Analysis Method of De-reflection Coefficient Short-time Window Spectrum Attenuation

Abstract

The present invention provides a de-reflection coefficient short-time window spectrum attenuation analysis method, which includes: step 1: resampling after spectrum domain transformation of seismic data; step 2: performing sliding time window analysis to calculate absorption attenuation coefficient; step 3: performing deconvolution to extract instantaneous wavelet; step 4: performing spectrum analysis on the instantaneous wavelet, fitting the attenuation coefficient of the high-frequency band to obtain the formation absorption value; step 5: obtaining the formation quality factor by the high-frequency band spectrum ratio method, subtracting the formation absorption values ​​of two adjacent time points, and obtaining the absorption anomaly value. The de-reflection coefficient short-time window spectrum attenuation analysis method can effectively remove the influence of the reflection coefficient and distinguish the strong amplitude (bright spot) anomaly caused by gas content from the strong amplitude anomaly caused by lithology.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical exploration seismic processing method research, and in particular to a de-reflection coefficient short-time window spectrum attenuation analysis method. Background Art

[0002] At present, the methods of identifying shallow gas reservoirs by seismic technology mainly include post-stack "bright spot" technology, spectrum decomposition technology, pre-stack AVO inversion technology, elastic parameter inversion technology, energy absorption analysis technology (EAA), etc. When using the "bright spot" technology to find natural gas reservoirs, the factors causing seismic amplitude anomalies are very complex, and the "bright spot" does not necessarily correspond to the gas content of the reservoir. Due to the influence of seismic resolution and 1 / 4 wavelength tuning, the spectrum decomposition technology has a blind spot in the identification of thin gas layers. When using AVO technology to detect the oil and gas content of the reservoir, the AVO characteristics of the gas-bearing reservoir are very complex and not all show enhanced characteristics. The change of formation water saturation is also closely related to the AVO response characteristics. The selection of seismic data processing parameters and the limited logging data will affect the AVO analysis, so the reliability of AVO analysis is affected to a certain extent. The elastic parameter inversion technology has enhanced the ability to identify thin gas layers, but the application of elastic parameter inversion technology requires certain full-wave logging data and pre-stack high-signal-to-noise ratio high-fidelity seismic data. The lack of pre-stack data in some areas limits the application of elastic parameter inversion technology. Energy absorption analysis technology (EAA) is based on the property that the high-frequency components of seismic waves attenuate sharply when passing through gas-bearing formations. By analyzing the spectrum of high-frequency components, the gas content of the reservoir can be detected.

[0003] Conventional energy attenuation analysis technology directly performs spectrum analysis in a time-sharing window on the seismic trace. The seismic spectrum is the product of the sub-wave spectrum and the reflection coefficient spectrum. The reflection coefficient is a combination of the stratigraphic framework sequence and does not represent the stratigraphic absorption characteristics. Since the reflection coefficient interferes with the seismic spectrum, the results of conventional energy attenuation analysis technology are greatly affected by the reflection coefficient, and it is difficult to distinguish whether the strong amplitude anomaly is caused by gas in the stratum or by high-resistance lithology, which greatly restricts the actual application effect of energy attenuation analysis.

[0004] In the Chinese patent application with application number: CN201410212287.4, a time-varying mixed phase seismic wavelet extraction method based on time-frequency spectrum simulation is involved, which is characterized in that: on the basis of the improved generalized S transform for non-stationary seismic records, a time-frequency filter is introduced for the first time to filter the time-frequency spectrum of the seismic record, and then the spectral simulation method is used to estimate the wavelet amplitude spectrum at each moment, and under the assumption that the wavelet phase is time-invariant, the high-order cumulant bispectral method is used to reconstruct the mixed phase spectrum of the wavelet from the seismic record with the time-varying wavelet amplitude spectrum removed, and finally the amplitude spectrum and phase spectrum are combined to realize the extraction of the time-varying mixed phase wavelet.

[0005] In the Chinese patent application with application number: CN201510109816.2, a method and device for oil and gas detection based on spectrum calculation absorption attenuation properties are involved. The method includes: taking a time window signal from the collected single-point seismic data to generate a time window seismic signal; performing a Hilbert transform on the time window seismic signal to generate an imaginary signal, and constructing a complex seismic signal with the time window seismic signal; constructing an objective function for solving the spectrum based on the complex seismic signal and the cost function under the L2 norm; iteratively solving the objective function to determine the spectrum; and determining the absorption attenuation properties based on the spectrum to perform oil and gas detection.

