elements in each directional derivative vector represent: the first-order directional derivative of the seismic attribute at the corresponding observation azimuth along the three-dimensional space; Each directional derivative vector is decomposed by orthogonal projection to obtain each seismic attribute tensor.

[0014] In an optional embodiment, when calculating the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, the processing module is further configured to: The seismic attribute data volume at each observation direction is decomposed in the direction to obtain each directional derivative vector.

[0015] In an optional embodiment, the upper left corner element in each seismic attribute tensor represents: edge anomaly information of the geological body in the north-south direction in the geographic coordinate system; The central element in each seismic attribute tensor represents: the edge anomaly information of the geological body in the east-west direction in the geographic coordinate system; The lower right corner element of each seismic attribute tensor represents the change information of the stratum in the geographic coordinate system.

[0016] In an optional embodiment, the elements in each seismic attribute tensor are: the second-order partial differential of the seismic attribute at the corresponding observation orientation along the three-dimensional space coordinate axis; When performing orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor, the processing module is also used to: The finite difference method is used to approximately solve the second-order partial differentials and obtain the numerical values ​​of the elements in each seismic attribute tensor.

[0017] In an optional embodiment, after calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor, the second fusion module is further configured to: The fusion results are used to predict the fracture system.

[0018] In a third aspect, an embodiment of the present application further provides an electronic device, including: processor; and Memory for storing programs, The program includes instructions, and when the instructions are executed by a processor, the processor executes the multi-azimuth seismic attribute tensor fusion method as described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present application further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the multi-azimuth seismic attribute tensor fusion method as described in the first aspect.

[0020] In a fifth aspect, the present application provides a computer program product, which, when called by a computer, enables the computer to execute the steps of the multi-azimuth seismic attribute tensor fusion method as described in the first aspect.

[0021] The beneficial effects of this application are as follows: In the multi-azimuth seismic attribute tensor fusion method provided in the embodiment of the present application, first, seismic attribute data bodies under multiple observation directions are extracted from five-dimensional seismic data, and the seismic attribute tensors corresponding to the seismic attribute data bodies under each observation direction are calculated respectively. Then, the values ​​of the elements with the same element position in each seismic attribute tensor are added to obtain a multi-azimuth seismic attribute tensor fusion operator. Finally, the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator is calculated to obtain the fusion result of the multi-azimuth seismic attribute tensor. In this way, since the sum of the main diagonal elements of the seismic attribute tensor in different coordinate systems is the same, that is, the first invariant of the seismic attribute tensor (that is, the sum of the main diagonal elements) is not affected by the selection of the observation coordinate system, and can directly reflect the true characteristics of the underground medium in the constitutive coordinate system. Therefore, the sum of the main diagonal elements in the multi-directional seismic attribute tensor fusion operator is calculated to obtain the fusion result of the multi-directional seismic attribute tensor, which eliminates the influence of the angle between the observation coordinate system and the constitutive coordinate system, makes full use of the observation azimuth information in the five-dimensional data, realizes the effective fusion of multi-directional seismic attribute information, reduces the uncertainty of the seismic attribute interpretation of multi-directional five-dimensional data, and the fracture details depicted by the fusion result are clearer and the fracture information is richer, which provides a new theoretical method and idea for the identification and prediction of oil and gas reservoir fracture systems, and improves the accuracy of the results of fracture system identification and prediction.

