A method, system and application for fine characterization of multi-scale depositional cycle facies space

Abstract

This invention discloses a method, system, and application for fine characterization of multi-scale sedimentary cycles facies space. This method combines variational mode decomposition (VMD) with synchronous compression optimal basis wavelet transform (MCB) to propose a workflow for multi-scale sedimentary cycle characterization. First, the relationship between geological structures at different scales and intrinsic mode functions (EMFs) is theoretically derived, and a method for determining the number of EMFs is proposed. Considering computational efficiency and the lateral continuity of the decomposition results, the variational mode decomposition algorithm is improved by determining the number of EMFs and their center frequencies based on the smoothed reflection coefficient amplitude spectrum. Based on this, multi-scale sedimentary cycle characterization is achieved by combining MCB with ridge extraction. The key parameters in the processing of this invention are all extracted from seismic data, making the operation simple, computationally fast, and suitable for processing massive amounts of seismic data. This provides an important tool for subsequent sequence stratigraphy research.

Description

Technical Field

[0001] This invention belongs to the field of petroleum geophysical exploration technology, and is mainly a multi-scale sedimentary cycle facies spatial fine characterization method, system and application for assisting sequence stratigraphy research. Background Technology

[0002] The fundamental tenet of sequence stratigraphy is that the geometry and lithology of stratigraphic units are controlled by four major factors: sea-level rise and fall, tectonic subsidence, sedimentary supply, and climate (McLaughlin et al., 2005). These factors operate at different scales and exhibit periodic variations; therefore, sedimentary sequences also possess multi-scale and periodic characteristics. Sedimentary cycles are the basic building blocks of sedimentary sequences, resulting from the superposition of periodic sedimentary events at different scales. Regional or global sedimentary events influence the superposition patterns of sedimentary sequences at different scales. Sedimentary cycles generally consist of several basic units, including transgressive and regressive cycles. In transgressive cycles, sandstone thickness increases with depth, while sediment grain size decreases, indicating a transgressive sedimentary sequence. In regressive cycles, sandstone thickness decreases with depth, while sediment grain size decreases, indicating a regressive sedimentary sequence (Melani et al., 2020; Rossi et al., 2020; Zhang et al., 2017). Typically, a large sequence stratigraphy consists of several smaller cycles, recording evidence of transgressive and regressive sequences in the sedimentary environment (Goldhammer et al., 1990; Melani et al., 2020). Therefore, the detailed characterization and division of multi-scale sedimentary cycles is the foundation for subsequent sedimentary environment studies, sequence stratigraphy analysis, and reservoir prediction, and is also an important step in establishing tectonic and stratigraphic frameworks.

[0003] Well logging technology, which performs detailed measurements of subsurface formations within wellbores, is the most common geophysical tool for characterizing and classifying multi-scale sedimentary cycles (Vermeer et al., 1992; Coconi-Morales et al., 2010). However, well logging data provides only a partial view and has limited spatial coverage. In contrast, seismic reflection data offers better spatial coverage, carrying lithological and physical property information about subsurface strata and containing a wealth of information about multi-scale sedimentary cycles (Zeng, 2013). Therefore, effectively extracting sedimentary cycle information from seismic data is crucial for the identification and interpretation of sedimentary cycles.

[0004] Data-driven decomposition methods have been used to extract sedimentary cycle information from seismic data. Liu et al. (2015) used empirical mode decomposition (EMD) to decompose seismic signals into intrinsic mode functions (EMFs) representing sedimentary cycles at different scales to aid in the characterization of multi-scale sedimentary cycles. However, EMD cannot arbitrarily determine the optimal number of EMFs. To address this issue, Li et al. (2017) introduced a variational mode decomposition (VMD) method, which allows for the arbitrarily defined number of EMFs. While this method offers greater flexibility, the interpretation of its results remains highly subjective and susceptible to the interpreter's experience. Therefore, a key problem that these data-driven decomposition methods need to solve is establishing a deterministic relationship between EMFs and geological structures at different scales. Furthermore, more reasonable criteria are needed to determine the optimal number of EMFs (Tian et al., 2022).

[0005] To address the limitations of previous work, the applicant initially proposed an enhanced multi-channel variational mode decomposition method and a workflow for seismic data decomposition to aid in multi-scale sequence stratigraphic interpretation (Tian et al., 2022). This method first clarifies the relationship between intrinsic mode functions and geological structures at different scales. Subsequently, it proposes a method for determining the number of intrinsic mode functions based on scale-space representation and compression mapping operators. Finally, it employs multi-channel variational mode decomposition to decompose seismic data, aiding in the interpretation of sedimentary sequence boundaries at different scales. However, the decomposition results from these mode decomposition methods—i.e., seismic reflection waveforms at different scales—still require interpretation by experienced interpreters to infer sedimentary sequences, thus hindering the direct division of sedimentary cycles.

[0006] Time-frequency analysis can intuitively reflect the instantaneous dominant frequency variation characteristics of seismic signals caused by thin-layer thickness variations within sedimentary cycles (Tian et al., 2022). Therefore, time-frequency analysis is also one of the most commonly used tools for characterizing sedimentary cycles. Accurately characterizing the dominant frequency variation characteristics caused by thin-layer thickness variations is a key issue in using time-frequency analysis to assist in the classification of sedimentary cycles. Pioneering research in this field can be traced back to 1980, when the Russian Institute of Exploration Geophysics proposed a sedimentary cycle classification method based on linear time-frequency transformation (Mushin et al., 2000).