[0006] In the Chinese patent application with application number: CN201010531211.X, a spectrum analysis method and device for seismic signals are involved, wherein the method includes: acquiring a seismic signal; performing a Hilbert transform operation on the seismic signal to obtain an analytical signal of the seismic signal; performing WVD processing on the analytical signal; determining a Gaussian window function and an attenuation coefficient based on the result of the WVD processing; and performing smoothed pseudo-WVD distribution time-frequency analysis processing on the result of the WVD processing based on the determined Gaussian window function and attenuation coefficient.

[0007] The above existing technologies are greatly different from the present invention and fail to solve the technical problem we want to solve. Therefore, we have invented a new de-reflection coefficient short-time window spectrum attenuation analysis method. Summary of the invention

[0008] The purpose of the present invention is to provide a method for performing de-reflection coefficient short-time window spectrum attenuation analysis and oil and gas detection using the instantaneous wavelet spectrum attenuation characteristics, which is an important tool for seismic data analysis and information interpretation.

[0009] The purpose of the present invention can be achieved by the following technical measures: a de-reflection coefficient short-time window spectrum attenuation analysis method, the de-reflection coefficient short-time window spectrum attenuation analysis method comprising:

[0010] Step 1: Resample the seismic data after spectral domain transformation;

[0011] Step 2: Perform sliding time window analysis and calculate the absorption attenuation coefficient;

[0012] Step 3: Perform deconvolution to extract instantaneous wavelets;

[0013] Step 4: Perform spectrum analysis on the instantaneous wavelet and fit the attenuation coefficient of the high frequency band to obtain the formation absorption value;

[0014] Step 5: Use the high-frequency spectrum ratio method to obtain the formation quality factor. Subtract the formation absorption values ​​at two adjacent time points to obtain the absorption anomaly value.

[0015] The purpose of the present invention can also be achieved by the following technical measures:

[0016] In step 1, the seismic data is transformed into the frequency domain, and the Nyquist frequency is increased by the method of frequency zero filling, and then inversely transformed into the time domain to obtain the resampled seismic record.

[0017] In step 1, the resampling of spectral domain seismic data must be performed before the sliding time window analysis. The resampling is completed in the spectral domain. The spectrum of the earthquake does not change before and after the resampling. It is a high-precision resampling algorithm.

[0018] In step 2, the time window length is 40-70 ms, and the time window step is equal to the sampling interval of the seismic record.

[0019] In step 2, the sliding window analysis is to calculate the absorption attenuation coefficient at each time point through the sliding window.

[0020] In step 3, pulse deconvolution is performed on the earthquakes in the sliding time window to extract the anti-wavelet, which is then inverted to obtain the instantaneous wavelet.

[0021] In step 4, the instantaneous wavelet is subjected to spectral analysis, and only the high frequency band of the spectrum is selected for energy absorption analysis. An exponential curve is used to fit the amplitude change from the peak frequency to the Nyquist frequency, and the quality factor Q is calculated using the obtained attenuation coefficient. Compared with the traditional spectral ratio method, the wavelet attenuation caused by the fluid is eliminated, and the multi-solution of the absorption anomaly is reduced.

[0022] In step 5, the formation absorption values ​​at two adjacent time points are subtracted, and the time window used must be small. It is assumed that the background value of the attenuation curve does not change between adjacent time windows. In this way, the attenuation parameters calculated in adjacent time windows are subtracted, which can be approximately considered to have removed the influence of the background trend. The attenuation curve after removing the background trend is actually an abnormal value that deviates from the background trend. In this way, the anomaly after removing the natural absorption background can better reflect the absorption attenuation effect of the target reservoir and is not limited by the burial depth of the formation.

[0023] The de-reflection coefficient short-time window spectrum attenuation analysis method of the present invention firstly improves the calculation accuracy of the instantaneous spectrum by resampling the seismic data before the sliding time window analysis, and provides reliable information input for the absorption attenuation analysis performed on the instantaneous spectrum; secondly, a new instantaneous wavelet extraction algorithm is provided by deconvolution, which has the advantages of mature theory, complete algorithm, and more reliable and accurate extracted wavelets compared with the conventional instantaneous wavelet extraction algorithm. The energy absorption analysis profile after de-reflection coefficient can better distinguish whether the absorption anomaly is caused by gas in the formation or by high-resistance lithology, which can be used as one of the detection indicators of gas content in the reservoir.