[0022] In addition, other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or may be understood by practicing the present application. The objectives and other advantages of the present application can be realized and obtained through the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described here are used to provide a further understanding of the present application, constitute a part of the present application, and do not constitute an improper limitation of the present application. In the drawings: Figure 1 A schematic diagram of an optional system architecture applicable to the embodiments of the present application; Figure 2 A schematic diagram of an implementation flow of a multi-azimuth seismic attribute tensor fusion method provided in an embodiment of the present application; Figure 3 Schematic diagram of underground media with vertical fractures in different coordinate systems provided in the embodiments of the present application; Figure 4 A schematic diagram of a three-dimensional forward model consisting of "sedimentary layer-weathering crust-buried hill interior" provided in an embodiment of the present application; Figure 5 A schematic diagram of a coherence property along a 0.15s time slice provided in an embodiment of the present application, wherein Figures (a) to (f) are schematic diagrams of the coherence property along a 0.15s time slice when the azimuth angles are 0°, 30°, 60°, 90°, 120°, and 150°, respectively; Figure 6 A schematic diagram of an advanced fault enhancement attribute along a 0.15s time slice provided in an embodiment of the present application, wherein Figures (a) to (f) are schematic diagrams of the advanced fault enhancement attribute along a 0.15s time slice at azimuth angles of 0°, 30°, 60°, 90°, 120°, and 150°, respectively; Figure 7 A comparison diagram of coherence properties along a 0.15s time slice under a forward model test provided in an embodiment of the present application, wherein Figure (a) is a schematic diagram of post-stack coherence properties, and Figure (b) is a schematic diagram of coherence property tensor parameters; Figure 8 A comparison diagram of advanced fault enhancement attributes along a 0.15s time slice under a forward model test provided in an embodiment of the present application, wherein Figure (a) is a schematic diagram of post-stack advanced fault enhancement attributes, and Figure (b) is a schematic diagram of advanced fault enhancement attribute tensor parameters; Figure 9 A schematic diagram of coherent attribute slices along a layer in a work area provided in an embodiment of the present application, wherein Figures (a) to (f) are schematic diagrams of coherent attribute slices along a layer when the azimuth angles are 0°, 30°, 60°, 90°, 120°, and 150°, respectively; Figure 10A schematic diagram of slices along a layer of advanced fault enhancement attributes within a work area provided in an embodiment of the present application, wherein Figures (a) to (f) are schematic diagrams of slices along a layer of advanced fault enhancement attributes at azimuth angles of 0°, 30°, 60°, 90°, 120°, and 150°, respectively; Figure 11 A comparison diagram of coherence attributes along layer slices in an actual work area test provided in an embodiment of the present application, wherein Figure (a) is a schematic diagram of post-stack coherence attributes, and Figure (b) is a schematic diagram of coherence attribute tensor parameters; Figure 12 A comparison diagram of advanced fault enhancement attributes along layer slices in an actual work area test provided in an embodiment of the present application, wherein Figure (a) is a schematic diagram of post-stack advanced fault enhancement attributes, and Figure (b) is a schematic diagram of advanced fault enhancement attribute tensor parameters; Figure 13 A schematic diagram of the imaging logging interpretation results of Well A provided in an embodiment of the present application; Figure 14 This is a partial magnified comparison diagram of the Well A region along a layer slice of advanced fault enhancement attributes under actual work area testing provided by an embodiment of the present application, wherein Figure (a) is a schematic diagram of the post-stack advanced fault enhancement attributes, and Figure (b) is a schematic diagram of the tensor parameters of the advanced fault enhancement attributes; Figure 15 A schematic structural diagram of a multi-azimuth seismic attribute tensor fusion device provided in an embodiment of the present application; Figure 16 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following describes embodiments of the present application in more detail with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of protection of the present application.

[0025] It should be understood that the various steps described in the method embodiments of the present application can be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0026] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "multiple" mentioned in this application are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] The following is a brief introduction to the design concept of the embodiment of this application: Underground reservoirs, having undergone multiple periods of sedimentation, diagenesis, and tectonic movement, commonly develop faults and fractures with a specific dominant orientation. These fault systems serve as important reservoir spaces and seepage pathways for oil and gas, controlling their generation and migration. Seismic exploration is a crucial tool for predicting oil and gas reservoirs. Using seismic data to identify and predict fault systems is key to characterizing oil and gas reservoirs. Seismic attributes, by mining information such as amplitude, frequency, absorption attenuation, and geometry contained in seismic data, can enhance abnormal information about target reservoirs and their geological structures. Seismic attributes are a crucial tool for effectively identifying oil and gas reservoirs and fault systems.

[0030] However, conventional attribute interpretation methods are often limited to 3D post-stack seismic data. Due to the narrow observation angle, the fracture system cannot be fully characterized, making it difficult to accurately identify oil and gas in reservoirs with strong heterogeneity and anisotropy. Thanks to the development of "two-width, one-height" seismic acquisition and processing technology, it is now possible to use 5D seismic data, which contains rich offset and azimuth information, to perform detailed descriptions and predictions of fracture systems in underground reservoirs.

[0031] However, the constitutive coordinate system of the underground medium (i.e., the natural orthogonal coordinate system with the underground medium as the research object) is usually inconsistent with the actual observation coordinate system. In other words, the observation orientation information in the five-dimensional data is not the true orientation of the underground medium. The existing multi-azimuth seismic attribute fusion method has not yet mathematically and physically analyzed the mapping relationship between seismic attributes in different coordinate systems, resulting in an unclear mechanism for the fusion of multi-azimuth seismic attributes in different coordinate systems. As a result, directly using five-dimensional data to interpret the true information of the underground reservoir fracture system still faces great challenges.

[0032] In an optional implementation method, an embodiment of the present application proposes a multi-directional seismic attribute tensor fusion method, which may specifically include: first extracting seismic attribute data bodies under multiple observation directions from five-dimensional seismic data, and calculating the seismic attribute tensor corresponding to the seismic attribute data body under each observation direction, wherein each seismic attribute tensor represents: the rate of change of the gradient of the seismic attribute under the corresponding observation direction along the three orthogonal directions of the three-dimensional space, and then adding the values ​​of the elements with the same element position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator, and finally calculating the sum of the main diagonal elements in the multi-directional seismic attribute tensor fusion operator to obtain the fusion result of the multi-directional seismic attribute tensor.

