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

Abstract

The embodiment of the application provides a kind of multi-azimuth seismic attribute tensor fusion method, device, equipment and medium, it is related to oil and gas exploration and development technical field.The method comprises: extracting multiple observation azimuths under seismic attribute data body from five-dimensional seismic data, respectively calculating the corresponding seismic attribute tensor of each observation azimuth under seismic attribute data body, wherein each seismic attribute tensor represents: the change rate of the gradient of seismic attribute in corresponding observation azimuth along the three orthogonal directions of three-dimensional space;The numerical value of the element with the same element position in each seismic attribute tensor is added to obtain a multi-azimuth 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.In this way, the obtained fusion result eliminates the influence of the angle between the observation coordinate system and the constitutive coordinate system, and effectively fuses the multi-azimuth seismic attribute information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a multi-azimuth seismic attribute tensor fusion method, device, equipment and medium. BACKGROUND

[0002] At present, in the field of oil and gas exploration and development, the identification and prediction of fault systems by using seismic data 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, and seismic attributes are important tools for effectively identifying and predicting fault systems.

[0003] In the prior art, single-azimuth post-stack three-dimensional seismic data is usually used for identification and prediction of fault systems, or five-dimensional seismic data is directly used for identification and prediction of fault systems.

[0004] However, when using single-azimuth three-dimensional post-stack seismic data to identify and predict fault systems, there is a problem of incomplete description of fault systems due to narrow observation system angle, so it is difficult to meet the oil and gas identification accuracy requirements of strongly 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 taking the underground medium as the research object) is usually inconsistent with the actual observation coordinate system, that is, the observation azimuth information in the five-dimensional data is not the true azimuth of the underground medium, so the identification and prediction effect of the underground reservoir fault system is poor. SUMMARY

[0005] The embodiments of the present application provide a multi-azimuth seismic attribute tensor fusion method, device, equipment and medium, which can effectively fuse multi-azimuth seismic attributes, thereby improving the result accuracy of fault system identification and prediction.

[0006] In a first aspect, the embodiments of the present application provide a multi-azimuth seismic attribute tensor fusion method, which comprises:

[0007] extracting seismic attribute data volumes in multiple observation azimuths from five-dimensional seismic data, and respectively calculating seismic attribute tensors corresponding to the seismic attribute data volumes in each observation azimuth, wherein each seismic attribute tensor represents the variation rate of the gradient of the seismic attribute in the corresponding observation azimuth along three orthogonal directions in three-dimensional space;

[0008] adding the values of elements with the same position in each seismic attribute tensor to obtain a multi-azimuth seismic attribute tensor fusion operator;

[0009] 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.

[0010] In an optional embodiment, the seismic attribute tensor corresponding to the seismic attribute data volume at each observation direction is calculated respectively, including:

[0011] The directional derivative vector corresponding to the seismic attribute data volume at each observation direction is calculated, wherein the element in each directional derivative vector represents the first-order directional derivative of the seismic attribute at the corresponding observation direction along the three-dimensional space;

[0012] Each directional derivative vector is orthogonally projected and decomposed to obtain each seismic attribute tensor.

[0013] In an optional embodiment, the directional derivative vector corresponding to the seismic attribute data volume at each observation direction is calculated, including:

[0014] The seismic attribute data volume at each observation direction is directionally decomposed to obtain each directional derivative vector.

[0015] In an optional embodiment, the upper-left element in each seismic attribute tensor represents the edge anomaly information of the geological body in the north-south direction under the geographic coordinate system;

[0016] The center element in each seismic attribute tensor represents the edge anomaly information of the geological body in the east-west direction under the geographic coordinate system;

[0017] The right-bottom element in each seismic attribute tensor represents the change information of the stratum under the geographic coordinate system.

[0018] In an optional embodiment, the element in each seismic attribute tensor is the second-order partial derivative of the seismic attribute at the corresponding observation direction along the three-dimensional space coordinate axis;

[0019] Then, each directional derivative vector is orthogonally projected and decomposed to obtain each seismic attribute tensor, including:

[0020] The second-order partial derivative is approximately solved by using the finite difference method to obtain the numerical value of the element in each seismic attribute tensor.

[0021] In an optional embodiment, after 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, the method further includes:

[0022] The fusion result is used to perform fracture system prediction.

[0023] In a second aspect, the embodiments of the present application further provide a multi-directional seismic attribute tensor fusion device, including:

[0024] The processing module is configured to extract a plurality of seismic attribute data volumes at different observation directions from the five-dimensional seismic data, and calculate a seismic attribute tensor corresponding to each seismic attribute data volume at each observation direction, wherein each seismic attribute tensor represents a rate of change of a gradient of a seismic attribute at the corresponding observation direction along three orthogonal directions in a three-dimensional space.

[0025] The first fusion module is configured to add values of elements with the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator.

[0026] The second fusion module is configured to calculate a sum of main diagonal elements in the multi-directional seismic attribute tensor fusion operator to obtain a fusion result of the multi-directional seismic attribute tensor.

[0027] In an optional embodiment, when the processing module calculates the seismic attribute tensor corresponding to each seismic attribute data volume at each observation direction, the processing module is further configured to:

[0028] calculate a directional derivative vector corresponding to each seismic attribute data volume at each observation direction, wherein each element in each directional derivative vector represents a first-order directional derivative of a seismic attribute at the corresponding observation direction along a three-dimensional space.

[0029] orthogonally project and decompose each directional derivative vector to obtain each seismic attribute tensor.

[0030] In an optional embodiment, when the processing module calculates the directional derivative vector corresponding to each seismic attribute data volume at each observation direction, the processing module is further configured to:

[0031] directionally decompose each seismic attribute data volume at each observation direction to obtain each directional derivative vector.