[0024] Based on the instantaneous wavelet analysis technology, the present invention obtains a more accurate instantaneous spectrum by resampling the seismic data, extracts the instantaneous wavelet by deconvolution, and then obtains the formation absorption anomaly value by energy absorption analysis as a detection index of formation gas content. This method can effectively remove the influence of the reflection coefficient and distinguish the strong amplitude (bright spot) anomaly caused by gas content from the strong amplitude anomaly caused by lithology. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of comparison of seismic records before and after resampling in a specific embodiment of the present invention;

[0026] Figure 2 It is a schematic diagram of the comparison between the waveform and the spectrum in the 40ms time window before and after the resampling of the seismic record in a specific embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a synthetic seismic record (top) seismic spectrum and instantaneous wavelet spectrum in a specific embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of the comparison between the waveform and the spectrum of the rematch spectrum wavelet extraction method in a specific embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the comparison between the waveform and the spectrum of the deconvolution wavelet extraction method in a specific embodiment of the present invention;

[0030] Figure 6 A schematic diagram of an instantaneous wavelet energy absorption analysis process in a specific embodiment of the present invention;

[0031] Figure 7 The absorption anomaly profile calculated by the de-reflection coefficient short-time window spectrum attenuation analysis in a specific embodiment of the present invention;

[0032] Figure 8 In a specific embodiment of the present invention, the abnormal profile of energy absorption of the reflection coefficient is not removed;

[0033] Fig. 9 The flowchart is a specific embodiment of the method for analyzing the attenuation of the de-reflection coefficient short-time window spectrum of the present invention. DETAILED DESCRIPTION

[0034] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0035] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0036] The de-reflection coefficient short-time window spectrum attenuation analysis method of the present invention comprises the following steps:

[0037] Step 1: Resample the spectral domain seismic data, transform the seismic data into the frequency domain, and increase the Nyquist frequency by the method of frequency zero filling, and then inversely transform it into the time domain to obtain the resampled seismic record;

[0038] The resampling of spectral domain seismic data must be performed before the sliding time window analysis. The resampling is completed in the spectral domain. The spectrum of the earthquake does not change before and after the resampling. It is a high-precision resampling algorithm.

[0039] Step 2: Sliding time window analysis, the time window length is generally 40-70ms, and the time window step can be equal to the sampling interval of the seismic record;

[0040] Step 3: Deconvolution to extract instantaneous wavelets. Perform pulse deconvolution on earthquakes within the sliding time window to extract anti-wavelets. Invert the anti-wavelets to obtain instantaneous wavelets.

[0041] The deconvolution method of extracting instantaneous wavelets is close to the real wavelets in both waveform and spectrum, especially in the high frequency band of the spectrum. Compared with the reciprocal spectrum method, the deconvolution wavelet extraction method has higher accuracy in the high frequency band, and the deconvolution method avoids the practice of artificially selecting separation points in the reciprocal spectrum method, so it is more reliable.

[0042] Step 4: Perform spectral analysis on the instantaneous wavelet, fit the attenuation coefficient of the high-frequency band to obtain the formation absorption value; perform spectral analysis on the instantaneous wavelet, and only select the high-frequency band of the spectrum for energy absorption analysis. Compared with the traditional spectral ratio method, this patented method eliminates the wavelet attenuation caused by fluid (oil, water) and reduces the multi-solution of absorption anomalies.

[0043] Step 5: Use the high-frequency spectrum ratio method to obtain the formation quality factor. Subtract the formation absorption values ​​at two adjacent time points to obtain the absorption anomaly value. When subtracting the formation absorption values ​​at two adjacent time points, the time window used must be small. It can be assumed that the background value of the attenuation curve does not change between adjacent time windows. In this way, the attenuation parameters calculated in adjacent time windows are subtracted, and it can be approximately considered that the influence of the background trend has been removed. The attenuation curve after removing the background trend is actually an anomaly that deviates from the background trend. In this way, the anomaly after removing the natural absorption background can better reflect the absorption attenuation effect of the target reservoir, and is not limited by the burial depth of the formation.

[0044] In a specific embodiment 1 of the present invention, Fig. 9 As shown, Fig. 9 The present invention is a flow chart of the de-reflection coefficient short-time window spectrum attenuation analysis method.

