Low-signal-to-noise-ratio seismic data Q factor extraction method, electronic equipment and storage medium

Abstract

The present application belongs to the technical field of seismic exploration, and specifically discloses a low signal-to-noise ratio seismic data Q factor extraction method, an electronic device and a storage medium. The method of the present application first performs data preparation, classifies VSP seismic data into first-class quality data-third-class quality data, and performs preprocessing, calculates the first-class quality factor, the second-class quality factor, and the third-class quality factor, then extracts the Q factor of the first-class quality factor, and then performs model training on the obtained Q factor to obtain a fitting formula, substitutes the longitudinal wave layer velocity of the second-class quality factor into the fitting formula to obtain a fitted Q factor, and substitutes the smoothed acoustic wave velocity of the third-class quality factor into the fitting formula to obtain the Q factor of the third-class quality factor. The present application is used for extracting the Q factor, and can improve the accuracy of the extracted Q factor.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of seismic exploration, and particularly relates to a low signal-to-noise ratio seismic data Q factor extraction method, an electronic device and a storage medium. BACKGROUND

[0002] In vertical seismic profile (VSP) data analysis, the Q factor is valued for two reasons: on the one hand, the inelastic attenuation can be regarded as an interference to the seismic wave, which must be eliminated by the inverse Q filtering method. Although the standardized inverse Q filtering method has been formed, and the interpretation quality of the seismic record is effectively improved, the Q factor estimation of the inverse filtering is not accurate, which is a long-term and difficult problem. On the other hand, it has been recognized that in the process of improving the interpretation quality of the seismic record, the Q factor is an important physical parameter of the seismic wave attenuation, so in addition to the velocity parameter, the Q factor is another seismic parameter that can be used for petrology and lithology interpretation.

[0003] Although there are many methods for calculating the Q factor in the prior art, the Q factor extraction is subject to many conditions at present, and it is extremely difficult to extract the Q factor in data with a low signal-to-noise ratio, and the accuracy of the extracted Q factor is not high. SUMMARY

[0004] To solve the above problems in the prior art, the first aspect of the application provides a low signal-to-noise ratio seismic data Q factor extraction method, which aims to obtain a Q factor with high accuracy.

[0005] To achieve the above object, the technical scheme adopted by the application is as follows:

[0006] A low signal-to-noise ratio seismic data Q factor extraction method, the method comprising the following steps performed in sequence:

[0007] S1, data preparation, collecting VSP seismic data and acoustic logging data of a target area, screening and classifying the VSP seismic data into first-class quality data to third-class quality data, performing data preprocessing on the first-class quality data to the third-class quality data to obtain first-class quality factors to third-class quality factors, and calculating the P-wave layer velocity of the first-class quality factor and the second-class quality factor and the acoustic wave velocity of the third-class quality factor;

[0008] S2, first-class Q factor sample extraction, calculating the Q factor of the first-class quality factor by using the spectral ratio method;

[0009] S3, Q factor model training, comprising the following steps performed in sequence:

[0010] a1) establishing a fitting relationship to correspond the Q factor of the first type of quality factor to the P-wave interval velocity of the same well, and obtaining an exponential formula of the Q factor of the first type of quality factor and the P-wave interval velocity by fitting, the exponential formula is denoted as:

[0011]

[0012] wherein i is the well number of the first type of quality factor, v is the P-wave interval velocity of the first type of quality factor, a i and b i are fitting parameters;

[0013] a2) fitting parameter optimization, performing median processing on the fitting parameters obtained in step a1) to obtain optimized fitting parameters, the optimized fitting parameters a m are denoted as:

[0014] a m = median(a i )

[0015] wherein median() is a median function, and the optimized fitting parameters b m are denoted as:

[0016] b m = median(b i );

[0017] a3) determining a fitting formula, re-establishing the fitting formula by using the optimized fitting parameters in step a2) to obtain the fitting formula trained by the Q factor of the first type of quality factor, the fitting formula is denoted as:

[0018]

[0019] wherein V is velocity;

[0020] S4, second type of Q factor fitting, substituting the P-wave interval velocity of the second type of quality factor into the fitting formula to obtain the fitted Q factor of the second type of quality factor;

[0021] S3, third type of Q factor fitting, performing multi-point smoothing processing on the acoustic velocity of the third type of quality data, substituting the smoothed acoustic velocity of the third type of quality factor into the fitting formula to obtain the Q factor of the third type of quality factor.

