Stratum quality factor Q estimation method based on spectral ratio method and depth consistency

Abstract

The invention discloses a method for estimating a stratum quality factor Q based on a spectral ratio method and depth consistency, which comprises the following steps of: intercepting deep and shallow layers of post-stack seismic data of a research area under different time windows to carry out spectral analysis, and estimating by using the spectral ratio method to obtain a quality factor Q value under different time windows; and constructing an unsteady-state inversion target function, solving unsteady-state inversion reflection coefficients corresponding to different quality factors Q, performing deep and shallow layer amplitude spectrum analysis on the obtained unsteady-state inversion reflection coefficients, and selecting the quality factor with the minimum relative error between the deep layer amplitude spectrum and the shallow layer amplitude spectrum as a final estimated stratum quality factor Qbest. Compared with the prior art, the method can provide a technology for stably solving the stratum quality factor Q.

Description

Technical Field

[0001] The invention relates to a seismic data processing method in the field of petroleum exploration, and in particular to a formation quality factor Q estimation method. Background Art

[0002] As the difficulty of oil and gas exploration increases, high-precision seismic data has become the key to determining the success or failure of oil and gas exploration and development. When seismic waves propagate in the stratum, the viscoelasticity of the stratum will absorb and attenuate the seismic waves, reducing the amplitude and frequency of the seismic waves, accompanied by phase distortion. This phenomenon of seismic wave attenuation caused by the viscoelasticity of the stratum will affect the quality of the collected seismic data, resulting in low resolution of seismic data and poor consistency of deep and shallow data, making it difficult to meet the requirements of high-precision seismic imaging. Therefore, for attenuated seismic data, it is particularly important to accurately obtain the formation quality factor Q to compensate for the formation absorption attenuation and improve the quality of seismic data.

[0003] At present, the methods for obtaining the formation quality factor Q mainly include time (frequency, time-frequency) domain method, Q inversion method, statistical method, wavelet comparison method, sparse criterion method, etc. The time (frequency, time-frequency) domain method mainly constructs the relationship between amplitude and formation quality factor Q in different domains to obtain the Q value, but it is greatly affected by factors such as seismic data quality and window length (Li Bo, Wen Xiaotao, Zhang Yijiang, et al. "Q value extraction based on Lucy-Richardson algorithm and generalized S transform". Petroleum Geophysical Prospecting, 2019, 58(5): 689-699.). The method of inverting Q mainly constructs the inversion objective function about Q and selects different inversion strategies to obtain the Q value, but the algorithm is complex and has high requirements on the quality of seismic data (Irving JD, Knight R J. Removal of wavelet dispersion from ground-penetrating radar data[J]. Geophysics, 2003, 68(3): 960-970.). Statistical methods, such as using the regression relationship between P-wave velocity and Q to obtain the Q value, but its regression relationship is limited by regional limitations (Zhao Qiufang. "Research on the Inversion Method and Application of Seismic Wave Absorption Attenuation Characteristics and Quality Factor Q in Near-Surface Formations". Henan Polytechnic University, 2018.). Wavelet comparison method (Cheng Liang, Zhang Zhijun, Zhang Shengqiang, et al. "A AVO Correction Method Based on Non-steady-state Inversion". Beijing: CN202211114964.X, 2022-12-09.), such as comparing the similarity of adjacent layer wavelets to obtain the Q value, but it is greatly affected by the time window and the signal-to-noise ratio of seismic data. The sparse criterion method uses the sparsity of the inversion results corresponding to different scans Q to estimate the optimal Q value (Gholami A. Semi-blind nonstationary deconvolution: Joint reflectivity and Q estimation [J]. Journal of Applied Geophysics, 2015, 117: 32-41.). This method believes that when the Q model is biased, the sparsity of the inversion results will be reduced. This conclusion has also been verified by a large number of numerical experiments, but there is no rigorous theoretical proof of the cause of this phenomenon. At present, there is no mature and stable method for estimating the formation quality factor Q. Summary of the invention

[0004] Taking into account the problems existing in the above-mentioned prior art and the low resolution of seismic data and low consistency in deep and shallow layers caused by formation absorption attenuation, the purpose of the present invention is to propose a method for estimating the formation quality factor Q based on the spectral ratio method and depth consistency. The conventional spectral ratio method is combined to design time windows of different sizes to obtain the Q value, and non-steady-state inversion is used to compensate for the attenuation of seismic data. The depth consistency of the inversion data is introduced to evaluate the optimal Q value, so as to realize the estimation of the formation quality factor Q.