[0045] Step 101: Resample the spectral domain seismic data, transform the seismic data into the frequency domain, increase the Nyquist frequency by using the method of frequency zero filling, and then inversely transform into the time domain to obtain a resampled seismic record.

[0046] In order to obtain a high-resolution attenuation profile, the time window length is generally 40-70ms. Assuming that the sampling interval of the seismic data is 4ms, there are only 11 sampling points in the 40ms time window. Such a small number of points is not enough to reflect the spectral characteristics of the seismic data. Within a certain time window length (40ms), in order to obtain the instantaneous spectrum of each point, it is necessary to increase the sampling rate of the seismic data before the sliding time window analysis.

[0047] Step 102: Sliding time window analysis, the time window length is generally 40-70ms, and the time window step length can be equal to the sampling interval of the seismic record.

[0048] Further sliding time window analysis is to calculate the absorption attenuation coefficient at each time point through the sliding time window.

[0049] Step 103: extracting instantaneous wavelets by deconvolution, performing pulse deconvolution on earthquakes within the sliding time window, extracting anti-wavelets, and inverting the anti-wavelets to obtain instantaneous wavelets;

[0050] The conventional algorithm first performs Fourier transform on the seismic record in a time window, calculates the logarithm in the frequency domain, and then converts the frequency domain to the time domain through inverse Fourier transform to obtain the reciprocal spectrum of the seismic record. The seismic wavelet and the reflection coefficient are located at the two ends of the reciprocal spectrum, so that the amplitude spectrum of the seismic wavelet can be separated by a time domain filter. A major problem with this method is that it is difficult to determine the dividing point between the reflection coefficient and the instantaneous wavelet on the reciprocal spectrum. We propose another method to extract instantaneous wavelets: extracting instantaneous wavelets through deconvolution. A major advantage of this method is that various relatively mature deconvolution methods can be used to design an instantaneous wavelet extraction algorithm.

[0051] Step 104: Perform spectrum analysis on the instantaneous wavelet, and fit the attenuation coefficient of the high frequency band to obtain the formation absorption value;

[0052] Furthermore, this method only selects the high frequency band of the spectrum for energy absorption analysis, uses an exponential curve to fit the amplitude change from the peak frequency to the Nyquist frequency, and uses the obtained attenuation coefficient to calculate the quality factor Q. Compared with the traditional spectrum ratio method, this patented method excludes the wavelet attenuation caused by fluid (oil, water) and only studies the influence of gas. The absorption anomaly calculated in this way only corresponds to the gas-bearing formation, reducing the multi-solution of the absorption anomaly.

[0053] Step 105: The formation quality factor is obtained by using the high-frequency spectrum ratio method, and the formation absorption values ​​at two adjacent time points are subtracted to obtain the absorption anomaly value.

[0054] Furthermore, the high-frequency spectrum ratio method is used to obtain the formation quality factor. The time window between two adjacent time points is very small, which can ensure that the background value of the attenuation curve is basically unchanged. In this way, the attenuation parameters calculated in adjacent time windows are subtracted, which can be approximately considered to have removed the influence of the background trend. The attenuation curve after removing the background trend is actually an outlier that deviates from the background trend. In this way, the anomaly after removing the natural absorption background can better reflect the absorption attenuation effect of the target reservoir, and is not limited by the depth of the formation.

[0055] The present invention performs instantaneous spectrum decomposition on amplitude-preserving seismic data to obtain instantaneous seismic wavelets, then fits the attenuation curve of the high-frequency band of the wavelet spectrum to obtain the formation absorption coefficient, reasonably estimates the background component of the absorption coefficient and subtracts it from the absorption coefficient to obtain the formation absorption anomaly profile, thereby realizing the detection of gas content in the reservoir.

[0056] In a specific embodiment 2 of the present invention, a de-reflection coefficient short-time window spectrum attenuation analysis technology is proposed. Based on the instantaneous wavelet analysis technology, a more accurate instantaneous spectrum is obtained by resampling the seismic data, and the instantaneous wavelet is extracted by deconvolution. Then, the formation absorption anomaly value is obtained by the energy absorption analysis method as a detection indicator of the gas content of the formation.