[0033] Using the above method, since the sum of the main diagonal elements of the seismic attribute tensor in different coordinate systems is the same, that is, the first invariant of the seismic attribute tensor (that is, the sum of the main diagonal elements) is not affected by the selection of the observation coordinate system, and can directly reflect the true characteristics of the underground medium in the constitutive coordinate system. Therefore, the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator is calculated to obtain the fusion result of the multi-azimuth seismic attribute tensor, which eliminates the influence of the angle between the observation coordinate system and the constitutive coordinate system, fully utilizes the observation azimuth information in the five-dimensional data, realizes the effective fusion of multi-azimuth seismic attribute information, reduces the uncertainty of the seismic attribute interpretation of multi-azimuth five-dimensional data, and the fracture details depicted by the fusion result are clearer and the fracture information is richer, which provides a new theoretical method and idea for the identification and prediction of oil and gas reservoir fracture systems, and improves the accuracy of the results of fracture system identification and prediction.

[0034] In particular, the preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. In addition, the embodiments of the present application and the features in the embodiments may be combined with each other if there is no conflict.

[0035] See Figure 1As shown, it is a schematic diagram of an optional system architecture applicable to an embodiment of the present application. The system architecture may include: terminal devices (101a, 101b) and a server 102. The terminal devices (101a, 101b) and the server 102 can exchange information via a communication network, where the communication network may use wireless communication and wired communication. Exemplarily, the terminal devices (101a, 101b) can access the network and communicate with the server 102 via cellular mobile communication technology. The cellular mobile communication technology may include, for example, fifth-generation mobile networks (5G) technology or next-generation mobile communication technology. Optionally, the terminal devices (101a, 101b) can access the network and communicate with the server 102 via short-range wireless communication. The short-range wireless communication method may include, for example, wireless fidelity (Wi-Fi) technology.

[0036] The embodiment of the present application does not impose any restrictions on the number of communication devices involved in the above system architecture. For example, the above system architecture may include more terminal devices, or may include fewer terminal devices, or may also include other network devices. Figure 1 As shown, only the terminal devices (101a, 101b) and the server 102 are described as examples. The following briefly introduces the above communication devices and their respective functions.

[0037] The terminal device (101a, 101b) is a device that can provide voice and / or data connectivity to users and can be a device that supports wired and / or wireless connection.

[0038] Exemplarily, the terminal devices (101a, 101b) may include, but are not limited to: mobile phones, tablet computers, laptop computers, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminal devices in industrial control, wireless terminal devices in unmanned driving, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, or wireless terminal devices in smart homes, etc.

[0039] In addition, a relevant client can be installed on the terminal device (101a, 101b), and the client can be software, such as an application (APP), a browser, a short video software, etc., or a web page, a mini-program, etc.; it should be noted that the terminal device (101a, 101b) in the embodiment of the present application can enable the above-mentioned client related to the fusion of multi-directional seismic attributes to send the collected five-dimensional seismic data to the server 102, so as to perform subsequent method steps such as the fusion of multi-directional seismic attributes.

[0040] Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0041] The multi-azimuth seismic attribute tensor fusion method provided by the exemplary embodiment of the present application is described below in combination with the above-mentioned system architecture and with reference to the accompanying drawings. It should be noted that the above-mentioned system architecture is only shown to facilitate understanding of the spirit and principles of the present application, and the implementation of the present application is not limited in this respect.

[0042] See Figure 2 As shown, it is a schematic diagram of the implementation process of a multi-directional seismic attribute tensor fusion method provided in an embodiment of the present application. The execution subject is taken as an example by a server. The specific implementation process of the method is as follows: S20: extracting seismic attribute data volumes at multiple observation positions from the five-dimensional seismic data, and respectively calculating seismic attribute tensors corresponding to the seismic attribute data volumes at each observation position.

[0043] Among them, each seismic attribute tensor represents: the rate of change of the gradient of the seismic attribute at the corresponding observation direction along the three orthogonal directions of the three-dimensional space.

[0044] In the embodiment of the present application, five-dimensional seismic data is based on traditional three-dimensional seismic data (including three spatial coordinate dimensions of x, y, and z), and further incorporates two key observation parameters, offset and azimuth, to form a five-dimensional information system (x, y, z, offset, azimuth), wherein the three-dimensional spatial coordinates x and y represent the horizontal position of the surface observation point, and z represents the underground depth or earthquake travel time, which is used to locate the spatial distribution of underground geological bodies; the offset distance refers to the horizontal distance between the seismic wave excitation point and the receiving point, reflecting the difference in the length of the seismic wave propagation path. Seismic data with different offset distances contain response characteristics of different depths of the underground medium; the azimuth refers to the observation direction, which represents the angle between the seismic wave propagation direction and the reference direction (e.g., due north).

