A method for depicting reservoir boundary by transient matching extrusion transformation

Abstract

The application discloses a transient matching squeeze transform method for reservoir boundary delineation. The method comprises the following steps: inputting seismic data to be analyzed; calculating group delay (GD) and a group delay band center in a short-time Fourier transform time-frequency domain of given data; calculating a group delay band boundary; matching all GD estimators in the group delay band with a GD estimator at the GD band center to obtain a matching GD estimator; squeezing time-frequency coefficients to obtain a transient matching squeeze transform (TMST); performing time-frequency transform on the seismic data according to the TMST to obtain time-frequency results; extracting a time sequence corresponding to a constant frequency from different seismic trace time-frequency spectrums obtained from the TMST, and obtaining a profile by synthesizing the time sequence, so that reservoir characteristics are represented and a reflection interface position is located. The method combines the advantages of a time redistribution synchronous squeeze transform (TSST) and a transient extraction transform (TET), and has strong sharpening performance and reconstruction performance.

Description

Technical Field

[0001] This invention relates to the fields of seismic signal processing and seismic reservoir identification, and in particular to a reservoir boundary characterization method based on transient matched squeeze transform (TMST). Background Technology

[0002] Time-frequency analysis (TFA) plays a crucial role in processing non-stationary signals such as seismic signals. Traditional time-frequency transforms, such as Short-Time Fourier Transform (STFT), Continuous Wavelet Transform, S-Transform (ST), and W-Transform, are limited by the Heisenberg uncertainty principle, requiring a trade-off between frequency and time resolution, resulting in time-frequency representations (TFRs) that are not ideal for seismic signal analysis. To overcome the shortcomings of traditional TFA methods, many time-frequency post-processing methods have been developed. Among them, the redistribution method (RM) provides a more concentrated TFR result by redistributing the time-frequency coefficients in the STFT domain to the centroid of the signal energy distribution, but RM loses the signal reconstruction capability of the TFA method. Synchronous Squeeze Transform (SST) redistributes the time-frequency coefficients only along the frequency direction, sharpening the original spectrum while preserving the reconstruction properties. Synchronous Extraction Transform (SET) obtains sparse TFR results by extracting the time-frequency coefficients at the fixed point of the instantaneous frequency (IF) estimator. However, the sharpening effect of SST and SET depends on the accuracy of the IF estimator. For signals with strong frequency variations, the basic IF estimator struggles to accurately estimate the IF of the signal.

[0003] For pulse-type signals, many researchers have improved the temporal resolution of Transform-Frequency Transform (TFR) results by reallocating time-frequency coefficients in the time domain. These methods are mainly divided into two categories: Time-Relocation-Based Transform-SST (TSST) and Transient Extraction Transform (TET) methods. TSST methods can reconstruct the original signal, but their sharpening effect is poor; TET methods have strong sharpening performance, but due to the loss of a large number of time-frequency coefficients, the signal reconstruction effect is poor. Therefore, it is necessary to develop a new time-frequency transform method that combines strong sharpening performance with good reconstruction performance. Summary of the Invention

[0004] The purpose of this invention is to propose a new time-frequency transformation method—Transient Matched Squeeze Transform (TMST)—for seismic reservoir boundary characterization, in order to solve the problem that existing TSST and TET methods cannot simultaneously achieve sharpening and reconstruction performance when processing seismic signals.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] Step 1: Input the seismic signal x(t) to be analyzed;

[0007] Step 2: Define the GD estimator by calculating the partial derivative of the STFT given the signal. Then calculate the group delay band center;

[0008] Step 3: Determine the left and right boundaries of the group delay band (GDB). and The GD estimators within the GDB are matched with the GD estimator at the center of the GD band to obtain the matched GD (MGD) estimator.

[0009] Step 4: Squeeze the time-frequency coefficients to obtain the Transient Matched Squeeze Transform (TMST) value Tm. x (t,ω);

[0010] Step 5: Perform time-frequency transformation on the seismic signal x(t) according to TMST to obtain the time-frequency result;

[0011] Step 6: Extract the time series corresponding to constant frequencies from the time spectra of different seismic traces obtained from TMST, and then synthesize them to obtain a profile map to characterize reservoir features and locate the reflection interface.

[0012] Preferably, in step 2, the partial derivative of its STFT is calculated to define the GD estimator, as follows:

[0013]

[0014] in, This represents the group delay estimator in the time-frequency domain of the STFT. This indicates taking the real part, where j is the imaginary unit. S represents the partial derivative with respect to the variable ω. x (t,ω) represents the STFT of signal x(t), where t is the time variable and ω is the frequency variable.

