Pre-stack seismic multi-attribute fusion reservoir prediction method, device, equipment and medium

Abstract

The invention discloses a pre-stack seismic multi-attribute fusion reservoir prediction method. The method comprises the following steps: acquiring pre-stack seismic trace gather data and preprocessing the pre-stack seismic trace gather data; based on the reservoir sediment source direction of the target area, a seismic body F1 in the direction perpendicular to the reservoir sediment source direction, a seismic body F2 in the direction parallel to the reservoir sediment source direction and a seismic body R with the maximum incidence angle are screened out from the seismic body data; calculating the seismic amplitude attribute FM of the target layer by using the seismic body F1 and the seismic body F2, and calculating the seismic frequency attribute RM of the target layer by using the seismic body R; and carrying out attribute fusion calculation on the normalized seismic attributes by taking lithology and fluid characteristics at the well point of the target stratum as constraints so as to obtain a seismic fusion attribute FFR of the target stratum. The invention further discloses a device, equipment and a readable medium applying the method. According to the method, the azimuth angle information and the incidence angle information of the pre-stack seismic gather are comprehensively utilized, accurate reservoir identification is realized through a fusion algorithm, so that accurate development of petroleum resources can be guided, and the oilfield development effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoirs, and in particular, to a pre-stack seismic multi-attribute fusion reservoir prediction method, device, equipment and medium. Background Art

[0002] With the deepening of oil exploration and development, the targets of oil and gas exploration have become increasingly complex and concealed, and the exploration means have become increasingly diversified and refined. In recent years, with the progress of reservoir prediction technology, new theories and technical means have emerged in an endless stream. While the targets of oil and gas exploration are constantly evolving, geophysical exploration technology has also made great progress, especially in reservoir prediction based on seismic data. Seismic data is a commonly used data for studying reservoirs, and seismic attribute fusion is an effective means for predicting reservoirs, which is of great significance for improving the oil recovery rate and the development effect of oilfields. Currently, the commonly used attribute fusion means are: fusing based on the amplitude, phase, frequency, etc. of post-stack seismic. Due to the insufficient resolution caused by the signal superposition of post-stack seismic itself, it has a good effect on relatively thick reservoirs, but the accuracy for thin reservoirs is poor.

[0003] Therefore, there is a need to improve the pre-stack seismic multi-attribute fusion reservoir prediction method in the prior art. Summary of the Invention

[0004] In view of this, the purpose of the embodiments of the present invention is to propose a pre-stack seismic multi-attribute fusion reservoir prediction method, which comprehensively utilizes the azimuth information and incident angle information of pre-stack seismic gathers to improve the reservoir prediction accuracy and enhance the oilfield development effect.

[0005] Based on the above purpose, the embodiments of the present invention provide a pre-stack seismic multi-attribute fusion reservoir prediction method, including the following steps:

[0006] Obtain pre-stack seismic gather data, preprocess the azimuth and incident angle information in the seismic gather data to obtain n seismic volumes with different azimuths and m seismic volumes with different incident angles;

[0007] Based on the reservoir sediment source direction of the target area, respectively screen out the seismic volume F 1 in the vertical azimuth and the seismic volume F 2 in the parallel azimuth from the n seismic volumes with different azimuths, and screen out the seismic volume R with the maximum incident angle from the m seismic volumes with different incident angles;

[0008] Respectively use the seismic volume F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer, and use the seismic volume R to obtain the seismic frequency attribute RM of the target layer;

[0009] Normalize the seismic amplitude attribute and the seismic frequency attribute respectively. Constrained by the lithology and fluid characteristics at the well points of the target layer, perform attribute fusion calculation on the normalized seismic amplitude attribute and the seismic frequency attribute to obtain the seismic fusion attribute FFR of the target layer.

[0010] In some embodiments, obtaining the pre-stack seismic gather data includes:

[0011] Obtain the pre-stack seismic gather data through seismic acquisition. Each seismic trace in the seismic gather data contains azimuth and incident angle information.

[0012] In some embodiments, the preprocessing of the azimuth and incident angle information in the seismic gather data includes:

[0013] Statistically calculate the azimuth range α 1 ~α 2 and the incident angle range β 1 ~β 2 in the pre-stack seismic gather data. Establish spatial grid coordinates based on a specific step size. Stack and calculate the seismic traces at each spatial grid point at a specific azimuth interval to obtain n seismic volumes with different azimuths. Stack and calculate the seismic traces at each spatial grid point at a specific incident angle interval to obtain m seismic volumes with different incident angles.