[0032] In an optional embodiment, a top-left element in each seismic attribute tensor represents edge abnormal information of a geological body in a north-south direction under a geographic coordinate system.

[0033] a center element in each seismic attribute tensor represents edge abnormal information of the geological body in an east-west direction under the geographic coordinate system.

[0034] a bottom-right element in each seismic attribute tensor represents variation information of a stratum under the geographic coordinate system.

[0035] In an optional embodiment, each element in each seismic attribute tensor is a second-order partial derivative of a seismic attribute at the corresponding observation direction along a three-dimensional space coordinate axis.

[0036] In an optional embodiment, when the processing module orthogonally projects and decomposes each directional derivative vector to obtain each seismic attribute tensor, the processing module is further configured to:

[0037] The finite difference method is used to approximately solve the second order partial derivatives, and the numerical value of each element in the seismic attribute tensor is obtained.

[0038] In an optional embodiment, after obtaining the fusion result of the multi-azimuth seismic attribute tensor by calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator, the second fusion module is further configured to:

[0039] The fusion result is used to predict a fracture system.

[0040] In a third aspect, the embodiments of the present application further provide an electronic device, comprising:

[0041] a processor; and

[0042] a memory for storing programs,

[0043] The programs include instructions, which, when executed by the processor, cause the processor to perform the multi-azimuth seismic attribute tensor fusion method according to the first aspect.

[0044] In a fourth aspect, the embodiments of the present application further provide a non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the multi-azimuth seismic attribute tensor fusion method according to the first aspect.

[0045] In a fifth aspect, the present application provides a computer program product, which, when invoked by a computer, causes the computer to perform the steps of the multi-azimuth seismic attribute tensor fusion method according to the first aspect.

[0046] The present application has the following beneficial effects:

[0047] In the multi-azimuth seismic attribute tensor fusion method provided in the embodiments of the present application, first, seismic attribute data volumes in multiple observation azimuths are extracted from five-dimensional seismic data, seismic attribute tensors corresponding to the seismic attribute data volumes in each observation azimuth are calculated respectively, then the values of elements with the same position in each seismic attribute tensor are added to obtain a multi-azimuth seismic attribute tensor fusion operator, and 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 tensors in different coordinate systems is the same, that is, the first invariant (i.e., the sum of the main diagonal elements) of the seismic attribute tensor is not affected by the selection of the observation coordinate system, and can directly reflect the real characteristics of the underground medium in the constitutive coordinate system, therefore, the fusion result of the multi-azimuth seismic attribute tensor obtained by calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator 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 multi-azimuth five-dimensional data seismic attribute interpretation, and the fusion result describes the fracture details more clearly and the fracture information is more abundant, thereby providing a new theoretical method and idea for oil and gas reservoir fracture system identification and prediction, and improving the result accuracy of fracture system identification and prediction.

[0048] Furthermore, other features and advantages of the present application will be set forth in the following specification, and in part will be apparent from the description, or can be learned by practice of the present application. The purposes and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings described here are used to provide further understanding of the present application, and form a part of the present application. They do not constitute an improper limitation on the present application. In the drawings:

[0050] Figure 1 An optional system architecture schematic diagram applicable to the embodiments of the present application;

[0051] Figure 2 An implementation flow schematic diagram of the multi-azimuth seismic attribute tensor fusion method provided in the embodiments of the present application;

[0052] Figure 3 A schematic diagram of an underground medium developing a vertical fracture in different coordinate systems provided in the embodiments of the present application;

[0053] Figure 4A schematic diagram of a three-dimensional forward model composed of a "depositional layer-weathered crust-buried mountain interior" provided for an embodiment of the present application;

[0054] Figure 5 A schematic diagram of coherent attributes along a 0.15s time slice provided for an embodiment of the present application, wherein Fig. (a)-Fig. (f) are schematic diagrams of coherent attributes along a 0.15s time slice when the azimuth angle is 0°, 30°, 60°, 90°, 120° and 150°, respectively;

[0055] Figure 6 A schematic diagram of advanced fault-enhanced attributes along a 0.15s time slice provided for an embodiment of the present application, wherein Fig. (a)-Fig. (f) are schematic diagrams of advanced fault-enhanced attributes along a 0.15s time slice when the azimuth angle is 0°, 30°, 60°, 90°, 120° and 150°, respectively;

[0056] Figure 7 A comparison diagram of coherent attributes along a 0.15s time slice under a forward model test provided for an embodiment of the present application, wherein Fig. (a) is a schematic diagram of post-stack coherent attributes, and Fig. (b) is a schematic diagram of coherent attribute tensor parameters;

[0057] Figure 8 A comparison diagram of advanced fault-enhanced attributes along a 0.15s time slice under a forward model test provided for an embodiment of the present application, wherein Fig. (a) is a schematic diagram of post-stack advanced fault-enhanced attributes, and Fig. (b) is a schematic diagram of advanced fault-enhanced attribute tensor parameters;

[0058] Figure 9 A schematic diagram of coherent attributes along a layer slice in a work area provided for an embodiment of the present application, wherein Fig. (a)-Fig. (f) are schematic diagrams of coherent attributes along a layer slice when the azimuth angle is 0°, 30°, 60°, 90°, 120° and 150°, respectively;

[0059] Figure 10 A schematic diagram of advanced fault-enhanced attributes along a layer slice in a work area provided for an embodiment of the present application, wherein Fig. (a)-Fig. (f) are schematic diagrams of advanced fault-enhanced attributes along a layer slice when the azimuth angle is 0°, 30°, 60°, 90°, 120° and 150°, respectively;

[0060] Figure 11 A comparison diagram of coherent attributes along a layer slice under a practical work area test provided for an embodiment of the present application, wherein Fig. (a) is a schematic diagram of post-stack coherent attributes, and Fig. (b) is a schematic diagram of coherent attribute tensor parameters;

