Method and device for predicting strong anti-noise carbonate reservoir

Abstract

The invention discloses a strong anti-noise carbonate reservoir prediction method and device. The method comprises the following steps: obtaining a cascade attribute body through seismic attribute cascade calculation; the obtained cascade attribute body and the seismic data are displayed in an overlapped mode, and the cascade attribute body is hollowed out; the value domain range of the hollowed-out cascade attribute body is calibrated to the value domain range of a wave impedance low-frequency component of logging statistics through a numerical calibration method, and a carbonate rock fracture-cavity body low-frequency model is established by taking the calibrated cascade attribute as a constraint; determining a wave impedance attribute body by taking the obtained carbonate rock fracture-cavity body low-frequency model as a constraint; and extracting an inter-layer minimum wave impedance attribute plane graph of the target layer by using the obtained wave impedance attribute body, and predicting plane distribution characteristics of different-scale reservoirs of carbonate rock caves and cracks.

Description

Technical Field

[0001] The invention belongs to the technical field of reservoir prediction in oil and gas seismic exploration and development interpretation, and in particular relates to a method and device for predicting carbonate reservoirs with strong noise resistance. Background Art

[0002] Carbonate reservoirs are one of the main reservoir types in the Tarim Basin. Unlike clastic reservoirs, carbonate reservoirs are mainly controlled by the influence of late diagenetic fracture transformation and karstification, forming different types of fractures, holes, caves, etc., which appear as fracture-cavity aggregates. As oil fields shift from the exploration stage to the development stage, the requirements for reservoir description accuracy are getting higher and higher. However, due to the different scales and diverse morphologies of carbonate reservoirs, they have strong heterogeneity characteristics and are difficult to predict, which brings great difficulties to the study of carbonate reservoirs.

[0003] Seismic inversion technology is the main means of quantitative reservoir prediction. Post-stack constrained sparse pulse inversion is a deterministic inversion method based on model constraints. Due to its strong noise resistance and high reliability, it is widely promoted in actual production. The objective function formula of post-stack constrained sparse pulse inversion is as follows:

[0004] f(r)=(sW·r) 2 +λ·f trend +α·f sparse +β·f smooth

[0005] Among them, f(r) is the objective function, r is the reflection coefficient, s is the seismic data, W is the seismic wavelet, and f trend is the low-frequency model constraint term, which uses the low-frequency trend of the well to supplement the low-frequency components missing from the band-limited seismic data. sparse is a sparsity constraint term used to ensure that the reflection coefficient is large and sparse above the noise that conforms to the Gaussian background, f smooth It is a spatial smoothness constraint term, which ensures the continuity of longitudinal wave impedance in the lateral direction. λ, α, and β are coefficient terms of the corresponding constraint terms. The larger the coefficient, the stronger the constraint on the inversion result.

[0006] However, for carbonate reservoirs, conventional seismic inversion methods can only identify large-scale reservoir types such as pores and caves, but cannot predict small-scale reservoirs such as fractures, which limits the application of such methods in carbonate reservoir research. In response to this problem, in recent years, some experts and scholars at home and abroad have carried out relevant research work and proposed a seismic inversion method based on phase control constraints. This method optimizes the low-frequency model to constrain the phase f trend, to achieve the purpose of overall prediction of carbonate fractures and caves. The basic principle can be summarized into three steps: first, using seismic attributes such as amplitude curvature to characterize the boundaries of carbonate fractures and caves, and to divide reservoir phases and non-reservoir phases; second, using seismic phases as constraints to establish attribute models; finally, using the attribute model in the inversion process to obtain phase-controlled constrained inversion results. This method is widely used in high signal-to-noise ratio areas in the Tarim Basin, and it supports the study of carbonate traps, targets and well locations. However, for low signal-to-noise ratio areas, due to the poor imaging quality of seismic data, conventional seismic attributes cannot clearly characterize the boundaries of geological anomalies, resulting in low accuracy and poor effect of phase-controlled inversion results.

[0007] In summary, it is urgent to propose a new phase-controlled inversion method to meet the needs of high-precision reservoir prediction in low signal-to-noise ratio areas. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a highly noise-resistant carbonate reservoir prediction method and device in view of the deficiencies in the above-mentioned prior art, which is used to solve the technical problem that carbonate fracture-cavity karst reservoirs in areas with low signal-to-noise ratio cannot be predicted with high precision.

