Pre-stack density stability inversion method and device

Abstract

The invention discloses a pre-stack density stability inversion method and device, and the method comprises the steps: carrying out the preprocessing of seismic data, and converting an offset data volume of the seismic data into an angle gather data volume; calculating the angle gather data volume according to a first elastic impedance formula to obtain first elastic impedance, and introducing the low-frequency model into the first elastic impedance to obtain second elastic impedance; and extracting an elastic parameter from the second elastic impedance according to the weighting coefficient. Compared with the prior art, the method can obtain a stable and high-precision density inversion result.

Description

Technical Field

[0001] The invention relates to the technical field of seismic data interpretation, and in particular to a pre-stack density stable inversion method and device. Background Art

[0002] Since Connolly first proposed the concept of elastic impedance (EI) in 1999, the theory has developed rapidly and has become an important tool in seismic inversion. The elastic impedance equation is derived based on the reflection coefficient approximation and is a generalization of post-stack wave impedance, although it is only a calculation quantity and has no clear physical meaning. The equation contains information on P- and S-wave velocities, density, and incident angles, and can extract a variety of elastic parameters to reflect reservoir lithology and fluid information. VerWest et al. (2000) derived the elastic impedance equation in the ray domain and extended the elastic impedance theory to the ray domain. With the development of multi-wave and multi-component seismic exploration technology, the concept of elastic impedance has been extended to converted shear waves, and Duffaut et al. (2000) derived the converted shear wave elastic impedance (SEI) equation.

[0003] However, Connolly's EI equation is affected by the incident angle, resulting in large changes in the elastic impedance value on a dimensional scale. Therefore, Whitecombe (2002) proposed the concept of extended elastic impedance (EEI) to standardize the equation. Martins (2002) extended the elastic impedance equation to weakly anisotropic media and applied it to actual data. Lu et al. (2004) applied the elastic impedance inversion method to extract target parameters in actual data.

[0004] Domestic scholars have also conducted a lot of research on elastic impedance inversion. Ni Yi (2003) proposed the normalized elastic impedance method, Ma Jinfeng et al. (2003) proposed the concepts of generalized elastic impedance (GEI) and Zoeppritz elastic impedance (ZEI), and successfully applied them to hydrocarbon detection. Gan Lideng et al. (2003) conducted a comparative analysis of the accuracy of reflection coefficients. Wang Baoli (2007) proposed an elastic wave impedance inversion method based on the Gray approximate equation, which can directly obtain the Lame constant. Yin Xingyao et al. (2010) proposed an elastic wave impedance equation expressed by fluid factor, Lame constant and density and the corresponding inversion method based on the approximate equation of porous fluid saturated elastic medium. Zong Zhaoyun (2011) used the sparse pulse inversion method under the Bayesian framework to realize the pre-stack elastic wave impedance inversion based on Gray approximation. Liu Xiaojing et al. (2016) applied the basis pursuit and reflection coefficient odd-even decomposition theory to prestack inversion, realized the inversion of Gassmann fluid terms and shear modulus, and analyzed the sensitivity of different elastic parameters to reservoir fluids. Zhang Fenglin (2017) introduced the Bayesian principle to construct regularization terms, and improved the inversion stability through sparse constraints and low-frequency soft constraints. Wang (2019) et al. used non-convex regularization to extract stable elastic impedance from seismic data, and then extracted longitudinal and transverse wave velocities and densities from the elastic impedance under the Bayesian inversion framework. Hao Yaju (2020) projected the seismic signal onto the odd and even sub-wave libraries to obtain the relative reflection coefficient of each channel, and then integrated the channels to obtain the relative elastic wave impedance, thereby improving the thin layer resolution and inversion accuracy.