[0061] Figure 12A contrast chart of a high-level fault enhancement attribute along a layer slice under actual work area testing provided by an embodiment of the present application, wherein figure (a) is a schematic diagram of a post-stack high-level fault enhancement attribute, and figure (b) is a schematic diagram of a high-level fault enhancement attribute tensor parameter;

[0062] Figure 13 A schematic diagram of an A well imaging logging interpretation result provided by an embodiment of the present application;

[0063] Figure 14 A local enlarged contrast chart of an A well area of a high-level fault enhancement attribute along a layer slice under actual work area testing provided by an embodiment of the present application, wherein figure (a) is a schematic diagram of a post-stack high-level fault enhancement attribute, and figure (b) is a schematic diagram of a high-level fault enhancement attribute tensor parameter;

[0064] Figure 15 A structural schematic diagram of a multi-azimuth seismic attribute tensor fusion device provided by an embodiment of the present application;

[0065] Figure 16 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0066] Embodiments of the present application will be described in more detail by referring to the attached drawings. Although certain embodiments of the present application are shown in the drawings, it is understood that the present application can be implemented in various forms and should not be interpreted as being limited to the embodiments set forth herein, but rather these embodiments are provided so as to more thoroughly and completely understand the present application. It is understood that the drawings and embodiments of the present application are for exemplary purposes only and are not intended to limit the scope of protection of the present application.

[0067] It should be understood that each of the steps recited in the method embodiments of the present application can be executed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0068] The term "comprising" and variations thereof as used in the present application are open-ended, that is, "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Related definitions are given throughout the description. It should be noted that the concepts mentioned in the present application are merely used to distinguish different devices, modules or units, and are not intended to limit the functions performed by these devices, modules or units in order or interdependence.

[0069] It should be noted that the modification of "one" and "multiple" mentioned in the present application is illustrative but not restrictive, and those skilled in the art should understand that "one or more" should be understood unless the context clearly indicates otherwise.

[0070] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present application are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0071] The design idea of the embodiments of the present application is briefly introduced as follows:

[0072] The underground reservoirs have experienced multiple stages of sedimentation burial depth, diagenetic process and tectonic movement, and generally develop faults and fractures with a certain main alignment direction. These fault systems, as important reservoir spaces and seepage channels for oil and gas, control the generation and migration of oil and gas. Seismic exploration is an important means for oil and gas reservoir prediction, and the identification and prediction of fault systems using seismic data are 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 the amplitude, frequency, absorption attenuation, geometry and other information contained in seismic data. Seismic attributes are important tools for effectively identifying oil and gas reservoirs and fault systems.

[0073] However, conventional attribute interpretation methods are mostly limited to three-dimensional post-stack seismic data, and there is a problem of incomplete description of fault systems due to narrow observation system angle, which is difficult to meet the oil and gas identification accuracy requirements of strongly heterogeneous and anisotropic reservoirs. Thanks to the development of "two wide and one high" seismic acquisition and processing technology, it is possible to use five-dimensional seismic data containing rich offset and azimuth information to carry out fine description and prediction of underground reservoir fault systems.

[0074] However, the constitutive coordinate system of the underground medium (i.e. the natural orthogonal coordinate system taking the underground medium as the research object) is usually inconsistent with the actual observation coordinate system, that is, the observation azimuth information in the five-dimensional data is not the true azimuth of the underground medium. The existing multi-azimuth seismic attribute fusion method has not analyzed the mapping relationship between seismic attributes in different coordinate systems from the mathematical and physical aspects, resulting in unclear multi-azimuth seismic attribute fusion mechanism in different coordinate systems, and leading to great challenges in directly using five-dimensional data to interpret the true information of underground reservoir fault systems.

[0075] In an optional implementation, the embodiment of the present application provides a multi-azimuth seismic attribute tensor fusion method, which can specifically include the following steps: first, extracting a plurality of seismic attribute data volumes in a plurality of observation azimuths from five-dimensional seismic data, and respectively calculating a seismic attribute tensor corresponding to each seismic attribute data volume in each observation azimuth, wherein each seismic attribute tensor represents a variation rate of a gradient of a seismic attribute in a corresponding observation azimuth along three orthogonal directions in a three-dimensional space; then adding values of elements with the same position in each seismic attribute tensor to obtain a multi-azimuth seismic attribute tensor fusion operator; and finally calculating a sum of main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator to obtain a fusion result of the multi-azimuth seismic attribute tensor.

[0076] In the above manner, 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 (i.e., the sum of the main diagonal elements) of the seismic attribute tensor is not affected by the selection of the observation coordinate system, and can directly reflect the real characteristics of the underground medium in the constitutive coordinate system, therefore, the fusion result of the multi-azimuth seismic attribute tensor obtained by calculating the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator 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 effective fusion of multi-azimuth seismic attribute information, reduces the uncertainty of multi-azimuth five-dimensional data seismic attribute interpretation, and makes the fracture details described by the fusion result clearer and the fracture information richer, thereby providing a new theoretical method and idea for identification and prediction of an oil and gas reservoir fracture system, and improving the result accuracy of fracture system identification and prediction.

[0077] Particularly, the preferred embodiments of the present application are described below in conjunction with the accompanying drawings of the specification, and 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, and the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0078] Reference is made to Figure 1As shown, it is an optional system architecture diagram applicable to the embodiments of the present application, which can include: terminal devices (101a, 101b) and a server 102. The terminal devices (101a, 101b) and the server 102 can interact information through a communication network, wherein the communication network can adopt a communication mode including: a wireless communication mode and a wired communication mode. For example, the terminal devices (101a, 101b) can access the network through a cellular mobile communication technology and communicate with the server 102. Wherein the cellular mobile communication technology includes, for example, a 5th generation mobile communication (5th generation mobile networks, 5G) technology or a next generation mobile communication technology. Alternatively, the terminal devices (101a, 101b) can access the network through a short-range wireless communication mode and communicate with the server 102. Wherein the short-range wireless communication mode includes, for example, a wireless fidelity (wireless fidelity, Wi-Fi) technology.