[0009] The present invention adopts the following technical solutions:

[0010] A method for predicting carbonate reservoirs with strong noise resistance comprises the following steps:

[0011] S1, obtain the cascade attribute volume through seismic attribute cascade calculation;

[0012] S2, superimposing the cascade attribute body obtained in step S1 with the seismic data and performing hollowing processing on the cascade attribute body;

[0013] S3, using a numerical calibration method, calibrating the range of the cascade attribute body hollowed out in step S2 to the range of the low-frequency component of the wave impedance of the logging statistics, and using the cascade attributes after calibration as constraints to establish a low-frequency model of carbonate fracture-cavity bodies;

[0014] S4, using the carbonate rock fracture-cave body low-frequency model obtained in step S3 as a constraint to determine the wave impedance attribute body;

[0015] S5. Using the wave impedance attribute body obtained in step S4, extract the minimum wave impedance attribute plane map between target layers, and predict the plane distribution characteristics of carbonate caves and fractures at different scales.

[0016] Specifically, step S1 is as follows:

[0017] S101, subtracting the in-phase homogeneous component from the original seismic data to obtain the seismic inhomogeneous component;

[0018] S102, taking the seismic inhomogeneous component obtained in step S101 as input, performing low-pass filtering, edge protection filtering and fractal weighted denoising in sequence to obtain a cascade attribute body.

[0019] Furthermore, in step S101, the seismic data volume is laterally smoothed to obtain in-phase homogeneous components of the seismic data.

[0020] Specifically, in step S2, the hollowing threshold value is adjusted until the cascade attribute body can fully reflect the boundary of the carbonate rock fracture-cavity body.

[0021] Specifically, step S3 is as follows:

[0022] S301. Based on the standard well and seismic interpretation horizon, a layered attribute model is established by using the layer interpolation method;

[0023] S302, low-pass filtering is performed on the standard well wave impedance curve using the threshold value of the missing low-frequency component, the value range of the low-frequency component of the wave impedance curve is counted, the hollow cascade attribute is scaled to the value range of the low-frequency component of the wave impedance counted by the well logging, and the scaled cascade attribute is used as the fracture-cavity zone attribute model;

[0024] S303, progressively merging the layered attribute model and the fracture-cavity zone attribute model to obtain a carbonate rock low-frequency model.

[0025] Furthermore, in step S301, based on the well logging data and seismic data, fine calibration of well seismic data is carried out, and the wells whose synthetic seismic records match the characteristics of the seismic trace group near the well are selected as standard wells.

[0026] Furthermore, in step S302, a threshold value of missing low-frequency components is determined by performing spectrum analysis on the target layer seismic data.

[0027] Furthermore, the hollow cascade attributes are calibrated to the value range of the low-frequency component of the wave impedance of the logging statistics using a numerical calibration method.

[0028] Specifically, in step S4, the wave impedance attribute body is obtained by post-stack sparse pulse inversion.

[0029] Another technical solution of the present invention is a highly noise-resistant carbonate reservoir prediction device, comprising:

[0030] A calculation module, which obtains a cascade attribute body through seismic attribute cascade calculation;

[0031] A processing module is used to overlay the cascade attribute body obtained by the calculation module with the seismic data and perform hollowing processing on the cascade attribute body;

[0032] The module is constructed, and the range of the cascade attribute body after hollowing out in the processing module is scaled to the range of the low-frequency component of the wave impedance of the logging statistics by using the numerical calibration method. The scaled cascade attribute is used as a constraint to establish a low-frequency model of carbonate fracture-cave bodies.

[0033] The attribute module uses the low-frequency model of carbonate fracture-cavity body obtained in the construction module as a constraint to determine the wave impedance attribute body;

[0034] The prediction module uses the wave impedance attribute body obtained by the attribute module to extract the minimum wave impedance attribute plane map between target layers and predict the plane distribution characteristics of reservoirs of different scales in carbonate caves and fractures.

[0035] Compared with the prior art, the present invention has at least the following beneficial effects:

[0036] A highly noise-resistant carbonate reservoir prediction method uses cascaded calculation attributes to effectively identify the boundaries of fracture zones in low signal-to-noise ratio areas, and uses this as a constraint to achieve quantitative prediction of carbonate reservoirs.

[0037] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0038] In summary, the accuracy of the characterization of the fracture zone by the cascade attributes of the method of the present invention is less affected by noise and has strong noise resistance, and is suitable for quantitative prediction of carbonate reservoirs in areas with low signal-to-noise ratio.