[0014] In some embodiments, the specific azimuth interval is 10° - 20°, and the specific incident angle interval is 10° - 15°.

[0015] In some embodiments, the range of n is (α 2 -α 1 ) / 20~(α 2 -α 1 ) / 10, and the range of m is (β 2 -β 1 ) / 10~(α 2 -α 1 ) / 15.

[0016] In some embodiments, respectively using the seismic volumes F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer includes:

[0017] Using the seismic volume F 1 to obtain the seismic amplitude attribute F 1 M of the target layer, and using the seismic volume F 2 to obtain the seismic amplitude attribute F 2 M of the target layer.

[0018] In some embodiments, respectively normalizing the seismic amplitude attribute and the seismic frequency attribute includes:

[0019] Normalize the seismic amplitude attribute F 1 M, the seismic amplitude attribute F 2 M and the seismic frequency attribute RM respectively.

[0020] In some embodiments, performing attribute fusion calculation on the normalized seismic amplitude attribute and seismic frequency attribute to obtain the target layer seismic fusion attribute FFR includes:

[0021] Performing attribute fusion calculation on the normalized seismic amplitude attribute and seismic frequency attribute based on the convolutional neural network algorithm to obtain the target layer seismic fusion attribute FFR, and outputting a planar map of the reservoir prediction result of the target layer based on the seismic fusion attribute.

[0022] Another aspect of the embodiments of the present invention further provides a pre-stack seismic multi-attribute fusion reservoir prediction device, which is applied to the above method, and the device includes:

[0023] A data acquisition module, configured to acquire pre-stack seismic gather data, preprocess the azimuth and incident angle information in the seismic gather data, and obtain n seismic volumes with different azimuths and m seismic volumes with different incident angles;

[0024] A data screening module, configured to screen out the seismic volume F in the direction perpendicular to the reservoir sediment source direction from the n seismic volumes with different azimuths based on the reservoir sediment source direction of the target area 1 and the seismic volume F in the parallel direction 2 , and screen out the seismic volume R with the maximum incident angle from the m seismic volumes with different incident angles;

[0025] A first calculation module, configured to respectively use the seismic volume F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer, and use the seismic volume R to obtain the seismic frequency attribute RM of the target layer;

[0026] A second calculation module, configured to respectively normalize the seismic amplitude attribute and the seismic frequency attribute, and perform attribute fusion calculation on the normalized seismic amplitude attribute and seismic frequency attribute with the lithology and fluid characteristics at the well points of the target layer as constraints to obtain the target layer seismic fusion attribute FFR.

[0027] Yet another aspect of the embodiments of the present invention further provides a computer device, including: at least one processor; and a memory, where the memory stores computer instructions that can be run on the processor, and when the instructions are executed by the processor, the steps of the above method are implemented.

[0028] In another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above method steps.

[0029] The present invention has at least the following beneficial technical effects:

[0030] By selecting the azimuthal seismic and incident angle seismic that are most suitable for reservoir prediction in the method of the present invention, that is, the seismic bodies in the perpendicular azimuth and parallel azimuth to the reservoir sediment source direction, and the seismic body with the maximum incident angle; the method of the present invention also extracts amplitude attributes for azimuthal seismic (perpendicular azimuth and parallel azimuth) respectively, and extracts frequency attributes for the seismic body with the maximum incident angle. These two attributes can better reflect the reservoir characteristics. Then, normalization processing and attribute fusion are carried out on this basis. The advantage of doing this is that it can improve the accuracy of reservoir prediction, thereby more efficiently developing the oil and gas resources stored in the reservoir. This method can be applied to different types of oilfields, and the calculation results are reasonable and the accuracy is relatively high. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other embodiments can be obtained based on these drawings.