[0005] The elastic impedance equation makes it possible to invert the far-offset angular stacking data using the acoustic impedance inversion technique, but its dimension changes with the incident angle, which brings inconvenience to the comprehensive analysis of AI and EI. In the absence of noise, the elastic impedance equation based on Connolly can be used to invert accurate elastic parameters, but as the noise increases, the errors of the obtained P- and S-wave velocities also gradually increase. Mallick et al. pointed out that when there is 2% random noise, this elastic impedance equation can no longer be used to invert reasonable elastic parameters. Therefore, many researchers have derived elastic impedance equations for various elastic parameter data bodies. In 2008, Wang Baoli proposed an elastic impedance equation based on the Fatti approximate formula and standardized it. It can directly invert the impedance data body, avoid the cumulative error of the calculation, and the inversion result is more accurate. Long-term practice has shown that direct inversion of elastic parameters is difficult and unstable, while inverting elastic impedance is a relatively stable process. Therefore, the parameter inversion method based on elastic impedance has been used in seismic inversion for a long time.

[0006] Density, as a key indicator of reservoir characteristics, is closely related to parameters such as porosity, fluid type, saturation and mineral content, and can intuitively reflect the changing laws of reservoirs and fluids. In the field of pre-stack inversion, improving the stability of density inversion is an important research direction. In AVO (Amplitude Variation with Offset inversion) inversion, the commonly used reflection coefficient approximation equation usually assumes that the seismic wavelet does not change with the offset distance, but in actual processing, factors such as dynamic correction stretching will cause the wavelet to change, making AVO attribute extraction complicated. In contrast, elastic impedance inversion uses different wavelet inversions for stacked gathers at different angles, taking into account the change of the wavelet with the offset distance, thereby providing more reliable elastic parameters. However, the existing elastic impedance equation has a deviation from the exact value when calculating the reflection coefficient, and this deviation increases with the increase of the angle, affecting the accuracy of the inversion result.

[0007] In addition, the traditional pre-stack parameter inversion method based on elastic impedance faces two major problems: first, the Aki-Richards reflection coefficient approximation formula, after linearization, becomes an expression of elastic impedance, but its accuracy is insufficient, which is inconsistent with the development trend of high-precision inversion; second, in the process of extracting elastic parameters using elastic impedance, the strong nonlinearity between elastic parameters and impedance increases the difficulty of parameter estimation, making it difficult to achieve the purpose of stable inversion. Summary of the invention

[0008] The technical problem to be solved by the present invention is to address at least one defect of the related technology mentioned in the above background technology: in the prior art, it is difficult to obtain stable and high-precision density inversion results, the conventional reflection coefficient approximation equation has a large deviation from the exact value, and the deviation gradually increases with the increase of the incident angle, and a pre-stack density stable inversion method and device are provided.

[0009] The technical solution adopted by the present invention to solve the technical problem is: constructing a pre-stack density stable inversion method, the method comprising the following steps:

[0010] Preprocessing the seismic data, converting the offset data volume of the seismic data into an angle gather data volume;

[0011] Calculate the angle gather data volume according to the first elastic impedance formula to obtain the first elastic impedance;

[0012] Introducing the low-frequency model into the first elastic impedance to obtain the second elastic impedance;

[0013] The elastic parameter is extracted from the second elastic impedance according to the weighting coefficient.

[0014] In some embodiments, the seismic data is preprocessed to convert the offset data volume of the seismic data into an angle gather data volume, which then includes: obtaining well logging data through well seismic calibration, extracting angle wavelets from the angle gather data volume of the seismic data and the well logging data;

[0015] Calculating the angle gather data body according to the first elastic impedance formula to obtain the first elastic impedance includes: using the first elastic impedance formula to calculate the angle gather data body of seismic data and logging data corresponding to the angle wavelet to obtain the first elastic impedance.

[0016] In some embodiments, the angle gather data volume includes angle gathers at three different angles.

[0017] In some embodiments, the low-frequency model is introduced into the first elastic impedance to obtain the second elastic impedance, and the method further includes:

[0018] The low-frequency components in the logging data are weighted and calculated to establish a low-frequency model.

[0019] In some embodiments, extracting elastic parameters from the second elastic impedance according to weighted coefficients may also include: solving a coefficient matrix using a coefficient estimation method with spatial variation characteristics to obtain weighted coefficients.