[0045] In an embodiment of the present application, seismic attribute data bodies at multiple observation positions are extracted from five-dimensional seismic data, and the following operations are performed for each of the multiple observation positions: a seismic attribute tensor corresponding to the seismic attribute data body at one observation position is calculated.

[0046] Among them, multiple observation directions include but are not limited to: 0°, 30°, 60°, 90°, 120° and 150°; seismic attributes reflect the distribution characteristics of underground geological bodies, and seismic attributes include: coherence attributes or advanced fault enhancement attributes, etc.; the seismic attribute data body is a three-dimensional data set containing seismic attribute information, with three-dimensional spatial coordinates (x, y, z), and is the specific carrier of seismic attributes in three-dimensional space.

[0047] Optionally, in an embodiment of the present application, a possible embodiment is provided for respectively calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation orientation, specifically performing the following operations: S200: Calculate the directional derivative vector corresponding to the seismic attribute data volume at each observation direction.

[0048] Among them, the elements in each directional derivative vector represent: the first-order directional derivative of the seismic attribute in the corresponding observation direction along the three-dimensional space.

[0049] In the embodiments of this application, seismic attributes highlight the distribution characteristics of geological bodies in three-dimensional space. Where fault systems develop, the values ​​of seismic attributes typically vary significantly. To characterize the edges of faults developing in different directions in three-dimensional space, the seismic attribute data volume at each observation orientation can be directional decomposed to obtain derivative vectors for each direction.

[0050] For example, assuming that a 3D seismic attribute data volume under an observation direction is A(x, y, z) ,but A(x, y, z) The directional derivative vector in three-dimensional space can be specifically expressed as follows: (1) in, g 1. g 2 and g 3 represent the first-order directional derivatives of the seismic attribute A along the x, y, and z axes, that is, the spatial variation rate of the seismic attribute A.

[0051] S201: Perform orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor.

[0052] In the embodiment of the present application, the seismic attribute tensor at an observation position can be specifically expressed as follows: (2) Among them, the elements in the earthquake attribute tensor are Indicates: earthquake attribute A along k The directional derivative of the axis is l The projection onto the x-axis can also be equivalent to the second-order partial differential of seismic attribute A along the x, y, and z axes in three-dimensional space, where the k-axis and the l-axis are any of the x, y, and z axes, respectively. In practical applications, the second-order partial differentials of seismic attributes cannot be directly measured. Finite difference methods can be used to approximate these second-order partial differentials and obtain the numerical values ​​of each element in the seismic attribute tensor. Specifically, the approximate solution is obtained by taking the differences of seismic attributes at different locations.

[0053] In the embodiment of the present application, the upper left corner element of each seismic attribute tensor represents: the edge anomaly information of the geological body in the north-south direction under the geographic coordinate system; the central element of each seismic attribute tensor represents: the edge anomaly information of the geological body in the east-west direction under the geographic coordinate system; the lower right corner element of each seismic attribute tensor represents: the change information of the stratum under the geographic coordinate system. In other words, the elements of the seismic attribute tensor are: T 11 Represents: edge anomaly information of geological bodies in the north-south direction in the geographic coordinate system, elements in the seismic attribute tensor T 22 Represents: edge anomaly information of geological bodies in the east-west direction in the geographic coordinate system, elements in the seismic attribute tensor T 33 Indicates: information on changes in strata under the geographic coordinate system.

[0054] In this way, the discontinuous spatial structure characteristics of seismic attributes can be enhanced based on the directional derivative vector.

[0055] In the examples of this application, when describing the subsurface medium, the first step is to select a coordinate system. Subsurface media generally exhibit anisotropy, and the anisotropy typically discussed is defined within the constitutive coordinate system. Seismic exploration forward and inverse problems are typically studied within the observational coordinate system. In practice, the constitutive coordinate system and the observational coordinate system may be at an angle, so a coordinate transformation matrix is ​​required to address the issue of misalignment.

[0056] Assume that the constitutive coordinate system is Indicates that its basis vector is , the observation coordinate system is oxyz Indicates that its basis vector is e i , then the direction cosine tensor between the basis vectors of the observation coordinate system and the constitutive coordinate system is The coordinate transformation matrix is ​​formed, which can be expressed as: (3) Among them, the subscript of the basis vector i The unit vector representing the coordinate axis in the rectangular coordinate system.