[0032] Figure 1 It is a flowchart of an embodiment of the pre-stack seismic multi-attribute fusion reservoir prediction method provided by the present invention;

[0033] Figure 2 It is the seismic bodies of 9 azimuths in the BSS block provided by an embodiment of the present invention;

[0034] Figure 3 It is the seismic bodies of 3 incident angles in the BSS block provided by an embodiment of the present invention;

[0035] Figure 4 It is the seismic amplitude attribute map of the target layer in the BSS block in the perpendicular azimuth to the reservoir sediment source direction provided by an embodiment of the present invention;

[0036] Figure 5 It is the seismic amplitude attribute map of the target layer in the parallel azimuth to the reservoir sediment source direction provided by an embodiment of the present invention;

[0037] Figure 6 It is the seismic frequency attribute map of the target layer in the BSS block provided by an embodiment of the present invention;

[0038] Figure 7Seismic fusion attribute map of the target layer of the BSS block provided by an embodiment of the present invention;

[0039] Figure 8 Reservoir prediction result map of the target layer of the BSS block provided by an embodiment of the present invention;

[0040] Figure 9 Schematic diagram of an embodiment of a pre-stack seismic multi-attribute fusion reservoir prediction device provided by the present invention;

[0041] Figure 10 Schematic diagram of an embodiment of a computer device provided by the present invention;

[0042] Figure 11 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Detailed implementation manners

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further elaborates on the embodiments of the present invention in detail with reference to specific embodiments and the accompanying drawings.

[0044] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above accompanying drawing descriptions are intended to cover non-exclusive inclusion; the terms "first", "second", etc. in the description and claims of the present invention or the above accompanying drawings are used to distinguish different objects and not to describe a specific order. The meaning of "a plurality" is two or more unless otherwise specifically defined.

[0045] As Figure 1 shown is a flowchart of an embodiment of a pre-stack seismic multi-attribute fusion reservoir prediction method provided by the present invention, including the following steps:

[0046] S1. Obtain pre-stack seismic gather data, preprocess the azimuth and incident angle information in the seismic gather data to obtain n seismic volumes with different azimuths and m seismic volumes with different incident angles;

[0047] S2. Based on the reservoir sediment source direction of the target area, respectively screen out the seismic volume F 1 in the vertical azimuth and the seismic volume F 2 in the parallel azimuth from the n seismic volumes with different azimuths, and screen out the seismic volume R with the maximum incident angle from the m seismic volumes with different incident angles;

[0048] S3. Respectively use the seismic volume F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer, and use the seismic volume R to obtain the seismic frequency attribute RM of the target layer;

[0049] S4. Normalize the seismic amplitude attribute and the seismic frequency attribute respectively, and with the lithology and fluid characteristics at the well points of the target layer as constraints, perform attribute fusion calculation on the normalized seismic amplitude attribute and the seismic frequency attribute to obtain the seismic fusion attribute FFR of the target layer.

[0050] Further, in S1, pre-stack seismic trace gather data is obtained through seismic acquisition, and each seismic trace contains azimuth and incident angle information. The preprocessing of the azimuth and incident angle information in the seismic trace gather data includes: statistically calculating the azimuth range α 1 ~α 2 and the incident angle range β 1 ~β 2 in the pre-stack seismic trace gather data, establishing a spatial grid coordinate based on a specific step size, performing stacking calculation on the seismic traces at each spatial grid point at a specific azimuth interval to obtain n seismic volumes with different azimuths, and performing stacking calculation on the seismic traces at each spatial grid point at a specific incident angle interval to obtain m seismic volumes with different incident angles. In some embodiments, the specific azimuth interval is 10° - 20°, and the specific incident angle interval is 10° - 15°. In some embodiments, the range of n is (α 2 - α 1 ) / 20~(α 2 - α 1 ) / 10, and the range of m is (β 2 - β 1 ) / 10~(α 2 - α 1 ) / 15.

[0051] Further, in S2, according to the reservoir sediment source direction of the target area, select the seismic volume F 1 (x, y, z) with the azimuth perpendicular to the reservoir sediment source direction from the n seismic volumes with different azimuths, and select the seismic volume F 2 (x, y, z) with the azimuth parallel to the reservoir sediment source direction from the n seismic volumes with different azimuths. Select the seismic volume R(x, y, z) with the maximum incident angle from the m seismic volumes with different incident angles, where x, y, z are spatial coordinates.

[0052] Further, in S3, use the above seismic volume F 1 (x, y, z) to obtain the seismic amplitude attribute F 1 M(x, y) of the target layer, use the above seismic volume F 2 (x, y, z) to obtain the seismic amplitude attribute F 2 M(x, y) of the target layer, and use the above seismic volume R(x, y, z) to obtain the seismic frequency attribute RM(x, y) of the target layer.

