Seismic data high-resolution processing method and device and electronic equipment

Abstract

The invention relates to the technical field of seismic data processing, and discloses a seismic data high-resolution processing method and device and electronic equipment. The method comprises the following steps: extracting seismic wavelets; determining the effective bandwidth of the seismic data; designing a high-frequency noise quantitative constraint maximum probability criterion and a constraint function, and solving the seismic reflection coefficient after final frequency expansion; the seismic reflection coefficient and the seismic wavelet are subjected to convolution to form a high-resolution data volume, and the seismic reflection coefficient is calculated through a high-frequency noise quantitative constraint function. According to the technical scheme, starting from seismic data analysis, the frequency band width of seismic data is gradually widened based on the high-frequency noise quantitative constraint function for suppressing high-frequency noise, and an accurate seismic reflection coefficient is approached; the constraint of improving the seismic data resolution in a limited bandwidth traditionally is eliminated, and the purpose of improving the seismic data resolution in a pass band is achieved; the seismic data after resolution processing is high in signal-to-noise ratio and high in signal fidelity.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular to a method, device and electronic equipment for high-resolution processing of seismic data. Background Art

[0002] High-resolution processing of seismic data is mainly achieved through deconvolution. According to the mathematical algorithm, deconvolution methods can be divided into two categories: one is linear deconvolution, such as least square deconvolution. Its characteristic is that it cannot produce high frequencies, and only in the frequency band where the effective signal occupies the vast majority, the purpose of improving resolution is achieved by raising the high frequencies in the band; the second is nonlinear deconvolution, such as sparse constrained deconvolution, which is characterized by adding various sparse constraints to make deconvolution a nonlinear problem. Since the various constraints added have the ability to create high frequencies to varying degrees, the defects of linear deconvolution band limitation are overcome, and the resolution of seismic data can be greatly improved.

[0003] A commonly used type of nonlinear deconvolution method is sparse constrained inversion. The advantage of this type of method is that, under the premise that the formation reflection coefficient is sparse, various sparse constraints are added in the inversion to improve the resolution of seismic data. This method is highly applicable to relatively simple geological targets. The disadvantage is that the formation reflection coefficient is not strictly sparse. Since the high-frequency information obtained by sparse constrained inversion is not controlled by the seismic data itself, and the various constraints are qualitative constraints, when faced with complex geological targets, it is easy to cause large differences in the inversion results and it is impossible to judge the authenticity.

[0004] In order to solve the problem that the inverted high-frequency information does not depend on the actual high-frequency data and has a large difference with the actual data, and the sparse constraint term is a qualitative constraint, a high-resolution processing method for seismic data based on high-frequency noise quantitative constraint function is proposed. Summary of the invention

[0005] The present application provides a method, device and electronic equipment for high-resolution processing of seismic data to solve the above-mentioned technical problems in the prior art.

[0006] According to one aspect of the present application, an embodiment provides a method for high-resolution processing of seismic data, comprising:

[0007] Extract seismic wavelets;

[0008] Determine the effective bandwidth of seismic data;

[0009] The seismic reflection coefficient is convolved with the seismic wavelet to form a high-resolution data volume, wherein the seismic reflection coefficient is calculated by a high-frequency noise quantitative constraint function.

[0010] In one embodiment, determining the effective bandwidth of seismic data includes:

[0011] Perform frequency division scanning on the post-stack processed seismic data and calculate the signal-to-noise ratio:

[0012]

[0013] Where SNR is the signal to noise ratio, M is the number of samples in the analysis time window, N is the number of seismic traces, and h is ij is the amplitude value of the seismic record, E is the square of the energy recorded, S is the effective seismic signal, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers;

[0014] The effective bandwidth is determined according to a frequency bandwidth having a signal-to-noise ratio greater than 1.

[0015] In one embodiment, the high-resolution processing method for seismic data includes:

[0016] The maximum probability criterion and constraint function of high-frequency noise quantitative constraint are designed to obtain the seismic reflection coefficient after final spectrum expansion, wherein the maximum probability criterion includes the effective bandwidth of the seismic data, the seismic reflection time and the threshold value of the corresponding reflection coefficient.

