Methods, apparatus, and media for predicting volcanic buried hill reservoirs based on residual diffraction waves.

Abstract

This invention relates to a method, apparatus, and medium for predicting volcanic buried hill reservoirs based on residual diffraction waves. The method includes: improving the resolution of post-stack seismic data volumes; sequentially performing bandpass filtering and diffusion filtering on the improved seismic data volumes to obtain diffused-filtered seismic data volumes; subtracting the diffused-filtered seismic data volumes from the improved seismic data volumes to obtain the difference between the two data volumes; performing bandpass filtering on the difference between the two data volumes to obtain post-stack residual diffraction wave data volumes; extracting seismic attributes from the post-stack residual diffraction wave data volumes; and obtaining an attribute volume characterizing the spatial distribution range of volcanic buried hill reservoirs based on the seismic attributes to predict the distribution of volcanic buried hill reservoirs. Therefore, this invention uses post-stack residual diffraction waves from seismic data to predict reservoirs, which is less affected by the amplitude of seismic reflected waves and yields more reliable results than conventional reflection wave prediction of volcanic buried hill reservoirs.

Description

Technical Field

[0001] This invention relates to a method, apparatus, equipment, and medium for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves, and pertains to the field of buried hill reservoir prediction in volcanic rocks. Background Technology

[0002] Because volcanic buried hills have undergone tectonic, weathering, and embankment processes, they have well-developed fracture systems, extensive dissolution cavities, and strong internal heterogeneity. Therefore, after the formation of reservoirs, the distribution and physical properties of these volcanic buried hills vary greatly in both vertical and horizontal directions, making reservoir prediction quite difficult.

[0003] In recent years, for large-scale strata that can be equivalent to layered homogeneous media, commonly used reservoir prediction methods employ reflection wave information from seismic waves, achieving satisfactory results. However, volcanic buried hills have complex structures, poor seismic data quality, and chaotic seismic data characteristics, weak signals, and well-developed diffraction waves within the buried hills. Therefore, using reflection wave information for reservoir prediction has inherent limitations, resulting in unsatisfactory performance.

[0004] Volcanic buried hills are massive, heterogeneous geological bodies containing both strong reflected wave energy and relatively strong diffracted wave energy. During seismic data processing, the velocity model within the buried hill is often inaccurate, resulting in partial imaging of diffracted wave energy. However, a significant amount of residual diffracted wave energy remains in the final post-stack seismic data; this portion is referred to as residual diffracted wave in post-stack seismic data. The more developed the fractures and dissolution pores within the volcanic buried hill reservoir, the stronger the heterogeneity, and the more residual diffracted wave energy remains in the post-stack seismic data. Residual diffracted wave energy shows a good correlation with the development of heterogeneous reservoirs, making reservoir prediction using residual diffracted wave energy from post-stack seismic data feasible. Utilizing diffracted wave energy has become an important method for predicting volcanic buried hill reservoirs. Reflected waves are generated when the scale of the subsurface geological body is larger than the seismic reflection wavelength, while diffracted waves are generated when the geological body scale is smaller than the seismic wavelength. Therefore, the reflected wave energy in post-stack seismic data is much greater than the diffracted wave energy, but the reflected wave frequency is much lower than the diffracted wave frequency. Therefore, how to separate the residual diffraction waves from the post-stack seismic data and use the residual diffraction waves for reservoir prediction is a current challenge in the prediction of buried hill reservoirs in volcanic rocks. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art. Therefore, in response to the above-mentioned problems, the object of the present invention is to provide a method, apparatus, device, and medium for predicting volcanic buried hill reservoirs based on residual diffraction waves, which can not only extract residual diffraction waves from post-stack seismic data, but also use residual diffraction waves to predict volcanic reservoirs.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] In a first aspect, the present invention provides a method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves, comprising:

[0008] The resolution of the post-stack seismic data volume to be predicted is improved.

[0009] The seismic data volume after resolution enhancement is sequentially subjected to bandpass filtering and diffusion filtering to obtain the diffusion-filtered seismic data volume.

[0010] Subtract the diffuse-filtered seismic data volume from the seismic data volume processed with higher resolution to obtain the difference between the two data volumes;

[0011] The difference between the two data volumes is processed by bandpass filtering to obtain the post-stack seismic residual diffraction wave data volume.