[0053] Further, in S4, the seismic amplitude attribute F 1 M, the seismic amplitude attribute F 2 M, and the seismic frequency attribute RM are normalized respectively. The normalized seismic amplitude attribute and seismic frequency attribute are used for attribute fusion calculation based on the convolutional neural network algorithm to obtain the seismic fusion attribute FFR of the target layer. Based on the seismic fusion attribute, a plane map of the reservoir prediction result of the target layer is output.

[0054] The following further explains the present invention with specific embodiments.

[0055] Select the BSS block of the Daqing Oilfield. Through seismic acquisition, prestack seismic gather data are obtained, and each seismic trace contains azimuth and incident angle information.

[0056] Statistics show that the azimuth range of the prestack seismic gathers in the BSS block is 0° to 180°, and the incident angle range is 0° to 45°.

[0057] The seismic traces at each spatial grid point in the BSS block are stacked and calculated at an azimuth interval of 200 to obtain 9 seismic volumes with different azimuths; the seismic traces at each spatial grid point are stacked and calculated at an incident angle interval of 15° to obtain 3 seismic volumes with different incident angles. Figure 2 are the seismic volumes of 9 azimuths, Figure 3 are the seismic volumes of 3 incident angles.

[0058] The sediment source direction of the reservoir in the BSS block is north-south. The seismic volume perpendicular to the sediment source direction of the reservoir is selected from the 9 seismic volumes with different azimuths, that is, the seismic volume of 80° to 100°; the seismic volume parallel to the sediment source direction of the reservoir is selected from the 9 seismic volumes with different azimuths, that is, the seismic volume of 0° to 20°. The seismic volume with the maximum incident angle is selected from the 3 seismic volumes with different incident angles.

[0059] Use the seismic volume of 80° to 100° perpendicular to the sediment source direction of the reservoir to obtain the seismic amplitude attribute F 1 M(x, y) of the target layer, use the seismic volume of 0° to 20° parallel to the sediment source direction of the reservoir to obtain the seismic amplitude attribute F 2 M(x, y) of the target layer, and use the seismic volume with the maximum incident angle to obtain the seismic frequency attribute RM(x, y) of the target layer. As Figure 4 shown is the seismic amplitude attribute map of the target layer in the BSS block perpendicular to the sediment source direction of the reservoir, Figure 5 shown is the seismic amplitude attribute map of the target layer in the BSS block parallel to the sediment source direction of the reservoir, Figure 6 shown is the seismic frequency attribute map of the target layer in the BSS block.

[0060] Respectively, for the seismic amplitude attribute F 1M(x, y), seismic amplitude attribute F 2 Normalize M(x, y) and seismic frequency attribute RM(x, y).

[0061] Taking the lithology and fluid characteristics at the well points of the target layer as constraints, perform attribute fusion calculation on the three normalized seismic attributes, and use the convolutional neural network algorithm to obtain the seismic fusion attribute FFR(x, y) of the target layer, thereby realizing reservoir prediction of the target layer. Figure 7 The figure shows the seismic fusion attribute map of the target layer in the BSS block. Figure 8 The figure shows the reservoir prediction result map of the target layer in the BSS block. The black part in the figure is the sand body part, that is, the oil-bearing reservoir, and the white part is the non-sand body, without oil. The result map output by the method of the present invention can quickly characterize the reservoir prediction result.

[0062] It should be particularly noted that each step in each embodiment of the above pre-stack seismic multi-attribute fusion reservoir prediction method can be mutually crossed, replaced, added, or deleted. Therefore, these reasonable permutation and combination transformations for the pre-stack seismic multi-attribute fusion reservoir prediction method should also fall within the protection scope of the present invention, and the protection scope of the present invention should not be limited to the embodiments.

