Gas-bearing reservoir identification method based on synchronous shrinkage transformation

Abstract

The invention relates to the technical field of gas-bearing reservoir identification, in particular to a gas-bearing reservoir identification method based on synchronous contraction transformation, which comprises the following implementation steps of: 1, inputting a seismic signal, and processing the seismic signal by using short-time Fourier transform (STFT); step 2, calculating a time frequency gravity center and a time width-bandwidth characteristic factor of the seismic signal according to an STFT result; step 3, constructing an energy extraction operator and an energy maintenance operator based on the obtained time frequency gravity center and the time width-bandwidth characteristic factor; 4, constructing a synchronous contraction transformation method based on the obtained operator and a synchronous extraction principle; 5, extracting low-frequency and high-frequency frequency characteristics of the synchronous contraction transformation method, and identifying the gas-bearing reservoir by utilizing different energy attenuation of the low frequency and the high frequency; the method is used for identifying the gas-bearing reservoir, and more obvious seismic response characteristics of the gas-bearing reservoir are obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of gas-bearing reservoir identification, and relates to a gas-bearing reservoir identification method based on synchronous contraction transformation. Background Art

[0002] Tight sandstone gas is an important type of natural gas resource in my country, with great exploration and development potential. However, due to the low permeability, low porosity and complex characteristics of tight sandstone gas reservoirs, the exploration and development of tight sandstone gas is difficult.

[0003] Time-frequency analysis can reveal the abnormal time-frequency response of seismic signals by mapping the signal from the one-dimensional time domain to the two-dimensional time-frequency domain, so it is used to identify gas-bearing reservoirs. The traditional STFT has a low time-frequency resolution and cannot accurately characterize the time-frequency distribution characteristics of tight sandstone gas reservoirs. High-precision time-frequency post-processing transforms, such as the synchronous extraction transform (SET), improve the time-frequency resolution, but because the energy in the frequency direction of the time-frequency spectrum is too focused, it cannot accurately characterize the gas-bearing response characteristics of the seismic signal of the reservoir. Summary of the invention

[0004] The present invention aims to solve the above problems in the prior art and propose a method for identifying gas-bearing reservoirs based on synchronous shrinkage transformation. The method comprises the following steps:

[0005] Step 1: Input the seismic signal to be analyzed and process it with STFT to obtain the time-frequency result of the signal. The key to this step is to convert the time domain signal into the time-frequency domain, thus providing a strong foundation for subsequent time-frequency analysis. At the same time, this process can reveal the changing characteristics of the signal over time and frequency, laying a good data foundation for further energy extraction.

[0006] Step 2: According to Calculate the time-frequency centroid of the seismic signal and the time-width-bandwidth characteristic factor This helps to accurately describe the distribution of signals in the time-frequency domain and provides a theoretical basis for the subsequent construction of time-frequency operators;

[0007] Step 3: According to and The calculation results of , construct two key operators: energy extraction operator and energy-preserving operator ; The function is to shrink the time-frequency spectrum of STFT synchronously to a given time-width-bandwidth interval, so as to retain the effective time-frequency energy while improving the time-frequency resolution; The purpose of the design is to ensure that the time-frequency transform method is consistent with the time-frequency energy of STFT.

[0008] Step 4: Based on the obtained and , combined with the principle of synchronous extraction, further construct the synchronous shrinkage transformation method ;

[0009] Step 5: When the seismic signal passes through the gas-bearing reservoir, the energy attenuation of the high-frequency component is obvious, while the attenuation of the low-frequency component is small. Therefore, the different energy attenuation of low and high frequencies is used to identify the gas-bearing reservoir.

[0010] Furthermore, in step 1, the input signal The result after STFT is:

[0011] ,

[0012] in Represents a window function The conjugate of Indicates time, Indicates frequency, represents the time-integrated variable, is the imaginary part of the complex number;

[0013] Next, define a representation Energy probability distribution function of any time-frequency point in the time-frequency surface :

[0014] ,

[0015] in represents the frequency integration variable;

[0016] Furthermore, in step 2, the time-frequency center of gravity and Calculate in time and frequency dimensions respectively:

[0017] ,

[0018] ,

[0019] in represents the real part of a complex number;

[0020] Calculated time-width-bandwidth characteristic factor and for:

[0021] ,

[0022] ,

[0023] Furthermore, in step three, the constructed for:

[0024] ,

[0025] It covers all the time-frequency energy within the signal time width and bandwidth, and improves the energy focusing of the time-frequency spectrum by shrinking this part of the time-frequency energy. However, retaining only part of the energy of the STFT time-frequency result will cause the new time-frequency spectrum energy to be unequal to the STFT result, so it is necessary to construct an energy preservation operator To ensure the consistency of time-frequency energy. It is designed based on energy conservation and Wigner-Ville distribution deconvolution algorithm and is defined as:

[0026] ,

[0027] in is the window function The Wigner-Ville distribution under , expressed as:

[0028] ,

[0029] Further, in step 4, the It is expressed as:

[0030] ,

[0031] The core idea is to achieve synchronous contraction processing of the signal's time-frequency energy by accurately capturing the signal's time width and bandwidth characteristics, thereby improving the accuracy of seismic signal analysis.

[0032] Furthermore, in step 5, the high-frequency and low-frequency features are The calculation results are carried out at low frequency and high frequency The feature extraction finally obtains the low-frequency profile and high frequency profile When the seismic signal passes through a gas-bearing reservoir, the high-frequency energy of the time spectrum will attenuate more than the low-frequency energy. In comparison, the high-frequency profile of gas-bearing reservoirs There will be obvious attenuation phenomenon, which is not obvious in non-gas reservoirs. It can improve the time-frequency resolution while maintaining the time-frequency energy integrity of the signal, thereby effectively improving the accuracy of seismic signal analysis and having significant advantages in identifying the gas content of reservoirs.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention proposes a method for identifying gas-bearing reservoirs based on synchronous shrinkage transformation. The method is based on the time-frequency spectrum of STFT, and by extracting the time-frequency energy within the range of the time-frequency spectrum center of gravity and the time-width-bandwidth characteristic factor, the energy focusing degree of the time-frequency spectrum is improved, and then an adaptive energy preservation operator is constructed to ensure the energy conservation of the time-frequency spectrum, and finally high-frequency and low-frequency profiles are extracted and used for gas-bearing reservoir identification. The method proposed by the present invention can not only improve the time-frequency positioning ability of the time-frequency spectrum, but also use it for reservoir gas-bearing identification, and can obtain more obvious time-frequency abnormal response characteristics of gas-bearing reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the prior art solutions and advantages, the following briefly introduces the drawings required to be used in the embodiments or the prior art descriptions:

[0036] Figure 1 It is a flow chart of the present invention;

[0037] Figure 2 is the actual seismic profile;

[0038] Figure 3 is the instantaneous amplitude spectrum of the seismic profile;

[0039] Figure 4 Time-frequency results calculated by different methods: (a), (b) STFT; (c), (d) SET; (e), (f) synchronous shrinkage transform method. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] Example 1: See Figure 1 , showing a process of some embodiments of a method for identifying gas-bearing reservoirs based on synchronous shrinkage transformation, comprising the following steps:

[0042] Input the seismic signal to be analyzed Perform STFT to obtain the time-frequency results of the signal:

[0043] ,

[0044] in Represents a window function The conjugate of Indicates time, Indicates frequency, represents the time-integrated variable, It is the imaginary part of the complex number; the time-domain seismic signal is converted into the time-frequency domain after STFT, which can effectively show the time-frequency characteristics of the signal and provide a basis for the subsequent operator construction.

[0045] Calculate the time-frequency center of gravity in the time-frequency spectrum based on the obtained STFT and the time-width-bandwidth characteristic factor First, define The energy probability distribution function of any time-frequency point in Satisfy the following formula:

[0046] ,

[0047] in represents the frequency integration variable;

[0048] based on Constructed time-frequency center of gravity and is defined as:

[0049] ,

[0050] ,

[0051] in represents the real part of a complex number;

[0052] At the same time, the time width and bandwidth The characteristic factors are calculated as follows:

[0053] ,

[0054] ,

[0055] Based on the obtained factors, construct the energy extraction operator , has the following expression:

[0056] ,

[0057] It covers all the time-frequency energy within the signal time width and bandwidth. If only this part of the energy in the STFT time-frequency result can be extracted, the accuracy of the time-frequency spectrum will be improved. However, retaining only part of the energy of the STFT time-frequency result will cause the new time-frequency spectrum energy to be unequal to the STFT result. Therefore, it is necessary to construct an energy preservation operator based on the law of energy conservation. To ensure the consistency of time-frequency energy. Drawing on the deconvolution idea of ​​Wigner-Ville distribution, is defined as:

[0058] ,

[0059] in is the window function The Wigner-Ville distribution under , expressed as:

[0060] ,

[0061] Based on the above-mentioned operators and synchronous extraction principle, a synchronous shrinkage transformation method is constructed. :

[0062] ,

[0063] The core is to use the time-frequency center of gravity and time-width-bandwidth characteristic factors to accurately describe the time-frequency characteristics of the signal, and through the cooperation of energy operators to effectively focus the time-frequency energy and obtain high-precision time-frequency characterization results;

[0064] By extracting Low frequency and high frequency Frequency characteristics, get low frequency profile and high frequency profile When seismic waves pass through a gas-bearing reservoir, the high-frequency portion of the time spectrum experiences stronger attenuation than the low-frequency portion. In contrast, high-frequency profiles in gas-bearing reservoirs The energy attenuation will be significantly weakened, while in the non-gas reservoir area, this attenuation phenomenon is not obvious. Therefore, this energy attenuation characteristic can be used to identify the existence of gas-bearing reservoirs.

[0065] Embodiment 2:

[0066] Let's take an actual earthquake data as an example. Figure 2 The seismic section is shown, where wells A and B are confirmed dry wells and gas wells. Figure 3 The instantaneous amplitude spectrum of the seismic profile basically outlines the basic form of the river reservoir. The present invention identifies the gas-bearing reservoir based on the obvious attenuation of the time-frequency energy of the high-frequency profile when the seismic signal passes through the gas-bearing reservoir. This is reflected in the seismic profile: compared with the time-frequency energy of the low-frequency profile, the time-frequency energy of the high-frequency profile at the position of well B will show obvious attenuation, while the time-frequency energy of the high-frequency profile at the position of well A will not change much.

[0067] Figure 4 The time-frequency profiles obtained by different methods are shown. Figure 4 (a), (c) and (e) are low-frequency (28 Hz) profiles. Figure 4 (b), (d) and (f) are high frequency (46 Hz) profiles. Figure 4 (a) and (b) are STFT results. It can be seen that STFT can identify the attenuation associated with gas-bearing reservoirs, but the reservoir boundary is relatively rough. Figure 4The SET high-frequency profiles shown in (c) and (d) have low energy at the locations of the two wells, which does not conform to the seismic response characteristics of the gas-bearing reservoir. The reason is that the SET spectrum only retains the time-frequency characteristics at the instantaneous frequency, and the time-frequency energy of other low-frequency or high-frequency in the frequency band is discarded. Therefore, the time-frequency energy of the entire river channel is low in the high-frequency profile. The results of the method proposed by the present invention are as follows Figure 4 As shown in (e) and (f), it not only improves the vertical characterization accuracy of the reservoir, but also has a more obvious energy attenuation at the location of well B, which demonstrates the effectiveness of the present invention in identifying gas-bearing reservoirs.

[0068] The above is a specific description of the implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for identifying gas-bearing reservoirs based on synchronous shrinkage transformation, characterized in that: The following steps are involved: Step 1: Input the seismic signal to be analyzed Perform STFT to obtain the time-frequency results of the signal: ， in Represents a window function The conjugate of Indicates time, Indicates frequency, represents the time-integrated variable, is the imaginary part of the complex number; definition The energy probability distribution function of any time-frequency point in Satisfy the following formula: ， in represents the frequency integration variable; Step 2: Based on the obtained Calculate the time-frequency center of gravity in the time-frequency spectrum and the time-width-bandwidth characteristic factor ; Step 3: Construct an energy extraction operator based on the obtained characteristic factors and energy-preserving operator ; Step 4: Based on the obtained and , and construct a synchronous shrinkage transformation method based on the synchronous extraction principle : ， Step 5: Extraction The low-frequency and high-frequency characteristics are used to identify gas-bearing reservoirs by using the different energy attenuation of low-frequency and high-frequency.

2. The method according to claim 1, characterized in that Step 2 and The expression includes the following formula: ， ， in represents the real part of the complex number; the and The expression includes the following formula: ， 。 3. The method according to claim 1, characterized in that Step 3 It has the following form of definition: ， Furthermore, the energy conservation operator constructed is defined as: ， in is the window function The Wigner-Ville distribution under , expressed as: 。 4. The method according to claim 1, characterized in that: The low-frequency and high-frequency profiles described in step 5 are first The calculation results are low frequency and high frequency The feature extraction finally obtains the low-frequency profile and high frequency profile , by comparison and The attenuation of the gas-bearing reservoir can be used to identify the gas-bearing reservoir.

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