[0017] In one embodiment, the design of the maximum probability criterion and constraint function of the high-frequency noise quantitative constraint to obtain the seismic reflection coefficient after the final spectrum extension includes:

[0018] Design the calculation formula of odd and even reflection coefficients and the expression formula of seismic signals;

[0019] Design a high-frequency noise quantitative constraint function, where the high-frequency noise quantitative constraint function is assumed to be F(p 1-2有效带宽 , t, α, β), the objective function uses the least squares method to minimize the noise, and the expression is as follows:

[0020]

[0021] Where M is the number of sampling points in the time window, N is the number of seismic traces, p is the initial effective bandwidth (p1, p2), t is the time position of each sampling point, α is the odd reflection coefficient, β is the even reflection coefficient, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers;

[0022] The simulated annealing method is used to solve the quantitative constraint function equation of high frequency noise;

[0023] Iterate step by step to obtain the final reflection coefficient after spectrum expansion;

[0024] In the design of the calculation formula for odd and even reflection coefficients, it is assumed that the t of a certain seismic reflection trace is i The reflection coefficient at the time position is R(t i ),but:

[0025] R(t i )=α i +β i

[0026] The Fourier transform yields:

[0027]

[0028] In the formula, α i , β i Represents t i Odd and even reflection coefficients at time position, t i is the time corresponding to the trace that produces the reflection coefficient, and f is the frequency of the seismic trace;

[0029] Among them, in the expression formula of the designed seismic signal, the expression formula of the seismic signal is:

[0030] H(f)=W(f)R(f)+N(f) (3)

[0031] Where H(f) is the seismic record, W(f) is the seismic wavelet, R(f) is the reflection coefficient, N(f) is the noise, and f represents the frequency of the seismic trace;

[0032] According to formula (2), we have:

[0033]

[0034] Where: α i is the odd reflection coefficient, β i is the even reflection coefficient, t i is the time corresponding to the trace that produces the reflection coefficient;

[0035] Assume that the seismic wavelet W(f) is known and the frequency is expressed as f c The reflection coefficient spectrum equation is expressed as:

[0036]

[0037] In the formula, Represent the deconvolution results of reflection coefficient and noise respectively, f c =(f1, f2, f3, f c ), where c represents the number of frequencies, then the vector expression is:

[0038] A=Qg+N (6)

[0039] Where:

[0040] A=[H(f1) / W(f1),H(f2) / W(f2),H(f3) / W(f3)...,H(f c ) / W(f c )] T

[0041]

[0042]

[0043] T is the transpose of the matrix.

[0044] In one embodiment, in the calculation of solving the high-frequency noise quantitative constraint function equation by the simulated annealing method, the initial effective bandwidth p1, p2, and the initial t i and the corresponding initial reflection coefficient α i , β i ; and calculate one by one according to the simulated annealing method, and finally obtain the reflection coefficient volume; the reflection coefficient volume is convolved with the wavelet to obtain the synthetic data volume S i .

[0045] In one embodiment, the stepwise iteration to obtain the final reflection coefficient after spectrum spreading includes:

[0046] The minimum residual S is obtained by subtracting the original data volume from the synthetic data volume. fe Error1 The data body is represented by the following formula:

[0047] S fe Error1 =S fe Residual1 +N1 fe (8)

[0048] In the formula, S fe Error1 Represents the minimum residual S fe Error1 Data body, S fe Residual1 Represents the effective signal remaining in the minimum residual, N1 fe represents the noise signal in the minimum residual;

[0049] For S fe Error1 The data volume is denoised, and the denoised data volume is scanned by frequency division to extend the high frequency to p3; the denoised result is combined with the synthetic data S i Add together to get the denoised seismic data S i+1 ;

[0050] The low-frequency end p1 remains unchanged, and p3 is used as the initial high-frequency p i新 A new round of iteration is carried out, and the frequency is widened from the initial p1-p2 to p1-p m , that is, the bandwidth changes to (p1-p2; p1-p3; p1-p4; p1-p m ) until the residual result is the smallest, and the values ​​of α and β corresponding to the final t are determined to calculate the final reflection coefficient.

[0051] According to one aspect of the present application, an embodiment provides a high-resolution seismic data processing device, comprising:

[0052] An extraction module, used to extract seismic wavelets;

[0053] A determination module for determining the effective bandwidth of seismic data; and

[0054] A formation module is provided for convolving a seismic reflection coefficient with a seismic wavelet to form a high-resolution data volume, wherein the seismic reflection coefficient is calculated by a high-frequency noise quantitative constraint function.

[0055] In one embodiment, the high-resolution processing device for seismic data also includes: a processing module; the processing module is used to design a maximum probability criterion and a constraint function for quantitative constraints on high-frequency noise, and to obtain the seismic reflection coefficient after the final spectrum expansion, wherein the maximum probability criterion includes the effective frequency band width of the seismic data, the seismic reflection time and the threshold value of its corresponding reflection coefficient.