[0063] Based on the above purpose, the second aspect of the embodiment of the present invention proposes a pre-stack seismic multi-attribute fusion reservoir prediction device. Figure 9 The figure shows a schematic diagram of an embodiment of the pre-stack seismic multi-attribute fusion reservoir prediction device provided by the present invention. As Figure 9 shown, the pre-stack seismic multi-attribute fusion reservoir prediction device of the embodiment of the present invention includes the following modules:

[0064] Data acquisition module 011, configured to acquire pre-stack seismic gather data, preprocess the azimuth and incident angle information in the seismic gather data, and obtain seismic volumes with n different azimuths and seismic volumes with m different incident angles;

[0065] Data screening module 012, configured to screen out the seismic volume F with the azimuth perpendicular to the reservoir sediment source direction and the seismic volume F with the parallel azimuth from the n seismic volumes with different azimuths based on the reservoir sediment source direction of the target area 1 and the seismic volume F with the parallel azimuth 2 , and screen out the seismic volume R with the maximum incident angle from the m seismic volumes with different incident angles;

[0066] The first calculation module 013, configured to respectively use the seismic volume F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer, and use the seismic volume R to obtain the seismic frequency attribute RM of the target layer;

[0067] The second calculation module 014 is configured to perform normalization processing on seismic amplitude attributes and seismic frequency attributes respectively, and perform attribute fusion calculation on the normalized seismic amplitude attributes and seismic frequency attributes with the lithology and fluid characteristics at the well points of the target layer as constraints to obtain the seismic fusion attribute FFR of the target layer.

[0068] For the above purpose, in the third aspect of the embodiments of the present invention, a computer device is proposed. Figure 10 Shown is a schematic diagram of an embodiment of the computer device provided by the present invention. As Figure 10 shown, the computer device of the embodiment of the present invention includes the following devices: at least one processor 021; and a memory 022, the memory 022 stores computer instructions 023 that can run on the processor, and when the instructions are executed by the processor, the steps of the above method are implemented.

[0069] The present invention also provides a computer-readable storage medium. Figure 11 Shown is a schematic diagram of an embodiment of the computer-readable storage medium provided by the present invention. As Figure 11 shown, the computer-readable storage medium 031 stores a computer program 032 that executes the above method when executed by a processor.

[0070] Finally, it should be noted that those of ordinary skill in the art can understand that all or part of the processes of implementing the above method embodiments can be completed by instructing relevant hardware through a computer program. The program of the pre-stack seismic multi-attribute fusion reservoir prediction method can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium of the program can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. The embodiments of the above computer program can achieve the same or similar effects as the corresponding foregoing method embodiments.

[0071] In addition, the method disclosed according to the embodiments of the present invention can also be implemented as a computer program executed by a processor, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the above functions defined in the method disclosed in the embodiments of the present invention are executed.

[0072] In addition, the above method steps and system units can also be implemented by using a controller and a computer-readable storage medium for storing a computer program that enables the controller to implement the above step or unit functions.

[0073] Those skilled in the art will also understand that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described generally in terms of their functionality. Whether such functionality is implemented as software or hardware depends upon the particular application and the design constraints imposed on the overall system. The functionality that can be implemented in various ways for each particular application by those skilled in the art, but such implementation decisions should not be construed as causing a departure from the scope of the disclosure of the embodiments of the present invention.

[0074] In one or more exemplary designs, the functionality can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality can be stored on or transmitted via a computer-readable medium as one or more instructions or code. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one location to another. The storage media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computer or a general purpose or special purpose processor. Additionally, any connection is properly termed a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0075] The above are exemplary embodiments of the disclosure of the present invention, but it should be noted that various changes and modifications can be made without departing from the scope of the disclosure of the embodiments of the present invention as defined by the claims. The functions, steps, and / or actions of the method claims according to the disclosed embodiments herein need not be performed in any particular order. Additionally, although the elements of the embodiments of the present invention disclosed herein may be described or claimed in a singular form, they can also be understood as plural unless explicitly limited to the singular.

[0076] It should be understood that, as used herein, unless the context clearly supports the exception, the singular form "a" is intended to also include the plural form. It should also be understood that the "and / or" used herein refers to any and all possible combinations of one or more of the associated listed items.

[0077] The serial numbers of the disclosed embodiments of the present invention above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0078] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc.