[0079] The embodiments of the present application do not make any limitation on the number of communication devices involved in the above system architecture, for example, the above system architecture can include more terminal devices, or can include fewer terminal devices, or can also include other network devices. For example, Figure 1 As shown, only the terminal devices (101a, 101b) and the server 102 are described as an example, and the above communication devices and their respective functions are briefly introduced below.

[0080] The terminal devices (101a, 101b) are devices that can provide voice and / or data connectivity to users, which can be devices supporting wired and / or wireless connection modes.

[0081] For example, the terminal devices (101a, 101b) can include but are not limited to: mobile phones, tablet computers, notebook computers, palm computers, mobile internet devices (mobile internet device, MID), wearable devices, virtual reality (virtual reality, VR) devices, augmented reality (augmented reality, AR) devices, wireless terminal devices in industrial control, wireless terminal devices in unmanned driving, wireless terminal devices in smart power grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, or wireless terminal devices in smart homes, etc.

[0082] In addition, the terminal device (101a, 101b) can be installed with a related client, which can be software such as an application (application, APP), a browser, a short video software, etc., or a webpage, an applet, etc.; it should be noted that the terminal device (101a, 101b) in the embodiment of the present application can make the above-mentioned client related to the fusion of multi-directional seismic attributes send the collected five-dimensional seismic data to the server 102, so as to subsequently perform the method steps of the fusion of multi-directional seismic attributes.

[0083] The server 102 can be a stand-alone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (content delivery network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0084] The multi-directional seismic attribute tensor fusion method provided by the example embodiment of the present application will be described below in combination with the above-mentioned system architecture and in reference to the accompanying drawings. It should be noted that the above-mentioned system architecture is only shown for the purpose of facilitating the understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect.

[0085] Referring to Figure 2 As shown in the figure, it is an implementation flowchart of a multi-directional seismic attribute tensor fusion method provided by an embodiment of the present application, and the execution subject is taken as an example of a server. The specific implementation process of the method is as follows:

[0086] S20: Extracting seismic attribute data volumes in multiple observation directions from the five-dimensional seismic data, and calculating the seismic attribute tensors corresponding to the seismic attribute data volumes in each observation direction.

[0087] Each seismic attribute tensor represents the rate of change of the gradient of the seismic attribute in the corresponding observation direction along the three orthogonal directions in the three-dimensional space.

[0088] In the embodiments of the present application, the five-dimensional seismic data is further integrated with two key observation parameters, offset and azimuth, on the basis of the traditional three-dimensional seismic data (containing three spatial coordinate dimensions x, y and z), 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, z represents the underground depth or seismic travel time, and is used to locate the spatial distribution of the underground geological body; the offset refers to the horizontal distance between the excitation point and the receiving point of the seismic wave, and reflects the length difference of the seismic wave propagation path, and the seismic data of different offsets contains the response characteristics of different depths of the underground medium; the azimuth refers to the observation azimuth, and represents the included angle between the seismic wave propagation direction and the reference direction (for example, the north direction).

[0089] In the embodiments of the present application, the seismic attribute data body under each observation azimuth is calculated by performing the following operations on the seismic attribute data body under each observation azimuth in the plurality of observation azimuths: calculating the seismic attribute tensor corresponding to the seismic attribute data body under one observation azimuth.

[0090] The plurality of observation azimuths include but are not limited to 0°, 30°, 60°, 90°, 120° and 150°; the seismic attribute reflects the distribution characteristics of the underground geological body, and the seismic attribute is coherence attribute or advanced fault enhancement attribute; the seismic attribute data body is a three-dimensional data set containing seismic attribute information, has three-dimensional spatial coordinates (x, y, z), and is a specific carrier of the seismic attribute in the three-dimensional space.

[0091] Optionally, in the embodiments of the present application, a possible embodiment is provided for calculating the seismic attribute tensor corresponding to the seismic attribute data body under each observation azimuth, and the following operations are performed:

[0092] S200: calculating the directional derivative vector corresponding to the seismic attribute data body under each observation azimuth.

[0093] The elements in each directional derivative vector represent the first-order directional derivative of the seismic attribute under the corresponding observation azimuth along the three-dimensional space.

[0094] In the embodiments of the present application, the seismic attribute highlights the distribution characteristics of the three-dimensional spatial geological body, and the value of the seismic attribute usually changes obviously at the development of the fault system. In order to edge depict the faults developed in different directions in the three-dimensional space, the directional decomposition is performed on the seismic attribute data body under each observation azimuth to obtain each directional derivative vector.

[0095] For example, assuming that the three-dimensional seismic attribute data body under one observation azimuth is A(x, y, z) then A(x, y, z) The directional derivative vector in the three-dimensional space can be specifically represented as follows:

[0096] (1)

[0097] in, g 1. g 2 and g 3 represents the first directional derivative of seismic attribute A along the x, y, and z axes, respectively, which is the spatial variation rate of seismic attribute A.

[0098] S201: Perform orthogonal projection decomposition on each directional derivative vector to obtain each seismic attribute tensor.

[0099] In this embodiment of the application, the seismic attribute tensor at an observation azimuth can be specifically represented as follows:

[0100] (2)

[0101] Among them, the elements in the earthquake attribute tensor Indicates: Earthquake attribute A along k The directional derivative of the axis at l The projection onto the axes can also be equivalent to the second-order partial derivatives of seismic attribute A along the x, y, and z axes in three-dimensional space, where the k-axis and l-axis are any one of the x, y, and z axes. In practical applications, the second-order partial derivatives of seismic attributes cannot be directly measured. The finite difference method can be used to approximate the second-order partial derivatives, obtaining the values ​​of the elements in each seismic attribute tensor. Specifically, this is achieved by using the difference approximation of seismic attributes at different locations.