[0056] According to one aspect of the present application, an embodiment provides an electronic device, including:

[0057] Memory; and

[0058] processor;

[0059] Wherein, the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement any of the methods described above.

[0060] According to one aspect of the present application, an embodiment provides a readable storage medium having computer instructions stored thereon; wherein, when the computer instructions are executed by a processor, any of the methods described above is implemented.

[0061] The technical solution of the above-mentioned embodiments of the present application starts with seismic data analysis, and based on the high-frequency noise quantitative constraint function for suppressing high-frequency noise, gradually broadens the frequency bandwidth of the seismic data and approaches the precise seismic reflection coefficient; it gets rid of the traditional constraint of improving the resolution of seismic data within a "limited bandwidth" and achieves the purpose of improving the resolution of seismic data within the passband; the seismic data after resolution processing has a high signal-to-noise ratio and high signal fidelity, and can well complete geological tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of a method for high-resolution processing of seismic data in an embodiment;

[0063] Figure 2 is a schematic diagram of decomposition of seismic reflection coefficients in an embodiment;

[0064] Figure 3 is a schematic diagram of simulated annealing calculation in an embodiment;

[0065] Figure 4 is a flow chart of an iterative method for obtaining a final reflection coefficient in an embodiment;

[0066] Figure 5 is a comparison diagram of the seismic profiles of the first iteration and the final iteration (third iteration) of the CR work area in an embodiment;

[0067] Figure 6 is a comparison diagram of the reflection coefficient calculated by logging of the S2 well in the MM area and the seismic reflection coefficient finally inverted in an embodiment;

[0068] Figure 7 is a comparative cross-section diagram of shale before and after inversion in the ZZ area in an embodiment;

[0069] Figure 8 It is a reservoir distribution comparison diagram before and after the inversion of the A1-A2-A3 well-connected profile in the TD area in an embodiment;

[0070] Fig. 9 In one embodiment, it is a comparison diagram of the well section before and after inversion of the KD893-KD894 well connection in the KD area;

[0071] Fig.10 is a flowchart of a method for high-resolution processing of seismic data in an embodiment;

[0072] Fig.11 The diagram is a schematic diagram of the structure of a high-resolution seismic data processing device in an embodiment. DETAILED DESCRIPTION

[0073] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0074] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0075] It should be noted that the terms "first", "second" etc. in the present application are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged in appropriate circumstances, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0076] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element. Moreover, in this application, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element through a third element.

[0077] Embodiment 1

[0078] Please refer to Figure 1 , Figure 2 An embodiment provides a method for high-resolution processing of seismic data, comprising the following steps:

[0079] S1. Extract seismic wavelets;

[0080] S2. Determine the effective bandwidth of seismic data;

[0081] S3. Design the maximum probability criterion and constraint function of quantitative constraint on high-frequency noise to obtain the seismic reflection coefficient after final spectrum expansion, wherein the maximum probability criterion includes the effective frequency bandwidth of the seismic data, the seismic reflection time and the threshold value of its corresponding reflection coefficient.

[0082] S4. The seismic reflection coefficient is convolved with the seismic wavelet to form a high-resolution data volume, wherein the seismic reflection coefficient is calculated by a high-frequency noise quantitative constraint function.

[0083] In one embodiment, step S2 includes the following steps:

[0084] S21. Perform frequency division scanning on the post-stack processed seismic data and calculate the signal-to-noise ratio, which is:

[0085]

[0086] Where SNR is the signal to noise ratio, M is the number of samples in the analysis time window, N is the number of seismic traces, and h is ij is the amplitude value of the seismic record, E is the square of the energy recorded, S is the effective seismic signal, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers;

[0087] S22. Determine the effective bandwidth (p1, p2) according to the frequency bandwidth whose signal-to-noise ratio is greater than 1.

[0088] In one embodiment, step S3 includes the following steps:

[0089] S31. Design calculation formula;

[0090] S32. Design a high-frequency noise quantitative constraint function, where the high-frequency noise quantitative constraint function is assumed to be F(p 1-2有效带宽 , t, α, β), the objective function uses the least squares method to minimize the noise, and the expression is as follows:

[0091]

[0092] Where M is the number of sampling points in the time window, N is the number of seismic traces, p is the initial effective bandwidth (p1, p2), t is the time position of each sampling point, α is the odd reflection coefficient, β is the even reflection coefficient, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers;

[0093] S33. Using simulated annealing method to solve the high frequency noise quantitative constraint function equation;

[0094] S34. Iterate step by step to obtain the final reflection coefficient after spectrum spreading.

