Method, device, medium and equipment for processing seismic data volume of mixed deposit reservoir

Abstract

The present application relates to a kind of mixed accumulation rock reservoir seismic data body processing method, device, medium and equipment, method includes: the shaping regularization algorithm is incorporated into centroid frequency shift method, the local centroid frequency and variance of seismic data are calculated channel by channel, obtain continuous time-varying Q factor;Based on two-dimensional hilbert transform estimation seismic data local dip, construct the structure-oriented filter operator of anti-noise, to seismic data is denoised, obtains denoised seismic data;Based on continuous time-varying Q factor, using anti-Q filter with stability factor, to seismic data is handled, obtains stable anti-Q filter seismic data;To denoised seismic data and stable anti-Q filter seismic data, after executing anti-Q filter operation, change will be converted into time domain signal from frequency domain signal, obtain attenuation compensation result.The method adapts to offshore deepwater, few well no well, structural complex block seismic data processing, and can be used for complex subtle trap identification, reservoir lateral prediction, stratum fine interpretation and oil and gas favorable zone evaluation.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for processing seismic data volumes of mixed sedimentary reservoirs, belonging to the field of geophysical exploration technology. Background Technology

[0002] With the deepening of unconventional oil and gas exploration and development, unconventional oil and gas has gradually become the main battleground for major oil and gas discoveries in my country. However, oil and gas resource exploration and development also face new technical challenges and difficulties. Taking the prediction of target reservoirs such as terrigenous clastic and carbonate mixed sedimentary reservoirs as an example, geophysical exploration technology mainly combines qualitative analysis of seismic attributes and quantitative prediction through seismic inversion to conduct comprehensive research on reservoir prediction. In particular, in offshore areas with few wells, improving the resolution of seismic data volumes is especially important.

[0003] As offshore oil and gas exploration continues to advance into deeper, deeper-water, and concealed mixed-sedimentary reservoirs, the geological conditions of the exploration targets are becoming increasingly complex. Mixed-sedimentary reservoirs formed by the intersedimentary deposition of terrigenous clastic and carbonate rocks are characterized by rapid lithological changes, strong lateral heterogeneity, thin reservoir thickness, and weak reflection signals. The single-layer thickness of these reservoirs is often near the seismic resolution limit, and they commonly exhibit geophysical response characteristics such as thin-layer interference, waveform overlay, and phase distortion. The seismic reflection boundaries at micro-fractures, bedding interfaces, and lithological boundaries are blurred, making them difficult to directly identify and characterize using conventional seismic profiles. Conventional seismic exploration processing methods are also insufficient to accurately characterize the spatial distribution and internal structure of the reservoirs. These special reservoirs differ from both pure clastic reservoirs and conventional carbonate reservoirs. Their sedimentary facies are varied, and their physical property differences are concealed, placing much higher technical demands on the signal-to-noise ratio, vertical resolution, and lateral imaging accuracy of seismic data than on conventional reservoirs. This also poses a significant challenge to seismic data volume processing and precise reservoir prediction.

[0004] In current exploration and production practices, offshore exploration blocks generally suffer from sparse well deployment and insufficient well control constraints. There is a lack of sufficient drilling data to calibrate seismic response characteristics, forcing subsequent interpretation and inversion work to rely entirely on post-stack seismic data. In deep-water offshore areas, the seabed topography is highly undulating, with numerous seafloor multiples, wind and wave interference, and uneven positioning of acquisition and observation systems. The original seismic data itself has a low signal-to-noise ratio, and the viscoelastic absorption and attenuation during propagation further exacerbates high-frequency energy loss, resulting in weakened deep-seated signals, narrowed bandwidth, and reduced resolution. The original seismic data is affected by multiple factors such as seafloor topography, stratum absorption attenuation, random interference, and coherent noise. It suffers from problems such as severe attenuation of effective signals, prominent low-frequency interference, loss of high-frequency information, and poor continuity of phase axes. This not only weakens the seismic reflection response of thin reservoirs, but also easily causes imaging artifacts such as false structures and false faults. It seriously restricts the accuracy of delineating favorable reservoirs, classifying sedimentary facies, and evaluating oil and gas reservoirs in mixed sedimentary rocks. There is an urgent need to develop a special seismic data volume refinement technology that is adapted to the characteristics of mixed sedimentary rock reservoirs in areas with few wells.