[0057] Step 1: Resample the spectral domain seismic data, transform the seismic data into the frequency domain, and increase the Nyquist frequency by the method of frequency zero filling, and then inversely transform it into the time domain to obtain the resampled seismic record;

[0058] Step 2: Sliding time window analysis, the time window length is generally 40-70ms, and the time window step can be equal to the sampling interval of the seismic record;

[0059] Step 3: Deconvolution to extract instantaneous wavelets. Perform pulse deconvolution on earthquakes within the sliding time window to extract anti-wavelets. Invert the anti-wavelets to obtain instantaneous wavelets.

[0060] Step 4: Perform spectrum analysis on the instantaneous wavelet and fit the attenuation coefficient of the high frequency band to obtain the formation absorption value;

[0061] Step 5: Use the high-frequency spectrum ratio method to obtain the formation quality factor. Subtract the formation absorption values ​​at two adjacent time points to obtain the absorption anomaly value.

[0062] The spectral domain seismic data resampling in step 1 is to convert the seismic data into the frequency domain, increase the Nyquist frequency by the method of frequency zero filling, and then inversely transform it into the time domain to obtain the resampled seismic record. Figure 1 Comparison of the resampled seismic records. It can be seen from the figure that the resampled seismic records are highly consistent with the original records, except that the sampling points have increased by 10 times. For the same 40ms time window, there are only 21 points before sampling, and after resampling, the number increases to 210 points. After the sampling points in the time window increase, the calculated spectrum is more accurate and reliable. Figure 2 Comparison of the waveform and spectrum before and after the resampling of the seismic record shows that the seismic waveform in the 40ms time window (0.45-0.49s) does not change much before and after the resampling, but the spectrum is quite different. The time domain waveform is very different before and after the resampling, but the sampling points have increased, which leads to the difference in the spectrum. This difference is entirely caused by too few FFT transformation points before sampling. In order to better obtain the spectrum in the short time window, the seismic data needs to be resampled before the short time window analysis.

[0063] Steps 2 and 3 are to achieve the instantaneous wavelet by removing the reflection coefficient. The seismic record can be regarded as the convolution of the wavelet and the reflection coefficient. The spectrum of the seismic record is equal to the spectrum of the wavelet multiplied by the spectrum of the reflection coefficient. The reflection coefficient is a combination of the stratigraphic frame sequence and does not represent the absorption characteristics of the stratigraphic formation. Since the reflection coefficient interferes with the seismic spectrum, the results of conventional energy attenuation analysis technology are greatly affected by the reflection coefficient, and the absorption anomaly obtained may not be caused by gas in the stratigraphic formation. In order to illustrate this problem, a simple example is designed. Attachment Figure 3 The amplitude spectrum and instantaneous wavelet spectrum of the synthetic record are shown in the figure. As can be seen from the figure, the instantaneous wavelet spectrum is quite different from the seismic spectrum, and the wavelet spectrum can basically be regarded as the envelope of the seismic spectrum. In this example, directly calculating the high-frequency attenuation parameters on the seismic spectrum will overestimate the absorption of the actual stratum and form a "false bright spot".

[0064] In a specific embodiment 3 of the present invention, if the instantaneous wavelet is obtained by using the conventional reciprocal spectrum method, it is necessary to determine the separation point of the wavelet spectrum and the reflection coefficient spectrum. If the separation point is not selected accurately, the spectrum of the separated wavelet and the real wavelet will differ greatly. Figure 4As shown in Figure 1, the energy absorption analysis on the separated wavelet spectrum will not give the accurate formation absorption coefficient. For the same model, we used the deconvolution method to extract the wavelet. Figure 5 Compared with the wavelet extracted by the deconvolution method, the wavelet extracted by the deconvolution method is close to the real wavelet in waveform and spectrum, especially in the high frequency band of the spectrum. Figure 4 By comparison, the wavelet extracted by deconvolution has higher accuracy (manifested in the high frequency band of the spectrum) than the wavelet extracted by the reciprocal spectrum method, and the deconvolution algorithm avoids the practice of artificially selecting separation points in the reciprocal spectrum method, making it more reliable.