[0007] However, the time-frequency spectrum of linear time-frequency analysis methods spreads along the frequency axis in the time-frequency plane (Huang et al., 2016), resulting in low time-frequency resolution and hindering the identification of sedimentary cycles. To address this deficiency, Daubechies et al. (2011) introduced the synchronous squeeze transform. The synchronous squeeze transform obtains a high-resolution time-frequency spectrum by synchronously squeezing the time-frequency spectrum along the frequency axis. Although this method provides a powerful tool for sedimentary cycle analysis, the choice of time-frequency atoms determines the interpretability of the results. Gao et al. (2001) argued that the time-frequency spectrum of continuous wavelet transform has stronger noise robustness and readability when the basis wavelet is very similar to the seismic wavelet. To better match the seismic wavelet, Gao et al. (2006) proposed a basis wavelet called the three-parameter wavelet. By adjusting its three parameters, its waveform can be matched with the seismic wavelet of the seismic data to be analyzed. Based on the criterion of matching the seismic wavelet, we propose an optimal basis wavelet construction method by combining the three-parameter wavelet and a deep neural network. This method first uses an alternating iterative deep neural network to estimate the seismic wavelet, and then uses a three-parameter wavelet to fit the seismic wavelet to obtain the optimal basis wavelet. Although the optimal basis wavelet transform achieves high-resolution spectral decomposition of seismic data (Tian et al., 2021), its frequency resolution is low, which is not conducive to characterizing the dominant frequency characteristics of sedimentary cycles.

[0008] To address the aforementioned limitations, we previously proposed an optimal basic wavelet transform based on synchronous compression transform, combining it with the optimal fundamental wavelet transform, to achieve optimal characterization of sedimentary cycles. This method achieves optimal representation of the dominant frequency characteristics of seismic data by defining the dominant frequency location condition of the synchronous compression transform and the similarity coefficient condition between the fundamental wavelet and the seismic wavelet, thereby achieving optimal characterization of sedimentary cycles (Tian et al., 2022). However, this method directly processes seismic data containing multi-scale sequence stratigraphy information, leading to the superposition of information from different scale sedimentary cycles in the temporal spectrum. Using such a temporal spectrum that simultaneously contains multi-scale sedimentary sequence information will reduce the accuracy of subsequent multi-scale sedimentary cycle unit division.

[0009] The above-mentioned existing technologies have the following disadvantages:

[0010] (1) The existing modal decomposition method for seismic data decomposition results in seismic reflection waveforms of different scales containing geological structural information at different scales. The results still need to be interpreted by experienced interpreters to infer sedimentary patterns, which will introduce human experience errors and make it difficult to directly divide sedimentary cycles.

[0011] (2) Existing sedimentary cycle characterization methods based on synchronous compression optimal basis wavelet transform directly process seismic data containing multi-scale sedimentary sequence information, resulting in the time spectrum containing sedimentary cycle information of different scales simultaneously. Using such a time spectrum with mixed multi-scale sedimentary cycle information will reduce the accuracy of subsequent multi-scale sedimentary cycle unit division. Summary of the Invention

[0012] To overcome the shortcomings of existing technologies, this invention aims to provide a refined characterization method for multi-scale sedimentary cycles in facies space. This method combines variational mode decomposition (VMD) with synchronous compression optimal basis wavelet transform (RCB) and proposes a workflow for multi-scale sedimentary cycle characterization. First, the relationship between geological structures at different scales and intrinsic mode functions (EMFs) is theoretically derived, and a method for determining the number of EMFs is proposed. Considering computational efficiency and the lateral continuity of the decomposition results, we improve the variational mode decomposition algorithm by determining the number of EMFs and their center frequencies based on the smoothed reflection coefficient amplitude spectrum. On this basis, a new method for extracting the instantaneous dominant frequency attribute of seismic data is proposed by combining RCB and ridge extraction, thereby achieving multi-scale sedimentary cycle characterization.

[0013] To achieve the above objectives, the present invention employs the following technical solution: a method for fine characterization of multi-scale sedimentary cyclic phase space, comprising the following steps:

[0014] Calculate the mean Fourier amplitude spectrum of two-dimensional seismic data;

[0015] Calculate the amplitude spectrum of the average reflection coefficient;

[0016] The smooth reflection coefficient amplitude spectrum is calculated using the scale-space representation method. Based on the relationship between the reflection coefficient amplitude spectrum and the empirical modes, the number of extrema of the smooth reflection coefficient spectrum is set to the number of intrinsic mode function components, and the extremum frequency is the center frequency of each intrinsic mode function component.

[0017] The variational mode decomposition method is used to decompose the input two-dimensional seismic data to obtain the intrinsic mode function components that can characterize the seismic reflection features of geological structures at different scales.

[0018] The synchronous compression optimal basis wavelet transform was used to calculate the time spectrum of each seismic trace for each two-dimensional intrinsic mode function component.

[0019] The instantaneous dominant frequency curve was extracted from the time spectrum of each seismic trace using a ridge line extraction method, resulting in a two-dimensional instantaneous dominant frequency attribute profile.

[0020] The transverse distribution pattern of sedimentary cycle units is explained by using instantaneous dominant frequency attribute profiles, which is a fine characterization of the multi-scale sedimentary cycle facies space.

[0021] Furthermore, calculating the average reflection coefficient amplitude spectrum involves extracting the seismic wavelet amplitude spectrum from the average Fourier amplitude spectrum of the seismic data using a compression mapping operator, and then dividing the average Fourier amplitude spectrum of the seismic data by the seismic wavelet amplitude spectrum to obtain the reflection coefficient amplitude spectrum.

