Seismic frequency expanding method, system and equipment for seismic frequency division and inverse Q filtering and medium

Abstract

The invention relates to a seismic frequency broadening method, system, equipment and medium for seismic frequency division and inverse Q filtering, and the method comprises the steps: carrying out the spectrum analysis of post-stack seismic data, and decomposing the original post-stack seismic data of a target interval into frequency division data bodies of different frequency bands through generalized S transformation; based on the deep logging curve, a Q value curve is obtained through estimation by means of a preset Q value fitting formula; establishing a three-dimensional Q-value data volume by using the Q-value curve obtained by fitting and combining a seismic interpretation result; on the basis of the three-dimensional Q-value data volume, performing inverse Q filtering processing on the obtained frequency division data volumes of the medium and high frequency bands, and combining the frequency division data volumes of the medium and high frequency bands after inverse Q filtering processing with the frequency division data volumes of the low frequency bands; and based on the combined post-stack seismic data volume, carrying out seismic frequency expansion by taking an artificially synthesized seismic record as quality control, and carrying out seismic interpretation and sedimentary unit fine description. The method can be widely applied to the technical field of oil-gas exploration and development.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a seismic frequency extension method, system, equipment and medium based on seismic frequency division and inverse Q filtering. Background Art

[0002] Seismic frequency bands play a vital role in fine seismic interpretation and structural characterization. However, due to the limitation of wavelet bandwidth, the lack of high-frequency and low-frequency information makes seismic data insufficient in identifying geological bodies and has low resolution, which brings obstacles to reservoir prediction. The low-frequency signal in seismic data represents the macroscopic information of geological bodies, and the high-frequency signal represents the microstructure inside the geological body. Broadband seismic data can effectively improve the ability to finely characterize underground structures and sediments, and plays a very important role in using seismic data for seismic phase identification and high-precision reservoir prediction.

[0003] 1. The patent "Phase-controlled random inversion thin reservoir prediction method based on seismic frequency extension processing" (patent number: CN201510151812.0) proposes a phase-controlled random inversion thin reservoir prediction method based on seismic frequency extension processing. This method uses post-stack seismic interpretation data and well logging for fine reservoir calibration, improves the resolution of seismic data of the target layer based on frequency extension technology, and combines sedimentary phase reservoirs to carry out random inversion and thin reservoir prediction. This method uses inverse Q energy compensation for well logging constrained frequency extension processing, and does not process seismic signals at different depths and different frequency bands.

[0004] 2. The document "Application of Bidirectional Spreading High-Resolution Seismic Technology in Wuxia Fault Zone" (Progress in Geophysics, Vol. 37, No. 1, 2022, pp. 0201-0212) proposes a bidirectional spread-frequency high-resolution post-stack seismic technology and uses it for the spread-frequency processing of 3D seismic data in the Wuxia Fault Zone of the Junggar Basin. This method can effectively widen the seismic frequency band. The method in this document is mainly used to improve the ability to characterize seismic fault information, while this patent is mainly used to improve the internal stratum reflection structure of thin reservoirs and sedimentary bodies. The two have different spread-frequency processing methods and purposes, and each has its own advantages.

[0005] 3. The paper “Time-space-variant adaptive inverse Q filtering” (Petroleum Science Bulletin, Vol. 4, No. 2, 2019, pp. 123-133). This paper designed the inverse Q filter gain parameters related to the local signal-to-noise ratio of the data, and made adaptive parameter adjustments for seismic signals with different signal-to-noise ratios. Summary of the invention

[0006] Conventional seismic data often have low main frequency and small effective frequency band width, which makes it difficult to meet the problem of fine characterization of sedimentary units such as fan deltas and their internal structures. The purpose of the present invention is to provide a seismic frequency division and inverse Q filtering seismic frequency extension method, system, equipment and medium to perform frequency division analysis on pre-stack seismic and perform stable inverse Q filtering on the frequency division data body. Actual cases show that the present invention can effectively increase the main frequency of earthquakes and widen the seismic frequency band width, thereby improving the ability to finely characterize seismic thin reservoirs and the internal structure of sedimentary units.