[0005] Current technologies for oil and gas exploration areas with few wells rely solely on existing post-stack seismic data from the exploration blocks. This data suffers from high seismic noise, low resolution, and limited information, thus restricting the accuracy of mixed sedimentary reservoir predictions. Existing conventional processing methods often employ a step-by-step, independent processing approach: first, uniform denoising followed by fixed Q-value attenuation compensation. This approach fails to consider the spatiotemporal variation of the formation's Q-factor, lacks structural boundary protection mechanisms during denoising, and easily smooths out weak reflective bedding and minor structures. Furthermore, subsequent inverse Q-filtering lacks stable constraints, easily amplifying residual noise and causing shallow-layer distortion and deep-layer inconsistencies in the processed profile. This fails to meet the production requirements for detailed interpretation of thin mixed sedimentary reservoirs, microstructures, and concealed traps. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method, apparatus, medium, and equipment for processing seismic data volumes in mixed sedimentary reservoirs. This method is suitable for actual seismic data processing in deep-water offshore areas with few or no wells and complex geological structures. It covers key technical aspects such as time-varying Q-factor inversion of seismic data, structure-guided edge preservation and denoising, and inverse Q-filter amplitude and phase compensation. It can serve core geophysical exploration operations such as identification of complex hidden traps, lateral reservoir prediction, detailed stratigraphic interpretation, and evaluation of favorable oil and gas zones.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for processing seismic data volumes of mixed sedimentary reservoirs includes: The shaping regularization algorithm is integrated into the centroid frequency shift method to calculate the local centroid frequency and variance of seismic data for each trace, thereby obtaining the continuous time-varying Q factor; Based on the local dip angle of seismic data estimated by two-dimensional Hilbert transform, a noise-resistant structure-guided filter operator is constructed to denoise the seismic data and obtain denoised seismic data. Based on the continuous time-varying Q factor, an inverse Q filter with a stability factor is used to process the seismic data to obtain stable inverse Q-filtered seismic data. After performing inverse Q-filtering with a stability factor on denoised seismic data and stable inverse Q-filtered seismic data, the frequency domain signal is converted into a time domain signal through inverse Fourier transform to obtain the final attenuation compensation result.

[0008] The method for processing seismic data volumes of mixed sedimentary reservoirs, preferably, uses the following formula for calculating the continuously time-varying Q-factor: ; In the formula, Indicates reference time The local centroid frequency at a given point corresponds to the local maximum value of each frequency component on the time spectrum; express Variance over time; t The travel time for seismic waves to propagate; Let be the local centroid frequency of the seismic signal at time t.

[0009] The method for processing seismic data volumes of mixed sedimentary reservoirs, preferably, involves local dip angles. The calculation formula is as follows: ; In the formula, F HTc F HTt These are the two-dimensional Hilbert transform component functions in the time and space directions, respectively; ε This is a numerical stability factor to avoid calculation anomalies caused by a denominator of zero.

[0010] The method for processing seismic data from mixed sedimentary reservoirs preferably involves denoising the seismic data to obtain denoised seismic data. The specific process is as follows: Structure-guided weighted median filtering is used to denoise seismic data. The filtering formula is as follows: ; In the formula, Weighting coefficients based on local correlation coefficients; To predict elements in the dataset; For copying operations; This is the filtering result.

[0011] In the aforementioned method for processing seismic data volumes of mixed sedimentary reservoirs, the stability factor is preferably designed as follows: Introducing a stabilizing factor Suppress noise amplification and define a stability factor term. : ; ; In the formula, As a stabilizing factor, its value is adjusted to balance the attenuation compensation intensity and noise suppression effect; This is the basic term for amplitude compensation; Angular frequency, For travel; The highest angular frequency of the seismic data; It is a continuously time-varying Q factor; This refers to the instantaneous travel time during the propagation of seismic waves.

[0012] The method for processing seismic data volumes of mixed sedimentary reservoirs, preferably, uses the following inverse Q-filter calculation formula with a stability factor: ; In the formula, This is the frequency domain expression for the seismic wave field; This is the frequency domain expression of the initial seismic wave field at position 0 during travel, i.e., the spectrum of the source wavelet.

[0013] A second aspect of the present invention provides a seismic data volume processing apparatus for mixed sedimentary reservoirs, comprising: The first processing unit is used to integrate the shaping regularization algorithm into the centroid frequency shift method, calculate the local centroid frequency and variance of the seismic data one channel at a time, and obtain the continuous time-varying Q factor. The second processing unit is used to estimate the local dip angle of seismic data based on the two-dimensional Hilbert transform, construct a noise-resistant structure-guided filter operator, denoise the seismic data, and obtain denoised seismic data. The third processing unit is used to process seismic data based on the continuous time-varying Q factor and using inverse Q filtering with a stability factor to obtain stable inverse Q-filtered seismic data. The fourth processing unit performs inverse Q filtering with a stability factor on the denoised seismic data and the stable inverse Q filtered seismic data, and then converts the frequency domain signal into a time domain signal through inverse Fourier transform to obtain the final attenuation compensation result.