[0022] Furthermore, a compression mapping operator is used to extract the seismic wavelet amplitude spectrum from the seismic data amplitude spectrum;

[0023] Introducing a compression mapping operator for calculating the seismic sub-wavelength spectrum, assuming the seismic sub-wavelength spectrum is within a specified frequency band. A function that exhibits smoothness and a single peak:

[0024] (2)

[0025] , ,make , Compression mapping operator P for

[0026] (3)

[0027] Set the initial value for the iteration to:

[0028] (4)

[0029] Iteratively using the compression mapping algorithm It converges to a fixed point. The fixed point corresponds to the estimated seismic sub-wave spectrum. ;

[0030] Using earthquake data amplitude spectrum Divide by the seismic wavelet amplitude spectrum Obtain the amplitude spectrum of the reflection coefficient :

[0031] (5)

[0032] here ,prevent .

[0033] Furthermore, based on the relationship between the reflection coefficient spectrum and empirical modes, the number of extrema of the smooth reflection coefficient spectrum is set to the number of intrinsic mode function components. The extremum frequencies include the center frequencies of each intrinsic mode function component. The relationship between the reflection coefficient amplitude spectrum and geological structures at different scales is derived theoretically, as shown in the following equation:

[0034] (1)

[0035] Seismic reflections of structures at different scales are defined as empirical mode functions, in equation (1) Representing the k The amplitude spectrum of the reflection coefficient corresponding to geological structures at various scales. Indicates the first k The th empirical mode function j The reflection coefficient value of each sampling point Indicates the first k Two reflection coefficient points at each scale m and n The time difference between them is defined as the one with the smaller absolute value. k Indicates large-scale structure, large value k Representing small-scale structures, equation (1) shows that... The frequency of the first extreme point and Inversely proportional.

[0036] Furthermore, the input two-dimensional seismic data is decomposed using the variational mode decomposition method to obtain intrinsic mode function components that can characterize the seismic reflection features of geological structures at different scales, including:

[0037] Signal separation and extraction are achieved by decomposing the signal into eigenmode functions with finite bandwidth, which is accomplished by solving the following optimization problem:

[0038] (10)

[0039] in, This represents single-channel seismic data. Indicates the first One eigenmode function Indicates the first The center frequencies of the eigenmode functions Indicates the first The Hilbert transform of each eigenmode function is used, and further, the Lagrange multiplier method shown in the following equation is used to solve the optimization problem above:

[0040] (11)

[0041] The value of the center frequency is determined by the frequency of the extreme point of the amplitude spectrum of the smooth reflection coefficient.

[0042] Furthermore, the synchronous squeezing optimal basis wavelet transform is used to calculate each seismic trace for each two-dimensional intrinsic mode function component, obtaining the time spectrum of each seismic trace, including:

[0043] The optimal basic wavelet is adopted as the basis wavelet within the synchronous compression transform framework. The optimal basic wavelet is obtained by fitting the seismic wavelet with a three-parameter wavelet. The three-parameter wavelet is defined in the time domain as follows:

[0044] (14)

[0045] in, , and These represent the modulation frequency, energy attenuation factor, and energy delay factor, respectively. express , and The set,

[0046] (15).

[0047] Based on the above-mentioned concept, this invention provides a multi-scale sedimentary cycle phase space fine characterization system, including a first calculation module, a second calculation module, a mapping module, a time-spectrum calculation module, and a characterization module;

[0048] The first calculation module is used to calculate the average Fourier amplitude spectrum and the average reflection coefficient amplitude spectrum of two-dimensional seismic data.

[0049] The second calculation module uses the scale-space representation method to calculate the smooth reflection coefficient amplitude spectrum. Based on the relationship between the reflection coefficient amplitude spectrum and the empirical modes, the number of extrema of the smooth reflection coefficient spectrum is set to the number of intrinsic mode function components, and the extremum frequency is the center frequency of each intrinsic mode function component.

[0050] The mapping module uses variational mode decomposition to decompose the input two-dimensional seismic data to obtain intrinsic mode function components that can characterize the seismic reflection features of geological structures at different scales.

[0051] The time-spectrum calculation module uses synchronous compression optimal basis wavelet transform to calculate the time-spectrum of each seismic trace for each two-dimensional intrinsic mode function component.

[0052] The characterization module uses the ridge extraction method to extract the instantaneous dominant frequency curve from the time spectrum of each seismic trace, obtains a two-dimensional instantaneous dominant frequency attribute profile, and uses the instantaneous dominant frequency attribute profile to interpret the lateral distribution law of sedimentary cycle units, that is, a fine characterization of the multi-scale sedimentary cycle facies space.

[0053] The present invention can also provide a computer device, including a processor and a memory, wherein the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and the processor can realize the multi-scale deposition cycle phase space fine characterization method of the present invention when executing the executable program.

[0054] Simultaneously, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, it can realize the multi-scale depositional cyclic phase space fine characterization method described in this invention.

[0055] In addition, the multi-scale sedimentary cycle facies space fine characterization method described in this invention can also be used to identify and interpret sedimentary cycle information extracted from seismic data.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This application considers that seismic reflection data simultaneously contain seismic reflection information of sedimentary sequence layers at different scales. It overcomes the problem that traditional methods cannot effectively separate and characterize the multi-scale sedimentary cycle information contained in seismic data. This application effectively separates seismic reflection wave characteristics at different scales from seismic data through an improved variational mode decomposition method. Based on this, the synchronous compression optimal fundamental wavelet transform accurately characterizes the variation patterns of sedimentary cycles at different scales. The instantaneous dominant frequency attribute proposed by the ridge extraction method effectively characterizes the lateral distribution range of sedimentary cycles at different scales. The key parameters in the processing of this invention are all extracted from the data, making the operation simple and the calculation fast, suitable for processing massive amounts of seismic data. This provides an important tool for subsequent sequence stratigraphy research. Attached Figure Description

[0058] Figure 1 This is a flowchart of the multi-scale deposition cycle characterization process of the present invention.