[0007] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a seismic frequency division and anti-Q filtering seismic frequency extension method, comprising the following steps: Perform spectrum analysis on post-stack seismic data, and use generalized S transform to decompose the original post-stack seismic data of the target layer into frequency-divided data volumes of different frequency bands; Based on the deep well logging curve, the Q value curve is estimated using the preset Q value fitting formula; The three-dimensional Q value data volume is established by using the fitted Q value curve and combining the seismic interpretation results. Based on the three-dimensional Q value data volume, an inverse Q filtering process is performed on each frequency division data volume of the obtained mid-high frequency band, and the frequency division data volume of the mid-high frequency band after the inverse Q filtering process is merged with each frequency division data volume of the low frequency band; Based on the merged post-stack seismic data volume, seismic frequency extension is performed using artificial synthetic seismic records as quality control, and seismic interpretation and detailed characterization of sedimentary units are carried out.

[0008] Further, the post-stack seismic data is subjected to spectral analysis, and the original post-stack seismic data of the target layer segment is decomposed into frequency-divided data bodies of different frequency bands by using generalized S transform, including; Acquire the seismic signal characteristics of the target layer based on the post-stack seismic data, and determine the number of frequency division data volumes and the frequency range of each frequency division data volume in the seismic frequency division processing according to the seismic signal characteristics; Based on the determined number of frequency-divided data volumes and frequency range, the post-stack seismic data of the target layer are frequency-divided and processed using generalized S transform to obtain multiple frequency-divided data volumes in different frequency bands.

[0009] Further, the Q value curve is estimated based on the deep well logging curve using a preset Q value fitting formula, including: The deep logging curve is converted into an initial Q value curve using a preset Q value fitting formula; The initial Q value curve is smoothed to obtain the final Q value curve.

[0010] Furthermore, the preset Q value fitting formula is:

[0011] In the formula, is the logging P-wave velocity.

[0012] Furthermore, the method of using the fitted Q-value curve in combination with the seismic interpretation results to establish a three-dimensional Q-value data body means: using the fitted Q-value curve in combination with known seismic interpretation stratigraphic information and acoustic logging information to establish a three-dimensional Q-value data body of the target layer segment through well-constrained velocity modeling technology.

[0013] Furthermore, the step of performing inverse Q filtering on each frequency division data body in the obtained mid-high frequency band includes: The anti-Q compensation is performed on the frequency division data volume and the three-dimensional Q value data volume of the mid-high frequency band, which is expressed as:

[0014] in, is the absorption attenuation measurement matrix, is the seismic data obtained after inverse Q filtering, The Fourier transform spectrum of the frequency-divided data volume representing the input mid-high frequency band; A non-sparse smooth constraint is imposed on the inverse Q compensation result, namely: (6) in, It is a trade-off factor used to adjust the resolution of noise and Q compensation.

[0015] Furthermore, the combined post-stack seismic data volume is used to perform seismic frequency extension using synthetic seismic records as quality control, and seismic interpretation and detailed characterization of sedimentary units are carried out, including: Based on the well logging curve, complete the well seismic calibration of artificial synthetic seismic records, spectrum analysis of seismic signals in the target layer, and analysis of the characteristics of well logging sedimentary phases; Seismic frequency extension was performed using well-seismic calibration, spectrum analysis and sedimentary facies characteristics as quality control; The processed topology data volume is used to carry out seismic interpretation and detailed characterization of sedimentary units.

[0016] In a second aspect, the present invention provides a seismic frequency division and anti-Q filtering seismic frequency extension system, comprising: The generalized S-transform module is used to perform spectrum analysis on post-stack seismic data and decompose the original post-stack seismic data of the target layer into frequency-divided data volumes of different frequency bands using the generalized S-transform; A Q value curve fitting module is used to estimate the Q value curve based on the deep well logging curve using a preset Q value fitting formula; A three-dimensional data volume building module is used to build a three-dimensional Q value data volume by using the fitted Q value curve combined with the seismic interpretation results; A frequency merging module, for performing an inverse Q filtering process on each frequency division data volume in the middle and high frequency bands based on the three-dimensional Q value data volume, and merging the frequency division data volume in the middle and high frequency bands after the inverse Q filtering process with each frequency division data volume in the low frequency band; The seismic frequency extension module is used to perform seismic frequency extension based on the merged post-stack seismic data volume, using artificial synthetic seismic records as quality control, and to carry out seismic interpretation and detailed characterization of sedimentary units.