[0015] Recalculate the group delay band center:

[0016]

[0017] Among them, t c denoted by , st indicates that the delay band is constrained.

[0018] Preferably, the left and right boundaries of the group delay band (GDB) in step 3 and

[0019]

[0020] in, Indicates the left boundary of the group delay band. This indicates taking the partial derivative with respect to the time variable. This indicates that in the GD estimator, time is t. c .

[0021] All GD estimators within the GDB are matched with the GD estimator at the center of the GD band to obtain the matched GD (MGD) estimator:

[0022]

[0023] in, This indicates a matching GD (MGD) estimator.

[0024] Preferably, step 4 yields the Transient Matched Squeeze Transform (TMST) value:

[0025]

[0026] Among them, Tm x (t,ω) represents the transient matching squeeze transform. δ(·) represents the infinite integration of the variable u, and δ(·) represents the unit impulse function.

[0027] Preferably, the Transient Matched Squeeze Transform (TMST) mentioned in step 4 can reconstruct the signal x(t) without loss of information, and its reconstruction formula is as follows:

[0028]

[0029] in, This represents the value of the Fourier transform of the window function at 0.

[0030] The reservoir boundary characterization method based on transient matched squeeze transformation (TMST) provided by this invention has the following beneficial effects:

[0031] The Transient Matched Squeeze Transform (TMST) of this invention squeezes dispersed time-frequency coefficients onto the central trajectory of the GD (Gross Divergence), achieving a more concentrated TFR (Time-Frequency Reflection) result than TSST and TET, effectively improving the resolution of time-frequency analysis of seismic signals. While achieving sharpening, it can also accurately reconstruct the original signal, overcoming the problem of poor reconstruction effect caused by the loss of a large number of time-frequency coefficients in TET. When applied to actual seismic data, it can effectively reveal the characteristics of seismic reflection interfaces and accurately locate the reflection interface positions, providing a more powerful tool for seismic interpretation. Attached Figure Description

[0032] Figure 1 This is a flowchart of the present invention;

[0033] Figure 2 This is a seismic profile of a region in China.

[0034] Figure 3 for Figure 2 20Hz frequency slice of a two-dimensional seismic profile;

[0035] (a) Short-Time Fourier Transform (STFT) results; (b) Time-Reassigned SST (TSST) results; (c) Time-Reassigned Second-Order TSST (TSST2) results; (d) Transient Extraction Transform (TET) results, with threshold γ. α =0.0001; (e) Based on the Transient Extraction Transform (TET) result, the threshold γ α =0.001; (f) Transient Matched Squeeze Transform (TMST). Detailed Implementation

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] See Figures 2-3 The performance of this method was tested using actual field data. Different time-frequency analysis methods were used to analyze seismic data from a certain region of China. This two-dimensional seismic data contains 210 seismic traces, 501 sampling time points, and a time interval of 1 ms. The specific implementation steps are as follows:

[0038] 1) Input the seismic data to be analyzed, and obtain the following: Figure 2 The original seismic profile shown;

[0039] 2) For the input seismic signal, calculate the partial derivative of its STFT to define the GD estimator. Then calculate the group delay band center;

[0040] First calculate the GD estimator have:

[0041]

[0042] in, This represents the group delay estimator in the time-frequency domain of the STFT. This indicates taking the real part, where j is the imaginary unit. S represents the partial derivative with respect to the variable ω. x (t,ω) represents the STFT of signal x(t), where t is the time variable and ω is the frequency variable.

[0043] Recalculate the group delay band center:

[0044]

[0045] Among them, t c Indicates the center of the group delay band.

[0046] 3) Determine the left and right boundaries of the group delay band (GDB). and Then, all GD estimators within the GDB are matched with the GD estimator at the center of the GD band to obtain the matched GD (MGD) estimator;

[0047] First calculate the left and right boundaries of the group delay band (GDB). and have:

[0048]

[0049] in, Indicates the left boundary of the group delay band. This indicates taking the partial derivative with respect to the time variable. This indicates that in the GD estimator, time is t. c .

[0050] Then, all GD estimators within the GDB are matched with the GD estimator at the center of the GD band to obtain the matched GD (MGD) estimator:

[0051]

[0052] in, This indicates a matching GD (MGD) estimator.

