A fine imaging method for small geological bodies based on FWI technology

Abstract

This invention discloses a fine imaging method for small geological bodies based on FWI technology, belonging to the field of small geological body imaging technology. The method includes acquiring seismic data of the small geological body, performing preliminary processing through regular noise suppression, and introducing Cadzow filtering to suppress residual noise in the triangular zone for reflective energy recovery, thus obtaining reflected wave data. Through seismic trace autocorrelation, the zero-point amplitude value of the autocorrelation is obtained, and surface consistency decomposition is performed to obtain the amplitude compensation components for each shot point, receiver point, and shot-receiver distance. The compensation coefficient of the seismic trace is then calculated, and surface consistency amplitude compensation is applied to the reflected wave data to recover the shallow reflected wave energy. Based on the shallow refracted waves, shallow velocities are directly calculated using forward modeling with FWI imaging technology for fine imaging of the small geological body. Therefore, this fine imaging method for small geological bodies based on FWI technology significantly improves the imaging accuracy of small geological bodies such as river channels and small faults.

Description

Technical Field

[0001] This invention relates to the field of small geological body imaging technology, and in particular to a method for fine imaging of small geological bodies based on FWI technology. Background Technology

[0002] Small geological bodies (such as faults, fractures, caves, and small reefs) are of significant geological importance in seismic exploration, but their imaging technology still faces many challenges. As oil and gas exploration extends to unconventional reservoirs and complex structural zones, the demand for high-precision imaging of small-scale geological bodies is becoming increasingly urgent. However, limited by factors such as seismic wavefield characteristics, data quality, and algorithm performance, existing technologies struggle to reliably identify and finely characterize small geological bodies.

[0003] Currently, small geological body imaging technology still has limitations:

[0004] (1) The energy of diffraction waves generated by small geological bodies is usually much lower than that of reflected waves, and they are easily affected by environmental noise and acquisition noise. Although traditional signal separation methods (such as singular value decomposition, SVD) can partially extract diffraction wave information, noise interference may lead to the loss of effective signals or false anomalies.

[0005] (2) The spatial scale of small geological bodies is often close to or lower than the seismic wavelength, making it difficult for conventional migration imaging methods (such as Kirchhoff migration) to distinguish their fine structure. Although high-resolution processing technology can partially improve the resolution, the imaging accuracy still cannot meet the needs of geological interpretation due to the contradiction between the bandwidth and signal-to-noise ratio of seismic data.

[0006] Therefore, there is an urgent need for a fine imaging method for small geological bodies that can overcome the influence of noise on reflected waves, accurately recover the reflected energy, and improve the accuracy of imaging small geological bodies. Summary of the Invention

[0007] The purpose of this invention is to provide a fine imaging method for small geological bodies based on FWI technology, which can overcome the limitations of existing technologies and improve the imaging accuracy of small geological bodies.

[0008] To achieve the above objectives, this invention provides a method for fine imaging of small geological bodies based on FWI technology, comprising the following steps:

[0009] S1. Obtain seismic data of small geological bodies, perform preliminary processing through regular noise suppression, and introduce Cadzow filtering to suppress residual noise in the triangular zone through reflective energy recovery noise suppression to obtain reflected wave data.

[0010] S2. By using the autocorrelation of the seismic trace, the zero-point amplitude value of the autocorrelation is obtained, and surface consistency decomposition is performed to obtain the amplitude compensation components of each shot point, receiver point and shot-receiver distance. Then, the compensation coefficient of the seismic trace is obtained, and surface consistency amplitude compensation is performed on the reflected wave data to recover the shallow reflected wave energy.

[0011] S3. Based on shallow refracted waves, shallow velocities are directly calculated using forward modeling with FWI imaging technology to perform fine imaging of small geological bodies.

[0012] Preferably, in S1, the Cadzow filter matrix is:

[0013]

[0014] in,

[0015]

[0016] In the formula, z i,j N represents the sample amplitude value of the i-th main survey line and the j-th connecting line. x N y The number of seismic traces in the main survey line direction and the connecting line direction, respectively, p = N. x / 2, q=N y / 2.

[0017] Preferably, S2 includes:

[0018] S21. Find the statistical energy of the gun point i0.

[0019] S22. Find the statistical energy of receiver point j0.

[0020] S23. Calculate the statistical energy of the common-shot distance k0.

[0021] S24. Calculate the total energy A of the survey line;

[0022] S25, Based on statistical energy The total energy A is used to balance the seismic trace (i0,j0,k0).

[0023] Preferably, in S25, the formula for balancing the (i0,j0,k0)th seismic trace is:

[0024] y(t,i0,j0,k0)=x(t,i0,j0,k0)*A 3 *A(i0,j0,k0);

[0025] in,

[0026]

[0027] In the formula, x(t,i0,j0,k0) is the input channel and y(t,i0,j0,k0) is the output channel.