[0102] In this embodiment, the top-left 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 center 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; and the bottom-right element of each seismic attribute tensor represents the strata variation information under the geographic coordinate system. In other words, the elements in the seismic attribute tensor... T 11 Representation: Elements in the seismic attribute tensor representing the north-south edge anomaly information of a geological body in a geographic coordinate system. T 22 Representation: East-west edge anomaly information of geological bodies in a geographic coordinate system; elements in the seismic attribute tensor. T 33 This indicates information about changes in strata within a geographic coordinate system.

[0103] In this way, the discontinuous spatial structure characteristics of seismic properties can be enhanced based on the directional derivative vector.

[0104] In this embodiment of the application, when describing the subsurface medium, a coordinate system must first be selected. Subsurface media generally exhibit anisotropic characteristics, and the anisotropy discussed is usually defined in the constitutive coordinate system. The forward and inverse problems of seismic exploration are usually studied in the observation coordinate system. In reality, there may be a certain angle between the constitutive coordinate system and the observation coordinate system. Therefore, it is necessary to establish a coordinate transformation matrix to handle the problem of coordinate system non-coincidence.

[0105] Assuming the constitutive coordinate system uses It is indicated that its basis vector is The observation coordinate system is used oxyz It is indicated 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 The coordinate transformation matrix is ​​formed and can be represented as:

[0106] (3)

[0107] Wherein, the subscript of the basis vector i It represents the unit vector of the coordinate axes in a rectangular coordinate system.

[0108] For subsurface media with vertical fracture systems, they can generally be considered equivalent to horizontally transverse isotropic (HTI) media. See also... Figure 3 The diagram shown is a schematic representation of underground media with vertical fractures developed in different coordinate systems according to embodiments of this 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 due north) is... φ (i.e., the azimuth of the earthquake observation), the x-axis of the observation coordinate system and the constitutive coordinate system of The included angle between the axes is φ sym The z-axis of the three coordinate systems coincides. At this point, the coordinate transformation matrix between the constitutive coordinate system and the observation coordinate system can be expressed as:

[0109] (4)

[0110] For example Figure 3 The seismic attribute tensors of the vertically fractured reservoir shown can be denoted as follows in both the constitutive and observation coordinate systems:

[0111] (5)

[0112] (6)

[0113] In different coordinate systems, the matrix corresponding to the seismic attribute tensor is subjected to the Bond transformation of matrix, that is:

[0114] (7)

[0115] Solving the simultaneous equations (4)-(7) can obtain:

[0116] (8)

[0117] Through mathematical operation on equation (8), it can be found that the elements of the seismic attribute tensor in different coordinate systems satisfy:

[0118] (9)

[0119] Equation (9) shows that the first invariant (i.e., the sum of the main diagonal elements) of the matrix of the seismic attribute tensor is not affected by the selection of the observation coordinate system, and can directly reflect the real characteristics of the underground medium in the intrinsic coordinate system.

[0120] S21: adding the numerical values of the elements with the same position in each seismic attribute tensor to obtain a multi-azimuth seismic attribute tensor fusion operator.

[0121] In the embodiments of the present application, the multi-azimuth seismic attribute tensor fusion operator can be specifically represented as follows:

[0122] (10)

[0123] wherein, m is the total number of multiple observation azimuths, φ k is the azimuth angle under the kth observation azimuth, and the multi-azimuth seismic attribute tensor fusion operator contains 9 components.

[0124] Optionally, it can be known from equation (2) that for the underground medium developing a vertical fault system, satisfies , and Therefore, the multi-azimuth seismic attribute tensor fusion operator in the embodiments of the present application can be further simplified to be represented by a 6-component matrix.

[0125] Thus, due to the existence of anisotropy, there is a difference in seismic data of each observation direction, and the recognition ability of different observation direction seismic attributes to different fracture system of different strikes is also different. In the tensor analysis, the same elements of the tensor matrix can be algebraically operated, thus, the values of the elements with the same position in each seismic attribute tensor are added to obtain a multi-azimuth seismic attribute tensor fusion operator, which can effectively fuse the seismic attributes of different observation directions. The multi-azimuth seismic attribute tensor fusion operator integrates the advantages of each direction, thereby covering a wider range of fracture strikes, avoiding the limitations of single-azimuth observation, and making the description of the fracture system more complete.

[0126] 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.

[0127] In the embodiment of the application, the sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator is calculated as C 11 + C 22 + C 33 to obtain the fusion result of the multi-azimuth seismic attribute tensor.

[0128] The sum of the main diagonal elements in the multi-azimuth seismic attribute tensor fusion operator can also be referred to as the first invariant in the multi-azimuth seismic attribute tensor fusion operator, and the fusion result of the multi-azimuth seismic attribute tensor can also be referred to as a seismic attribute tensor parameter.

[0129] In the embodiment of the application, the fusion result of the multi-azimuth seismic attribute tensor can be specifically represented as follows:

[0130] (11)

[0131] Thus, from equation (9), the sum of T 11 , T 22 and T 33 is equal, and the fusion result calculated by equation (11) q σ eliminates the influence of the angle between the observation coordinate system and the constitutive coordinate system, which shows that the fusion result fully utilizes the azimuth information in the five-dimensional seismic data, can realize effective fusion of multi-azimuth seismic attributes, and reduces the uncertainty of multi-azimuth five-dimensional seismic data interpretation.