[0057] For underground media with vertical fracture systems, they can usually be equivalent to horizontal transverse isotropic media (HTI). Figure 3 As shown in the figure, it is a schematic diagram of the underground medium with vertical fractures under different coordinate systems in the embodiment of the present application. It is assumed that the angle between the x-axis of the observation coordinate system and the x-axis of the geographic coordinate system (where the x-axis is parallel to the north direction) is φ (i.e., earthquake observation orientation), the x-axis of the observation coordinate system and the constitutive coordinate system of The angle between the axes is φ sym , the z-axes of the three coordinate systems coincide. At this time, the coordinate transformation matrix between the constitutive coordinate system and the observation coordinate system can be expressed as: (4) For Figure 3 For the vertical fault reservoir shown in the figure, the seismic attribute tensors in the constitutive coordinate system and the observation coordinate system can be expressed as: (5) (6) In different coordinate systems, the matrix corresponding to the seismic attribute tensor obeys the Bond transformation of the matrix, that is: (7) Solving the simultaneous equations (4)-(7) yields: (8) By performing mathematical operations on Equation (8), it can be found that the seismic attribute tensor elements in different coordinate systems satisfy: (9) Equation (9) shows that the first invariant of the matrix of the seismic attribute tensor (i.e., the sum of the main diagonal elements) is not affected by the choice of the observation coordinate system and can directly reflect the true characteristics of the underground medium in the constitutive coordinate system.

[0058] S21: Add the values ​​of the elements with the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator.

[0059] In the embodiment of the present application, the multi-azimuth seismic attribute tensor fusion operator can be specifically expressed as follows: (10) in, m is the total number of multiple observation positions, φ k is the azimuth angle at the kth observation azimuth, and the multi-azimuth seismic attribute tensor fusion operator contains 9 components.

[0060] Alternatively, from formula (2), it can be seen that for underground media with a vertical fracture system, 、 and Therefore, the multi-azimuth seismic attribute tensor fusion operator in the embodiment of the present application can be further simplified into a 6-component matrix for representation.

[0061] In this way, due to the existence of anisotropy, there are differences in the seismic data of each observation direction, and the seismic attributes of different observation directions also have different abilities to identify underground fault systems with different strikes. In tensor analysis, the same elements of the tensor matrix can be subjected to algebraic operations. Therefore, the values ​​of the elements with the same position in each seismic attribute tensor are added together to obtain a multi-directional seismic attribute tensor fusion operator, which can effectively fuse the seismic attributes of different observation directions. The multi-directional seismic attribute tensor fusion operator integrates the advantageous information of each direction, thereby covering a wider range of fault strikes, avoiding the limitations of single-directional observations, and making the characterization of the fault system more complete.

[0062] S22: Calculate the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor.

[0063] In the embodiment of the present application, the multi-azimuth seismic attribute tensor fusion operator is calculated. C 11 + C 22 + C 33, and obtain the fusion result of multi-directional seismic attribute tensor.

[0064] Among them, the sum of the main diagonal elements in the multi-directional seismic attribute tensor fusion operator can also be called the first invariant in the multi-directional seismic attribute tensor fusion operator, and the fusion result of the multi-directional seismic attribute tensor can also be called the seismic attribute tensor parameter.

[0065] In the embodiment of the present application, the fusion result of the multi-azimuth seismic attribute tensor can be specifically expressed as follows: (11) Thus, from formula (9), we can see that under different coordinate systems T 11 、 T 22 and T 33 The sum of is equal, the fusion result calculated by formula (11) q σ The influence of the angle between the observation coordinate system and the constitutive coordinate system is eliminated, which shows that the fusion result makes full use of the azimuth information in the five-dimensional seismic data, can realize the effective fusion of multi-azimuth seismic attributes, and reduce the uncertainty of seismic attribute interpretation of multi-azimuth five-dimensional seismic data.

[0066] Furthermore, in an embodiment of the present application, after obtaining the fusion results of the multi-directional seismic attribute tensors, the fusion results are used to perform fault system prediction.

[0067] Optionally, in the embodiment of the present application, the fusion results can also be used for river channel identification. That is to say, the application scenarios of the fusion results in the embodiment of the present application are not limited.

[0068] In summary, in the multi-directional seismic attribute tensor fusion method of the example provided in the embodiment of the present application, first, the seismic attribute data bodies under multiple observation directions are extracted from the five-dimensional seismic data, and the seismic attribute tensors corresponding to the seismic attribute data bodies under each observation direction are calculated respectively. Then, the values ​​of the elements with the same element position in each seismic attribute tensor are added to obtain a multi-directional seismic attribute tensor fusion operator. Finally, the sum of the main diagonal elements in the multi-directional seismic attribute tensor fusion operator is calculated to obtain the fusion result of the multi-directional seismic attribute tensor. In this way, the influence of the angle between the observation coordinate system and the constitutive coordinate system is eliminated, the effective fusion of multi-directional seismic attribute information is achieved, and the accuracy of the results of fault system identification and prediction is improved.

[0069] In the embodiment of the present application, in order to further verify the feasibility and effectiveness of the multi-azimuth seismic attribute tensor fusion method proposed in the embodiment of the present application, the verification includes the following two aspects: forward model testing and actual work area testing.

[0070] To further illustrate the feasibility and effectiveness of this application, two examples are listed below: Example 1: Forward model test, see Figure 4-Figure 8 .