[0059] Figure 2 These are examples of model data; (a) is the established geological model containing two sedimentary cycle scales: large scale (one transgressive cycle unit, three regressive cycle units) and small scale (two transgressive cycle units, five regressive cycle units); (b) is the reflection coefficient corresponding to the model; (c) is the synthetic seismic record; (d) is the intrinsic mode function 1 calculated using the variational mode decomposition method; (e) is the intrinsic mode function 2 calculated using the variational mode decomposition method; (f) is the temporal spectrum of the synthetic seismic record; (g) is the temporal spectrum of intrinsic mode function 1; (h) is the instantaneous dominant frequency curve of intrinsic mode function 1; (i) is the temporal spectrum of intrinsic mode function 2; and (j) is the instantaneous dominant frequency curve of intrinsic mode function 2.

[0060] Figure 3 Figure (a) shows the amplitude spectrum of the model data; (b) shows the amplitude spectrum (dashed line), reflection coefficient amplitude spectrum (solid line), and smooth spectrum (dotted line) of the model seismic record. Figure (c) shows the reflection coefficient amplitude spectrum (solid line), reflection coefficient smooth spectrum (dotted line), intrinsic mode function 1 (dotted line), and intrinsic mode function 2 (dashed line), respectively.

[0061] Figure 4 These are actual data examples; where (a) is the seismic profile of the target work area; (c) and (e) are the intrinsic mode functions 1 and 2 of the seismic profile obtained by variational mode decomposition, respectively; (b), (d) and (f) are the instantaneous dominant frequency attributes of the seismic profile to be analyzed, intrinsic mode function 1 and intrinsic mode function 2, respectively; the curve at the well point represents the logging acoustic impedance curve.

[0062] Figure 5 This is the result of characterizing and classifying sedimentary cycles at two scales using the proposed method at Well 2. The results clearly demonstrate the effectiveness of the proposed method in decomposing the characteristics of seismic signals at two scales. The synchronous compression optimal basis wavelet transform can effectively characterize the dominant frequency variation characteristics of sedimentary cycles at different scales, and the sedimentary cycle unit division results based on this effectively match the well logging lithology interpretation results. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0064] Sedimentary cycles are a sequence concept describing the repeated deposition of sedimentary processes and conditions in the same order. They are characterized by alternating changes in lithology and lithofacies, representing the interaction between rock deposition and erosion in geological history. The formation and evolution of sedimentary cycles are influenced by factors including global climate change, plate tectonics, sea-level changes, and the sonic velocity of river erosion. Seismic data contains rich information about subsurface sedimentary bodies. Using time-frequency analysis techniques, one-dimensional time-domain seismic records can be transformed into a two-dimensional time-frequency domain, thereby extracting more information. In the time-frequency map, the distribution and movement trends of amplitude energy clusters can be observed. Low-frequency information corresponds to coarser-grained rock compositions or thicker strata, while high-frequency information corresponds to finer-grained rock compositions or thinner strata. The type of sedimentary cycle can be determined based on the direction and shape of the amplitude energy cluster movement. Combined with geological background knowledge, the identified sedimentary cycles can be interpreted, and their formation and evolution processes can be analyzed. This sedimentary cycle information can guide reservoir prediction and oil and gas exploration.

[0065] The present invention will now be described in further detail with reference to the accompanying drawings:

[0066] The multi-scale depositional cyclic phase space fine characterization method of the present invention is as follows: Figure 1 As shown; specifically including the following steps:

[0067] 1) Acquire two-dimensional seismic data and calculate its mean Fourier spectrum using Fourier transform. .

[0068] 2) Calculate the average reflection coefficient amplitude spectrum of the original 2D seismic data; this includes two steps:

[0069] ① The wavelet amplitude spectrum of seismic data is extracted from the amplitude spectrum of seismic data using a compression mapping operator;

[0070] A compression mapping operator is introduced for calculating the seismic sub-wavelength spectrum, which assumes that the seismic sub-wavelength spectrum is within a specified frequency band. A function that exhibits smooth and unimodal behavior. Definition.

[0071] (2)

[0072] Obviously .make , Compression mapping operator P It can be defined as

[0073] (3)

[0074] Set the initial value for the iteration to:

[0075] (4)

[0076] Iteratively using the compression mapping algorithm It converges to a fixed point. This fixed point corresponds to the estimated seismic sub-wave spectrum. .

[0077] ②Use seismic data amplitude spectrum Divide by the seismic wavelet amplitude spectrum Obtain the amplitude spectrum of the reflection coefficient :

[0078] (5)

[0079] here ,prevent .

[0080] 3) The smooth reflection coefficient amplitude spectrum is calculated using a scale-space representation method:

[0081] The kernel function used in the scale-space representation framework is:

[0082] (6)

[0083] here n Representative scale parameter, reflection coefficient amplitude spectrum The scale-space representation is defined as:

[0084] (7)

[0085] Here, * denotes the convolution symbol, and the scale-space representation result is the original reflection coefficient amplitude spectrum. The global trend (i.e., smoothing results). With the scaling parameter... n The increase, It will become smoother, and the number of maxima frequencies will decrease. Here, we use the center frequency of the seismic data. f c Determine parameters n :

[0086] (8)

[0087] in , is an empirical parameter. f c Determined by the following formula:

[0088] (9)

[0089] For each earthquake dataset, n The optimal values ​​are all determined, which directly affect the number of extreme values ​​in the scale-space representation. This dependence stems from the inherent characteristics of seismic data itself.