[0012] Seismic attributes are extracted from the residual diffraction wave data volume of post-stack earthquakes. Based on the seismic attributes, an attribute volume is obtained that characterizes the spatial distribution range of volcanic buried hill reservoirs, and the distribution of volcanic buried hill reservoirs is predicted.

[0013] Furthermore, the resolution of the post-stack seismic data volume to be predicted is improved by: using the hybrid phase wavelet deconvolution method to extract the hybrid phase wavelet in the composite spectral domain, calculating the anti-wavelet given the desired wavelet, and convolving it with the seismic trace to form a frequency-upgraded seismic record, thereby improving the longitudinal resolution of the seismic data.

[0014] Furthermore, the improved seismic data volume is sequentially subjected to bandpass filtering and diffusion filtering to obtain the diffusion-filtered seismic data volume, including:

[0015] By using bandpass filtering technology, the improved seismic data volume is filtered to obtain the reflected wave seismic data volume;

[0016] Diffusion filtering based on reflected wave seismic data volume includes: directional smoothing of the seismic phase axis, suppression of anisotropic noise caused by residual diffraction waves, extraction of seismic reflected wave data volume, removal of residual diffraction wave energy, and obtaining a seismic data volume of an approximately equivalent layered homogeneous medium.

[0017] Furthermore, bandpass filtering technology is used to filter the seismic data volume after resolution improvement, including: performing time-spectrum analysis on the seismic data volume before resolution improvement, selecting the width between two frequency values ​​where the amplitude spectrum is equal to 1 / 5 of the maximum value of the spectrum as the reflection wave bandwidth, selecting the highest frequency of this bandwidth as the upper limit value of the bandpass filter, and selecting 0 to the upper limit value as the bandpass filter parameters of the reflection wave to obtain the reflection wave seismic data volume.

[0018] Furthermore, the difference between the two data volumes is bandpass filtered to obtain the post-stack seismic residual diffraction data volume, including: bandpass filtering the difference between the two data volumes, determining the diffraction filtering parameters using time-spectrum analysis, and obtaining the post-stack seismic residual diffraction data volume.

[0019] Furthermore, the seismic properties of the post-stack earthquake residual diffraction data volume include the instantaneous amplitude and root mean square amplitude of the diffraction data volume.

[0020] Furthermore, based on seismic attributes, an attribute body is obtained to characterize the spatial distribution range of volcanic buried hill reservoirs, and the distribution of volcanic buried hill reservoirs is predicted. This includes: the larger the anomaly value of the attribute body, the stronger the heterogeneity of the volcanic buried hill and the more developed the reservoir, thereby realizing the prediction of the distribution range of volcanic buried hill reservoirs.

[0021] Secondly, the present invention also provides a device for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves, comprising:

[0022] The data processing unit is configured to perform resolution enhancement processing on the post-stack seismic data volume to be predicted.

[0023] The diffusion filtering unit is configured to sequentially perform bandpass filtering and diffusion filtering on the seismic data volume after resolution enhancement processing to obtain the diffusion-filtered seismic data volume.

[0024] The difference calculation unit is configured to subtract the diffuse-filtered seismic data volume from the resolution-upgraded seismic data volume to obtain the difference between the two data volumes.

[0025] The difference filtering unit is configured to perform bandpass filtering on the difference between two data volumes to obtain the post-stack seismic residual diffraction wave data volume.

[0026] The reservoir prediction unit is configured to extract the seismic attributes of the post-stack seismic residual diffraction wave data volume, obtain the attribute volume characterizing the spatial distribution range of volcanic buried hill reservoirs based on the seismic attributes, and predict the distribution of volcanic buried hill reservoirs.

[0027] Thirdly, the present invention also provides an electronic device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods.

[0028] Fourthly, the present invention also provides a computer-readable storage medium for storing one or more programs, said one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods.

[0029] Because the present invention adopts the above technical solution, it has the following characteristics:

[0030] 1. This invention proposes the concept of post-stack residual diffraction waves in seismic data, which is applicable to all volcanic rock buried hill exploration; based on post-stack seismic data volumes, it solves the pain point of insufficient oil and gas exploration data.

[0031] 2. This invention utilizes post-stack seismic data for related processing, which reduces computational load and increases efficiency compared to pre-stack seismic data;

[0032] 3. This invention uses residual diffraction waves after seismic data stacking to predict reservoirs. It is less affected by the amplitude of seismic reflected waves and the results are more reliable than conventional reflected wave prediction of volcanic buried hill reservoirs.