[0028] Preferably, S3 determines the shallow velocity using FWI imaging technology, including:

[0029] Least Squares FWI objective function:

[0030] FWI gradient function:

[0031] In the formula, Let be the gradient of the objective function with respect to the model parameter m, D be the actual earthquake observation data, U be the earthquake simulation data, ⊙ be the Hadamard product, A be the wave propagation operator, Q be the source term, and P be the gradient of the objective function with respect to the model parameter m. r For the projection operator of the receiving point.

[0032] Therefore, the present invention employs the above-mentioned method for fine imaging of small geological bodies based on FWI technology, which has the following technical advantages:

[0033] This invention addresses residual noise in triangular zones by suppressing noise through reflected energy recovery, and then effectively recovers the energy of reflected waves through surface uniformity amplitude compensation. Simultaneously, based on FWI imaging technology, it directly calculates shallow velocities using refracted waves, improving the imaging accuracy of small geological bodies and significantly enhancing the characteristics of small geological bodies such as river channels and small faults.

[0034] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0035] Figure 1 This is a flowchart of a fine imaging method for small geological bodies based on FWI technology;

[0036] Figure 2 This is a comparison of regular noise suppression in an embodiment of a fine imaging method for small geological bodies based on FWI technology, where (a) is before suppression and (b) is after suppression;

[0037] Figure 3 This is a comparison of noise suppression based on reflective energy recovery in an embodiment of a fine imaging method for small geological bodies based on FWI technology, where (a) is before suppression and (b) is after suppression;

[0038] Figure 4 This is a comparison of reflected wave surface uniformity amplitude compensation in an embodiment of a fine imaging method for small geological bodies based on FWI technology, where (a) is before restoration and (b) is after restoration;

[0039] Figure 5This is a schematic diagram of shallow reflected and refracted wave information in an embodiment of a fine imaging method for small geological bodies based on FWI technology.

[0040] Figure 6 This is a fine imaging profile of a small geological body in an embodiment of a fine imaging method for small geological bodies based on FWI technology, wherein (a) is a conventional offset imaging profile without processing for residual noise from the triangular zone, and (b) is an FWI imaging profile after processing for residual noise from the triangular zone.

[0041] Figure 7 This is an example of a fine imaging slice of a small geological body based on FWI technology. (a) is a schematic diagram of the slice location, (b) is a conventional offset imaging slice without processing for residual noise from the triangular zone, and (c) is an FWI imaging slice processed for residual noise from the triangular zone. Detailed Implementation

[0042] The present invention will be explained in more detail through the following embodiments. The purpose of disclosing the present invention is to protect all changes and modifications within the scope of the present invention. The present invention is not limited to the following embodiments.

[0043] like Figure 1 As shown, this invention provides a fine imaging method for small geological bodies based on FWI technology, including regular noise suppression, reflective energy recovery noise suppression, reflective wave surface uniformity amplitude compensation, and FWI imaging technology, as detailed below:

[0044] Following standard processing procedures, regular noise suppression is applied to seismic data from small geological bodies. For example... Figure 2 As shown, conventional noise suppression only processes seismic data through regular noise suppression, but residual noise still exists in the triangular zone, which leads to higher short-path energy and affects the accuracy of surface uniform energy statistics.

[0045] To address the aforementioned issues, this embodiment introduces Cadzow filtering to suppress residual noise in the triangular zone, achieving restorative noise suppression of reflected energy and ensuring that the seismic data before surface consistency processing contains only reflected waves.

[0046] The Cadzow filter constructs a high-dimensional Cadzow matrix in the frequency domain based on the difference in coherence between the effective signal and random noise. This matrix is ​​then subjected to singular value decomposition, and rank reduction is used to suppress random noise. Specifically, for seismic data s(x,y,t), where x represents the direction of the main seismic line, y represents the direction of the connecting line, and t represents the time direction, a Fourier transform is performed along the time direction to obtain complex-domain seismic data z(x,y,ω). Slices of data at each frequency f are then sequentially taken to form a complex matrix A.

[0047]

[0048] In the formula, z i,j N represents the sample amplitude value of the i-th main survey line and the j-th connecting line. x N y These represent the number of seismic traces along the main survey line and the connecting line, respectively. By employing a high-dimensional folding and combination method, the complex matrix A is transformed into a combination of high-dimensional matrices, thereby further enhancing the coherence of the effective signal. The matrix corresponding to the Cadzow filtering method is:

[0049]

[0050] in,

[0051]

[0052] In general, p = N x / 2, q=N y / 2.

[0053] like Figure 3 As shown, reflective energy restorative noise suppression can effectively remove residual noise, ensuring that all data after reflective energy restorative noise suppression are valid reflected waves, thus laying a data foundation for surface uniformity energy compensation of reflected waves.

[0054] Then, by using the autocorrelation of the seismic traces, the zero-point amplitude value of the autocorrelation is obtained, and surface consistency decomposition is performed to obtain the amplitude compensation components of each shot point, receiver point, and shot-receiver distance. Furthermore, the compensation coefficient of the seismic traces is calculated to achieve surface consistency amplitude compensation of the reflected waves, including:

[0055] A. Calculate the statistical energy of the gun point i0.