[0017] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any method.

[0018] In a fourth aspect, the present invention provides a computing device, comprising: one or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, and the one or more programs include instructions for executing any method.

[0019] The present invention adopts the above technical solution, which has the following advantages: 1. The present invention adopts seismic frequency division and inverse Q filtering, which can effectively widen the seismic frequency band width under well constraints and improve the resolution of post-stack seismic data.

[0020] 2. The present invention decomposes the seismic signal into different frequency bands through seismic frequency division analysis, and focuses on enhanced spectrum spreading processing for medium and high frequency signals, making the seismic spectrum spreading more targeted for the target layer.

[0021] 3. The present invention constructs inverse Q filtering parameters through fine comparison of well seismic data volume. For a specific work area, the processing parameters and spectrum extension results of this method are more targeted.

[0022] Therefore, the present invention can be widely applied in the technical field of oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings: Figure 1 It is a flow chart of the earthquake frequency division and anti-Q filtering earthquake frequency extension method provided in an embodiment of the present invention; Figure 2 is a flow chart of the spectrum spreading technology provided in an embodiment of the present invention; Figure 3 It is an amplitude spectrum of a target layer section in a certain work area provided in an embodiment of the present invention; Figure 4 This is a comparison of seismic traces before and after frequency spreading processing of a well A in a certain work area provided in an embodiment of the present invention; Figure 5 It is a comparison of amplitude spectra before and after frequency spreading processing of seismic traces near a well A in a certain work area provided in an embodiment of the present invention; Figure 6a and Figure 6b This is a comparison of the seismic profile of a well B in a certain work area before and after the frequency extension processing provided in an embodiment of the present invention, wherein: Figure 6a Before processing, Figure 6b After processing. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] Conventional seismic data has a limited bandwidth. Due to the need to take into account the quality of seismic signals at different depths, deep earthquakes often have severe attenuation of high-frequency signals and a low main frequency, making it difficult to conduct fine structural analysis of seismic sedimentary phases and characterize thin reservoirs.

[0027] Based on this, in some embodiments of the present invention, a seismic frequency extension method based on seismic frequency division and inverse Q filtering is provided, comprising the following steps: performing S transformation on post-stack seismic data to obtain frequency division data bodies of different frequency bands; performing spectrum analysis on each frequency division data body, establishing a Q value data body using a Q value curve fitted by an acoustic logging curve, performing inverse Q value filtering on the mid-high frequency band frequency division data body, and then merging each frequency division data body to obtain a post-stack seismic data body after frequency extension. The present invention can effectively improve the longitudinal resolution of seismic data by performing inverse Q filtering on the mid-high frequency band seismic body after seismic frequency division, and provide a basis for fine seismic interpretation and fine seismic dissection of sedimentary systems. It can be used for marine clastic reservoir prediction and fine sedimentary phase characterization.

[0028] Correspondingly, in some other embodiments of the present invention, a seismic frequency extension system, device and medium based on seismic frequency division and inverse Q filtering are provided.

[0029] Example 1 like Figure 1 As shown, the present invention provides a seismic frequency division and anti-Q filtering seismic frequency extension method, which comprises the following steps: 1) Perform spectrum analysis on post-stack seismic data and use generalized S transform to decompose the original post-stack seismic data of the target layer into frequency-divided data volumes of different frequency bands; 2) Based on the deep well logging curve, the Q value curve is estimated using the preset Q value fitting formula; 3) Using the fitted Q value curve and combining it with the seismic interpretation results, a three-dimensional Q value data volume is established; 4) Based on the three-dimensional Q value data volume, perform inverse Q filtering on each frequency division data volume of the middle and high frequency bands obtained in step 1), and merge the frequency division data volume of the middle and high frequency bands after the inverse Q filtering with each frequency division data volume of the low frequency band; 5) Based on the merged post-stack seismic data volume, seismic frequency extension is performed using artificial synthetic seismic records as quality control, and seismic interpretation and detailed characterization of sedimentary units are carried out.