[0005] The present invention provides a method for estimating a formation quality factor Q based on a spectral ratio method and depth consistency, comprising:

[0006] S1: Intercept different time windows T k The spectrum analysis of deep and shallow parts of post-stack seismic data in the lower study area was performed to obtain spectrum analysis results of deep and shallow seismic data;

[0007] S2: according to the spectrum analysis result, the spectral ratio method is used to estimate the formation quality factor Q value, and the estimated quality factor Q value under different time windows is obtained;

[0008] S3: constructing a non-steady-state inversion objective function, and obtaining a corresponding non-steady-state inversion reflection coefficient using the different quality factor Q values ​​and the non-steady-state inversion objective function;

[0009] S4: Perform deep and shallow amplitude spectrum analysis on the obtained non-steady-state inversion reflection coefficient to select the quality factor corresponding to the highest consistency between the deep amplitude spectrum and the shallow amplitude spectrum as the final estimated formation quality factor Q best .

[0010] In some embodiments, the different time windows include a shallow data time window T k_shallow and deep data time window T k_deep , from the earthquake data d obs The shallow seismic data d obs (T k_shallow ) and deep seismic data d obs (T k_deep ).

[0011] In some embodiments, the method further includes obtaining a reflection coefficient by using a non-steady-state inversion, and the expression of the non-steady-state inversion objective function is as follows:

[0012]

[0013] Among them, d obs For observing earthquake data, G(Q eff ) is the non-steady-state seismic wavelet, Q eff is the equivalent Q value of the formation, r is the reflection coefficient, λ is the regularization parameter, and p is the value of the p-norm.

[0014] In some embodiments, S4 further includes obtaining the relative error erro(Q) of the deep and shallow layer amplitude spectrum of different non-steady-state inversion reflection coefficients. k ) represents consistency, and the expression is as follows:

[0015]

[0016] Among them, || is the absolute value operator, FFT() is the Fourier transform operation, abs[] represents the complex number amplitude operation, r shallow (Q k ) is any reflection coefficient r(Q k ) of the shallow part, r deep (Q k ) is any reflection coefficient r(Q k ) deep part.

[0017] In some embodiments, the quality factor corresponding to the minimum relative error is selected as the final estimated formation quality factor Q best , the expression is as follows:

[0018] Q best =min[erro(Q1),erro(Q2),…,erro(Q k ),…,erro(Q N )]

[0019] Among them, min[] represents the minimization algorithm, Q best ∈[Q1,Q2,…,Q k ,…,Q N ].

[0020] Compared with the prior art, the beneficial technical effects and technical progress achieved by the present invention are as follows:

[0021] 1) By intercepting the deep and shallow post-stack seismic data of the study area under different time windows, the spectrum analysis is performed. The spectrum analysis results contain the attenuation degree of the seismic data from shallow to deep. Therefore, the formation quality factor Q is obtained according to the absorption attenuation law of the formation during the propagation of seismic waves, which can be better applied to the attenuation compensation of seismic data;

[0022] 2) Combining the spectral ratio method and non-steady-state inversion, the optimal Q value is evaluated by using the relative error of the deep and shallow amplitude spectra of the non-steady-state inversion reflection coefficient. Compared with the conventional spectral ratio method, the influence of the time window on the estimation of Q by the spectral ratio method is reduced to a certain extent, thus achieving the purpose of stably obtaining the formation quality factor Q. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for estimating formation quality factor Q based on spectral ratio method and depth consistency of the present invention;

[0024] Figure 2 2 is a diagram of an embodiment of a reflection coefficient model, (2a) the reflection coefficient model designed by the present invention; (2b) the comparison of the deep and shallow amplitude spectra of the reflection coefficient model designed by the present invention, (2c) a synthetic attenuation seismic profile (with 5% random noise), wherein the initial seismic wavelet is a Ricker wavelet with a main frequency of 30 Hz, and the given formation quality factor Q is 40, (2d) the comparison of the deep and shallow amplitude spectra of the synthetic attenuation seismic profile;

[0025] Figure 3 It is a diagram of an embodiment of the present invention, (3a) relative errors of the deep and shallow layer amplitudes of the non-steady-state inversion reflection coefficient corresponding to different Q values, when the relative error is the smallest, the corresponding Q value is 37, that is, the estimated final formation quality factor Q is 37, these Q values ​​are the Q values ​​extracted by opening different time windows on the attenuation seismic record using the spectral ratio method (as shown in Table 1), (3b) non-steady-state inversion reflection coefficient profile corresponding to the optimal Q value (that is, equivalent to Q = 37), (3c) comparison of the deep and shallow layer amplitude spectra of the non-steady-state inversion reflection coefficient corresponding to the optimal Q value (that is, equivalent to Q = 37). DETAILED DESCRIPTION

[0026] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] like Figure 1 As shown, the overall process of the method for estimating the formation quality factor Q based on the spectral ratio method and the consistency of depth of the present invention includes the following steps:

[0028] Step 1: Cut different time windows T k (1≤k≤N, N is the number of time windows) post-stack seismic data d in the study area obs The deep d obs (T k_deep ) and shallow d obs (T k_shallow ) part, performing spectrum analysis on the intercepted deep and shallow seismic data to obtain spectrum analysis results of the deep and shallow seismic data, that is, obtaining amplitude spectra of the deep and shallow seismic data, and analyzing the attenuation degree of the seismic data from shallow to deep by comparing the amplitude spectra of the deep and shallow seismic data;

[0029] Step 2: According to the spectrum comparison analysis results of the deep and shallow seismic data mentioned above, the logarithm of the amplitude spectrum ratio of the deep and shallow layers is obtained, the frequency band range is set, and then the spectral ratio method is used to estimate the formation quality factor Q value, and the initial estimation sequence of formation quality factors corresponding to different time windows Q1, Q2, ..., Q k ,…,Q N ; Specifically, the shallow data time window T k_shallow and deep data time window Tk_deep The size is T k ;

[0030] Step 3: Construct the unsteady-state inversion objective function based on the unsteady-state forward model, and use different quality factors Q1, Q2, ..., Q k ,…,Q N The corresponding unsteady inversion reflection coefficient is obtained by the unsteady inversion objective function, which is expressed as a sequence r(Q1), r(Q2),…, r(Q k ),…,r(Q N ), the non-steady-state inversion objective function expression is as follows:

[0031]

[0032] Among them, d obs For observing earthquake data, G(Q eff ) is the non-steady-state seismic wavelet, Q eff is the equivalent Q value of the formation, r is the reflection coefficient sequence, M represents the reflection coefficient length, r j is the jth element of the reflection coefficient r (1≤j≤M), λ is the regularization parameter, and p is the value of the p-norm, ranging from 0 <p≤1;

[0033] Step 4: Perform deep and shallow layer amplitude spectrum analysis on the non-steady-state inversion reflection coefficient obtained in step 3 to obtain different non-steady-state inversion reflection coefficients r(Q1), r(Q2),…, r(Q k ),…,r(Q N ) of the deep and shallow layer amplitude spectrum relative error erro(Q k ) characterizes the consistency, that is, the relative error between the shallow amplitude spectrum and the deep amplitude spectrum is used to characterize the shallow-depth consistency of the inversion data. The expression is as follows:

[0034]

[0035] Among them, || is the absolute value operator, FFT() is the Fourier transform operation, abs[] represents the complex number amplitude operation, r shallow (Q k ) is any reflection coefficient r(Q k ) of the shallow part, r deep (Q k ) is any reflection coefficient r(Q k ), f represents the frequency, f min is the selected minimum frequency, f max is the maximum frequency selected;

[0036] Step 5: Select the quality factor corresponding to the minimum relative error as the final estimated formation quality factor Q best , the expression is as follows:

[0037] Q best =min[erro(Q1),erro(Q2),…,erro(Q k ),…,erro(Q N )] (3)

[0038] Among them, min[] represents the minimization algorithm, Q best ∈[Q1,Q2,…,Q k ,…,Q N ].

[0039] In summary, the technical solution adopted by the present invention mainly includes three aspects: first, for the attenuated post-stack seismic data, time windows of different depths and shallow layers are intercepted for spectrum analysis, and the spectral ratio method is used to obtain the formation quality factor Q obtained by time windows of different lengths; second, the quality factor Q under different time windows obtained by the spectral ratio method is combined with non-steady-state inversion to obtain the inversion reflection coefficient; third, the relative error of the deep and shallow layer amplitude spectrum of the non-steady-state inversion reflection coefficient corresponding to different Q is obtained to measure the depth consistency of the inversion data, and the Q value corresponding to the minimum relative error is selected as the final estimated formation quality factor. Compared with the conventional spectral ratio method, the present invention reduces the influence of the time window on the estimation of Q by the spectral ratio method to a certain extent, and the combination of non-steady-state inversion and depth consistency to evaluate the Q value is more stable.

[0040] In order to better verify the feasibility of the above steps, a test example of the reflection coefficient model is designed below. Figure (2a) shows the reflection coefficient model, and the sampling interval is set to 2ms. Figure (2b) shows the comparison of the deep and shallow layer amplitude spectrum of the reflection coefficient in Figure (2a). It can be seen that the amplitude and frequency of the deep and shallow layers of the actual formation reflection coefficient are relatively small. The Ricker wavelet with a main frequency of 30Hz is selected as the initial seismic wavelet, and the formation quality factor Q is set to 40. The attenuated seismic profile of Figure (2c) is synthesized with the reflection coefficient model shown in Figure (2a), and 5% random noise is added. The spectrum analysis of the deep and shallow layers of the attenuated seismic profile in Figure (2d) shows that the attenuation effect of the formation quality factor Q leads to a large difference in energy and frequency between the deep and shallow layers. The high frequency and amplitude of deep seismic are rapidly attenuated, and the main frequency of deep seismic data is reduced to about 20Hz.