[0071] In order to verify the feasibility of the multi-azimuth seismic attribute tensor fusion method proposed in this application, a three-dimensional buried hill forward model with the characteristics of "sedimentary layer, weathering crust, and developed three-dimensional fracture network system" was designed. Figure 4 The figure shows a schematic diagram of a three-dimensional forward model consisting of a "sedimentary layer-weathering crust-buried hill interior" in an embodiment of the present application. The model contains 375 lines, 375 channels, and 276 sampling points per channel. Two major faults develop in the north-south and east-west directions of the model, and a hidden fault develops in the northeast-southwest direction. The distribution characteristics of the fractures in the buried hill interior are relatively complex. Based on the seismic equivalent medium theory, anisotropic parameters are calculated using the model's parameters such as the longitudinal wave velocity, shear wave velocity, density, and fracture density. These parameters are then used to synthesize an azimuthal seismic data set with six observation orientations (wherein the azimuth angles are 0°, 30°, 60°, 90°, 120°, and 150°, respectively).

[0072] Based on the synthetic azimuthal seismic dataset, the coherence attributes and advanced fault enhancement attributes of the azimuthal seismic dataset are extracted respectively. Figure 5 and Figure 6 The time slices of the coherence attributes and advanced fault enhancement attributes of the 6 observation positions of the 3D forward model at time 0.15s are shown respectively. Figure 5 (a)-(f) are schematic diagrams of the coherence properties along the 0.15s time slice when the azimuth angles are 0°, 30°, 60°, 90°, 120° and 150°, respectively. Figure 6 (a)-(f) in the figure are schematic diagrams of advanced fault enhancement attributes along a 0.15s time slice at azimuths of 0°, 30°, 60°, 90°, 120°, and 150°, respectively. Coherence attributes and advanced fault enhancement attributes are geometric attributes, which mainly use waveform discontinuities and other characteristics to reflect the geometric shape of geological structures and stratigraphic discontinuities. Figure 5 and Figure 6 It can be seen from the figure that due to the widespread development of fault systems in the weathering crust and buried hill interior of the model, there are obvious response differences in the seismic attribute slices of the six observation directions.

[0073] Based on the extracted coherence attributes and advanced fault enhancement attributes, fusion tests of multi-azimuth seismic attributes are carried out respectively, and the fusion results are compared with conventional post-stack attributes, such as Figure 7 and Figure 8 As shown, Figure 7 (a) is a schematic diagram of the post-stack coherence properties under the forward model test. Figure 7(b) is a schematic diagram of the coherent attribute tensor parameters under the performance model test. Figure 8 (a) Schematic diagram of the post-stack advanced fault enhancement properties under the model test. Figure 8 (b) Schematic diagram of the advanced fault enhancement attribute tensor parameters under the model test. Figure 5 and Figure 7 、 Figure 6 and Figure 8 It can be seen that the coherent attribute tensor parameters (i.e., the fusion result of multi-directional coherent attributes) and the advanced fault enhancement tensor parameters (i.e., the fusion result of multi-directional advanced fault enhancement attributes) retain the overall trend of post-stack attributes while integrating seismic attribute information from different observation directions. Compared with conventional post-stack attribute results, the fracture characteristics in the fusion results of the proposed method are clearer and contain multi-directional fracture information, which can more comprehensively describe the underground structural characteristics.

[0074] Example 2: Actual work area test, see Figures 9-14 .

[0075] To further validate the feasibility and effectiveness of the multi-azimuth seismic attribute tensor fusion method proposed in this application, an application test was conducted in a deep buried-hill fractured reservoir in eastern my country. This buried-hill reservoir features complex structure, multi-scale fractures, and significant heterogeneity and anisotropy. Due to its deep burial depth (over 3,500 meters), the seismic response exhibits characteristics such as weak to moderate, short-axis, and chaotic reflections, making reservoir seismic identification challenging. To suppress random noise and improve the signal-to-noise ratio of the seismic data, the amplitude-preserved azimuth observation data were stacked in azimuth using the equal coverage criterion. This data was then divided into six azimuth seismic data volumes, with stacked azimuth angles of 0°, 30°, 60°, 90°, 120°, and 150°, respectively.

[0076] Figure 9 and Figure 10 The coherent attributes and advanced fault enhancement attributes of the work area are shown in the slices along the layer, where Figure 9 (a)-(f) are schematic diagrams of coherence properties along the slice when the azimuth angles are 0°, 30°, 60°, 90°, 120° and 150° respectively. Figure 10 (a)-(f) are schematic diagrams of the slices of the advanced fault enhancement attributes along the layer at azimuths of 0°, 30°, 60°, 90°, 120° and 150°, respectively. It can be seen that there are obvious differences in the attribute slices at different observation azimuths. Then, the multi-azimuth seismic attribute tensor fusion method was applied, and the fusion results of the multi-azimuth seismic attribute tensor (i.e., seismic attribute tensor parameters) were compared with the conventional post-stack attributes, as shown in Figure 2. Figures 11 to 12 As shown, Figure 11(a) is a schematic diagram of the post-stack coherence properties under actual work area testing. Figure 11 (b) is a schematic diagram of the coherent attribute tensor parameters under actual work area testing. Figure 12 (a) is a schematic diagram of the post-stack advanced fault enhancement attributes under actual work area testing. Figure 12 (b) Schematic diagram of the advanced fault enhancement attribute tensor parameters under actual work area testing.