[0095] In one embodiment, step S31 specifically includes the following steps:

[0096] S311. Design a calculation formula for odd and even reflection coefficients;

[0097] A. Assume that t of a seismic reflection trace i The reflection coefficient at the time position is R(t i ),but:

[0098] R(t i )=α i +β i

[0099] B. Fourier transform to get:

[0100]

[0101] In the formula, α i , β i Represents t i Odd and even reflection coefficients at time position, t i is the time corresponding to the trace that produces the reflection coefficient, and f is the frequency of the seismic trace;

[0102] S312. Design an expression formula for seismic signals;

[0103] A. The expression formula of seismic signal is:

[0104] H(f)=W(f)R(f)+N(f) (3)

[0105] Where H(f) is the seismic record, W(f) is the seismic wavelet, R(f) is the reflection coefficient, N(f) is the noise, and f represents the frequency of the seismic trace;

[0106] B. According to formula (2), we have:

[0107]

[0108] Where: α i is the odd reflection coefficient, β i is the even reflection coefficient, t i is the time corresponding to the trace that produces the reflection coefficient;

[0109] C. Assume that the seismic wavelet W(f) is known and the frequency is expressed as f c The reflection coefficient spectrum equation is expressed as:

[0110]

[0111] In the formula, Represent the deconvolution results of reflection coefficient and noise respectively, f c =(f1, f2, f3, f c ), where c represents the number of frequencies, then the vector expression is:

[0112] A=Qg+N (6)

[0113] Where:

[0114] A=[H(f1) / W(f1),H(f2) / W(f2),H(f3) / W(f3)...,H(f c ) / W(f c )] T

[0115]

[0116]

[0117] T is the transpose of the matrix.

[0118] In one embodiment, in step S33, the initial effective bandwidths p1 and p2 are given, and the initial t i and the corresponding initial reflection coefficient α i , β i ; and calculate one by one according to the simulated annealing method, and finally obtain the reflection coefficient volume; the reflection coefficient volume is convolved with the wavelet to obtain the synthetic data volume S i .

[0119] In one embodiment, step S34 includes the following steps:

[0120] S341. Use the original data volume and the synthetic data volume to subtract to obtain the minimum residual S fe Error1 The data body is represented by the following formula:

[0121] S fe Error1 =S fe Residual1 +N1 fe (8)

[0122] In the formula, S fe Error1 Represents the minimum residual S fe Error1 Data body, S fe Residual1 Represents the effective signal remaining in the minimum residual, N1 fe represents the noise signal in the minimum residual;

[0123] S342. fe Error1 The data volume is denoised, and the denoised data volume is scanned by frequency division to extend the high frequency to p3; the denoised result is combined with the synthetic data S i Add together to get the denoised seismic data S i+1 ;;

[0124] S343. The low-frequency end p1 remains unchanged, and p3 is used as the initial high-frequency p i新 A new round of iteration is carried out, and the frequency is widened from the initial p1-p2 to p1-p m , that is, the bandwidth changes to (p1-p2; p1-p3; p1-p4; p1-p m ) until the residual result is the smallest, and the values ​​of α and β corresponding to the final t are determined to calculate the final reflection coefficient.

[0125] The high-resolution processing method of seismic data of the present application is based on the quantitative constraint function of high-frequency noise. Starting from the analysis of seismic data, the maximum probability criterion and quantitative constraint function for suppressing high-frequency noise are designed; a multiple iteration method is adopted to gradually broaden the frequency band width of the seismic data and approach the accurate seismic reflection coefficient; it gets rid of the traditional constraint of improving the resolution of seismic data within the "limited bandwidth" and achieves the purpose of improving the resolution of seismic data within the passband; the seismic data after resolution processing has a high signal-to-noise ratio and high signal fidelity, and can well complete geological tasks.

[0126] Embodiment 2

[0127] Please refer to Figure 1-Figure 5 , Fig.10 An embodiment provides a method for high-resolution processing of seismic data. The following will explain the technical solution of the present application with the ideas of the inventor, which will help those skilled in the art to be familiar with the ideas of the present application. Among them, some technical points are explained in detail, so that those skilled in the art can fully understand the technical solution of the present application.