[0030] Furthermore, the above step 1) includes the following steps: 1.1) Based on the post-stack seismic data, the amplitude spectrum and phase spectrum, main frequency, bandwidth and other seismic signal characteristics of the target layer are obtained, and the number of frequency division data volumes and the frequency range of each frequency division data volume for seismic frequency division processing are determined; 1.2) Based on the determined number of frequency-divided data volumes and frequency range, the post-stack seismic data of the target layer are frequency-divided and processed using the generalized S transform to obtain multiple frequency-divided data volumes in different frequency bands.

[0031] Furthermore, in the above step 1.2), when the seismic wave propagates in the underground medium, the non-complete elasticity and non-uniformity of the underground medium cause the amplitude energy of the seismic wave to attenuate and phase distort, resulting in reduced resolution of the seismic data. Due to the absorption and attenuation of the underground medium, the energy attenuation and velocity dispersion of the high-frequency components of the seismic wave are faster than those of the low-frequency components. Therefore, the amount of information retained by the high-frequency components is lower than that of the low-frequency components, which increases the difficulty of seismic data interpretation to a certain extent. This attenuation mainly comes from two parts, one is the geometric attenuation caused by the propagation properties of the seismic wave, and the other is the inherent attenuation caused by the internal factors of the underground medium, which is represented by the quality factor Q. The Q value can quantitatively represent the absorption attenuation, which can provide an effective reference for oil and gas detection, reservoir prediction, etc., and also provide a reliable basis for absorption attenuation compensation.

[0032] Frequency analysis of seismic data is achieved through generalized S-transform. S-transform is a method of time-frequency analysis that combines the advantages of short-time Fourier transform (STFT) and wavelet transform and has variable frequency resolution.

[0033] The S transform is defined as: , (1) in, is the time shift parameter, indicating the position of the window on the time axis; is a frequency variable; is the Gaussian window function, expressed as: (2) This function reflects the seismic signal The localization degree of the low-frequency component can ensure a wider time window, while the high-frequency component has a narrower time window, thereby providing frequency-adaptive time resolution. The original seismic signal can be decomposed into seismic signals of different frequency bands by using the generalized S transform. Then, the high-frequency band signal is enhanced by using the inverse Q filtering method, which can effectively improve the vertical resolution of the seismic signal.

[0034] Furthermore, the above step 2) includes the following steps: 2.1) The deep logging curve, i.e., the P-wave velocity curve, is converted into an initial Q-value curve using a preset Q-value fitting formula; 2.2) Smoothing the initial Q value curve in step 2.1) and fitting to obtain a Q value curve.

[0035] Furthermore, in the above step 2.1), this embodiment adopts the Q value fitting formula proposed by Li Qingzhong in 1994 to perform Q value curve fitting, which is expressed as: (3) In the formula, is the logging P-wave velocity.

[0036] Furthermore, in the above step 3), using the fitted Q value curve in combination with the seismic interpretation results to establish a three-dimensional Q value data body means: using the fitted Q value curve in combination with known seismic interpretation layer information and acoustic logging information, through the well constraint velocity modeling technology, to establish a three-dimensional Q value data body of the target layer. The method for establishing the three-dimensional Q value data body can adopt the technology known to those skilled in the art, and the present invention does not limit this.

[0037] Furthermore, in the above step 4), the inverse Q filtering method can compensate and correct the seismic data in terms of amplitude attenuation and phase distortion, thereby improving the resolution of the seismic data. Inverse Q filtering requires the quality factor Q value of the formation, and its accuracy directly affects the accuracy of the inverse Q filtering. From the existing drilling speed, the inverse Q filtering quality factor Q is obtained, which can, to a certain extent, construct a relatively stable inversion model.