[0077] contrast Figure 9 and Figure 11 It can be seen that the fault system in the work area is densely distributed in a network shape, with an overall trend of northeast-southwest, and the fault system in the northern part of the work area is more developed. From the yellow arrows in the figure, it can be seen that there are significant differences in the coherent attributes in different directions. Compared with the post-stack coherent attributes, the multi-directional coherent attribute tensor parameters integrate the multi-directional coherent attribute information, so that the fault information contained in different directions can be enhanced, and the coherent attribute differences between different directions can also be retained, which can clearly indicate the information of a small fault with a nearly north-south trend in the southern part of the work area (as shown by the arrow in the figure).

[0078] contrast Figure 10 and Figure 12 It can be seen that compared with the post-stack advanced fault enhancement attributes, the multi-directional advanced fault enhancement attribute fusion results (i.e., advanced fault enhancement attribute tensor parameters) describe the underground fracture system through different observation directions, and the fracture information depicted is richer (as shown by the arrows in the figure). The advanced fault enhancement attribute tensor parameters not only integrate the fracture information from different observation directions, but also have a resolution significantly better than the post-stack attribute tensor parameter attributes and the attribute tensor parameter attributes of a single direction.

[0079] Figure 13 for Figure 12 The imaging logging interpretation results at the location of Well A and the statistical results of imaging logging show that high-conductivity fractures and faults are developed at Well A, and the strikes of high-conductivity fractures and faults are all in the northeast-southwest direction. Figure 14 This is a local enlarged image of the high-level fault enhancement attribute along the layer slice in the A well area. It can be seen that the post-stack high-level fault enhancement attribute is difficult to identify the fracture at the A well location, while the high-level fault enhancement attribute tensor parameter indicates the fault at the A well location, and the predicted fault system strikes in the northeast-southwest direction, which is consistent with the Figure 13 The results of the imaging logging interpretation in the 2016-07-18 are consistent, which confirms the feasibility and effectiveness of the prediction results of the multi-azimuth seismic attribute tensor fusion method.

[0080] Furthermore, based on the same technical concept, the embodiment of the present application provides a multi-directional seismic attribute tensor fusion device, which is used to implement the above-mentioned method flow of the embodiment of the present application. Figure 15As shown, the multi-azimuth seismic attribute tensor fusion device 1500 may include: a processing module 1501, a first fusion module 1502 and a second fusion module 1503, wherein: Processing module 1501 is used to extract seismic attribute data volumes at multiple observation locations from the five-dimensional seismic data, and calculate the seismic attribute tensor corresponding to the seismic attribute data volume at each observation location, wherein each seismic attribute tensor represents: the rate of change of the seismic attribute gradient at the corresponding observation location along three orthogonal directions in the three-dimensional space; The first fusion module 1502 is used to add the values ​​of the elements with the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator; The second fusion module 1503 is used to calculate the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor.

[0081] In an optional embodiment, when respectively calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation orientation, the processing module 1501 is further configured to: Calculate the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, wherein the elements in each directional derivative vector represent: the first-order directional derivative of the seismic attribute at the corresponding observation azimuth along the three-dimensional space; Each directional derivative vector is decomposed by orthogonal projection to obtain each seismic attribute tensor.

[0082] In an optional embodiment, when calculating the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, the processing module 1501 is further configured to: The seismic attribute data volume at each observation direction is decomposed in the direction to obtain each directional derivative vector.

[0083] In an optional embodiment, the upper left corner element in each seismic attribute tensor represents: edge anomaly information of the geological body in the north-south direction in the geographic coordinate system; The central element in each seismic attribute tensor represents: the edge anomaly information of the geological body in the east-west direction in the geographic coordinate system; The lower right corner element of each seismic attribute tensor represents the change information of the stratum in the geographic coordinate system.

[0084] In an optional embodiment, the elements in each seismic attribute tensor are: the second-order partial differential of the seismic attribute at the corresponding observation orientation along the three-dimensional space coordinate axis; When performing orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor, the processing module 1501 is further used to: The finite difference method is used to approximately solve the second-order partial differentials and obtain the numerical values ​​of the elements in each seismic attribute tensor.

[0085] In an optional embodiment, after calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor, the second fusion module 1503 is further configured to: The fusion results are used to predict the fracture system.