[0128] The high-resolution processing method of seismic data includes the following steps:

[0129] S1. Extract seismic wavelet W(t);

[0130] S2. Determine the effective bandwidth of the seismic data; perform frequency division scanning on the post-stack processed seismic data and calculate the signal-to-noise ratio:

[0131]

[0132] Where SNR is the signal to noise ratio, M is the number of samples in the analysis time window, N is the number of seismic traces, and h is ij is the amplitude value of the seismic record, E is the square of the energy recorded, S is the effective seismic signal, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers;

[0133] Then determine the "effective bandwidth" (p1, p2) based on the frequency bandwidth where the signal-to-noise ratio is greater than 1;

[0134] S3. Design the maximum probability criterion and constraint function of high-frequency noise quantitative constraint to obtain the final seismic reflection coefficient after spectrum expansion; wherein, the maximum probability criterion includes relevant control parameters such as the effective frequency bandwidth of seismic data, seismic reflection time and its corresponding reflection coefficient threshold value; specifically:

[0135] S31 design calculation formula:

[0136] ① Calculation formula for odd and even reflection coefficients

[0137] A. Assume that t of a seismic reflection trace i The reflection coefficient at the time position is R(t i ),but:

[0138] R(t i )=α i +β i (See attached Figure 2 )

[0139] B. Fourier transform to get:

[0140]

[0141] In the formula, α i , β i Represents t i Odd and even reflection coefficients at time position, t i is the time corresponding to the trace that produces the reflection coefficient, and f is the frequency of the seismic trace;

[0142] ②Expression formula of seismic signal

[0143] A. According to the principle of seismic data processing, in the frequency domain, the seismic signal can be expressed as:

[0144] H(f)=W(f)R(f)+N(f) (3)

[0145] Where H(f) is the seismic record, W(f) is the seismic wavelet, R(f) is the reflection coefficient, N(f) is the noise, and f represents the frequency of the seismic trace;

[0146] B. According to formula (2), we have:

[0147]

[0148] Where: α i is the odd reflection coefficient, β i is the even reflection coefficient, t i is the time corresponding to the trace that produces the reflection coefficient;

[0149] C. Assume that the seismic wavelet W(f) is known and the frequency is expressed as f c The reflection coefficient spectrum equation is expressed as:

[0150]

[0151] In the formula, Represent the deconvolution results of reflection coefficient and noise respectively, f c =(f1, f2, f3, f c ), where c represents the number of frequencies, then the vector expression is:

[0152] A=Qg+N (6)

[0153] Where:

[0154] A=[H(f1) / W(f1),H(f2) / W(f2),H(f3) / W(f3)...,H(f c ) / W(f c )] T

[0155]

[0156]

[0157] T is the transpose of the matrix.

[0158] S32. Design a quantitative constraint function for high frequency noise; in detail:

[0159] Assume that the quantitative constraint function of high-frequency noise is F(p 1-2有效带宽 , t, α, β), the objective function uses the least squares method to minimize the noise, and the expression is as follows:

[0160]

[0161] Where M is the number of sampling points in the time window, N is the number of seismic traces, p is the initial effective bandwidth (p1, p2), t is the time position of each sampling point, α is the odd reflection coefficient, β is the even reflection coefficient, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers;

[0162] S33. Using simulated annealing method to solve the high frequency noise quantitative constraint function equation;

[0163] In the calculation, the initial effective bandwidth (p1, p2) and the initial t i and the corresponding initial reflection coefficient α i , βi ; Calculate one by one according to the simulated annealing method, and finally get the reflection coefficient volume (the calculation process is shown in the attached Figure 3 ), the reflection coefficient volume is convolved with the wavelet to obtain the synthetic data volume (S i );

[0164] S34. Iterate step by step to obtain the final reflection coefficient after spectrum spreading (see Appendix Figure 4 ); specifically including:

[0165] S34. Using the original data volume and the synthetic data volume (S i The minimum residual S is obtained by subtracting fe Error1 The data body is represented by the following formula:

[0166] S fe Error1 =S fe Residual1 +N1 fe (8)

[0167] In the formula, S fe Error1 Represents the minimum residual S fe Error1 Data body, S fe Residual1 Represents the effective signal remaining in the minimum residual, N1 fe represents the noise signal in the minimum residual;

[0168] S342. fe Error1 The data volume is denoised, and the denoised data volume is scanned by frequency division to extend the high frequency to p3; the denoised result is combined with the synthetic data S i Add together to get the denoised seismic data S i+1 ;

[0169] S343. The low-frequency end p1 remains unchanged, and p3 is used as the initial high-frequency p i新 A new round of iteration is carried out, and the frequency is widened from the initial p1-p2 to p1-p m , that is, the bandwidth changes to (p1-p2; p1-p3; p1-p4; p1-p m ) until the residual result is the smallest, and the values ​​of α and β corresponding to the final t are determined to calculate the final reflection coefficient;

[0170] S4. The final seismic reflection coefficient calculated by the high-frequency noise quantitative constraint function is convolved with the seismic wavelet to form a high-resolution data volume.