[0038] The absorption attenuation during seismic wave propagation can be expressed as: (4) Where M represents the number of sampling points of seismic recording signals, is the angular frequency, is the Fourier transform spectrum of seismic data, is the seismic data after Q value compensation, is the Q absorption attenuation factor.

[0039] In the Q value compensation process, the input is the seismic data and the previously estimated Q value, and the seismic wavelet is not used. The output is the seismic data after Q value compensation. Formula (4) is discretized into a linear equation system and written as the following relationship: (5) in, is the absorption attenuation measurement matrix, is the seismic data obtained after inverse Q filtering, Represents the Fourier transform spectrum of the input seismic data. Because the seismic record curve obtained by inverse Q compensation is smooth and non-sparse, in order to find the optimal solution, this embodiment imposes a non-sparse smooth constraint condition on the inverse Q compensation result, namely: (6) in, As a compromise adjustment factor, used to adjust the resolution of noise and Q compensation, the least squares QR decomposition algorithm is used to optimize the solution to obtain a stable inverse Q filtering result.

[0040] Furthermore, in the above step 5), if Figure 2 As shown, the following steps are included: 5.1) Based on the well logging curve, complete the well seismic calibration of artificial synthetic seismic records, spectrum analysis of seismic signals in the target layer, and analysis of the characteristics of well logging sedimentary phases.

[0041] Among them, the well seismic calibration of artificial synthetic seismic records, the spectrum analysis of seismic signals of the target layer segment and the analysis of the characteristics of the logging sedimentary phases can be performed using the techniques known to those skilled in the art, which will not be elaborated in the present invention.

[0042] 5.2) Use well-seismic calibration, spectrum analysis and sedimentary facies characteristics as quality control for seismic frequency extension.

[0043] In this embodiment, the processing parameters are optimized and the processing effect is improved by comparing and analyzing the artificial synthetic seismic records and the seismic traces near the well before and after processing; the well seismic calibration is mainly to clarify the target layer range of the post-stack seismic frequency division processing; the spectrum analysis is mainly to obtain the frequency band range of the target layer segment for the frequency extension processing and to check the spectrum changes before and after the frequency extension; the sedimentary phase characteristic analysis is to accurately identify the sedimentary unit on the seismic profile and to clarify whether the frequency extension processing has achieved the requirement of enhancing the internal details of the sedimentary body.

[0044] 5.3) Use the processed topology data volume to carry out seismic interpretation and detailed characterization of sedimentary units.

[0045] Example 2 This example takes a lacustrine clastic rock formation in a certain work area as an example, and analyzes the spectrum of different depths (shallow, medium and deep) Figure 3 ), the main frequency of the shallow layer is 20Hz, the effective frequency band width is 5-50Hz, the main frequency of the middle and deep layer is only 18Hz, and the effective frequency band width is 5-45Hz. It is difficult to use the existing seismic data to accurately characterize the sedimentary units.

[0046] Figure 4 These are the comparison results of seismic traces near Well A before and after frequency extension in the work area. Figure 5 This is a comparison of the amplitude spectra of the A-well cross-well seismic profile before and after the extension processing. The main frequency of the seismic is extended from 20Hz to 28Hz, and the effective bandwidth is extended from 5-50Hz to 5-65Hz. After the extension processing, the weak signal is effectively enhanced, and the complex wave event axis of the deep reflection signal is decomposed. According to the characteristics of the seismic spectrum, 20Hz and 30Hz Ricker wavelets are used to make artificial synthetic seismic records, which are compared with the cross-well seismic profiles before and after the extension. After the extension processing, the seismic profile has a good correspondence with the 30Hz Ricker wavelet artificial synthetic record, and the enhanced medium and high frequency signals have a corresponding relationship with the thin reservoir. Among them, the weak reflection at 1910ms is enhanced, the complex wave at 1925ms is decomposed, and the formation contact relationship at 2260ms is clearer. The calibration results of the artificial synthetic seismic records before and after the extension provide quality control for the extension processing, verify the credibility of the extension processing parameters and processing results; on the other hand, it also shows that the method of the present invention can effectively improve the fine characterization ability of thin reservoir lithology changes and formation structure changes.