[0132] Further, in the embodiment of the application, after obtaining the fusion result of the multi-azimuth seismic attribute tensor, the fusion result is used for fracture system prediction.

[0133] Optionally, in the embodiments of the present application, the fusion result can also be used for river channel recognition, that is, the application scenario of the fusion result in the embodiments of the present application is not limited.

[0134] To sum up, in the multi-azimuth seismic attribute tensor fusion method provided in the examples of the embodiments of the present application, first, the seismic attribute data volumes in multiple observation azimuths are extracted from the five-dimensional seismic data, the seismic attribute tensors corresponding to the seismic attribute data volumes in each observation azimuth are calculated respectively, then the values of the elements with the same position in each seismic attribute tensor are added to obtain a multi-azimuth seismic attribute tensor fusion operator, and 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, the influence of the angle between the observation coordinate system and the constitutive coordinate system is eliminated, the effective fusion of multi-azimuth seismic attribute information is realized, and the result accuracy of the fracture system identification and prediction is improved.

[0135] In the embodiments 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 embodiments of the present application, the verification includes the following two aspects: forward modeling test and actual work area test.

[0136] To further illustrate the feasibility and effectiveness of the present application, two embodiments are listed below:

[0137] Embodiment 1: forward modeling test, see Figures 4-8 .

[0138] In order to verify the feasibility of the multi-azimuth seismic attribute tensor fusion method proposed in the present application, a three-dimensional buried hill forward model with the characteristics of “sedimentary layer, weathering crust, and development of three-dimensional joint system inside” is designed, as shown in Figure 4 , which is a schematic diagram of the three-dimensional forward model composed of “sedimentary layer-weathering crust-buried hill inside” in the embodiments of the present application. The model contains 375 lines, 375 channels, and 276 sampling points per channel. The north-south direction and the east-west direction of the model develop two large faults, the north-east-south-west direction develops a hidden fault, and the fracture distribution characteristics of the buried hill inside are relatively complex. Through the parameters of the longitudinal wave velocity, the transverse wave velocity, the density, the fracture density, etc. of the model, the anisotropy parameters are calculated based on the seismic equivalent medium theory, and the azimuthal seismic data sets with six observation azimuths (wherein the azimuth angles are 0°, 30°, 60°, 90°, 120° and 150° respectively) are synthesized through these parameters.

[0139] Based on the synthesized azimuthal seismic data sets, the coherence attribute and the advanced fault enhancement attribute of the azimuthal seismic data sets are extracted, Figure 5 and Figure 6The time slices of the coherence attribute and the advanced fault enhancement attribute of the 3D forward model at time 0.15s in six observation directions are respectively shown. Among them, Figure 5 Figures (a)-(f) in (a)-(f) are respectively schematic diagrams of the coherence attribute along the 0.15s time slice when the azimuth angle is 0°, 30°, 60°, 90°, 120° and 150°, Figure 6 Figures (a)-(f) in (a)-(f) are respectively schematic diagrams of the advanced fault enhancement attribute along the 0.15s time slice when the azimuth angle is 0°, 30°, 60°, 90°, 120° and 150°. The coherence attribute and the advanced fault enhancement attribute belong to the geometric attribute, which mainly reflects the geometric shape of the geological structure and the stratigraphic discontinuity by using the discontinuity and other characteristics of the waveform, Figure 5 and Figure 6 It can be seen from (a)-(f) that, due to the development of the fracture system in the weathering crust and the inner buried hill of the model, there are obvious response differences in the seismic attribute slices of the six observation directions.

[0140] Based on the extracted coherence attribute and advanced fault enhancement attribute, the fusion test of multi-azimuth seismic attribute is respectively carried out, and the fusion results are compared with the conventional post-stack attribute, as shown in (a)-(f), Figure 7 and Figure 8 Among them, Figure 7 (a) is a schematic diagram of the post-stack coherence attribute under the forward model test, Figure 7 (b) is a schematic diagram of the coherence attribute tensor parameter under the forward model test, Figure 8 (a) is a schematic diagram of the post-stack advanced fault enhancement attribute under the forward model test, Figure 8 (b) is a schematic diagram of the advanced fault enhancement attribute tensor parameter under the forward model test. By comparing Figure 5 and Figure 7 , Figure 6 and Figure 8 It can be seen that the coherence attribute tensor parameter (i.e., the fusion result of the multi-azimuth coherence attribute) and the advanced fault enhancement tensor parameter (i.e., the fusion result of the multi-azimuth advanced fault enhancement attribute) retain the overall trend of the post-stack attribute while integrating the seismic attribute information from different observation directions. Compared with the conventional post-stack attribute result, the fracture characteristics in the fusion result of the method are clearer and contain multi-azimuth fracture information, which can more comprehensively describe the underground structure characteristics.

[0141] Example 2: Actual work area test, see Figures 9-14 .

[0142] To further verify the feasibility and effectiveness of the multi-azimuth seismic attribute tensor fusion method proposed in this application, a deep buried hill fractured reservoir work area in eastern China is selected for application testing. The buried hill reservoir has complex structure, multi-scale fracture development, and obvious anisotropy and heterogeneity characteristics. Due to the deep burial depth (burial depth exceeding 3500 meters), the seismic response presents the characteristics of medium-weak, short axis and chaotic reflection, and the reservoir seismic identification is difficult. In order to suppress random noise and improve the signal-to-noise ratio of seismic data, the azimuthal observation data after amplitude preservation processing are stacked according to the equal coverage number rule, and they are divided into 6 azimuthal seismic data bodies, in which the stacked azimuth angles are 0°, 30°, 60°, 90°, 120° and 150° respectively.