[0014] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the seismic data volume processing method for mixed sedimentary reservoirs described in any of the preceding claims.

[0015] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the seismic data volume processing method for mixed sedimentary reservoirs described in any one of the above-described methods.

[0016] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention uses the local centroid frequency shift method to estimate the Q value. This method has a certain degree of noise resistance and can avoid the weak signal loss caused by conventional denoising algorithms, thus obtaining more accurate Q value estimation results. The application of robust structure-guided filtering can remove more random noise while protecting the weak effective signal. This step can avoid the impact of subsequent "noise explosion". The application of stable inverse Q filtering can ensure that the final high-resolution, high signal-to-noise ratio seismic data is obtained.

[0017] 2. The time-varying attenuation factor of the target is obtained using the local centroid frequency shift method. This operation avoids the accuracy loss in attenuation factor estimation caused by the destruction of weak signals in traditional denoising algorithms. Local tilt angle information is calculated using 2D Hilbert transform, and weighted median filtering is applied based on the tilt angle information to obtain the denoising result. Applying the attenuation factor to the denoising result yields the final processed result.

[0018] 3. The method of this invention is suitable for the actual working conditions of seismic data processing in deep-water offshore areas with few or no wells and complex structures. It covers key technical aspects such as Q-factor time-varying inversion of seismic data, structure-guided edge preservation and denoising, and inverse Q-filter amplitude and phase compensation. It can serve core geophysical exploration operations such as identification of complex hidden traps, lateral reservoir prediction, fine interpretation of strata, and evaluation of favorable oil and gas zones. It is especially suitable for special exploration zones such as deep-sea shelf mixed facies, platform margin mixed facies, marine-continental transitional facies, and inland lacustrine clastic-carbonate mixed basins. It can also be extended to the fine processing of weak reflection and low signal-to-noise ratio seismic data such as tight sandstone and carbonate fracture-vuggy reservoirs. Attached Figure Description

[0019] Figure 1 This is a flowchart of the seismic data volume processing method for mixed sedimentary reservoirs provided by the present invention; Figure 2 The raw data is provided by a method for processing seismic data volumes of mixed sedimentary reservoirs according to an embodiment of the present invention. Figure 3 This is a cross-sectional view of the continuously time-varying Q value provided in this embodiment of the present invention; Figure 4 This is a diagram showing the robust structure-guided filtering processing result provided in this embodiment of the invention; Figure 5 This is a diagram showing the removal of random noise provided in this embodiment of the invention; Figure 6 This is a diagram showing the final processing result provided in this embodiment of the invention; Figure 7 The spectrum of the nasal drops provided in this embodiment of the invention is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," "third," "fourth," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0022] 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.

[0023] Mixed sedimentary reservoirs are controlled by a combination of factors including terrigenous sediment supply, paleogeographic changes, sea-level rise and fall, hydrodynamic conditions, and paleoclimate. They exhibit diverse sedimentary systems and complex lithological assemblages, retaining characteristics of interbedded clastic sand-mud and rhythmic sedimentation, while also exhibiting features of carbonate reef-shoals, grain shoals, dolomitization, and porous-fractured reservoirs. Vertically, thin interbedded layers are frequently superimposed, and lateral lithological facies transitions are abrupt, resulting in extremely strong subsurface heterogeneity. This leads to severe distortion of seismic reflection waveforms, dispersion of effective reflection energy, prominent thin-layer tuning effects, and widespread development of weakly reflective strata. Mixed sedimentary reservoirs possess dual sedimentary characteristics of clastic and carbonate rocks, with complex geological structures, rapid lateral lithological changes, well-developed thin interbedded layers, and weak seismic reflection signals. Conventional seismic processing methods are insufficient to achieve effective signal fidelity and detailed characterization.

[0024] Based on the above-mentioned technical problems, this invention provides a method for processing seismic data volumes of mixed sedimentary reservoirs. This method is suitable for the actual working conditions of seismic data processing in deep water offshore, areas with few or no wells, and structurally complex blocks. It covers key technical aspects such as time-varying Q-factor inversion of seismic data, structure-guided edge preservation and denoising, and inverse Q-filter amplitude and phase compensation. It can serve core geophysical exploration operations such as identification of complex hidden traps, lateral prediction of reservoirs, detailed interpretation of formations, and evaluation of favorable oil and gas zones.