[0039] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The superimposed seismic sections of different earthquake amplitudes are shown, among which (a) is the amplitude curvature superimposed seismic section, (b) is the structure tensor superimposed seismic section, and (c) is the cascade attribute superimposed seismic section;

[0041] Figure 2 Establishment of phase-controlled model (① layered mean model; ② optimization of hollowing display below the cascade attribute threshold; ③ attribute model merging);

[0042] Figure 3 are seismic profiles, where (a) is the seismic profile through Well B, (b) is the post-stack phase-controlled inversion profile, and (c) is the conventional post-stack inversion profile;

[0043] Figure 4 It is a well-connected seismic section and a post-stack phase-controlled inversion section;

[0044] Figure 5 The prediction plane diagrams are shown in Figure 1, where (a) is the conventional inversion reservoir prediction and (b) is the post-stack phase-controlled inversion reservoir prediction. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "one side", "one end", "one side" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0049] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0050] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0051] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0052] The present invention provides a method and device for predicting carbonate reservoirs with strong noise resistance. By optimizing multiple seismic attributes and adopting a multi-attribute cascade method, the boundaries of carbonate fractures and caves in low signal-to-noise ratio areas are effectively characterized, and the boundaries are used as seismic phases to further constrain the inversion process, thereby achieving high-precision prediction of carbonate fractures and caves in low signal-to-noise ratio areas.

[0053] The present invention provides a method and device for predicting carbonate reservoirs with strong noise resistance, comprising the following steps:

[0054] S1. Calculate earthquake cascade attributes. Through earthquake attribute cascade calculation, the contour of the broken zone in the low signal-to-noise ratio area can be depicted;

[0055] S101, performing lateral smoothing processing on the seismic data volume to obtain in-phase homogeneous components of the seismic data, and subtracting the in-phase homogeneous components from the original seismic data to obtain inhomogeneous components;

[0056] S102: Taking the seismic inhomogeneous component obtained in step S101 as input, low-pass filtering, edge protection filtering and fractal weighted denoising are performed in sequence to obtain a final cascade attribute body.

[0057] Through low-pass filtering and edge protection filtering, high-frequency noise is suppressed and the outline of the broken zone is highlighted. Using the filtered attribute body, according to the waveform similarity, the weighted coefficients of adjacent sample points in the neighborhood to the target sample point are calculated, and weighted averaging is performed to obtain the final cascade attribute body.

[0058] S2, superimposing and displaying the cascade attribute body obtained in step S1 with the seismic data, hollowing out the cascade attributes, and adjusting the hollowing threshold value until the cascade attributes can fully reflect the boundaries of the carbonate rock fracture-cavity body;

[0059] See also Figure 1Different cascade attribute threshold values ​​have different ranges for describing the boundaries of carbonate fracture-cavity zones. The carbonate fracture-cavity zones show a comprehensive response characteristic of beaded and chaotic shapes in seismic data, which can be identified by the naked eye. Therefore, the cascade attributes are superimposed on the seismic data, and the cascade attributes are adjusted to semi-transparent colors and hollowed out. The hollowing threshold value is adjusted until the cascade attributes can fully reflect the boundaries of carbonate fracture-cavity bodies, and the hollowed-out cascade attributes are obtained. Compared with single seismic attributes such as amplitude curvature and structural tensor, the cascade attributes have higher accuracy in describing the fracture and rupture zones in low signal-to-noise ratio areas.

[0060] S3, using a numerical calibration method, calibrating the range of the cascade attribute value after hollowing out in step S2 to the range of the low-frequency component of the wave impedance of the logging statistics, and using the cascade attribute after calibration as a constraint to establish a low-frequency model of carbonate rock fracture-cavity body;

[0061] See also Figure 2 , the phase-controlled modeling method is used to establish a low-frequency model of carbonate rocks, which is divided into three steps:

[0062] S301. Based on the well logging data and seismic data, fine well-seismic calibration is carried out, and wells with good calibration effects are selected as standard wells. Based on the standard wells and seismic interpretation layers, a layered attribute model is established by using the interpolation method along the layers;

[0063] S302, by performing spectrum analysis on the seismic data of the target layer, determining the threshold value of the missing low-frequency component, using the threshold value to perform low-pass filtering on the wave impedance curve of the standard well, statistically analyzing the value range of the low-frequency component of the wave impedance curve, and using the numerical calibration method to calibrate the hollow cascade attributes to the value range of the low-frequency component of the wave impedance statistically obtained by well logging, and using the calibrated cascade attributes as the fracture-cavity zone attribute model;

[0064] S303, progressively merging the layered attribute model and the fracture-cavity zone attribute model to obtain a carbonate rock low-frequency model.