[0022] As a limitation, in the step S1, the VSP seismic data is screened and classified, including two screening conditions, the first screening condition is that the data with a signal-to-noise ratio greater than 10; the second screening condition is that the data with a signal-to-noise ratio greater than 2 and less than 10;

[0023] The data satisfying both the first and second screening conditions is classified as first quality data, the data satisfying the first screening condition but not the second screening condition is classified as second quality data, and the data not satisfying both screening conditions is classified as third quality data.

[0024] As a second limitation, in the step S1, the data preprocessing includes bad trace elimination, three-component rotation and wave field separation, and does not include a step of destroying the relative relationship of amplitudes.

[0025] As a third limitation, the calculation of the first and second quality factors and the P-wave interval velocity in the step S1 includes:

[0026] The first and second quality factors are used to pick up the first arrival, and the down-going wave field is separated out.

[0027] The first and second quality factors are used to pick up the first arrival, and the down-going wave field is separated out.

[0028] The third quality factor is calculated according to the acoustic logging data.

[0029] As a fourth limitation, the number n of points in the multi-point smoothing processing in the step S4 satisfies the following condition:

[0030]

[0031] Wherein, N vsp is the acquisition interval of VSP seismic data, N ac is the acquisition interval of acoustic logging data, and int() is an integer function.

[0032] The second aspect of the present application discloses an electronic device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the low signal-to-noise ratio Q factor extraction method when executing the computer program.

[0033] The third aspect of the present application discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the low signal-to-noise ratio Q factor extraction method.

[0034] Compared with the prior art, the beneficial effects obtained by the present application are:

[0035] (1) The method of the present application uses a Q factor model to train a fitting formula, and a more accurate Q factor is obtained after fitting the Q factor, so that stable and reliable stratum rock absorption parameters are obtained, and relatively reliable seismic processing parameters are provided for inverse Q filtering.

[0036] (2) The method effectively improves the interpretation quality of the seismic record, and is beneficial to the popularization and application of the seismic processing technology in the low signal-to-noise ratio seismic exploration area.

[0037] To sum up, the application can further improve the accuracy of the extracted Q factor, effectively improve the interpretation quality of the seismic record, obtain stable and reliable stratum rock absorption parameters, and is beneficial to the popularization and application of the seismic processing technology in the low signal-to-noise ratio seismic exploration area. BRIEF DESCRIPTION OF DRAWINGS

[0038] The application will be further described in detail below in combination with the drawings and specific embodiments.

[0039] Figure 1 The flowchart of the embodiment of the application;

[0040] Figure 2 The quality factor Q value sample graph extracted by the first type of quality factor of the embodiment of the application;

[0041] Figure 3 The longitudinal wave layer velocity extracted by the first type of quality factor of the embodiment of the application;

[0042] Figure 4 The quality factor Q value sample graph extracted by the second type of quality factor of the embodiment of the application;

[0043] Figure 5 The longitudinal wave layer velocity extracted by the second type of quality factor of the embodiment of the application;

[0044] Figure 6 The quality factor Q value sample graph obtained by fitting the second type of quality factor of the embodiment of the application;

[0045] Figure 7 The quality factor Q value sample graph obtained by fitting the third type of quality data of the embodiment of the application. DETAILED DESCRIPTION

[0046] In order to better explain the application and facilitate understanding, the preferred embodiments of the application are described in detail below through specific embodiments in combination with the drawings.

[0047] Embodiment 1: A low signal-to-noise ratio seismic data Q factor extraction method

[0048] The embodiment provides a low signal-to-noise ratio seismic data Q factor extraction method, which comprises the following steps performed in sequence:

[0049] S1, data preparation, collecting VSP seismic data and acoustic logging data of the research target area, screening and classifying the VSP seismic data into first quality data to third quality data, pre-processing the first quality data to the third quality data to obtain first quality factor to third quality factor, and calculating the P-wave interval velocity of the first quality factor and the second quality factor and the acoustic wave velocity of the third quality factor;

[0050] The VSP seismic data refers to the original seismic data collected in the research target area by using the VSP observation method, which is different from the surface seismic data. The VSP seismic data is screened and classified including two screening conditions. The first screening condition is that the first break is crisp and the first break wave is clear, and the signal-to-noise ratio is greater than 10. The second screening condition is that the data signal-to-noise ratio is high, the first break wave amplitude attenuation law, and the signal-to-noise ratio is greater than 2 and less than 10. The data that meet the first screening condition and the second screening condition are classified as first quality data. The data that meet the first screening condition but do not meet the second screening condition are classified as second quality data. The data that do not meet the two screening conditions are classified as third quality data. Table 1 shows the classification standards of the three types of quality data.