[0047] Figure 6a and Figure 6bThis is a comparison of the seismic profile of Well B in the work area before and after frequency extension processing. The well encountered a lake bottom fan at 1790ms-1920ms. The resolution of the original post-stack seismic data is limited, and the internal structure is difficult to analyze. After frequency extension processing, the seismic reflection complex wave phase axis is decomposed, the details of the lake bottom fan in the target layer are increased, and the contact relationship of the formation is improved, which is conducive to further fine characterization of the internal structure of the lake bottom fan.

[0048] Example 3 The above-mentioned embodiment 1 provides a seismic frequency extension method based on seismic frequency division and inverse Q filtering. Correspondingly, this embodiment provides a seismic frequency extension system based on seismic frequency division and inverse Q filtering. The system provided in this embodiment can implement the seismic frequency extension method based on seismic frequency division and inverse Q filtering of embodiment 1, and the system can be implemented by software, hardware or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the process described in this embodiment is relatively simple, and the relevant parts can refer to the partial description of embodiment 1. The embodiment of the system provided in this embodiment is only schematic.

[0049] The seismic frequency extension system based on seismic frequency division and inverse Q filtering provided in this embodiment includes: The generalized S-transform module is used to perform spectrum analysis on post-stack seismic data and decompose the original post-stack seismic data of the target layer into frequency-divided data volumes of different frequency bands using the generalized S-transform. A Q value curve fitting module is used to estimate the Q value curve based on the deep well logging curve using a preset Q value fitting formula; A three-dimensional data volume building module is used to build a three-dimensional Q value data volume by using the fitted Q value curve combined with the seismic interpretation results; A frequency merging module, for performing an inverse Q filtering process on each frequency division data volume in the middle and high frequency bands based on the three-dimensional Q value data volume, and merging the frequency division data volume in the middle and high frequency bands after the inverse Q filtering process with each frequency division data volume in the low frequency band; The seismic frequency extension module is used to perform seismic frequency extension based on the merged post-stack seismic data volume, using artificial synthetic seismic records as quality control, and to carry out seismic interpretation and detailed characterization of sedimentary units.

[0050] Example 4 This embodiment provides a processing device corresponding to the seismic frequency division and inverse Q filtering based seismic frequency spreading method provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0051] The processing device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The memory stores a computer program that can be run on the processor, and the processor executes the seismic frequency division and inverse Q filtering method provided in this embodiment 1 when running the computer program.

[0052] Preferably, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0053] Preferably, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors of various types, which are not limited here.

[0054] Example 5 The seismic frequency extension method based on seismic frequency division and inverse Q filtering of this embodiment 1 can be specifically implemented as a computer program product, which may include a computer-readable storage medium carrying computer-readable program instructions for executing the seismic frequency extension method based on seismic frequency division and inverse Q filtering described in this embodiment 1.

[0055] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0056] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 One or more processes and / or boxes Figure 1 A function specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A seismic frequency division and inverse Q filtering seismic frequency extension method, characterized in that: The following steps are involved: Perform spectrum analysis on post-stack seismic data, and use generalized S transform to decompose the original post-stack seismic data of the target layer into frequency-divided data volumes of different frequency bands; Based on the deep well logging curve, the Q value curve is estimated using the preset Q value fitting formula; The three-dimensional Q value data volume is established by using the fitted Q value curve and combining the seismic interpretation results. Based on the three-dimensional Q value data volume, an inverse Q filtering process is performed on each frequency division data volume of the obtained mid-high frequency band, and the frequency division data volume of the mid-high frequency band after the inverse Q filtering process is merged with each frequency division data volume of the low frequency band; Based on the merged post-stack seismic data volume, seismic frequency extension is performed using artificial synthetic seismic records as quality control, and seismic interpretation and detailed characterization of sedimentary units are carried out.