[0065] See also Figure 3 and Figure 4 The low-frequency model is used to constrain the inversion process, and the phase-controlled inversion wave impedance attribute body is obtained to realize the arrangement and quantitative prediction of carbonate fracture-cavity bodies. Compared with the conventional inversion results, the reservoir positions predicted by the new method are empty and lost, which is consistent with the actual drilling results.

[0066] S4, using the carbonate rock fracture-cave low-frequency model obtained in step S3 as a constraint, performing post-stack sparse pulse inversion to obtain a wave impedance attribute body;

[0067] S5. Using the wave impedance attribute body obtained in step S4, extract the minimum wave impedance attribute plane map between target layers, and predict the plane distribution characteristics of carbonate caves and fractures at different scales.

[0068] See also Figure 5 , using the wave impedance attribute body obtained by phase-controlled inversion, the minimum wave impedance attribute plane map between target layers is extracted to predict the plane distribution characteristics of carbonate caves and fractures at different scales.

[0069] In another embodiment of the present invention, a highly noise-resistant carbonate reservoir prediction device is provided, which can be used to implement the above-mentioned highly noise-resistant carbonate reservoir prediction method. Specifically, the highly noise-resistant carbonate reservoir prediction device includes a calculation module, a processing module, a construction module, an attribute module and a prediction module.

[0070] Among them, the calculation module obtains the cascade attribute body through the cascade calculation of seismic attributes;

[0071] A processing module is used to overlay the cascade attribute body obtained by the calculation module with the seismic data and perform hollowing processing on the cascade attribute body;

[0072] The module is constructed, and the range of the cascade attribute body after hollowing out in the processing module is scaled to the range of the low-frequency component of the wave impedance of the logging statistics by using the numerical calibration method. The scaled cascade attribute is used as a constraint to establish a low-frequency model of carbonate fracture-cave bodies.

[0073] The attribute module uses the low-frequency model of carbonate fracture-cavity body obtained in the construction module as a constraint to determine the wave impedance attribute body;

[0074] The prediction module uses the wave impedance attribute body obtained by the attribute module to extract the minimum wave impedance attribute plane map between target layers and predict the plane distribution characteristics of reservoirs of different scales in carbonate caves and fractures.

[0075] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the strong noise-resistant carbonate reservoir prediction method, including:

[0076] The cascade attribute body is obtained by cascading seismic attributes; the obtained cascade attribute body is superimposed and displayed with the seismic data, and the cascade attribute body is hollowed out; the value range of the hollowed-out cascade attribute body is scaled to the value range of the low-frequency component of the wave impedance of the logging statistics by the numerical calibration method, and the scaled cascade attributes are used as constraints to establish a low-frequency model of carbonate fracture-cavity bodies; the wave impedance attribute body is determined by using the obtained low-frequency model of carbonate fracture-cavity bodies as a constraint; the obtained wave impedance attribute body is used to extract the minimum wave impedance attribute plane map between target layers, and the plane distribution characteristics of reservoirs of different scales of carbonate caves and fractures are predicted.

[0077] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating device of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0078] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the strong noise-resistant carbonate reservoir prediction method in the above embodiment; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium:

[0079] The cascade attribute body is obtained by cascading seismic attributes; the obtained cascade attribute body is superimposed and displayed with the seismic data, and the cascade attribute body is hollowed out; the value range of the hollowed-out cascade attribute body is scaled to the value range of the low-frequency component of the wave impedance of the logging statistics by the numerical calibration method, and the scaled cascade attributes are used as constraints to establish a low-frequency model of carbonate fracture-cavity bodies; the wave impedance attribute body is determined by using the obtained low-frequency model of carbonate fracture-cavity bodies as a constraint; the obtained wave impedance attribute body is used to extract the minimum wave impedance attribute plane map between target layers, and the plane distribution characteristics of reservoirs of different scales of carbonate caves and fractures are predicted.

[0080] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0081] The present invention solves the problem of quantitative prediction of carbonate fracture-cave karst reservoirs in low signal-to-noise ratio areas, improves reservoir prediction accuracy, and lays a foundation for subsequent carbonate reservoir research in low signal-to-noise ratio areas.

[0082] The invention was applied to conduct experiments in the Tazhong A block in the Tarim Basin. The signal-to-noise ratio of seismic data in this area is relatively low, the buried depth of the target layer is more than 5,000 meters, and the application effect is good.

[0083] In previous studies, the conventional phase-controlled inversion method could not accurately depict the boundaries of carbonate fractures and caves, and could not provide a constructive basis for trap research, well location demonstration, and development plan adjustment. After using the present invention, the reservoir prediction accuracy is significantly improved, and the reservoir matching rate with drilling and logging responses reaches 89%, providing strong support for the trap research and well location deployment of carbonate fracture-cave karst reservoirs in low signal-to-noise ratio areas.