[0056] B. Calculate the statistical energy at receiver point j0.

[0057] C. Calculate the statistical energy of the common-shot distance k0.

[0058] D. Calculate the total energy A of the survey line.

[0059] E. The formula for balancing the (i0,j0,k0)th seismic trace is:

[0060] y(t,i0,j0,k0)=x(t,i0,j0,k0)*A 3 *A(i0,j0,k0);

[0061]

[0062] Where x(t,i0,j0,k0) is the input channel and y(t,i0,j0,k0) is the output channel.

[0063] Through the aforementioned surface uniform amplitude compensation for reflected waves, the energy of shallow reflected waves can be effectively recovered. Compared with conventional surface uniform amplitude compensation that includes residual noise from the triangular zone, the energy characteristics of small geological bodies are more pronounced. Figure 4 As shown.

[0064] On the other hand, besides energy feature recovery, velocity accuracy also affects the imaging quality of small geological bodies. In this embodiment, the migration velocity is updated using FWI, which has higher accuracy than conventional velocity iteration, to improve the imaging of small geological bodies. Figure 5 As shown, conventional migration velocity iterative imaging methods update velocities by flattening the gathers, but there is little effective reflected wave information in the shallow layers, resulting in limited velocity update accuracy and difficulty in updating areas with low shallow coverage times. In contrast, shallow refracted wave information is very rich. By using FWI imaging technology, the formation velocity can be directly calculated using forward modeling of refracted waves, without relying on gather quality, resulting in higher accuracy in shallow velocity updates.

[0065] The process of obtaining shallow layer velocities using FWI is as follows:

[0066] Least Squares FWI objective function:

[0067] FWI gradient function:

[0068] Time-delay FWI assumptions:

[0069] Delay FWI residual:

[0070] Time-delay FWI gradient function:

[0071] In the formula, Let be the gradient of the objective function with respect to the model parameter m; D be the actual seismic observation data; U(m) be the seismic simulation data based on the model parameter m; ⊙ be the Hadamard product; A be the wave propagation operator, which depends on the model parameter m (such as velocity, density, etc.). Characterizes the effect of changes in medium parameters on the propagation operator; Q is the source term; P r For the projection operator of the receiving point, t is time. For example... Figure 5 As shown, FWI can accurately determine shallow velocities, significantly improving the imaging quality of small geological bodies.

[0072] from Figure 6 and Figure 7It can be seen that through the above-mentioned fine imaging processing of small geological bodies, not only are the cross-sectional imaging of small geological bodies such as river channels and small faults significantly improved, but their spatial distribution on the slices also shows a great improvement compared with conventional processing.

[0073] Therefore, the present invention employs the above-mentioned method for fine imaging of small geological bodies based on FWI technology, which significantly improves the imaging accuracy of small geological bodies such as river channels and small faults.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for fine imaging of small geological bodies based on FWI techniques, characterized in that, Includes the following steps: S1. Obtain seismic data of small geological bodies, perform preliminary processing through regular noise suppression, and introduce Cadzow filtering to suppress residual noise in the triangular zone through reflective energy recovery noise suppression to obtain reflected wave data. S2. By using the autocorrelation of the seismic trace, the zero-point amplitude value of the autocorrelation is obtained, and surface consistency decomposition is performed to obtain the amplitude compensation components of each shot point, receiver point and shot-receiver distance. Then, the compensation coefficient of the seismic trace is obtained, and surface consistency amplitude compensation is performed on the reflected wave data to recover the shallow reflected wave energy. S3. Based on shallow refracted waves, shallow velocities are directly calculated using forward modeling with FWI imaging technology to perform fine imaging of small geological bodies. S2 includes: S21, find shotpoint statistical energy ; S22, Find the receiving point Statistical energy ; S23, Determine the common gun-receiver distance. Statistical energy ; S24. Calculate the total energy of the survey line. ; S25, Based on statistical energy , , and total energy Balance Earthquake track; In S25, the balance is... The formula for seismic traces is: ； in, ； In the formula, For input channel, For output channels.

2. The method for fine imaging of small geological bodies based on FWI technology according to claim 1, characterized in that, In S1, the Cadzow filter matrix is: D ； in, ； In the formula, For the first The first main survey line The amplitude values ​​of the sample points of the connecting line, , These represent the number of seismic traces along the main survey line and the connecting line, respectively. , .

3. The method for fine imaging of small geological bodies based on FWI technology according to claim 1, characterized in that, S3 uses FWI imaging technology to determine shallow velocities, including: Least Squares FWI objective function: ; FWI gradient function: ; In the formula, Let m be the gradient of the objective function with respect to the model parameters m. Based on actual earthquake observation data, For earthquake simulation data, The product is the Hadamard product, where A is the product with the seismic wave propagation operator. For the focal term, For the projection operator of the receiving point.

Images