[0020] In some embodiments, a coefficient estimation method with spatial variation characteristics is used to solve a coefficient matrix to obtain weighted coefficients, including: performing statistical analysis on the second elastic impedance, establishing a coefficient matrix for the second elastic impedance with bimodal or multi-modal characteristics, and solving the weighted coefficients using singular value decomposition.

[0021] In some embodiments, the first elastic impedance and the second elastic impedance include longitudinal wave velocity, shear wave velocity, and density, respectively.

[0022] In some embodiments, the first elastic impedance formula is: an elastic impedance formula based on power series approximation.

[0023] In some embodiments, the seismic data is preprocessed, including: fitting missing data based on existing data.

[0024] The present invention also constructs a pre-stack density stable inversion device, which comprises:

[0025] one or more processors;

[0026] A storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the pre-stack density stability inversion method as described in any of the above embodiments.

[0027] By implementing the present invention, the following beneficial effects are achieved:

[0028] The present invention uses a pre-stack density stable inversion method to pre-process seismic data, convert the offset data volume of the seismic data into an angle gather data volume, calculate the angle gather data volume according to the first elastic impedance formula to obtain the first elastic impedance, introduce the low-frequency model into the first elastic impedance to obtain the second elastic impedance, and extract elastic parameters from the second elastic impedance according to the weighting coefficient. Compared with the prior art, the implementation of the present invention can obtain stable and high-precision density inversion results. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0030] Figure 1 A flow chart of an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0031] Figure 2 A comparison diagram of the reflection coefficients of the first type AVO model in an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0032] Figure 3 A comparison diagram of the reflection coefficient error of the first type AVO model in an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0033] Figure 4 A comparison diagram of reflection coefficients of the second type AVO model in an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0034] Figure 5 A comparison diagram of reflection coefficient errors of the second type AVO model in an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0035] Figure 6 A comparison diagram of reflection coefficients of the third type AVO model in an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0036] Figure 7 A comparison diagram of reflection coefficient errors of the third type AVO model in an embodiment of the pre-stack density stability inversion method of the present invention is shown;

[0037] Figure 8 The diagram shows the elastic impedance inversion result at three angles when the signal-to-noise ratio is 0 in one embodiment of the pre-stack density stable inversion method of the present invention;

[0038] Fig. 9 The figure shows the elastic impedance inversion result at three angles when the signal-to-noise ratio is 10 in one embodiment of the pre-stack density stable inversion method of the present invention;

[0039] Fig.10The figure shows the elastic impedance inversion result at three angles when the signal-to-noise ratio is 5 in one embodiment of the pre-stack density stable inversion method of the present invention;

[0040] Fig.11 The inversion result diagram of the elastic parameters of the present invention is shown when the signal-to-noise ratio is 0 in one embodiment of the pre-stack density stable inversion method of the present invention;

[0041] Fig.12 The inversion result diagram of the elastic parameters of the Connolly elastic impedance equation when the signal-to-noise ratio is 0 in one embodiment of the pre-stack density stability inversion method of the present invention is shown;

[0042] Fig.13 The inversion result diagram of the elastic parameters of the present invention is shown in an embodiment of the pre-stack density stable inversion method of the present invention when the signal-to-noise ratio is 10;

[0043] Fig.14 The inversion result diagram of the elastic parameters of the Connolly elastic impedance equation when the signal-to-noise ratio is 10 in one embodiment of the prestack density stability inversion method of the present invention is shown;

[0044] Fig.15 The inversion result diagram of the elastic parameters of the present invention is shown in an embodiment of the pre-stack density stable inversion method of the present invention when the signal-to-noise ratio is 5;

[0045] Fig.16 The inversion result diagram of the elastic parameters of the Connolly elastic impedance equation when the signal-to-noise ratio is 5 in one embodiment of the prestack density stability inversion method of the present invention is shown;

[0046] Fig.17 The inversion result diagram of the elastic impedance of the 10° well bypass channel in one embodiment of the pre-stack density stability inversion method of the present invention is shown;