[0033] In summary, this invention can be widely applied to the exploration of buried hills in volcanic rocks. Attached Figure Description

[0034] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0035] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of seismic data from a buried volcanic hill in an oil field after resolution enhancement processing, according to one embodiment of the present invention.

[0037] Figure 3 This is seismic data of an approximately equivalent layered homogeneous medium from a buried hill of volcanic rock in an oilfield, as described in one embodiment of the present invention.

[0038] Figure 4 In one embodiment of the present invention, the difference between two data volumes is obtained by subtracting diffusion-filtered seismic data from the improved resolution seismic data of an oil field.

[0039] Figure 5 This is a schematic diagram of the result of bandpass filtering on the difference to obtain the post-stack seismic residual diffraction wave data volume in one embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram illustrating the predicted distribution range of volcanic buried hill reservoirs in one embodiment of the present invention;

[0041] Figure 7 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0042] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0043] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0044] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "above," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure.

[0045] Isolating residual diffraction waves from post-stack seismic data and using them for reservoir prediction remains a challenge in predicting buried hill reservoirs in volcanic rocks. This invention provides a method, apparatus, device, and medium for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves. The method includes: improving the resolution of the post-stack seismic data volume to be predicted; performing bandpass filtering on the improved seismic data volume; performing diffusion filtering on the bandpass-filtered seismic data volume to obtain a diffused-filtered seismic data volume; subtracting the diffused-filtered seismic data volume from the improved seismic data volume to obtain the difference between the two seismic data volumes; performing bandpass filtering on the difference to remove high-frequency scattering noise, obtaining the residual diffraction wave data volume from the post-stack seismic data; and extracting diffraction wave attributes from the residual diffraction wave data volume to predict the distribution range of buried hill reservoirs in volcanic rocks. Therefore, this invention predicts reservoirs using residual diffraction waves from post-stack seismic data, which is less affected by the amplitude of seismic reflected waves and yields more reliable results than conventional methods using reflected waves to predict buried hill reservoirs in volcanic rocks.

[0046] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0047] Example 1: As Figure 1 As shown in this embodiment, the method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves includes:

[0048] S1. Improve the resolution of the post-stack seismic data volume to be predicted, and increase the seismic data bandwidth as much as possible while maintaining the signal-to-noise ratio.

[0049] In this embodiment, the diffracted wave is generated by a small-scale geological body. The frequency band of the diffracted wave is higher than that of the reflected wave. Improving the resolution is equivalent to improving the bandwidth of the seismic data, thereby recovering the diffracted wave signal.

[0050] Furthermore, this embodiment employs a hybrid phase wavelet deconvolution method to extract hybrid phase wavelets in the composite spectral domain, calculate the anti-wavelet given the desired wavelet, and convolve it with the seismic trace to form a frequency-upgraded seismic record, thereby improving the longitudinal resolution of the seismic data and obtaining a post-stack seismic data volume containing the reflected wave energy of low-frequency post-stack seismic data and the residual diffraction wave energy of mid-frequency post-stack seismic data.

[0051] In this embodiment, residual diffraction waves in post-stack seismic data are defined as: residual diffraction waves that are not fully repositioned after seismic data processing. Post-stack seismic data is the result of imaging using seismic reflection waves, and partially imaging diffraction waves as well. During the seismic data imaging process, imaging is focused on seismic reflection waves, without paying attention to diffraction wave imaging. Because the formation mechanisms of reflected waves and diffraction waves are different, geological bodies larger than the wavelength generate seismic reflection waves, while inhomogeneous bodies smaller than or comparable to the wavelength of seismic waves (such as faults, cracks, cavities, and boundaries) act as diffraction points, generating seismic diffraction waves. The energy of diffraction waves is relatively weaker than that of reflected waves, and the frequency of diffraction waves is higher than that of reflected waves. In homogeneous strata, there are fewer diffraction waves, the velocity model is relatively accurate, and reflected waves are fully imaged; however, in strata with severe heterogeneity, the velocity model is not accurately established, and after imaging with reflected waves, diffraction waves can only be partially imaged. In the final post-stack seismic data, a large number of residual diffraction waves remain, which are called residual diffraction waves in post-stack seismic data. This reflects the heterogeneity of the strata. In buried volcanic hills, the more residual diffraction waves from post-stack seismic data, the more developed the fractures and dissolution pores, and the better the reservoir properties. There is a good correlation between residual diffraction waves and the development of heterogeneous reservoirs, and residual diffraction waves from post-stack seismic data can be used to predict buried volcanic hill reservoirs.