[0090] Based on the relationship between the reflection coefficient amplitude spectrum and the empirical modes, the number of extrema of the smooth reflection coefficient amplitude spectrum is set to the number of intrinsic mode function components, and the extremum frequency is the center frequency of each intrinsic mode function component.

[0091] 4) The input two-dimensional seismic data is decomposed using the variational mode decomposition method to obtain the intrinsic mode function components that can characterize the seismic reflection features of geological structures at different scales.

[0092] Variational mode decomposition (VMD) is an adaptive and non-recursive signal processing method that separates and extracts signals by decomposing them into a series of eigenmode functions with finite bandwidth. This is achieved by solving the following optimization problem:

[0093] (10)

[0094] in, This represents single-channel seismic data. Indicates the first One eigenmode function Indicates the first The center frequencies of the eigenmode functions Indicates the first The Hilbert transform of each eigenmode function is used, and further, the Lagrange multiplier method shown in the following equation is used to solve the optimization problem above:

[0095] (11)

[0096] It is important to note that in this invention, the center frequency of each intrinsic mode function is fixed when solving the above formulas and does not change with the iteration process. The value of the center frequency is determined by the frequency of the extreme points of the amplitude spectrum of the smoothed reflection coefficient. There are two reasons for this approach: First, considering the similarity of waveforms generated by structures of the same scale, different frequency components of the amplitude spectrum can represent the characteristics of geological structures at different scales. Second, to ensure computational efficiency and lateral continuity of the calculation results, since variational mode decomposition is a single-channel calculation method, not fixing the center frequency when calculating two-dimensional seismic data would lead to poor lateral continuity of the decomposition results.

[0097] 5) The time spectrum is obtained by using the synchronous compression optimal basis wavelet transform to calculate each seismic trace of each two-dimensional intrinsic mode function component.

[0098] Synchronous compression optimal basis wavelet transform Defined as:

[0099] (12)

[0100] in, It is a continuous wavelet transform of the signal; t , a and These represent time, scale, and frequency, respectively. It is the instantaneous frequency of the signal, defined as:

[0101] (13)

[0102] This invention employs the optimal fundamental wavelet as the base wavelet within the synchronous compression transform framework. The optimal fundamental wavelet achieves the best match with the seismic wavelet by fitting a three-parameter wavelet to the seismic wavelet. The three-parameter wavelet is defined in the time domain as follows:

[0103] (14)

[0104] in, , and These represent the modulation frequency, energy attenuation factor, and energy delay factor, respectively. express , and The set. Furthermore.

[0105] (15).

[0106] Wavelet transform can transform one-dimensional time-domain seismic records into two-dimensional time-frequency domains, thereby extracting more information to reveal changes in rock properties within sedimentary bodies and providing important facies markers for sedimentary facies research.

[0107] 6) The ridge line extraction method is used to extract the instantaneous dominant frequency curve from the time spectrum of each seismic trace to obtain a two-dimensional instantaneous dominant frequency attribute profile.

[0108] 7) Use the instantaneous dominant frequency attribute to explain the lateral distribution pattern of sedimentary cycle units.

[0109] To verify the effectiveness of the proposed method, a design was created. Figure 2 (a) shows a sedimentary cycle model with two scales. From a large-scale perspective, the model includes one transgressive cycle unit and three regressive cycle units. From a small-scale perspective, the model includes two transgressive cycle units and five regressive cycle units. Generally, large-scale sedimentary sequence boundaries correspond to strong seismic reflection amplitudes and have large reflection coefficients; small-scale sequence boundaries correspond to weak seismic reflection amplitudes and have small reflection coefficients. Therefore, this invention is based on... Figure 2 The sedimentary cycle geological model shown in (a) was designed with Figure 2 (b) shows the reflection coefficient values ​​of the geological model. The reflection coefficient value at the boundary of a large-scale sedimentary cycle unit is 0.08, and the reflection coefficient value at the boundary of a small-scale sedimentary cycle unit is 0.04. The reflection coefficients were obtained by convolving the reflection coefficients with a 30Hz dominant frequency zero-phase Ricker wavelet. Figure 2 (c) shows the synthetic seismic record. It can be based on... Figure 2 (c) The characteristics of the medium-strong reflection waveform can be used to approximate the location of the boundary of the large-scale cyclic unit, but it is not accurate. Figure 2 (f) shows the time spectrum of the synthetic seismic record calculated using the synchronous compression optimal basis wavelet transform. It is easy to see that this time spectrum simultaneously contains two frequency variation components, which reflect the instantaneous dominant frequency variation trends caused by two scale sedimentary cycles. Here, the low-frequency component characterizes the thickness variation trend of the large-scale sedimentary cycle, and the high-frequency component characterizes the thickness variation trend of the small-scale sedimentary cycle. (Directly using...) Figure 2 The time spectrum shown in (f) can approximate the division of sedimentary cycles of two scales. However, the overlap of the two scales can easily cause division errors. In particular, it is impossible to determine which scale of sedimentary cycle caused the frequency characteristics at the overlapping position of the two scales.