Claims

1. A pre-stack seismic multi-attribute fusion reservoir prediction method, characterized in that, it includes: Obtain pre-stack seismic gather data, preprocess the azimuth and incident angle information in the seismic gather data to obtain n seismic volumes with different azimuths and m seismic volumes with different incident angles; Based on the source direction of the reservoir sediments in the target area, the seismic volumes F perpendicular to the source direction of the reservoir sediments are selected from the n seismic volumes with different azimuths. 1 and the earthquake body F in parallel orientation 2 , select the seismic volume R with the largest incident angle from the m seismic volumes with different incident angles; Respectively utilize the seismic volume F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer, and utilize the seismic volume R to obtain the seismic frequency attribute RM of the target layer; Normalize the seismic amplitude attribute and the seismic frequency attribute respectively, and perform attribute fusion calculation on the normalized seismic amplitude attribute and the seismic frequency attribute with the lithology and fluid characteristics at the well points of the target layer as constraints to obtain the seismic fusion attribute FFR of the target layer.

2. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 1, characterized in that, Obtaining pre-stack seismic gather data includes: Obtain pre-stack seismic gather data through seismic acquisition, and each seismic trace in the seismic gather data contains azimuth and incident angle information.

3. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 1, characterized in that, Preprocessing the azimuth and incident angle information in the seismic gather data includes: Statistically analyze the azimuth range α 1 ~α 2 in the pre-stack seismic gather data, and the incident angle range β 1 ~β 2 . Establish spatial grid coordinates based on a specific step size, perform stacking calculations on the seismic traces at each spatial grid point at specific azimuth intervals to obtain n seismic volumes with different azimuths, and perform stacking calculations on the seismic traces at each spatial grid point at specific incident angle intervals to obtain m seismic volumes with different incident angles.

4. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 3, characterized in that, The specific azimuth interval is 10° - 20°, and the specific incident angle interval is 10° - 15°.

5. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 4, characterized in that, The range of n is (α 2 -α 1 ) / 20 to (α 2 -α 1 ) / 10, and the range of m is (β 2 -β 1 ) / 10 to (α 2 -α 1 ).

6. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 1, characterized in that, Using the seismic body F respectively 1 and F 2 Obtaining the seismic amplitude attribute FM of the target layer includes: Using the seismic volume F 1 to obtain the seismic amplitude attribute F of the target layer 1 M, using the seismic volume F 2 to obtain the seismic amplitude attribute F of the target layer 2 M.

7. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 6, characterized in that, Normalizing the seismic amplitude attribute and the seismic frequency attribute respectively includes: Normalize the seismic amplitude attribute F 1 M, the seismic amplitude attribute F 2 M and the seismic frequency attribute RM respectively.

8. The pre-stack seismic multi-attribute fusion reservoir prediction method according to claim 7, characterized in that, Performing attribute fusion calculation on the normalized seismic amplitude attribute and the seismic frequency attribute to obtain the seismic fusion attribute FFR of the target layer includes: Perform attribute fusion calculation on the normalized seismic amplitude attribute and the seismic frequency attribute based on the convolutional neural network algorithm to obtain the seismic fusion attribute FFR of the target layer, and output the planar map of the reservoir prediction result of the target layer based on the seismic fusion attribute.

9. A pre-stack seismic multi-attribute fusion reservoir prediction device, characterized in that, it includes: A data acquisition module configured to obtain pre-stack seismic gather data, preprocess the azimuth and incident angle information in the seismic gather data to obtain n seismic volumes with different azimuths and m seismic volumes with different incident angles; A data screening module, configured to screen out seismic body F with a perpendicular azimuth to the reservoir sediment source direction from seismic bodies with n different azimuth angles based on the reservoir sediment source direction in the target area 1 and seismic body F with a parallel azimuth 2 , and screen out seismic body R with the maximum incident angle from seismic bodies with m different incident angles; The first calculation module is configured to respectively use the seismic volume F 1 and F 2 to obtain the seismic amplitude attribute FM of the target layer, and use the seismic volume R to obtain the seismic frequency attribute RM of the target layer; A second calculation module configured to normalize the seismic amplitude attribute and the seismic frequency attribute respectively, and perform attribute fusion calculation on the normalized seismic amplitude attribute and the seismic frequency attribute with the lithology and fluid characteristics at the well points of the target layer as constraints to obtain the seismic fusion attribute FFR of the target layer.

10. A computer device, characterized in that, it includes: At least one processor; And A memory, the memory stores computer instructions that can be run on the processor, and when the instructions are executed by the processor, the steps of the method according to any one of claims 1 - 8 are implemented.

11. A computer-readable storage medium storing a computer program, wherein, when the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-8.

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