[0171] In one embodiment, Figure 5This is a comparison diagram of the seismic profile of the XX60-XX49-XX69 survey line after the first iteration (upper) and the final iteration (the third iteration S3) of the CR work area. From the comparative analysis of the upper and lower seismic profiles, it can be seen that the resolution of the seismic data has been greatly improved, with its main frequency at around 45Hz, and the bandwidth widened from 5-100Hz to 5Hz-140Hz, which can complete the geological task well.

[0172] In general, this application proposes a high-resolution seismic processing method based on a high-frequency noise quantitative constraint function, which includes four steps:

[0173] Step 1: Extract seismic wavelet W(t);

[0174] Step 2: Determine the effective bandwidth of seismic data;

[0175] Step 3: Design the maximum probability criterion and constraint function of high-frequency noise quantitative constraint to obtain the final seismic reflection coefficient after spectrum extension;

[0176] Step 4: The final seismic reflection coefficient calculated using the high-frequency noise quantitative constraint function is convolved with the seismic wavelet to form a high-resolution data volume.

[0177] The high-resolution seismic processing method based on the quantitative constraint function of high-frequency noise of the present invention starts with seismic data analysis, designs the maximum probability criterion and quantitative constraint function for suppressing high-frequency noise; adopts the method of multiple iterations to gradually broaden the bandwidth of seismic data and approach the accurate seismic reflection coefficient; gets rid of the traditional constraint of improving the resolution of seismic data within the "limited bandwidth", and achieves the purpose of improving the resolution of seismic data within the passband; improves the signal-to-noise ratio and signal fidelity of seismic data after resolution processing, and can well complete geological tasks. The well seismic calibration effect is good in the prediction of thin interlayer reservoirs.

[0178] Embodiment 3

[0179] Please refer to Figure 6-Figure 10 An embodiment provides a high-resolution seismic processing method based on a high-frequency noise quantitative constraint function, which is described below through a case study to facilitate further understanding of the technical solution.

[0180] Among them, the high-resolution seismic data processing method based on high-frequency noise quantitative constraint function was used to process high-resolution seismic data of post-stack three-dimensional data in multiple work areas. The results showed that the formation reflection coefficient calculated by logging data and the seismic reflection coefficient of the wellside seismic trace extracted after high-resolution data processing had a good correspondence. Compared with the original seismic data of several seismic work areas and the post-stack high-resolution seismic data processing results, the latter not only effectively broadened the seismic frequency band and improved the resolution to varying degrees, but also made the geological phenomena clearer and effectively improved the ability to solve geological problems.

[0181] Attached Figure 6 This is a comparison chart of the reflection coefficient calculated by logging and the inversion results of Well S2 in the MM area. Red represents the formation reflection coefficient calculated using logging data, and blue represents the formation reflection coefficient inverted by the present invention. It can be seen from the figure that the relative relationship between the seismic reflection coefficient calculated using the actual logging data of Well S2 and the reflection coefficient of the wellside seismic trace extracted using the present invention is good, indicating that the inversion result is really reliable.

[0182] Attached Figure 7 The comparison profile of shale in ZZ area before and after inversion. From the figure, it can be seen that the shale on the original seismic profile is a set of strong reflection features (upper), and the high-resolution seismic data processed by the present invention can decompose the original set of strong reflections into upper and lower sets of strong reflections (lower).

[0183] Attached Figure 8 This is a comparison diagram of reservoir distribution before and after inversion of the A1-A2-A3 well profile in the TD area. From the figure, it can be seen that the three wells on the original seismic profile all encountered a set of continuously released thick reservoirs (shown by the yellow dotted line), while the actual drilling data revealed that there were two sets of thin reservoirs separated by a set of mudstone interlayers; the high-resolution seismic data processed by the present invention showed that the two sets of thin reservoirs not only had lateral discontinuity, but also uneven distribution, which was in good agreement with the actual drilling data.