[0110] To better utilize seismic data to assist in the division of sedimentary cycles at different scales, this invention further employs variational mode decomposition to decompose the seismic waveform features at different scales contained in the seismic data. Subsequently, synchronous compression optimal basis wavelet transform is used to extract sedimentary cycle information at different scales from the eigenmode functions. This method first uses Fourier transform to calculate the amplitude spectrum of the synthetic seismic trace, such as... Figure 3 (a) As shown by the dashed line. Subsequently, the seismic wavelet amplitude spectrum is extracted from the synthetic seismic trace amplitude spectrum using a compression mapping method, and the reflection coefficient amplitude spectrum is obtained by dividing the two, as shown below. Figure 3 (a) As shown by the solid line. Furthermore, a scale-space representation is used to calculate the smoothed reflection coefficient spectrum, as shown below. Figure 3 (a) As shown by the dotted line. The smoothed reflection coefficient spectrum reveals two extrema, corresponding to frequencies of 21 Hz and 52 Hz. Therefore, the number of intrinsic mode functions is set to two, with the center frequencies of intrinsic mode function 1 and intrinsic mode function 2 fixed at 21 Hz and 52 Hz, respectively. With the above variational mode decomposition parameters, the synthetic seismic trace is decomposed using the variational mode decomposition method to obtain... Figure 2 Eigenmode function 1 and eigenmode function 2 are shown in (d) and (e). It is easy to see that eigenmode function 1 reflects the strong amplitude and low frequency seismic reflection waveform characteristics corresponding to large-scale sedimentary cycles, while eigenmode function 2 reflects the weak amplitude and high frequency seismic waveform characteristics corresponding to small-scale sedimentary cycles. Figure 3 In (b), the dotted and dashed lines represent the amplitude spectra of intrinsic mode function 1 and intrinsic mode function 2, respectively. It can be seen that intrinsic mode function 1 and intrinsic mode function 2 reflect the low-frequency large-scale and high-frequency small-scale characteristics of seismic data, respectively. To better characterize sedimentary cycles, further, the synchronous compression optimal basis wavelet transform was used to calculate the intrinsic mode functions 1 and 2. Figure 2 The time-frequency spectra shown in (g) and (i) are readily apparent. Figure 2 (g) The instantaneous dominant frequency variation characteristics caused by the thickness variation of large-scale sedimentary cycles are clearly depicted. To more clearly characterize the sedimentary cycles and assist in the division of sedimentary cycle units, the time-spectrum ridge extraction method is used to obtain... Figure 2 The instantaneous dominant frequency curve shown in (h) is smoothed. Based on the smoothed trend of the instantaneous dominant frequency curve, one transgressive and three regressive sedimentary cycles within a large-scale sedimentary cycle can be accurately delineated. This result is consistent with... Figure 2 The geological model shown in (a) has a good match. Similarly, Figure 2 (i) The instantaneous dominant frequency characteristics caused by the thickness variation of small-scale sedimentary cycles are clearly depicted. The temporal spectral ridge extraction method is used to calculate... Figure 2The instantaneous dominant frequency curve shown in (j) is smoothed. Based on the smoothed trend of the instantaneous dominant frequency curve, two transgressive and five regressive sedimentary cycles within the small-scale sedimentary cycle can be accurately divided. This result is consistent with... Figure 2 The geological model shown in (a) has a good matching relationship.

[0111] As demonstrated by the model examples, the variational mode decomposition method can effectively separate the seismic reflection waveform characteristics caused by sedimentary cycles at different scales. Based on variational mode decomposition, the time-frequency spectra calculated using the synchronous squeezing optimal basis wavelet transform for different intrinsic mode functions contain only the instantaneous frequency characteristics of a single-scale sedimentary cycle. This is highly helpful in subsequent multi-scale sedimentary cycle unit delineation.

[0112] Figure 4(a) shows the actual two-dimensional seismic profile used to verify the workflow proposed in this invention. The data comes from a craton basin rich in oil and gas, covering an area of ​​250,000 square kilometers. Layer T3 in Figure 4(a) is the main source rock formed during the maximum flooding period, corresponding to a suite of oil shale exceeding 100 meters in thickness. Between T2 and T3 are thin interbedded sandstone and mudstone layers formed during alternating stages of deep-lake shale and turbidite sediments, forming the main reservoir. It is important to note that the thin interbedded layers between T2 and T3 belong to a fourth-order sequence within two third-order sequences (i.e., the high-water-stand systems tract and the low-water-stand systems tract). The purpose of this invention is to use the proposed method to assist in the characterization and classification of sedimentary cycles at two scales (i.e., third-order and fourth-order sequences).

[0113] The smooth reflection coefficient spectrum calculated using the technical process proposed in this invention contains two extreme points, with corresponding extreme frequencies of 22Hz and 48Hz. These two extreme frequencies represent the frequency characteristics of the third and fourth sequence layers on the seismic profile, respectively. Therefore, this invention sets the number of intrinsic mode functions to two, and the center frequencies of intrinsic mode function 1 and intrinsic mode function 2 to 22Hz and 48Hz, respectively. Figure 4 (c) and (e) are the intrinsic mode functions 1 and 2 obtained by the empirical mode decomposition method, respectively. Figure 4 (c) It can better characterize the low-frequency seismic reflection characteristics of the third-order sequence. Figure 4 (e) It can better characterize the high-frequency seismic reflection characteristics of the fourth-order sequence.

[0114] Furthermore, the synchronous squeezing optimal basis wavelet transform and ridge extraction method are used to... Figure 4 The seismic profiles shown in (a), (c) and (e) were calculated to obtain the following results: Figure 4 The instantaneous dominant frequency attribute profiles shown in (b), (d), and (f) are compared with... Figure 4 Compared to (d), Figure 4(b) has poor lateral continuity because the original seismic data contains seismic reflection characteristics of two scale sequences. The time spectrum obtained by directly using the synchronous compression optimal fundamental wavelet transform method has two components. Based on this, it is difficult to accurately extract the instantaneous dominant frequency characteristics of the time spectrum using the ridge extraction method. Figure 4 The instantaneous dominant frequency attribute profiles shown in (d) and (f) exhibit good lateral continuity, representing large-scale (third-order sequence) and small-scale (fourth-order sequence) sedimentary cyclic units, respectively. At five well sites, this invention delineated sedimentary cyclic units based on instantaneous dominant frequency attributes, and the delineation results of the two scales of sedimentary cyclic units show a good match with the formation thickness variation patterns represented by the well logging impedance curves.