[0051] Table 1 Classification standards of three types of quality data

[0052]

[0053] In the data preprocessing of the VSP seismic data in this embodiment, bad channel rejection, three-component rotation and wave field separation are included, and amplitude compensation and automatic gain steps that destroy the relative relationship of amplitudes are not included.

[0054] Before calculating the P-wave interval velocity of the first quality factor and the second quality factor, the first break of the first quality factor is picked up, and the downgoing wave field is separated to make the subsequent Q factor calculation more stable. The first break of the second quality factor is picked up. The first break of the first quality factor and the second quality factor are used, and the well offset and shot point depth information are combined through the Geoeast software to calculate the P-wave interval velocity of the first quality factor and the second quality factor, respectively. The well offset and shot point depth information are known when collecting the data of the research target area in step S1. The acoustic wave velocity of the third quality factor is calculated according to the acoustic logging data.

[0055] S2, first Q factor sample extraction, the Q factor of the first quality factor is calculated by using the spectral ratio method.

[0056] The spectral ratio method for calculating the Q factor of the first type quality factor is based on the method recorded in the article "The determination of the seismic quality factor Q from VSP data: a comparison of different computational methods[J]. Geophysical Prospecting, 1991, 39(1): 1-27."

[0057] Figure 2 The Q value sample extracted by the first type quality factor is shown, the horizontal coordinate in the figure is depth, unit: m, and the vertical coordinate is Q value, dimensionless; Figure 3 The P-wave interval velocity of the first type quality factor is shown, the horizontal coordinate in the figure is depth, unit: m, and the vertical coordinate is velocity, unit: m / s; Figure 4 The Q value sample extracted by the second type quality factor is shown, the horizontal coordinate in the figure is depth, unit: m, and the vertical coordinate is Q value, dimensionless; Figure 5 The P-wave interval velocity of the second type quality factor is shown, the horizontal coordinate in the figure is depth, unit: m, and the vertical coordinate is velocity, unit: m / s. Figure 2 And Figure 4 It is concluded that the Q factor error of the second type quality factor is too large to be applied.

[0058] S3, Q factor model training, including the following steps in turn:

[0059] a1) Establish a fitting relationship to one-to-one correspond the Q factor of the first type quality factor and the P-wave interval velocity of the same well, and fit to obtain the exponential formula of the Q factor of the first type quality factor and the P-wave interval velocity, the exponential formula is recorded as:

[0060]

[0061] Wherein, i is the well number of the first type quality factor, v is the P-wave interval velocity of the first type quality factor, a i and b i are fitting parameters.

[0062] a2) Fitting parameter optimization, median processing is performed on the fitting parameters obtained in step a1) to obtain optimized fitting parameters, and the optimized fitting parameters a m are recorded as:

[0063] a m = median(a i )

[0064] Wherein, median() is a median function, and the optimized fitting parameters b m are recorded as:

[0065] b m = median(b i ).

[0066] a3) determining a fitting formula, reestablishing the fitting formula using the optimized fitting parameters in step a2), obtaining the fitting formula trained by the Q factor of the first type of quality factor, and the fitting formula is denoted as:

[0067]

[0068] wherein, V is the velocity.

[0069] S4, the second type of Q factor fitting is obtained by substituting the P-wave velocity of the second type of quality factor into the fitting formula, and the Q factor of the second type of quality factor is obtained after fitting; the acoustic wave velocity of the third type of quality factor is subjected to multi-point smoothing processing, and the acoustic wave velocity of the third type of quality factor after smoothing processing is substituted into the fitting formula to obtain the Q factor of the third type of quality factor.

[0070] The number of points n of multi-point smoothing processing satisfies the following conditions:

[0071]

[0072] wherein, N vsp is the acquisition interval of VSP seismic data, N ac is the acquisition interval of acoustic logging data, and int() is the integer function.