[0053] 4) The time-frequency coefficients are squeezed to obtain the Transient Matched Squeeze Transform (TMST) value:

[0054]

[0055] Among them, Tm x (t,ω) represents the transient matching squeeze transform. δ(·) represents the infinite integration of the variable u, and δ(·) represents the unit impulse function.

[0056] 5) Perform time-frequency transformation on the seismic data using TMST to obtain time-frequency results for different seismic traces;

[0057] 6) Extract the time series corresponding to constant frequencies from the time spectra of different seismic traces obtained from TMST, and then synthesize them to obtain profile maps to characterize reservoir features and locate the reflection interface.

[0058] Through the Figure 3 A comparative analysis of (a)(b)(c)(d)(e) reveals the following:

[0059] (a) The TFR results generated by the traditional TFA method STFT have a coarse TF response, and the TF energy is roughly distributed around the reflectivity location, which is not conducive to accurately identifying the location of the reflective interface.

[0060] (b) and (c) Using TSST and TSST2 with compressed TF coefficients, the constant spectral lines obtained by TSST and TSST2 are more sparse than those of conventional STFT. It can be seen that the sparsity effect of TSST2 is better than that of TSST. However, both methods show dispersion of TF energy around the reflective interface.

[0061] (d) and (e) Compared to TSST and TSST2, TET uses an extraction operator to sharpen the STFT time spectrum, which can better describe the location of the reflecting interface. Here, we use two versions of TET to analyze the response curves. (d) Threshold γ α =0.0001,(e)γ α =0.001, we can see that the TET time-frequency results are sparser with a smaller threshold, but the choice of threshold is challenging in practice;

[0062] (f)TMST uses a compression operator to redistribute the TF coefficients, which combines the advantages of TET, resulting in sparser time-frequency results with higher time resolution. Therefore, it can be concluded that the proposed TMST is superior in revealing the characteristics of reflective interfaces.

[0063] The above analysis shows that, in reservoir boundary characterization, TMST's TFR has sparser time-frequency results, higher temporal resolution, and is more advantageous in revealing reflection interface characteristics, making it more promising than other methods in the interpretation of actual seismic data.

[0064] This invention is based on Time-Reassigned SST (TSST) and Transient Extraction Transform (TET), combining the lossless reconstruction performance of Time-Reassigned SST (TSST) and the strong sharpening performance of Transient Extraction Transform (TET), and is a new time-frequency transform that has both strong sharpening and reconstruction performance.

[0065] Although specific embodiments of the invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of this patent. Various modifications and variations that can be made by a person skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of this patent.

Claims

1. A method for characterizing reservoir boundaries based on transient matching squeeze transformation, characterized in that, Includes the following steps: Step 1: Input the seismic signal to be analyzed ; Step 2: Define the GD estimator by calculating the partial derivative of the STFT given the signal. , ,in, This represents the group delay estimator in the time-frequency domain of the STFT. Indicates taking the real part, It is the imaginary unit. Represents the variable Find the partial derivative. Indicates signal The STFT below, For time variables, It is a frequency variable; then the group delay band center is calculated. Step 3: Determine the left and right boundaries of the group delay band and , , Indicates the center of the group delay band. Indicates the left boundary of the group delay band. This indicates taking the partial derivative with respect to the time variable. Indicates the time taking in the GD estimator Then, all GD estimators within the group delay band are matched with the GD estimator at the center of the GD band to obtain the matched GD estimator. , ; Step 4: Squeeze the time-frequency coefficients to obtain the transient matched squeezed transform value. , , Represents the variable Perform infinite integration, Represents the unit impulse function; Step 5: Apply transient matched compression transform to the seismic signal Perform time-frequency transformation to obtain the time-frequency result; Step 6: Extract the time series corresponding to constant frequencies from the time spectra of different seismic traces obtained by transient matching compression transformation, and then synthesize them to obtain a profile map to characterize reservoir features and locate the reflection interface.

2. The reservoir boundary characterization method based on transient matching squeeze transformation according to claim 1, characterized in that, The group delay band center can be represented as: (1) in, Indicates the center of the group delay band. It indicates that it is subject to restrictions.

3. The reservoir boundary characterization method based on transient matching squeeze transformation according to claim 1, characterized in that, The transient matched squeeze transform obtained in step 4 is a lossless inverse transform, with no information loss, and the signal can be reconstructed. Its inverse transform expression is: (2) in, This represents the value of the Fourier transform of the window function at 0.

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