2. The earthquake frequency division and anti-Q filtering earthquake frequency extension method according to claim 1, characterized in that: The post-stack seismic data is subjected to spectral analysis, and the original post-stack seismic data of the target layer segment is decomposed into frequency-divided data bodies of different frequency bands by using the generalized S transform, including; Acquire the seismic signal characteristics of the target layer based on the post-stack seismic data, and determine the number of frequency division data volumes and the frequency range of each frequency division data volume in the seismic frequency division processing according to the seismic signal characteristics; Based on the determined number of frequency-divided data volumes and frequency range, the post-stack seismic data of the target layer are frequency-divided and processed using generalized S transform to obtain multiple frequency-divided data volumes in different frequency bands.

3. The earthquake frequency division and anti-Q filtering earthquake frequency extension method according to claim 1, characterized in that: The method of estimating a Q value curve based on a deep well logging curve using a preset Q value fitting formula includes: The deep logging curve is converted into an initial Q value curve using a preset Q value fitting formula; The initial Q value curve is smoothed to obtain the final Q value curve.

4. The earthquake frequency division and anti-Q filtering earthquake frequency extension method according to claim 3, characterized in that: The preset Q value fitting formula is: In the formula, is the logging P-wave velocity.

5. The earthquake frequency division and anti-Q filtering earthquake frequency extension method according to claim 1, characterized in that: The method of using the fitted Q-value curve in combination with the seismic interpretation results to establish a three-dimensional Q-value data body refers to: using the fitted Q-value curve in combination with known seismic interpretation layer information and acoustic logging information, and through well-constrained velocity modeling technology, a three-dimensional Q-value data body of the target layer segment is established.

6. The earthquake frequency division and anti-Q filtering earthquake frequency extension method according to claim 1, characterized in that: The step of performing an inverse Q filtering process on each frequency division data body in the obtained mid-high frequency band comprises: The anti-Q compensation is performed on the frequency division data volume and the three-dimensional Q value data volume of the mid-high frequency band, which is expressed as: in, is the absorption attenuation measurement matrix, is the seismic data obtained after inverse Q filtering, The Fourier transform spectrum of the frequency-divided data volume representing the input mid-high frequency band; A non-sparse smooth constraint is imposed on the inverse Q compensation result, namely: （6） in, It is a trade-off factor used to adjust the resolution of noise and Q compensation.

7. The earthquake frequency division and anti-Q filtering earthquake frequency extension method according to claim 1, characterized in that: Based on the merged post-stack seismic data volume, the seismic frequency extension is performed using artificial synthetic seismic records as quality control, and seismic interpretation and detailed characterization of sedimentary units are carried out, including: Based on the well logging curve, complete the well seismic calibration of artificial synthetic seismic records, spectrum analysis of seismic signals in the target layer, and analysis of the characteristics of well logging sedimentary phases; Seismic frequency extension was performed using well-seismic calibration, spectrum analysis and sedimentary facies characteristics as quality control; The processed topology data volume is used to carry out seismic interpretation and detailed characterization of sedimentary units.

8. A seismic frequency division and anti-Q filtering seismic frequency extension system, characterized in that: include: The generalized S-transform module is used to perform spectrum analysis on post-stack seismic data and decompose the original post-stack seismic data of the target layer into frequency-divided data volumes of different frequency bands using the generalized S-transform. A Q value curve fitting module is used to estimate the Q value curve based on the deep well logging curve using a preset Q value fitting formula; A three-dimensional data volume building module is used to build a three-dimensional Q value data volume by using the fitted Q value curve combined with the seismic interpretation results; A frequency merging module, for performing an inverse Q filtering process on each frequency division data volume in the middle and high frequency bands based on the three-dimensional Q value data volume, and merging the frequency division data volume in the middle and high frequency bands after the inverse Q filtering process with each frequency division data volume in the low frequency band; The seismic frequency extension module is used to perform seismic frequency extension based on the merged post-stack seismic data volume, using artificial synthetic seismic records as quality control, and to carry out seismic interpretation and detailed characterization of sedimentary units.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that: include: One or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, wherein the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

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