[0184] Attached Fig. 9 This is a comparison diagram of the well profile of two development wells KD893-KD894 in the KD area before and after inversion. After drilling, there was a lot of controversy about the seismic geological stratification. The high-resolution seismic data processed by the present invention show that the seismic geological strata of the two wells are clear. In the area shown by the red circle, the geological phenomena are clear due to the improved seismic resolution.

[0185] Embodiment 4

[0186] Please refer to Fig.11 , an embodiment provides a high-resolution seismic data processing device in the following form:

[0187] 1. Extraction module

[0188] The extraction module 10 is used to extract seismic wavelets;

[0189] 2. Determine the module

[0190] The determination module 20 is used to determine the effective bandwidth of the seismic data;

[0191] 3. Processing module

[0192] The processing module 30 is used to design the maximum probability criterion and constraint function of the quantitative constraint of high-frequency noise, and obtain the seismic reflection coefficient after the final spectrum expansion, wherein the maximum probability criterion includes the effective frequency bandwidth of the seismic data, the seismic reflection time and the threshold value of the corresponding reflection coefficient; the specific processing method is described in the above embodiment;

[0193] 4. Forming modules

[0194] The forming module 40 is used for convolving the seismic reflection coefficient with the seismic wavelet to form a high-resolution data volume, wherein the seismic reflection coefficient is calculated by a high-frequency noise quantitative constraint function.

[0195] It should be noted that the above-mentioned high-resolution seismic data processing device is used to implement the high-resolution seismic data processing method in the above-mentioned embodiments, and each module in the device corresponds to each step in the method.

[0196] Embodiment 5

[0197] Based on the same inventive concept, an embodiment of the present application provides an electronic device, comprising: a memory and a processor; wherein the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor as a high-resolution seismic data processing method described in any one of the above-mentioned embodiments.

[0198] Embodiment 6

[0199] Based on the same inventive concept, an embodiment of the present application provides a readable storage medium, on which computer instructions are stored; wherein, when the computer instructions are executed by a processor, the high-resolution processing method for seismic data described in any one of the above embodiments is implemented.

[0200] Among them, the above one or more computer instructions can be formed into a program.

[0201] The above program can be run in the processor, or it can be stored in the memory (or computer-readable medium), which includes permanent and non-permanent, removable and non-removable media. Information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0202] These computer programs can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps.

[0203] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for high-resolution processing of seismic data, characterized in that: include: Extract seismic wavelets; Determine the effective bandwidth of seismic data; The seismic reflection coefficient is convolved with the seismic wavelet to form a high-resolution data volume, wherein the seismic reflection coefficient is calculated by a high-frequency noise quantitative constraint function.

2. A method for high-resolution processing of seismic data according to claim 1, characterized in that: Determining the effective bandwidth of seismic data includes: Perform frequency division scanning on the post-stack processed seismic data and calculate the signal-to-noise ratio: Where SNR is the signal to noise ratio, M is the number of samples in the analysis time window, N is the number of seismic traces, and h is ij is the amplitude value of the seismic record, E is the square of the energy recorded, S is the effective seismic signal, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers; The effective bandwidth is determined according to a frequency bandwidth having a signal-to-noise ratio greater than 1.

3. A method for high-resolution processing of seismic data according to claim 1, characterized in that: The high-resolution processing method for seismic data comprises: The maximum probability criterion and constraint function of high-frequency noise quantitative constraint are designed to obtain the seismic reflection coefficient after final spectrum expansion, wherein the maximum probability criterion includes the effective bandwidth of the seismic data, the seismic reflection time and the threshold value of the corresponding reflection coefficient.