[0073] As shown in Figure 6 , the Q value sample after fitting of the second type of quality factor, compared with the Q value sample before fitting, the error of the Q value sample after fitting is reduced; Figure 4 As shown in Figure 7 , the Q value sample after fitting of the third type of quality factor, the error of the Q value sample after fitting is reduced.

[0074] Embodiment 2: An electronic device and a storage medium for extracting a Q factor of low signal-to-noise ratio seismic data

[0075] The electronic device in the embodiment adopts a computer device, including a memory and a processor, the memory stores a computer program, and the processor can realize the steps of the method for extracting a Q factor of low signal-to-noise ratio seismic data in embodiment 1 when executing the computer program.

[0076] A computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, can implement the steps of the low signal-to-noise ratio seismic data Q factor extraction method in Embodiment 1. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

Claims

1. A low signal-to-noise seismic data Q factor extraction method, characterized in that, The method comprises the following steps in sequence: S1, data preparation, collecting VSP seismic data and sonic logging data of a target area, screening and classifying the VSP seismic data into first-class quality data to third-class quality data, pre-processing the first-class quality data to the third-class quality data to obtain first-class quality factors to third-class quality factors, and calculating the P-wave interval velocity of the first-class quality factors and the second-class quality factors and the sonic velocity of the third-class quality factors; S2, first-class Q factor sample extraction, calculating the Q factor of the first-class quality factors by using the spectral ratio method; S3, Q factor model training, comprising the following steps in sequence: a1) establishing a fitting relationship, one-to-one correspondence between the Q factor of the first-class quality factors and the P-wave interval velocity of the same well, fitting to obtain an exponential formula of the Q factor of the first-class quality factors and the P-wave interval velocity, denoted as: wherein, is the well number for the first quality factor, is the P-wave interval velocity for the first quality factor, and is a fitting parameter; a2) fitting parameter optimization, the fitting parameters obtained in step a1) are subjected to median processing to obtain optimized fitting parameters, the optimized fitting parameters is denoted as: wherein is the median function, the optimized fitting parameters denoted as: ； a3) determining the fitting formula, re-establishing the fitting formula using the optimized fitting parameters in step a2) to obtain the fitting formula trained by the Q factor of the first-class quality factors, denoted as: wherein V is the velocity; S4, second-class Q factor fitting acquisition, substituting the P-wave interval velocity of the second-class quality factors into the fitting formula to obtain the fitted Q factor of the second-class quality factors; third-class Q factor fitting acquisition, performing multi-point smoothing processing on the sonic velocity of the third-class quality data, and substituting the smoothed sonic velocity of the third-class quality factors into the fitting formula to obtain the Q factor of the third-class quality factors.

2. The low signal-to-noise seismic data Q-factor extraction method of claim 1, wherein, In the step S1, the screening and classification of the VSP seismic data comprises two screening conditions, the first screening condition is data with a signal-to-noise ratio greater than 10, and the second screening condition is data with a signal-to-noise ratio greater than 2 and less than 10.

3. The low signal-to-noise seismic data Q-factor extraction method of claim 1 or 2, wherein, In the step S1, the data preprocessing comprises bad channel rejection, three-component rotation and wave field separation, and does not include steps that destroy the relative amplitude relationship.

4. The low signal-to-noise seismic data Q-factor extraction method of claim 1 or 2, wherein, In the step S1, the calculation of the P-wave interval velocity of the first-class quality factors and the second-class quality factors comprises: picking up the first arrival of the first-class quality factors and separating out the downgoing wave field; picking up the first arrival of the second-class quality factors; using the first arrivals of the first-class quality factors and the second-class quality factors, and combining the well source distance and shot point depth information of data acquisition, respectively calculating the P-wave interval velocity of the first-class quality factors and the second-class quality factors; calculating the sonic velocity of the third-class quality factors according to the sonic logging data.

5. The low signal-to-noise seismic data Q-factor extraction method of claim 1 or 2, wherein, The number of points in the multi-point smoothing process in the step S4 satisfies the following conditions: wherein, is the acquisition interval for VSP seismic data, is the acquisition interval for sonic logging data, is a rounding function. 6.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to realize the steps of the method of any one of claims 1-4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the steps of the method of any one of claims 1-4.

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