[0084] In summary, the method and device for predicting carbonate reservoirs with strong noise resistance of the present invention improve the quantitative prediction accuracy of carbonate reservoirs in areas with low signal-to-noise ratio.

[0085] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A strong noise-resistant carbonate reservoir prediction method, characterized in that: The following steps are involved: S1, obtain the cascade attribute volume through seismic attribute cascade calculation; S2, superimposing the cascade attribute body obtained in step S1 with the seismic data and performing hollowing processing on the cascade attribute body; S3, using a numerical calibration method, calibrating the range of the cascade attribute body hollowed out in step S2 to the range of the low-frequency component of the wave impedance of the logging statistics, and using the cascade attributes after calibration as constraints to establish a low-frequency model of carbonate fracture-cavity bodies; S4, using the carbonate rock fracture-cave body low-frequency model obtained in step S3 as a constraint to determine the wave impedance attribute body; S5. Using the wave impedance attribute body obtained in step S4, extract the minimum wave impedance attribute plane map between target layers, and predict the plane distribution characteristics of carbonate caves and fractures at different scales.

2. The method for predicting carbonate reservoirs with strong noise resistance according to claim 1, characterized in that: Step S1 is specifically as follows: S101, subtracting the in-phase homogeneous component from the original seismic data to obtain the seismic inhomogeneous component; S102, taking the seismic inhomogeneous component obtained in step S101 as input, performing low-pass filtering, edge protection filtering and fractal weighted denoising in sequence to obtain a cascade attribute body.

3. The method for predicting carbonate reservoirs with strong noise resistance according to claim 2, characterized in that: In step S101, the seismic data volume is laterally smoothed to obtain in-phase homogeneous components of the seismic data.

4. The method for predicting carbonate reservoirs with strong noise resistance according to claim 1, characterized in that: In step S2, the hollowing threshold value is adjusted until the cascade attribute body can fully reflect the boundary of the carbonate rock fracture-cavity body.

5. The method for predicting carbonate reservoirs with strong noise resistance according to claim 1, characterized in that: Step S3 is specifically as follows: S301. Based on the standard well and seismic interpretation horizon, a layered attribute model is established using the layer interpolation method; S302, low-pass filtering is performed on the standard well wave impedance curve using the threshold value of the missing low-frequency component, the value range of the low-frequency component of the wave impedance curve is counted, the hollow cascade attribute is scaled to the value range of the low-frequency component of the wave impedance counted by the well logging, and the scaled cascade attribute is used as the fracture-cavity zone attribute model; S303, progressively merging the layered attribute model and the fracture-cavity zone attribute model to obtain a carbonate rock low-frequency model.

6. The method for predicting carbonate reservoirs with strong noise resistance according to claim 5, characterized in that: In step S301, based on the well logging data and seismic data, fine calibration of well seismic data is carried out, and the wells whose synthetic seismic records match the characteristics of the seismic trace wave group near the well are selected as standard wells.

7. The method for predicting carbonate reservoirs with strong noise resistance according to claim 5, characterized in that: In step S302, a threshold value of missing low-frequency components is determined by performing spectrum analysis on the target layer seismic data.

8. The method for predicting carbonate reservoirs with strong noise resistance according to claim 7, characterized in that: The hollow cascade attributes are scaled to the value range of the low-frequency component of the wave impedance of the logging statistics using the numerical calibration method.

9. The method for predicting carbonate reservoirs with strong noise resistance according to claim 1, characterized in that: In step S4, the wave impedance attribute volume is obtained by post-stack sparse pulse inversion.

10. A highly noise-resistant carbonate reservoir prediction device, characterized in that: include: A calculation module, which obtains a cascade attribute body through seismic attribute cascade calculation; A processing module is used to overlay the cascade attribute body obtained by the calculation module with the seismic data and perform hollowing processing on the cascade attribute body; The module is constructed, and the range of the cascade attribute body after hollowing out in the processing module is scaled to the range of the low-frequency component of the wave impedance of the logging statistics by using the numerical calibration method. The scaled cascade attribute is used as a constraint to establish a low-frequency model of carbonate fracture-cave bodies. The attribute module uses the low-frequency model of carbonate fracture-cavity body obtained in the construction module as a constraint to determine the wave impedance attribute body; The prediction module uses the wave impedance attribute body obtained by the attribute module to extract the minimum wave impedance attribute plane map between target layers and predict the plane distribution characteristics of reservoirs of different scales in carbonate caves and fractures.

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