[0025] like Figure 1 As shown, the method for processing seismic data volumes of mixed sedimentary reservoirs provided by this invention includes the following specific steps: Step 1: Continuous Time-Varying Q-Factor Estimation The shaping regularization algorithm is incorporated into the centroid frequency shift method to calculate the local centroid frequency and variance of seismic data for each trace, thereby obtaining the continuously time-varying Q factor. The specific process is as follows: (1) The expression for the Q factor is: The instantaneous centroid frequency and instantaneous spectral bandwidth are obtained using spectral information at fixed time points. The instantaneous centroid frequency... It can be represented as: (1) in, Indicates in t The amplitude spectrum at time step. By incorporating the shaping regularization operator into the instantaneous centroid frequency, local centroid frequencies characterizing local properties can be constructed. For a two-dimensional seismic profile, the centroid frequency can be described in vector form: (2) Where U represents the diagonal operator, and M and n are the vector forms of the numerator and denominator in formula (1), respectively.

[0026] Formula (2) is considered as a linear inverse problem and solved by adding an integer regularization constraint term. Local centroid frequency It can be represented as: (3) Where L represents the integer regularization operator, I is the unit operator, and U T The transpose of the diagonal operator can be specifically represented as: (4) in, Let be the weight coefficients. Combining this with the scaling operator λ, an iterative algorithm can quickly converge to: (5) The local centroid frequency is calculated using information from each time point. Therefore, even if data is missing in some places, a reasonable local centroid frequency can be obtained continuously and smoothly.

[0027] By replacing the instantaneous centroid frequency and instantaneous variance in equation (4) with the local centroid frequency and local variance, the Q factor in the viscoelastic medium can be estimated by the following formula: (6) in: Indicates reference time The local centroid frequency at a given point corresponds to the local maximum value of each frequency component on the time spectrum; express Variance over time; t The travel time for seismic waves to propagate; Let be the local centroid frequency of the seismic signal at time t.

[0028] Step 2: Robust Structure-Guided Filtering Mode To address the poor noise immunity of traditional plane wave decomposition filters (PWD) for tilt estimation, this invention estimates the local tilt angle of seismic data using two-dimensional Hilbert transform, constructs a noise-resistant structure-guided filter operator, and denoises the seismic data to obtain denoised seismic data. This achieves a balance between noise suppression and structural protection. The specific implementation process is as follows: (1) Local dip estimation: The seismic wave field is defined as ,in c Indicates the Dao sign. t For travel, a two-dimensional Hilbert transform is used instead of traditional derivative operations. The component functions of the two-dimensional Hilbert transform in the time and space directions are F. HTc F HTt The local dip angle is estimated by the following formula. : (7) in, To avoid computational anomalies caused by a denominator of zero, the stability of tilt angle estimation under noisy environments is significantly improved.

[0029] (2) Structure-guided weighted median filtering: The seismic profile is regarded as a set of seismic traces. The prediction operator of adjacent traces is constructed by combining local dip angles to realize the recursive prediction of seismic data.

[0030] Weighted median filtering is used to denoise the data. The filtering formula is as follows: (8) in, These are weighting coefficients based on local correlation coefficients, used to highlight the contribution of the effective signal; To predict the elements in the dataset; For copying operations; This is the filtering result.

[0031] Step 3: Stabilize the inverse Q filter attenuation compensation Based on the continuous time-varying Q-factor obtained in step 1, an inverse Q-filter with a stability factor is used to process the seismic data, resulting in stable inverse Q-filtered seismic data. This allows for amplitude compensation and phase correction while avoiding noise amplification. The specific process is as follows: (1) Inverse Q filter The stable inverse Q-filter formula based on the generalized Q-model is: (9) in, This is the frequency domain expression for the seismic wave field. This is the frequency domain expression for the initial seismic wavefield at position 0 during travel, i.e., the spectrum of the source wavelet. Angular frequency, For travel. The highest angular frequency of the seismic data (determined by data spectrum analysis); , The term is the time-varying Q-factor obtained in step 1; the exponential term is the phase correction term, which compensates for phase distortion during propagation.

[0032] (2) Stability factor design Introducing a stabilizing factor Suppress noise amplification and define a stability factor term. : (10) (11) in, As a stabilizing factor, its value is adjusted to balance the attenuation compensation intensity and noise suppression effect; This is the basic term for amplitude compensation; This refers to the instantaneous travel time during the propagation of seismic waves.