4. A method for high-resolution processing of seismic data according to claim 3, characterized in that: The design of the maximum probability criterion and constraint function of the quantitative constraint of high-frequency noise and the obtaining of the seismic reflection coefficient after the final spectrum extension include: Design the calculation formula of odd and even reflection coefficients and the expression formula of seismic signals; Design a quantitative constraint function for high-frequency noise, where the quantitative constraint function for high-frequency noise is expressed by F(p, t, α, β), and the objective function uses the least squares method to minimize the noise, which is expressed as follows: Where M is the number of sampling points in the time window, N is the number of seismic traces, p is the initial effective bandwidth (p1, p2), t is the time position of each sampling point, α is the odd reflection coefficient, β is the even reflection coefficient, i is the number of the seismic record sampling point, j is the number of the seismic trace, i∈(1,2,3...M), j∈(1,2,3...N), where M and N are natural numbers; The simulated annealing method is used to solve the quantitative constraint function equation of high frequency noise; Iterate step by step to obtain the final reflection coefficient after spectrum expansion; In the design of the calculation formula for odd and even reflection coefficients, it is assumed that the t of a certain seismic reflection trace is i The reflection coefficient at the time position is R(t i ),but: R(t i )=a i +b i The Fourier transform yields: In the formula, α i , β i Represents t i Odd and even reflection coefficients at time position, t i is the time corresponding to the trace that produces the reflection coefficient, and f is the frequency of the seismic trace; Among them, in the expression formula of the designed seismic signal, the expression formula of the seismic signal is: H(f)=W(f)R(f)+N(f) (3) Where H(f) is the seismic record, W(f) is the seismic wavelet, R(f) is the reflection coefficient, N(f) is the noise, and f represents the frequency of the seismic trace; According to formula (2), we have: Where: α i is the odd reflection coefficient, β i is the even reflection coefficient, t i is the time corresponding to the trace that produces the reflection coefficient; Assume that the seismic wavelet W(f) is known and the frequency is expressed as f c The reflection coefficient spectrum equation is expressed as: In the formula, Represent the deconvolution results of reflection coefficient and noise respectively, f c =(f1,f2,f3...f c ), where c represents the number of frequencies, then the vector expression is: A=Qg+N (6) Where: <h2 style=";text-align:left;direction:ltr">A = [H(f1) / W(f1),H(f2) / W(f2),H(f3) / W(f3)...,H(f<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> ) / W(f<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> )]<h2 style=";text-align:left;direction:ltr"> T T is the transpose of the matrix.

5. A method for high-resolution processing of seismic data according to claim 4, characterized in that: In the calculation of the high-frequency noise quantitative constraint function equation using the simulated annealing method, the initial effective bandwidth p1, p2, and initial t i and the corresponding initial reflection coefficient α i , β i ; and calculate one by one according to the simulated annealing method, and finally obtain the reflection coefficient volume; the reflection coefficient volume is convolved with the wavelet to obtain the synthetic data volume S i .

6. A method for high-resolution processing of seismic data according to claim 4, characterized in that: The step-by-step iteration to obtain the final reflection coefficient after spectrum spreading includes: The minimum residual S is obtained by subtracting the original data volume from the synthetic data volume. fe Error1 The data body is represented by the following formula: S fe Error1 =S fe Residual1 +N1 fe (8) In the formula, S fe Error1 Represents the minimum residual S fe Error1 Data body, S fe Residual1 Represents the effective signal remaining in the minimum residual, N1 fe represents the noise signal in the minimum residual; For S fe Error1 The data volume is denoised, and the denoised data volume is scanned by frequency division to extend the high frequency to p3; the denoised result is combined with the synthetic data S i Add together to get the denoised seismic data S i+1 ; The low-frequency end p1 remains unchanged, and p3 is used as the initial high-frequency p i新 A new round of iteration is carried out, and the frequency is widened from the initial p1-p2 to p1-p m , that is, the bandwidth changes to (p1-p2; p1-p3; p1-p4; p1-p m ) until the residual result is the smallest, and the values ​​of α and β corresponding to the final t are determined to calculate the final reflection coefficient.

7. A high-resolution seismic data processing device, characterized in that: include: An extraction module, used to extract seismic wavelets; A determination module is used to determine the effective bandwidth of seismic data; and A formation module is provided for convolving a seismic reflection coefficient with a seismic wavelet to form a high-resolution data volume, wherein the seismic reflection coefficient is calculated by a high-frequency noise quantitative constraint function.

8. The high-resolution seismic data processing device according to claim 7, characterized in that: The high-resolution processing device for seismic data also includes: a processing module; the processing module is used to design a maximum probability criterion and a constraint function for quantitative constraints on high-frequency noise, and to obtain the seismic reflection coefficient after the final spectrum expansion, wherein the maximum probability criterion includes the effective frequency band width of the seismic data, the seismic reflection time and the threshold value of the corresponding reflection coefficient.

9. An electronic device, characterized in that: include: Memory; and processor; The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method described in any one of claims 1-6.

10. A readable storage medium, characterized in that: The readable storage medium stores computer instructions; wherein, when the computer instructions are executed by a processor, the method described in any one of claims 1 to 6 is implemented.

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