[0143] Figure 9 and Figure 10 respectively show the layer slices of the coherent attribute and the advanced fault enhancement attribute in the work area, wherein, Figure 9 (a)-(f) in are respectively the schematic diagrams of the layer slices of the coherent attribute when the azimuth angles are 0°, 30°, 60°, 90°, 120° and 150°, Figure 10 (a)-(f) in are respectively the schematic diagrams of the layer slices of the advanced fault enhancement attribute when the azimuth angles are 0°, 30°, 60°, 90°, 120° and 150°, and it can be seen that there are obvious differences in the attribute slices of different observation azimuths. Then, the multi-azimuth seismic attribute tensor fusion method is applied respectively, and the fusion results (i.e., the seismic attribute tensor parameters) of the multi-azimuth seismic attribute tensor are compared with the conventional post-stack attributes, as shown in Figures 11 to 12 , wherein, Figure 11 (a) is a schematic diagram of the post-stack coherent attribute under actual work area testing, Figure 11 (b) is a schematic diagram of the coherent attribute tensor parameter under actual work area testing, Figure 12 (a) is a schematic diagram of the post-stack advanced fault enhancement attribute under actual work area testing, Figure 12 (b) is a schematic diagram of the advanced fault enhancement attribute tensor parameter under actual work area testing.

[0144] It can be seen from Figure 9 and Figure 11 that the fracture system in the work area is densely distributed in a network, and the overall trend is northeast-southwest, and the fracture system in the north of the work area is more developed. It can be seen from the yellow arrow in the figure that there are significant differences in the coherent attributes of different azimuths. Compared with the post-stack coherent attribute, the multi-azimuth coherent attribute tensor parameter fuses the multi-azimuth coherent attribute information, so that the fracture information jointly contained by different azimuths is enhanced, the differences between the coherent attributes of different azimuths are preserved, and a small fracture information with a nearly south-north trend in the south of the work area can be clearly indicated (as shown by the arrow in the figure).

[0145] ComparisonFigure 10 and Figure 12 It can be seen that, compared with the post-stack advanced fault enhancement attribute, the multi-azimuth advanced fault enhancement attribute fusion result (i.e., the advanced fault enhancement attribute tensor parameter) describes the underground fracture system through different observation azimuths, and the fracture information described is more abundant (as indicated by the arrows in the figure). The advanced fault enhancement attribute tensor parameter not only fuses the fracture information from different observation azimuths, but also has a resolution significantly better than that of the post-stack attribute tensor parameter attribute and the single-azimuth attribute tensor parameter attribute.

[0146] Figure 13 For Figure 12 The imaging logging statistical result shows that the high-conductivity fracture and the fault are developed at the position of Well A, and the strike of the high-conductivity fracture and the fault is the northeast-southwest direction. Figure 14 It can be seen from the local enlarged view of the advanced fault enhancement attribute along the layer slice of the Well A region that the post-stack advanced fault enhancement attribute is difficult to identify the fracture at the position of Well A, while the advanced fault enhancement attribute tensor parameter indicates the fracture at the position of Well A, and the predicted fracture system strike is the northeast-southwest direction, which is consistent with the imaging logging interpretation result in Figure 13 , thereby verifying the feasibility and effectiveness of the multi-azimuth seismic attribute tensor fusion method.

[0147] Further, based on the same technical concept, the embodiments of the present application provide a multi-azimuth seismic attribute tensor fusion device, which is used to implement the method flow of the embodiments of the present application. For example, as shown in Figure 15 , the multi-azimuth seismic attribute tensor fusion device 1500 can include a processing module 1501, a first fusion module 1502, and a second fusion module 1503, where:

[0148] The processing module 1501 is configured to extract seismic attribute data volumes in multiple observation azimuths from five-dimensional seismic data, and calculate a seismic attribute tensor corresponding to each seismic attribute data volume in each observation azimuth, where each seismic attribute tensor represents the variation rate of the gradient of the seismic attribute in the corresponding observation azimuth along three orthogonal directions in a three-dimensional space.

[0149] The first fusion module 1502 is configured to add the numerical values of the elements with the same position in each seismic attribute tensor to obtain a multi-azimuth seismic attribute tensor fusion operator.

[0150] The second fusion module 1503 is configured 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.

[0151] In an optional embodiment, when calculating the seismic attribute tensor corresponding to the seismic attribute data volume at each observation direction respectively, the processing module 1501 is further configured to:

[0152] calculate a directional derivative vector corresponding to the seismic attribute data volume at each observation direction, wherein an element in each directional derivative vector represents a first-order directional derivative of the seismic attribute at the corresponding observation direction along a three-dimensional space;

[0153] orthogonally project and decompose each directional derivative vector to obtain each seismic attribute tensor.

[0154] In an optional embodiment, when calculating the directional derivative vector corresponding to the seismic attribute data volume at each observation direction, the processing module 1501 is further configured to:

[0155] directionally decompose the seismic attribute data volume at each observation direction to obtain each directional derivative vector.

[0156] In an optional embodiment, a top-left element in each seismic attribute tensor represents edge anomaly information of the geological body in the north-south direction under a geographic coordinate system;

[0157] a center element in each seismic attribute tensor represents edge anomaly information of the geological body in the east-west direction under the geographic coordinate system;

[0158] a bottom-right element in each seismic attribute tensor represents variation information of the stratum under the geographic coordinate system.

[0159] In an optional embodiment, an element in each seismic attribute tensor is a second-order partial derivative of the seismic attribute at the corresponding observation direction along a three-dimensional space coordinate axis;

[0160] When orthogonally projecting and decomposing each directional derivative vector to obtain each seismic attribute tensor, the processing module 1501 is further configured to:

[0161] approximate and solve the second-order partial derivative by using a finite difference method to obtain a numerical value of the element in each seismic attribute tensor.

[0162] In an optional embodiment, after 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, the second fusion module 1503 is further configured to:

[0163] perform fracture system prediction by using the fusion result.