[0033] Step 4: After performing an inverse Q-filter operation with a stability factor on the denoised seismic data obtained in Step 2 and the stable inverse Q-filtered seismic data obtained in Step 3, the frequency domain signal is converted into a time domain signal through inverse Fourier transform to obtain the final attenuation compensation result.

[0034] like Figure 2-7 As shown, it demonstrates the effectiveness of the present invention in processing data. Figure 2 This represents the raw data, which contains significant noise interference, especially in the deep layers (3–4 s), where the in-phase axes are cluttered and blurred, weak reflection signals are masked by noise, resulting in low resolution and an inability to clearly identify deep geological structures. This invention calculates Q-value information (…). Figure 3The results show that the Q-value is high (mainly red) in the shallow layer (1–1.5s), indicating weak medium attenuation; as the depth increases (2–4s), the Q-value gradually decreases (mainly blue-green), which is consistent with the actual law that "the greater the depth, the stronger the attenuation" of underground media. This shows that the time-varying Q-factor estimation is highly accurate, capturing the spatial-temporal variation characteristics of medium attenuation, and providing reliable parameters for subsequent attenuation compensation.

[0035] Structure-guided filtering is used to remove random noise. Figure 4 Compared to the original data, the denoising results show significantly improved continuity and clarity of the phase axes, substantial suppression of random noise, and complete preservation of structural information in the shallow and deep layers (such as bedding and potential faults). The removed random noise profile ( Figure 5 The results also show that the method loses almost no effective signal, which indicates that the robust structure-guided filtering achieves a balance between "denoising and structural protection", providing high-quality "clean data" for subsequent Q compensation and avoiding noise interference in the compensation process.

[0036] By combining the continuously time-varying Q factor, the final filtering result can be obtained. Figure 6 Comparing the denoising results with the original data, the continuity and clarity of the phase axis of the final filtered result were further improved. In particular, the weak reflection signal in the deep layers (3–4 s) changed from "blurred and indistinguishable" to "continuous and identifiable," and the high-frequency components were effectively recovered. This indicates that the stable inverse Q filter, supported by a precise Q factor, successfully compensated for the attenuated seismic signal. At the same time, due to the previous denoising steps, no noise amplification problem occurred, ultimately achieving the dual goals of "resolution improvement and signal-to-noise ratio maintenance."

[0037] This invention also includes the spectrum diagrams of the original data, the direct inverse Q filter result, and the data processed by this invention. Figure 7 As shown in the figure, the energy of the original seismic record (solid black line) is mainly concentrated in the low-frequency band, while the energy in the high-frequency band (above 40Hz) decays rapidly, indicating severe loss of high-frequency information and insufficient resolution in the original data. If the original data is directly subjected to inverse Q filtering, its spectrum is represented by the blue dashed line. The results show that the amplitude of the high-frequency band (20-60Hz) is significantly increased, but the curve fluctuates greatly, and the attenuation in the high-frequency band (above 60Hz) is still significant, with a potential for noise amplification. The processing results of this invention (pink dotted line) show that the amplitude of the mid-to-high frequency band (20-60Hz) is significantly higher than the former two, the energy retention in the high-frequency band (above 60Hz) is better, and the curve is smoother, indicating that this method effectively suppresses noise while compensating for high-frequency attenuation.

[0038] The seismic data processing method of this invention significantly enhances the high-frequency energy of seismic signals and recovers more effective frequency components through an optimized inverse Q-filter with a stability factor and noise control mechanism. Compared with direct inverse Q-filter, it performs better in maintaining spectral smoothness (suppressing noise) and expanding high-frequency bandwidth (improving resolution), providing richer frequency information for the detailed interpretation of underground geological structures (such as reservoir identification and fault analysis).

[0039] A second aspect of the present invention provides a seismic data volume processing apparatus for mixed sedimentary reservoirs, comprising: The first processing unit is used to integrate the shaping regularization algorithm into the centroid frequency shift method, calculate the local centroid frequency and variance of the seismic data one channel at a time, and obtain the continuous time-varying Q factor. The second processing unit is used to estimate the local dip angle of seismic data based on the two-dimensional Hilbert transform, construct a noise-resistant structure-guided filter operator, denoise the seismic data, and obtain denoised seismic data. The third processing unit is used to process seismic data based on the continuous time-varying Q factor and using inverse Q filtering with a stability factor to obtain stable inverse Q-filtered seismic data. The fourth processing unit performs inverse Q filtering with a stability factor on the denoised seismic data and the stable inverse Q filtered seismic data, and then converts the frequency domain signal into a time domain signal through inverse Fourier transform to obtain the final attenuation compensation result.