[0086] Based on the description of the above method embodiment and apparatus embodiment, the exemplary embodiments of the present invention further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and when executed by the at least one processor, the computer program causes the electronic device to perform a method according to an embodiment of the present invention.

[0087] An embodiment of the present application further provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute a method according to an embodiment of the present application.

[0088] An embodiment of the present application further provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present application.

[0089] See Figure 16 As shown, the structural block diagram of the electronic device 1600 that can be used as the server or client of the present application will now be described, which is an example of a hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0090] like Figure 16As shown, electronic device 1600 includes a computing unit 1601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1602 or a computer program loaded from a storage unit 1608 into a random access memory (RAM) 1603. RAM 1603 may also store various programs and data required for the operation of device 1600. Computing unit 1601, ROM 1602, and RAM 1603 are interconnected via a bus 1604. An input / output (I / O) interface 1605 is also connected to bus 1604.

[0091] Multiple components within electronic device 1600 are connected to I / O interface 1605, including an input unit 1606, an output unit 1607, a storage unit 1608, and a communication unit 1609. Input unit 1606 can be any type of device capable of inputting information into electronic device 1600. Input unit 1606 can receive input numeric or character information and generate key input signals related to user settings and / or function control of the electronic device. Output unit 1607 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 1608 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 1609 allows electronic device 1600 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or chipset, such as a Bluetooth device, a WiFi device, a Worldwide Interoperability for Microwave Access (WiMax) device, a cellular communication device, and / or the like.

[0092] Computing unit 1601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing unit 1601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 1601 performs the various methods and processes described above. For example, in some embodiments, the multi-azimuth seismic attribute tensor fusion method described above can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1608. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 1600 via ROM 1602 and / or communication unit 1609. In some embodiments, computing unit 1601 can be configured to perform the multi-azimuth seismic attribute tensor fusion method described above by any other suitable means (e.g., via firmware).

[0093] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM) or flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receives machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0098] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0099] Furthermore, it should be understood that what is disclosed above is merely a preferred embodiment of the present application and certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope covered by the present application.

Claims

1. A multi-azimuth seismic attribute tensor fusion method, characterized in that: include: Extract seismic attribute data volumes at multiple observation locations from five-dimensional seismic data, and calculate the seismic attribute tensor corresponding to each seismic attribute data volume at each observation location. Each seismic attribute tensor represents: the rate of change of the seismic attribute gradient at the corresponding observation location along three orthogonal directions in three-dimensional space; Adding the values ​​of elements with the same element position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator; The sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator is calculated to obtain a fusion result of the multi-azimuth seismic attribute tensor.

2. The method according to claim 1, wherein The method of respectively calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation position includes: Calculating the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, wherein the elements in each directional derivative vector represent: the first-order directional derivative of the seismic attribute at the corresponding observation azimuth along the three-dimensional space; Perform orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor.

3. The method according to claim 1, wherein The calculating of the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth includes: Directional decomposition is performed on the seismic attribute data volume at each observation azimuth to obtain each directional derivative vector.

4. The method according to claim 1, wherein The upper left corner element of each seismic attribute tensor represents: edge anomaly information of the geological body in the north-south direction in the geographic coordinate system; The central element in each seismic attribute tensor represents: edge anomaly information of the geological body in the east-west direction in the geographic coordinate system; The lower right corner element of each seismic attribute tensor represents: the change information of the stratum in the geographic coordinate system.

5. The method according to claim 2, wherein The elements in each seismic attribute tensor are: the second-order partial differential of the seismic attribute at the corresponding observation position along the three-dimensional space coordinate axis; Then, performing orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor includes: The second-order partial differential is approximately solved by using a finite difference method to obtain the numerical value of the element in each seismic attribute tensor.

6. The method according to claim 1, wherein After calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor, the method further includes: The fusion results are used to predict the fracture system.

7. A multi-azimuth seismic attribute tensor fusion device, characterized in that: include: A processing module is used to extract seismic attribute data volumes at multiple observation positions from the five-dimensional seismic data, and calculate the seismic attribute tensor corresponding to the seismic attribute data volume at each observation position, wherein each seismic attribute tensor represents: the rate of change of the seismic attribute gradient at the corresponding observation position along three orthogonal directions in the three-dimensional space; a first fusion module, configured to add the values ​​of elements in the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator; The second fusion module is used to calculate the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain the fusion result of the multi-azimuth seismic attribute tensor.

8. The device according to claim 7, wherein When respectively calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation position, the processing module is further configured to: Calculating the directional derivative vector corresponding to the seismic attribute data volume at each observation azimuth, wherein the elements in each directional derivative vector represent: the first-order directional derivative of the seismic attribute at the corresponding observation azimuth along the three-dimensional space; Perform orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor.

9. An electronic device comprising: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

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