[0065] Figure 6 It is the main process of instantaneous wavelet energy absorption analysis. First, the instantaneous wavelet is extracted by time window, and the instantaneous wavelet is Fourier transformed to obtain the spectrum of the instantaneous wavelet. The absorption attenuation fitting is performed on the high frequency band of the instantaneous wavelet spectrum to obtain an attenuation coefficient. This coefficient indicates the average absorption characteristics of the formation in the short time window. When encountering gas-bearing formations, the high frequency band of the instantaneous wavelet will be drastically attenuated, and the obtained attenuation coefficient is relatively large. In order to eliminate the influence of attenuation caused by the increase of burial depth caused by the earth filtering, it is necessary to use the trend analysis method to separate the background trend of the attenuation curve. Since the time window used in our analysis is small (40ms), it can be assumed that the background amount of the attenuation curve is basically unchanged between adjacent time windows. In this way, the attenuation parameters calculated by adjacent time windows are subtracted, and it can be approximately considered that the influence of the background trend is removed. The attenuation curve after removing the background trend is actually an outlier that deviates from the background trend. In this way, the anomaly after removing the natural absorption background can better reflect the absorption attenuation effect of the target reservoir, which is not limited by the burial depth of the formation.

[0066] Figure 7 It is the absorption anomaly profile calculated by the short-time window spectrum attenuation analysis of the actual data de-reflection coefficient. The circles in the figure correspond to gas-bearing strata. It can be seen from the figure that the response of the gas-bearing strata on the absorption anomaly profile is relatively obvious and relatively easy to identify.

[0067] Figure 8 The absorption anomaly profile obtained by directly performing energy absorption analysis on the seismic spectrum without removing the influence of the reflection coefficient can be seen from the figure. The absorption anomaly profile is greatly affected by the reflection coefficient and cannot effectively distinguish whether the absorption anomaly is caused by gas in the formation or by high-resistance lithology. This proves the necessity of performing spectrum attenuation analysis after removing the reflection coefficient in the present invention.

[0068] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention. Except for the technical features described in the specification, all are known technologies to those skilled in the art.

Claims

1. A method for analyzing the attenuation of a short-time window spectrum of a de-reflection coefficient, characterized in that: The de-reflection coefficient short-time window spectrum attenuation analysis method includes: Step 1: Resample the seismic data after spectral domain transformation; Step 2: Perform sliding time window analysis and calculate the absorption attenuation coefficient; Step 3: Perform deconvolution to extract instantaneous wavelets; Step 4: Perform spectrum analysis on the instantaneous wavelet and fit the attenuation coefficient of the high frequency band to obtain the formation absorption value; Step 5: The formation quality factor is obtained by using the high-frequency spectrum ratio method. The formation absorption values ​​at two adjacent time points are subtracted to obtain the absorption anomaly value. In step 1, the seismic data is transformed into the frequency domain, and the Nyquist frequency is increased by the method of frequency zero filling, and then inversely transformed into the time domain to obtain the resampled seismic record; In step 1, the resampling of spectral domain seismic data must be done before the sliding window analysis. The resampling is done in the spectral domain. The spectrum of the earthquake does not change before and after the resampling. This is a high-precision resampling algorithm. In step 3, pulse deconvolution is performed on the earthquakes in the sliding time window to extract the anti-wavelet, which is then inverted to obtain the instantaneous wavelet.

2. The de-reflection coefficient short-time window spectrum attenuation analysis method according to claim 1 is characterized in that: In step 2, the time window length is 40-70 ms, and the time window step is equal to the sampling interval of the seismic record.

3. The de-reflection coefficient short-time window spectrum attenuation analysis method according to claim 2 is characterized in that: In step 2, the sliding window analysis is to calculate the absorption attenuation coefficient at each time point through the sliding window.

4. The de-reflection coefficient short-time window spectrum attenuation analysis method according to claim 1, characterized in that: In step 4, the instantaneous wavelet is subjected to spectral analysis, and only the high frequency band of the spectrum is selected for energy absorption analysis. An exponential curve is used to fit the amplitude change from the peak frequency to the Nyquist frequency, and the quality factor Q is calculated using the obtained attenuation coefficient. Compared with the traditional spectral ratio method, the wavelet attenuation caused by the fluid is eliminated, and the multi-solution of the absorption anomaly is reduced.

5. The de-reflection coefficient short-time window spectrum attenuation analysis method according to claim 1, characterized in that: In step 5, the formation absorption values ​​at two adjacent time points are subtracted. The time window used must be small. It is assumed that the background value of the attenuation curve does not change between adjacent time windows. In this way, the attenuation parameters calculated in adjacent time windows are subtracted, which can be considered to remove the influence of the background trend. The attenuation curve after removing the background trend is actually an outlier that deviates from the background trend; in this way, the anomaly after removing the natural absorption background can better reflect the absorption attenuation effect of the target reservoir and is not limited by the burial depth of the formation.

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