[0052] S2. Bandpass filtering is applied to the improved seismic data volume to obtain a seismic data volume containing reflected wave energy and a small portion of diffracted wave energy. Diffusion filtering is then applied to the bandpass-filtered seismic data volume to remove the remaining diffracted wave energy, resulting in a seismic data volume of reflected waves from an approximately equivalent layered homogeneous medium.

[0053] In this embodiment, the method for obtaining a reflected seismic data volume of an approximately equivalent layered homogeneous medium includes:

[0054] S21. Use bandpass filtering technology to filter the seismic data volume after the resolution is improved.

[0055] In this embodiment, the reflected wave energy in the seismic data is much greater than the residual diffracted wave energy, and the reflected wave frequency is lower than the diffracted wave frequency. Time-spectrum analysis is performed on the seismic data volume before resolution improvement. In the time spectrum, most of the reflected wave energy is concentrated in the harmonic components near the dominant frequency. The width between two frequency values ​​where the amplitude spectrum is equal to 1 / 5 of the maximum value of the spectrum is selected as the reflected wave bandwidth. The highest frequency of this bandwidth is selected as the upper limit of the bandpass filter. The bandpass filter parameters of the reflected wave are selected from 0 to the upper limit, and the reflected wave seismic data volume is obtained. This part of the reflected wave seismic data volume contains a small portion of the residual diffracted wave.

[0056] S22. Based on the reflected wave seismic data volume, diffusion filtering (diffusion filtering is coherent enhanced anisotropic filtering) is performed to directionally smooth the seismic phase axis, suppress anisotropic noise caused by residual diffraction waves, improve the lateral continuity of the seismic phase axis, enhance the imaging capability of reflected waves for approximately equivalent layered homogeneous media, extract the seismic reflected wave data volume, remove the residual diffraction wave energy, improve lateral continuity, and obtain the seismic data volume of approximately equivalent layered homogeneous media.

[0057] S3. Subtract the diffuse-filtered seismic data volume from the seismic data volume after resolution improvement to obtain the difference between the two data volumes. This difference represents the residual diffraction wave and scattering noise in the post-stack seismic data.

[0058] S4. Bandpass filtering is performed on the difference in step S3 to remove high-frequency scattering noise and obtain the post-stack seismic residual diffraction wave data volume. The larger the value of the data volume, the more residual diffraction waves there are, the stronger the heterogeneity of the volcanic buried hill, and the more developed the reservoir.

[0059] In this embodiment, the method for obtaining the residual diffraction wave of the post-stack seismic data includes: performing bandpass filtering on the difference in step S3, determining the diffraction wave filtering parameters using time-spectrum analysis, and obtaining the residual diffraction wave data volume of the post-stack seismic data.

[0060] S5. Based on the post-stack seismic residual diffraction data volume, seismic attributes are extracted. The instantaneous amplitude, root mean square amplitude, and other amplitude attributes of the diffraction data volume are obtained to obtain the attribute volume that characterizes the spatial distribution range of volcanic rock buried hill reservoirs. The larger the anomaly value of the attribute volume, the stronger the heterogeneity of the volcanic rock buried hill and the more developed the reservoir, thereby achieving the purpose of predicting the distribution range of volcanic rock buried hill reservoirs.

[0061] In this embodiment, the residual diffraction waves from post-stack seismic data are used to perform diffraction wave attribute analysis and extraction, thereby predicting the distribution range of volcanic buried hill reservoirs.

[0062] The following example, using the prediction of a buried hill reservoir in a volcanic rock as a specific embodiment, further illustrates the volcanic rock buried hill reservoir prediction method based on residual diffraction waves of the present invention.

[0063] The method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves provided in this embodiment includes:

[0064] 1. The post-stack seismic data used for predicting buried hill reservoirs in this oilfield were processed to improve resolution, resulting in a seismic data volume capable of recovering the reflected wave energy of low-frequency seismic data and the residual diffraction wave energy of mid-frequency seismic data, such as... Figure 2 As shown.