[0115] Figure 5 The image shows the characterization results of sedimentary cycles at two scales at Well 2. The spectrum of the original well-side seismic trace, directly calculated using the synchronous compression optimal fundamental wavelet transform, contains two frequency components, representing large-scale sedimentary cycles (third-order sequence) and small-scale sedimentary cycles (fourth-order sequence), respectively. Due to the presence of multiple frequency components, the ridge extraction method cannot accurately calculate the instantaneous dominant frequency from the time spectrum of the original seismic trace. Figure 4 (b) explains the poor lateral continuity of the instantaneous dominant frequency profile. This overlapping of multi-scale information in the time spectrum is detrimental to the interpretation of multi-scale sedimentary cycles. The intrinsic mode functions 1 and 2 obtained using the proposed VMD decomposition process reflect the seismic reflection waveform characteristics of sedimentary sequences at two different scales. Further, the time spectra calculated from intrinsic mode functions 1 and 2 using synchronous compression optimal fundamental wavelet transform, which contains no other frequency components, effectively characterize the variation patterns of large-scale and small-scale sedimentary cycles. The instantaneous dominant frequency curve extracted using the ridge extraction method effectively characterizes the variation trend of stratigraphic thickness in sedimentary cycles at different scales, and the sedimentary cycle units delineated accordingly can well match the well logging lithology interpretation results. These results further verify the effectiveness of the proposed method in delineating multi-scale sedimentary cycles.

[0116] On the other hand, the present invention can also provide a multi-scale sedimentary cycle phase space fine characterization system, including a first calculation module, a second calculation module, a mapping module, a time-spectrum calculation module, and a characterization module;

[0117] The first calculation module is used to calculate the average Fourier amplitude spectrum and the average reflection coefficient amplitude spectrum of two-dimensional seismic data.

[0118] The second calculation module uses the scale-space representation method to calculate the smooth reflection coefficient amplitude spectrum. Based on the relationship between the reflection coefficient amplitude spectrum and the empirical modes, the number of extrema of the smooth reflection coefficient spectrum is set to the number of intrinsic mode function components, and the extremum frequency is the center frequency of each intrinsic mode function component.

[0119] The mapping module uses variational mode decomposition to decompose the input two-dimensional seismic data to obtain intrinsic mode function components that can characterize the seismic reflection features of geological structures at different scales.

[0120] The time-spectrum calculation module uses synchronous compression optimal basis wavelet transform to calculate the time-spectrum of each seismic trace for each two-dimensional intrinsic mode function component.

[0121] The characterization module uses the ridge extraction method to extract the instantaneous dominant frequency curve from the time spectrum of each seismic trace, obtains a two-dimensional instantaneous dominant frequency attribute profile, and uses the instantaneous dominant frequency attribute profile to interpret the lateral distribution law of sedimentary cycle units, that is, a fine characterization of the multi-scale sedimentary cycle facies space.

[0122] In summary, this invention provides a method for fine characterization of the phasic space of multi-scale sedimentary cycles. This method takes into account that seismic reflection data simultaneously contains seismic reflection information of sedimentary sequences at different scales. It overcomes the problem that traditional methods cannot effectively separate and characterize the multi-scale sedimentary cycle information contained in seismic data. This technique effectively separates the seismic reflection wave characteristics of different scales from seismic data by improving the variational mode decomposition method. Based on this, the time spectrum calculated by the synchronous compression optimal fundamental wavelet transform can accurately characterize the variation law of sedimentary cycles at different scales. Combined with the instantaneous dominant frequency attribute proposed by the ridge extraction method, it can effectively characterize the lateral distribution range of sedimentary cycles at different scales.

[0123] On the other hand, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the implementation of the multi-scale depositional cyclic phase space fine characterization method described in the present invention.

[0124] The computer device may be a laptop, a desktop computer, or a workstation.

[0125] The present invention can also provide a computer device, including a processor and a memory, wherein the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and the processor can realize the multi-scale deposition cyclic phase space fine characterization method of the present invention when executing the computer executable program.

[0126] The processor can be a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or an off-the-shelf programmable gate array (FPGA).

[0127] The memory described in this invention can be an internal storage unit of a laptop, desktop computer, or workstation, such as memory or hard disk; or it can be an external storage unit, such as a portable hard disk or flash memory card.

[0128] Computer-readable storage media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media can include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. Random access memory can include resistive random access memory (ReRAM) and dynamic random access memory (DRAM).

[0129] In summary, this invention discloses a method for fine characterization of the facies space of multi-scale sedimentary cycles. This method takes into account that seismic reflection data simultaneously contains seismic reflection information of sedimentary sequence stratigraphy at different scales. By improving the variational mode decomposition method, this invention can effectively separate the characteristics of seismic reflection waves at different scales from seismic data. Based on this, the time-spectrum calculated using the synchronous compression optimal fundamental wavelet transform can accurately characterize the variation patterns of sedimentary cycles at different scales. Combined with the instantaneous dominant frequency attribute proposed by the ridge extraction method, it can effectively characterize the lateral distribution range of sedimentary cycles at different scales, overcoming the problem that traditional methods cannot effectively separate and characterize the multi-scale sedimentary cycle information contained in seismic data. The key parameters in the processing of this invention are all extracted from seismic data, making the operation simple, the calculation speed fast, and suitable for processing massive amounts of seismic data. This provides an important tool for subsequent sequence stratigraphy research.

