A method for characterizing tight sand thin layers based on adaptive transient extraction W transform

Abstract

The application discloses a compact sandstone thin layer characterization method based on adaptive transient extraction W transform, which comprises the following steps: (1) inputting post-stack seismic data x(t), wherein t is time; (2) estimating chirp rate c(t,f), wherein f is frequency; (3) constructing a chirp modulation window w(t,f,c(t,f)); (4) calculating W transform WT(τ,f,c) with the chirp modulation window; (5) defining a transient extraction operator wherein τ is a time delay parameter, and is a group delay estimation quantity; (6) calculating adaptive transient extraction W transform ATEWT(τ,f) of the seismic data; (7) performing feature extraction with frequency f0 to obtain a single frequency profile ATEWT(τ,f0). On the basis of the W transform, the chirp modulation window obtained from the main frequency estimation is constructed, and the convergent transient extraction operator under the group delay estimation quantity is defined, so that the time response characteristics of the thin layer can be better described by using the seismic data.

Description

Technical Field

[0001] This invention relates to a seismic signal time response analysis method, and proposes a method for characterizing tight sandstone thin layers based on adaptive transient extraction W-transform. This method has strong time positioning capability and can well describe the boundary structure characteristics of thin layers. Background Technology

[0002] Tight sandstone gas is the most important and realistic source to replace conventional oil and gas resources within the long-term vision of 2035. Tight sandstone reservoirs, as a key and challenging area in the exploration and development of tight sandstone gas, possess complex characteristics such as thin and narrow vertical layers, dispersed development, poor physical properties, strong heterogeneity, and complex gas-water distribution. The thin-layer seismic reflection signals are constantly affected by complex factors such as single-layer thickness, differences in physical properties, and fluid dynamics, significantly limiting the effective use of conventional seismic exploration processing and interpretation methods.

[0003] Time-frequency analysis maps one-dimensional seismic signals to a two-dimensional time-frequency plane, effectively characterizing the frequency features hidden in seismic reflection signals and locating changing frequencies in time. This has been widely applied in reservoir characterization and channel detection. The W-transform, a traditional time-frequency analysis method defining a Gaussian window function with symmetric frequency weights, improves the time-spectrum resolution in the low-frequency range and has received widespread attention in recent years; however, it still suffers from insufficient resolution in thin reservoir applications. The Transient Extraction Operator (TEO), as a time-frequency post-processing operator for achieving high-level time localization, exhibits significant interference in practical applications. This is due to non-convergence issues in TEO theory, requiring new definitions and restrictions to obtain better reservoir time response characteristics. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention proposes a method for characterizing tight sandstone thin layers based on adaptive transient extraction of the W-transform. In this method, we introduce a chirp modulation window for dominant frequency estimation based on the W-transform, and define a transient extraction operator under the group delay estimator, thus redefining and restricting its convergence, thereby obtaining better thin-layer time response characteristics.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows: a method for characterizing tight sandstone thin layers based on adaptive transient extraction W-transform, comprising the following steps:

[0006] (1) Input the post-stack seismic data x(t), where t is time;

[0007] (2) Estimate the chirp rate c(t,f), where f is the frequency;

[0008] (3) Construct the chirp modulation window w(t,f,c(t,f));

[0009] (4) Calculate the W transform WT(τ,f,c) with a chirp modulation window;

[0010] (5) Define the transient extraction operator Where τ is the time delay parameter. For convergent group delay estimators;

[0011] (6) Calculate the adaptive transient extraction W transform ATEWT(τ,f) of seismic data;

[0012] (7) Perform feature extraction at frequency f0 to obtain the single-frequency profile ATEWT(τ,f0).

[0013] As a preferred option: In step (2), the specific method for estimating the chirp rate c(t,f) is as follows:

[0014]

[0015] Where f is the frequency, and f0(τ)≠0 represents the dominant frequency of a waveform.

[0016] As a preferred embodiment: In step (3), the specific chirp modulation window w(t,f,c(t,f)) is constructed as follows:

[0017]

[0018] Where w(t,f;τ) is a Gaussian window function with symmetric frequency weights, and can be in the form of:

[0019]

[0020] Where k is a scalar factor, f0(τ)≠0 represents the dominant frequency of a waveform, and Δf(τ)=f0(τ)-f is the difference between the dominant frequency and the instantaneous frequency.

[0021] Preferably, in step (4), the W transform expression with the chirp modulation window is:

[0022]

[0023] The transient extraction operator is defined based on this result.

[0024] As a preferred option: the transient extraction operator defined in step (5) for:

[0025]

[0026] Where τ is the time delay parameter, specifically the group delay estimate. The expression is:

[0027]

[0028] in The imaginary part is represented by the following formula: [Further details about the formula are needed for the imaginary part]. The convergence of this formula is reflected in the constraints on the second and third derivatives with respect to frequency.

[0029]

[0030] in It represents the nth derivative with respect to time.

[0031] As a preferred embodiment: In step (6), the adaptive transient extraction W transform ATEWT(τ,f) is defined as:

[0032]

[0033] Based on the calculation results, feature extraction at a certain frequency can yield a single-frequency profile.