[0065] 2. Bandpass filtering was used to filter the improved seismic data volume. Time-spectrum analysis was employed to determine the filtering parameters. In the time spectrum, the low-frequency, high-amplitude energy is predominantly the response of reflected waves. Parameters were obtained from the time-spectrum analysis, and bandpass filtering was used to obtain the reflected wave seismic data volume. This portion of the seismic data volume consists mostly of reflected waves, containing a small portion of residual diffracted waves. Based on the bandpass-filtered seismic data, diffusion filtering was performed to remove residual anisotropic energy, improve lateral continuity, and obtain seismic data representing an approximately equivalent layered homogeneous medium. The seismic reflected wave data volume was then extracted, as shown below. Figure 3 As shown.

[0066] 3. Subtract the diffuse-filtered seismic data volume from the seismic data volume obtained in step 2 after the resolution improvement in step 1. The difference between the two data volumes is the residual diffraction wave and scattering noise of the stacked seismic data.

[0067] 4. Perform bandpass filtering on the difference in step 3. Use time-frequency spectrum analysis to determine the filtering parameters. In the time spectrum, most of the energy in the mid-frequency and weak amplitude regions is diffraction wave, while the energy in the high-frequency and weak amplitude regions is scattering noise. Obtain the bandpass filtering parameters from the time-frequency spectrum analysis, remove the high-frequency scattering noise, and obtain the remaining diffraction wave data volume of the post-stack seismic data.

[0068] 5. Based on the residual diffraction wave data volume of the post-stack seismic data in step 4, diffraction wave seismic attribute analysis and extraction are performed to obtain the attribute volume that characterizes the spatial distribution range of volcanic rock buried hill reservoirs, and finally predict the distribution of volcanic rock buried hill reservoirs.

[0069] Example 2: Following the method for predicting buried volcanic hill reservoirs based on residual diffraction waves provided in Example 1, this example provides a device for predicting buried volcanic hill reservoirs based on residual diffraction waves. The device provided in this example can implement the method for predicting buried volcanic hill reservoirs based on residual diffraction waves described in Example 1. This device can be implemented through software, hardware, or a combination of both. For ease of description, this example is described by dividing the function into various units. Of course, in implementation, the functions of each unit can be implemented in one or more software and / or hardware components. For example, the device may include integrated or separate functional modules or units to perform the corresponding steps in the methods of Example 1. Since the device in this example is basically similar to the method example, the description process of this example is relatively simple. For relevant details, please refer to the description in Example 1. The example of the device for predicting buried volcanic hill reservoirs based on residual diffraction waves provided by this invention is merely illustrative.

[0070] Specifically, this embodiment provides a device for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves, comprising:

[0071] The data processing unit is configured to perform resolution enhancement processing on the post-stack seismic data volume to be predicted.

[0072] The diffusion filtering unit is configured to sequentially perform bandpass filtering and diffusion filtering on the seismic data volume after resolution enhancement processing to obtain the diffusion-filtered seismic data volume.

[0073] The difference calculation unit is configured to subtract the diffuse-filtered seismic data volume from the resolution-upgraded seismic data volume to obtain the difference between the two data volumes.

[0074] The difference filtering unit is configured to perform bandpass filtering on the difference between two data volumes to obtain the post-stack seismic residual diffraction wave data volume.

[0075] The reservoir prediction unit is configured to extract the seismic attributes of the post-stack seismic residual diffraction wave data volume, obtain the attribute volume characterizing the spatial distribution range of volcanic buried hill reservoirs based on the seismic attributes, and predict the distribution of volcanic buried hill reservoirs.

[0076] Example 3: This example provides an electronic device corresponding to the volcanic buried hill reservoir prediction method based on residual diffraction waves provided in Example 1. The electronic device can be an electronic device for the client, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Example 1.

[0077] like Figure 7 As shown, the electronic device includes a processor, memory, communication interface, and bus. The processor, memory, and communication interface are connected via the bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the method of Embodiment 1. The implementation principle and technical effects are similar to those of Embodiment 1, and will not be repeated here. Those skilled in the art will understand that... Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computing device on which the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0078] In a preferred embodiment, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), and optical discs.

[0079] In a preferred embodiment, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0080] Example 4: This example provides a computer program product. The computer program product may include a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by the computer, the computer can execute the method provided in Example 1 above. Its implementation principle and technical effects are similar to those in Example 1, and will not be repeated here.

[0081] In a preferred embodiment, the computer-readable storage medium may be a tangible device for holding and storing instructions used by an instruction execution device, such as, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided in Embodiment 1 above.