[0041] For the attenuation seismic profile, deep and shallow seismic data of different length time windows are selected, and the equivalent Q value of the formation is obtained using the spectral ratio method, as shown in Table 1.

[0042] Table 1

[0043]

[0044] The initial seismic wavelet, attenuation seismic profile and a series of equivalent Q values ​​are used as inputs of formula (1). The reflection coefficient is obtained by combining the non-steady-state inversion. The deep and shallow layers of the non-steady-state inversion reflection coefficient are intercepted for spectrum analysis, and the relative error of the deep and shallow amplitude spectra is obtained. Figure 2 a is the relative error of the deep and shallow layer amplitude spectrum of the non-steady-state inversion reflection coefficient obtained by using the spectral ratio method in step 2 with different Q values. By comparison, it can be seen that when the equivalent Q estimated by the spectral ratio method is 37, the relative error of the deep and shallow layer amplitude of the non-steady-state inversion reflection coefficient is the smallest. Therefore, the optimal Q value obtained is 37, which is equivalent to the actual Q value set. Figure 2 b and 2c are comparisons of the non-steady-state inversion reflection coefficient corresponding to Q=37 and its corresponding deep and shallow layer amplitude spectrum.

[0045] In summary, the present invention is based on seismic wave attenuation and compensation, and innovatively proposes a Q estimation method based on the spectral ratio method and depth consistency. Compared with the existing spectral ratio method for estimating formation quality factors, it uses non-steady-state inversion technology and depth consistency to evaluate formation quality factors, which increases the stability of Q estimation. This method evaluates Q from a non-steady-state perspective, thereby improving the influence of the time window on the traditional spectral ratio method for obtaining formation quality factors, providing a new idea for Q estimation and better serving the attenuation compensation of subsequent seismic data.

[0046] The above embodiments are only used to illustrate the present invention, wherein the ideas and methods of each step may be varied, and any equivalent transformations and improvements based on the technical solution of the present invention should not be excluded from the protection scope of the present invention.

[0047] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications made without departing from the principle of the present invention should also be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for estimating formation quality factor Q based on spectral ratio method and depth consistency, characterized in that: include: S1: Intercept different time windows T k The spectrum analysis of deep and shallow parts of post-stack seismic data in the lower study area was performed to obtain spectrum analysis results of deep and shallow seismic data; S2: according to the spectrum analysis result, the spectral ratio method is used to estimate the formation quality factor Q value, and the estimated quality factor Q value under different time windows is obtained; S3: constructing a non-steady-state inversion objective function, and obtaining a corresponding non-steady-state inversion reflection coefficient using the different quality factor Q values ​​and the non-steady-state inversion objective function; S4: Perform deep and shallow amplitude spectrum analysis on the obtained non-steady-state inversion reflection coefficient to select the quality factor corresponding to the highest consistency between the deep amplitude spectrum and the shallow amplitude spectrum as the final estimated formation quality factor Q best .

2. The method for estimating formation quality factor Q based on spectral ratio method and depth consistency according to claim 1, characterized in that: Different time windows include shallow data time window T k_shallow and deep data time window T k_deep From earthquake data obs Separately intercept the shallow seismic data d obs (T k_shallow ) and deep seismic data d obs (T k_deep ).

3. The method for estimating formation quality factor Q based on spectral ratio method and depth consistency according to claim 1, characterized in that: Further, the reflection coefficient is obtained by using the non-steady-state inversion objective function, and the expression is as follows: Among them, d obs For observing earthquake data, G(Q eff ) is the non-steady-state seismic wavelet, Q eff is the equivalent Q value of the formation, r is the reflection coefficient sequence, M represents the reflection coefficient length, r j is the jth element of the reflection coefficient r, λ is the regularization parameter, and p is the value of the p-norm.

4. The method for estimating formation quality factor Q based on spectral ratio method and depth consistency according to claim 1, characterized in that: The step S4 further includes obtaining the relative error erro(Q k ) represents consistency, and the expression is as follows: Among them, || is the absolute value operator, FFT() is the Fourier transform operation, abs[] represents the complex number amplitude operation, r shallow (Q k ) is an arbitrary reflection coefficient r(Q k ) of the shallow part, r deep (Q k ) is an arbitrary reflection coefficient r(Q k ), f represents the frequency, f min is the selected minimum frequency, f max is the maximum frequency selected.

5. The method for estimating formation quality factor Q based on spectral ratio method and depth consistency according to claim 1, characterized in that: The quality factor corresponding to the minimum relative error is selected as the final estimated formation quality factor Q best , the expression is as follows: Q best =min[error(Q1),error(Q2),…,error(Q k ),…,error(Q N )] Among them, min[] represents the minimization algorithm, Q best ∈[Q1,Q2,…,Q k ,…,Q N ].

Images