[0040] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the seismic data volume processing method for mixed sedimentary reservoirs described in any of the preceding claims.

[0041] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the seismic data volume processing method for mixed sedimentary reservoirs described in any one of the above-described methods.

[0042] This invention is described based on flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts 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 device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0043] 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.

[0044] 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.

[0045] 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 processing seismic data volumes of mixed sedimentary reservoirs, characterized in that, include: The shaping regularization algorithm is integrated into the centroid frequency shift method to calculate the local centroid frequency and variance of seismic data for each trace, thereby obtaining the continuous time-varying Q factor; Based on the local dip angle of seismic data estimated by two-dimensional Hilbert transform, a noise-resistant structure-guided filter operator is constructed to denoise the seismic data and obtain denoised seismic data. Based on the continuous time-varying Q factor, an inverse Q filter with a stability factor is used to process the seismic data to obtain stable inverse Q-filtered seismic data. After performing inverse Q-filtering with a stability factor on denoised seismic data and stable inverse Q-filtered seismic data, the frequency domain signal is converted into a time domain signal through inverse Fourier transform to obtain the final attenuation compensation result.

2. The method for processing seismic data volumes of mixed sedimentary reservoirs according to claim 1, characterized in that, The formula for calculating the continuously time-varying Q factor is as follows: ； In the formula, Indicates reference time The local centroid frequency at a given point corresponds to the local maximum value of each frequency component on the time spectrum; express Variance over time; t The travel time for seismic waves to propagate; Let be the local centroid frequency of the seismic signal at time t.

3. The method for processing seismic data volumes of mixed sedimentary reservoirs according to claim 2, characterized in that, Local tilt angle The calculation formula is as follows: ； In the formula, F HTc F HTt These are the two-dimensional Hilbert transform component functions in the time and space directions, respectively; ε This is a numerical stability factor to avoid calculation anomalies caused by a denominator of zero.

4. The method for processing seismic data volumes of mixed sedimentary reservoirs according to claim 3, characterized in that, The process of denoising seismic data to obtain denoised seismic data is as follows: Structure-guided weighted median filtering is used to denoise seismic data. The filtering formula is as follows: ； In the formula, These are weighting coefficients based on local correlation coefficients; To predict elements in the dataset; For copying operations; This is the filtering result.

5. The method for processing seismic data volumes of mixed sedimentary reservoirs according to claim 4, characterized in that, The stability factor is designed as follows: Introducing a stabilizing factor Suppress noise amplification and define a stability factor term. : ； ； In the formula, As a stabilizing factor, its value is adjusted to balance the attenuation compensation intensity and noise suppression effect; This is the basic term for amplitude compensation; Angular frequency, For travel; The highest angular frequency of the seismic data; It is a continuously time-varying Q factor; This refers to the instantaneous travel time during the propagation of seismic waves.

6. The method for processing seismic data volumes of mixed sedimentary reservoirs according to claim 5, characterized in that, The formula for calculating the inverse Q filter with a stabilization factor is as follows: ； In the formula, This is the frequency domain expression for the seismic wave field; This is the frequency domain expression of the initial seismic wave field at position 0 during travel, i.e., the spectrum of the source wavelet.

7. A device for processing seismic data volumes of mixed sedimentary reservoirs, characterized in that, include: The first processing unit is used to integrate the shaping regularization algorithm into the centroid frequency shift method, calculate the local centroid frequency and variance of the seismic data one channel at a time, and obtain the continuous time-varying Q factor. The second processing unit is used to estimate the local dip angle of seismic data based on the two-dimensional Hilbert transform, construct a noise-resistant structure-guided filter operator, denoise the seismic data, and obtain denoised seismic data. The third processing unit is used to process seismic data based on the continuous time-varying Q factor and using inverse Q filtering with a stability factor to obtain stable inverse Q-filtered seismic data. The fourth processing unit performs inverse Q filtering with a stability factor on the denoised seismic data and the stable inverse Q filtered seismic data, and then converts the frequency domain signal into a time domain signal through inverse Fourier transform to obtain the final attenuation compensation result.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the seismic data volume processing method for mixed rock reservoirs according to any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the seismic data volume processing method for mixed sedimentary reservoirs according to any one of claims 1-6.

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