[0082] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In the description of this specification, the terms "a preferred embodiment," "furthermore," "specifically," "in this embodiment," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0083] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves, characterized in that, include: The resolution of the post-stack seismic data volume to be predicted is improved. The seismic data volume after resolution enhancement is sequentially subjected to bandpass filtering and diffusion filtering to obtain the diffusion-filtered seismic data volume. Subtract the diffuse-filtered seismic data volume from the seismic data volume processed with higher resolution to obtain the difference between the two data volumes; The difference between the two data volumes is processed by bandpass filtering to obtain the post-stack seismic residual diffraction wave data volume. Seismic attributes are extracted from the residual diffraction wave data volume of post-stack earthquakes. Based on the seismic attributes, an attribute volume is obtained that characterizes the spatial distribution range of volcanic buried hill reservoirs, and the distribution of volcanic buried hill reservoirs is predicted. To improve the resolution of the predicted post-stack seismic data volume, the following steps are taken: the mixed phase wavelet is extracted in the complex spectral domain using the mixed phase wavelet deconvolution method, the anti-wavelet is calculated given the desired wavelet, and the convolution is performed with the seismic trace to form a frequency-upgraded seismic record, thereby improving the longitudinal resolution of the seismic data. The upscaled seismic data volume is then subjected to bandpass filtering and diffusion filtering sequentially to obtain the diffusion-filtered seismic data volume, which includes: By using bandpass filtering technology, the improved seismic data volume is filtered to obtain the reflected wave seismic data volume; Diffusion filtering based on reflected wave seismic data volume includes: directional smoothing of the seismic phase axis, suppression of anisotropic noise caused by residual diffraction waves, extraction of seismic reflected wave data volume, removal of residual diffraction wave energy, and obtaining a seismic data volume of an approximately equivalent layered homogeneous medium.

2. The method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves according to claim 1, characterized in that, Bandpass filtering is used to filter the seismic data volume after resolution improvement. This includes: performing time-spectrum analysis on the seismic data volume before resolution improvement, selecting the width between two frequency values ​​where the amplitude spectrum is equal to 1 / 5 of the maximum value of the spectrum as the reflection wave bandwidth, selecting the highest frequency of this bandwidth as the upper limit value of the bandpass filter, and selecting values ​​from 0 to the upper limit value as the bandpass filter parameters for the reflection wave to obtain the reflection wave seismic data volume.

3. The method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves according to claim 1, characterized in that, The difference between the two data volumes is bandpass filtered to obtain the post-stack seismic residual diffraction data volume. This includes: performing bandpass filtering on the difference between the two data volumes, determining the diffraction filtering parameters using time-spectrum analysis, and obtaining the post-stack seismic residual diffraction data volume.

4. The method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves according to claim 1, characterized in that, The seismic properties of the post-stack earthquake residual diffraction data volume include the instantaneous amplitude and root mean square amplitude of the diffraction data volume.

5. The method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves according to claim 1, characterized in that, Based on seismic attributes, attribute volumes are obtained to characterize the spatial distribution range of volcanic buried hill reservoirs. The distribution of volcanic buried hill reservoirs is predicted, including: the larger the anomaly value of the attribute volume, the stronger the heterogeneity of the volcanic buried hill and the more developed the reservoir, thereby realizing the prediction of the distribution range of volcanic buried hill reservoirs.

6. An apparatus for implementing the method for predicting buried hill reservoirs in volcanic rocks based on residual diffraction waves as described in any one of claims 1-5, characterized in that, include: The data processing unit is configured to perform resolution enhancement processing on the post-stack seismic data volume to be predicted. The diffusion filtering unit is configured to sequentially perform bandpass filtering and diffusion filtering on the seismic data volume after resolution enhancement processing to obtain the diffusion-filtered seismic data volume. The difference calculation unit is configured to subtract the diffuse-filtered seismic data volume from the resolution-upgraded seismic data volume to obtain the difference between the two data volumes. The difference filtering unit is configured to perform bandpass filtering on the difference between two data volumes to obtain the post-stack seismic residual diffraction wave data volume. The reservoir prediction unit is configured to extract the seismic attributes of the post-stack seismic residual diffraction wave data volume, obtain the attribute volume characterizing the spatial distribution range of volcanic buried hill reservoirs based on the seismic attributes, and predict the distribution of volcanic buried hill reservoirs.

7. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 5.

8. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 5.

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