[0034] Compared with existing technologies, the advantages of this invention are as follows: by introducing a chirp modulation window for dominant frequency estimation and defining a new transient extraction operator, the convergence of the group delay estimator is redefined and restricted, thus proposing a method for characterizing tight sandstone thin layers based on adaptive transient extraction W-transform. This method can better capture the temporal response characteristics of thin layers in seismic data and obtain more seismic data information. Attached Figure Description

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

[0036] Figure 2 This is a seismic thin-layer profile of a tight sandstone gas field in western Sichuan, China.

[0037] Figure 3 The results of W-transform processing of a thin layer of dense sandstone are as follows: (a) is a single-frequency profile extracted from 32Hz features, and (b) is a single-frequency profile extracted from 50Hz features.

[0038] Figure 4 The results of the method of the present invention for processing a thin layer of dense sandstone are as follows: (a) is a single-frequency profile extracted from 32Hz features, and (b) is a single-frequency profile extracted from 50Hz features. Detailed Implementation

[0039] The invention will now be further described with reference to the accompanying drawings.

[0040] Example 1: See Figure 1 A method for characterizing tight sandstone thin layers based on adaptive transient extraction of W-transform includes the following steps:

[0041] (1) Input the post-stack seismic data x(t), where t is time;

[0042] (2) Estimating the chirp rate

[0043]

[0044] Where f is the frequency, and f0(τ)≠0 represents the dominant frequency of a waveform;

[0045] (3) Constructing the chirp modulation window

[0046]

[0047] Where w(t,f;τ) is a Gaussian window function with symmetric frequency weights, and can be in the form of:

[0048]

[0049] Where k is a scalar factor, f0(τ)≠0 represents the dominant frequency of a waveform, and Δf(τ)=f0(τ)-f is the difference between the dominant frequency and the instantaneous frequency;

[0050] (4) Calculate the W transform with a chirp modulation window:

[0051]

[0052] (5) Define the transient extraction operator

[0053]

[0054] Where τ is the time delay parameter, specifically the group delay estimate. The expression is:

[0055]

[0056] in The imaginary part is represented by the following formula: [Further details about the formula are needed for the imaginary part]. The convergence of this formula is reflected in the constraints on the second and third derivatives with respect to frequency.

[0057]

[0058] in This represents the nth derivative with respect to time.

[0059] (6) Calculate the adaptive transient extraction W transform of seismic data:

[0060]

[0061] (7) Perform feature extraction at frequency f0 to obtain the single-frequency profile ATEWT(τ,f0);

[0062] The invention first inputs post-stack seismic data; the second and third steps estimate the chirp rate and construct a chirp modulation window; the fourth step calculates the W transform with the chirp modulation window; the fifth step defines a transient extraction operator based on the results; the sixth step calculates the adaptive transient extraction W transform result of the seismic data; and the final step performs feature extraction at a certain frequency on the calculation results to obtain a single-frequency profile.

[0063] Example 2:

[0064] Figure 2 This is a seismic thin-layer profile of a tight sandstone gas field in western Sichuan, China.

[0065] Figure 3 In the middle (a) and (b), the 32Hz and 50Hz single-frequency profiles extracted after processing the tight sandstone thin layer by W transform are shown respectively. It can be seen that although W transform has better hierarchical properties at 32Hz than at 50Hz, the time-frequency resolution still needs to be improved.

[0066] Figure 4 In Figures (a) and (b), the 32Hz and 50Hz single-frequency profiles extracted from the thin layer of dense sandstone after processing by the method of this invention are shown respectively. Compared with the results of W-transformation, the seismic profiles processed by the method of this invention, whether at 32Hz or 50Hz, retain the continuity of seismic data, have accurate time positioning, clearer geological stratification, richer texture details, and more reasonable results.

[0067] The above examples are only used to illustrate the present invention. The implementation steps of the method can be changed. All equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A method for characterizing tight sandstone thin layers based on adaptive transient extraction of W-transform, characterized in that: Includes the following steps: (1) Input post-stack seismic data ,in For time; (2) Estimate the chirp rate, specifically the chirp rate. The estimation method is as follows: ， in For frequency, ≠0 indicates the dominant frequency of a waveform; (3) Constructing the chirp modulation window ; (4) Calculate the W transform with a chirp modulation window. ; (5) Define the transient extraction operator ,in Delay parameters, As a group delay estimator, its convergence is reflected in the constraints on the second and third derivatives with respect to frequency. ,in Indicates the imaginary part. This represents the nth derivative with respect to time. (6) Calculate the adaptive transient extraction W transform of seismic data ; (7) The frequency is Feature extraction yields single-frequency profiles. .

2. The method for characterizing tight sandstone thin layers based on adaptive transient extraction of W-transform according to claim 1, characterized in that: In step (3), the specific chirp modulation window The construction is as follows: ， in It is a Gaussian window function with symmetric frequency weights, in the form of: ， in It is a scalar factor. This represents the dominant frequency of a waveform. The difference between the dominant frequency and the instantaneous frequency.

3. The method for characterizing tight sandstone thin layers based on adaptive transient extraction of W-transform according to claim 1, characterized in that: In step (4), the W transform expression with the chirp modulation window is: ， The transient extraction operator is defined based on this result.

4. The method for characterizing tight sandstone thin layers based on adaptive transient extraction of W-transform according to claim 1, characterized in that: In step (6), the defined adaptive transient extraction W transform is... for: ， Based on the calculation results, feature extraction at a certain frequency can yield a single-frequency profile.

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