[0130] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method of fine characterization of a multi-scale depositional cycle facies space, characterized in that, Includes the following steps: Calculate the mean Fourier amplitude spectrum of two-dimensional seismic data; Calculate the average reflection coefficient amplitude spectrum of two-dimensional seismic data; specifically, this includes extracting the seismic wavelet amplitude spectrum from the average Fourier amplitude spectrum of the seismic data using a compression mapping operator, dividing the average Fourier amplitude spectrum of the seismic data by the seismic wavelet amplitude spectrum to obtain the reflection coefficient amplitude spectrum, and extracting the seismic wavelet amplitude spectrum from the seismic data amplitude spectrum using a compression mapping operator. Introducing a compression mapping operator for calculating the seismic sub-wavelength spectrum, assuming the seismic sub-wavelength spectrum is within a specified frequency band. A function that exhibits smoothness and a single peak: , , let , , compression mapping operator P be Set the initial value for the iteration to: （4） By a compression mapping algorithm iteration to a fixed point corresponding to an estimated seismic wavelet spectrum ; Using seismic data amplitude spectrum Divide by seismic wavelet amplitude spectrum Get reflectivity amplitude spectrum : wherein , prevent ; The smooth reflection coefficient amplitude spectrum is calculated using a scale-space representation method. Based on the relationship between the reflection coefficient amplitude spectrum and empirical modes, the number of extrema of the smooth reflection coefficient spectrum is set to the number of intrinsic mode function components, and the extremum frequency is the center frequency of each intrinsic mode function component. Specifically, the relationship between the reflection coefficient amplitude spectrum and geological structures at different scales is derived theoretically, as shown in the following equation: Seismic reflections of structures at different scales are defined as empirical mode functions, as shown in equation (1). Representing the k The amplitude spectrum of the reflection coefficient corresponding to geological structures at various scales. Indicates the first k The th empirical mode function j The reflection coefficient value of each sampling point Indicates the first k Two reflection coefficient points at each scale m and n The time difference between them The frequency of the first extreme point and Inversely proportional; The input two-dimensional seismic data is decomposed using the variational mode decomposition method to obtain eigenmode function components that can characterize the seismic reflection features of geological structures at different scales; specifically, these include: Signal separation and extraction are achieved by decomposing the signal into eigenmode functions with finite bandwidth, which is accomplished by solving the following optimization problem: in, This represents single-channel seismic data. Indicates the first One eigenmode function Indicates the first The center frequencies of the eigenmode functions Indicates the first The Hilbert transform of each eigenmode function is used, and further, the Lagrange multiplier method shown in the following equation is used to solve the optimization problem above: The value of the center frequency is determined by the frequency of the extreme points of the smooth reflection coefficient amplitude spectrum; The synchronous compression optimal basis wavelet transform was used to calculate the time spectrum of each seismic trace for each two-dimensional intrinsic mode function component. The instantaneous dominant frequency curve was extracted from the time spectrum of each seismic trace using a ridge line extraction method, resulting in a two-dimensional instantaneous dominant frequency attribute profile. The transverse distribution pattern of sedimentary cycle units is explained by using instantaneous dominant frequency attribute profiles, which is a fine characterization of the multi-scale sedimentary cycle facies space.

2. The method of claim 1, wherein, The synchronous compression optimal basis wavelet transform was used to calculate the time spectrum of each seismic trace for each two-dimensional intrinsic mode function component, and the time spectrum of each seismic trace was obtained as follows: The optimal basic wavelet is adopted as the basis wavelet within the synchronous compression transform framework. The optimal basic wavelet is obtained by fitting the seismic wavelet with a three-parameter wavelet. The three-parameter wavelet is defined in the time domain as follows: wherein, , and represent the modulation frequency, the energy attenuation factor and the energy delay factor, respectively, represent the set of , and , 。 3. A multi-scale depositional cycle facies space fine characterization system, characterized in that, The method for fine characterization of the spatial phasor phases of the multiple scales described in claim 1 or 2 is applicable. It includes a first calculation module, a second calculation module, a mapping module, a time-spectrum calculation module, and a representation module; The first calculation module is used to calculate the average Fourier amplitude spectrum and the average reflection coefficient amplitude spectrum of two-dimensional seismic data. The second calculation module uses the scale-space representation method to calculate the smooth reflection coefficient amplitude spectrum. Based on the relationship between the reflection coefficient amplitude spectrum and the empirical modes, the number of extrema of the smooth reflection coefficient spectrum is set to the number of intrinsic mode function components, and the extremum frequency is the center frequency of each intrinsic mode function component. The mapping module uses variational mode decomposition to decompose the input two-dimensional seismic data to obtain intrinsic mode function components that can characterize the seismic reflection features of geological structures at different scales. The time-spectrum calculation module uses synchronous compression optimal basis wavelet transform to calculate the time-spectrum of each seismic trace for each two-dimensional intrinsic mode function component. The characterization module uses the ridge extraction method to extract the instantaneous dominant frequency curve from the time spectrum of each seismic trace, obtains a two-dimensional instantaneous dominant frequency attribute profile, and uses the instantaneous dominant frequency attribute profile to interpret the lateral distribution law of sedimentary cycle units, that is, a fine characterization of the multi-scale sedimentary cycle facies space.

4. A computer device, comprising: It includes a processor and a memory, the memory being used to store a computer-executable program, the processor reading part or all of the computer-executable program from the memory and executing it, and the processor executing part or all of the computed executable program is able to implement the multi-scale depositional cyclic phase space fine characterization method as described in claim 1 or 2.

5. A computer readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the multi-scale depositional cyclic phase space fine characterization method as described in claim 1 or 2.

6. Use of the method for fine characterization of a multiscale depositional cycle facies space according to claim 1 or 2, characterized in that, Used to identify and interpret sedimentary cycle information extracted from seismic data.

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