[0164] Based on the description of the method embodiments and the device embodiments, the exemplary embodiments of the present application further provide an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication. The memory stores a computer program capable of being executed by the at least one processor, and the computer program, when executed by the at least one processor, is configured to cause the electronic device to perform the method according to the embodiments of the present application.

[0165] The embodiments of the present application further provide a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is configured to cause the computer to perform the method according to the embodiments of the present application.

[0166] The embodiments of the present application further provide a computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer, is configured to cause the computer to perform the method according to the embodiments of the present application.

[0167] Referring to Figure 16 As shown in FIG. 16, a block diagram of an electronic device 1600 that can be used as a server or a 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 a wide variety of digital electronic computing devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computing devices. The electronic device can also represent a wide variety of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in FIG. 16, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0168] As Figure 16 shown in FIG. 16, the electronic device 1600 includes a computing unit 1601 that 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 into a random access memory (RAM) 1603 from a storage unit 1608. In the RAM 1603, various programs and data required for the operation of the device 1600 can also be stored. The computing unit 1601, the ROM 1602, and the RAM 1603 are connected to each other through a bus 1604. An input / output (I / O) interface 1605 is also connected to the bus 1604.

[0169] The various components in the electronic device 1600 are connected to the I / O interface 1605, including an input unit 1606, an output unit 1607, a storage unit 1608, and a communication unit 1609. The input unit 1606 can be any type of device that can input information to the electronic device 1600, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 1607 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1608 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 1609 allows the electronic device 1600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can 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.

[0170] The computing unit 1601 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the 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 appropriate processor, controller, microcontroller, and the like. The computing unit 1601 performs various methods and processes described above. For example, in some embodiments, the above-described multi-azimuth seismic attribute tensor fusion method can be implemented as a computer software program, which is tangibly embodied in a machine-readable medium, such as the storage unit 1608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 1600 via the ROM 1602 and / or the communication unit 1609. In some embodiments, the computing unit 1601 can be configured to perform the above-described multi-azimuth seismic attribute tensor fusion method by any other appropriate means, such as by means of firmware.

[0171] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The program code can be entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine or entirely on a remote machine or server.

[0172] In the context of the present application, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include one or more lines of electrical conductors, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] As used in the present application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0174] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, 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, speech, or tactile input.

[0175] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, 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 a local area network (LAN), a wide area network (WAN), and the Internet.

[0176] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0177] And, it should be understood that all the disclosed herein are only preferred embodiments of the application, and cannot be used to limit the scope of the application, therefore, any equivalent changes made on the basis of the claims of the application, still belong to the scope of the application.

Claims

1. A multi-azimuth seismic attribute tensor fusion method, characterized in that, The method comprises the following steps: extracting a plurality of seismic attribute data volumes under different observation directions from five-dimensional seismic data; calculating a directional derivative vector corresponding to each seismic attribute data volume under each observation direction, wherein each element in the directional derivative vector represents a first-order directional derivative of the seismic attribute under the corresponding observation direction along a three-dimensional space; orthogonal projection decomposing each directional derivative vector to obtain a seismic attribute tensor corresponding to each seismic attribute data volume under each observation direction, wherein each seismic attribute tensor represents a rate of change of the gradient of the seismic attribute under the corresponding observation direction along three orthogonal directions of the three-dimensional space; adding values of elements with the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator; calculating a sum of main diagonal elements in the multi-directional seismic attribute tensor fusion operator to obtain a fusion result of the multi-directional seismic attribute tensor.

2. The method of claim 1, wherein, The calculation of the directional derivative vector corresponding to each seismic attribute data volume under each observation direction comprises: directional decomposition of the seismic attribute data volume under each observation direction to obtain the directional derivative vector.

3. The method of claim 1, wherein, The upper-left element in each seismic attribute tensor represents edge anomaly information of a geological body in a north-south direction under a geographic coordinate system; the center element in each seismic attribute tensor represents edge anomaly information of the geological body in an east-west direction under the geographic coordinate system; the lower-right element in each seismic attribute tensor represents variation information of a stratum under the geographic coordinate system.

4. The method of claim 1, wherein, Each element in each seismic attribute tensor is a second-order partial derivative of the seismic attribute under the corresponding observation direction along a three-dimensional coordinate axis; The orthogonal projection decomposition of each directional derivative vector to obtain the seismic attribute tensor corresponding to each seismic attribute data volume under each observation direction comprises: approximate solving of the second-order partial derivative by using a finite difference method to obtain values of elements in the seismic attribute tensor.

5. The method of claim 1, wherein, After the calculation of 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, the method further comprises: fracture system prediction by using the fusion result.

6. A multi-azimuth seismic attribute tensor fusion apparatus, characterized in that, The method comprises the following steps: extracting a plurality of seismic attribute data volumes under different observation directions from five-dimensional seismic data by using a processing module; calculating a directional derivative vector corresponding to each seismic attribute data volume under each observation direction, wherein each element in the directional derivative vector represents a first-order directional derivative of the seismic attribute under the corresponding observation direction along a three-dimensional space; orthogonal projection decomposing each directional derivative vector to obtain a seismic attribute tensor corresponding to each seismic attribute data volume under each observation direction, wherein each seismic attribute tensor represents a rate of change of the gradient of the seismic attribute under the corresponding observation direction along three orthogonal directions of the three-dimensional space; a first fusion module is configured to add values of elements with the same position in each seismic attribute tensor to obtain a multi-directional seismic attribute tensor fusion operator; a second fusion module is configured to calculate a sum of main diagonal elements in the multi-directional seismic attribute tensor fusion operator to obtain a fusion result of the multi-directional seismic attribute tensor.

7. An electronic device comprising: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the method of any of claims 1-5. The computer instructions are for causing a computer to perform the method of any of claims 1-5.

8. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, ​

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