[0047] Fig.18 The inversion result diagram of the elastic impedance of the 20° well bypass channel in one embodiment of the pre-stack density stability inversion method of the present invention is shown;

[0048] Fig.19 The inversion result diagram of the elastic impedance of the 30° well bypass channel in one embodiment of the pre-stack density stability inversion method of the present invention is shown;

[0049] Fig. 20 The inversion result diagram of the actual well bypass P-wave velocity, S-wave velocity and density of one embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0050] Fig.21 A 10° stack gather diagram of an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0051] Fig. 22A 20° angle stack gather diagram of an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0052] Fig.23 A 30° angle stack gather diagram of an embodiment of the pre-stack density stable inversion method of the present invention is shown;

[0053] Fig.24 The figure shows the inversion result of the longitudinal wave velocity in one embodiment of the pre-stack density stable inversion method of the present invention;

[0054] Fig.25 The figure shows the inversion result of the shear wave velocity in one embodiment of the pre-stack density stable inversion method of the present invention;

[0055] Fig.26 The figure shows the density inversion result of one embodiment of the pre-stack density stable inversion method of the present invention. DETAILED DESCRIPTION

[0056] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0057] It should be noted that the flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0058] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0059] Some embodiments of the present invention disclose a method for prestack density stability inversion, such as Figure 1 As shown, the method comprises the following steps:

[0060] Preprocessing the seismic data, converting the offset data volume of the seismic data into an angle gather data volume;

[0061] Calculate the angle gather data volume according to the first elastic impedance formula to obtain the first elastic impedance;

[0062] Introducing the low-frequency model into the first elastic impedance to obtain the second elastic impedance;

[0063] The elastic parameter is extracted from the second elastic impedance according to the weighting coefficient.

[0064] In some embodiments, the seismic data is preprocessed, including: fitting missing data based on existing data.

[0065] In some embodiments, the offset data volume refers to the data set involved in the process of "deflecting" the recorded data back to its original real spatial position in seismic data processing, and the angle gather data volume is a data set that classifies and organizes seismic data according to specific incident angles or reflection angles.

[0066] In some embodiments, the first elastic impedance is well bypass elastic impedance (EI) or relative elastic impedance, and the second elastic impedance is absolute elastic impedance.

[0067] In some embodiments, the first elastic impedance formula is: an elastic impedance formula based on power series approximation, and the power series approximation is specifically: first, the reflection coefficient formula is expressed as a power series form with respect to the ray parameter p using Taylor expansion, and then approximation is performed by retaining only the quadratic term of the ray parameter p. The four-term approximation formula obtained by this method shows higher accuracy than the traditional linear approximation formula in the test of four types of AVO models, and in terms of the sensitivity matrix, the condition number with respect to the three elastic parameters is also significantly improved. The following is the derivation process of the elastic impedance formula based on power series approximation, and the specific power series approximation reflection coefficient expression of the three elastic parameters is shown as follows:

[0068]

[0069] Where R(θ) is the formation reflection coefficient, α, β, ρ are the P-wave velocity, S-wave velocity and density respectively, Δα, Δβ and Δρ are the differences in P-wave velocity and S-wave density between the lower and upper layers on both sides of the interface respectively. are the average values ​​of the longitudinal wave velocity, shear wave velocity and density on both sides of the interface, θ is the average value of the incident angle and the reflection angle, and γ is the ratio of the average values ​​of the shear and longitudinal wave velocities of the upper and lower layers.

[0070] For small to moderate changes in wave impedance, the reflection coefficient expressed as the logarithm of the wave impedance is accurate:

[0071]

[0072] Where EI is the elastic impedance, ΔEI represents the change in elastic impedance. Based on the relationship in equation (2), equation (1) is rewritten to obtain:

[0073]

[0074] Taylor expansion of the fourth term on the right side of the equation and ignoring higher-order terms yields:

[0075]

[0076] Integrate both sides of the equation, set the constant to 0, and let is a constant, we can get:

[0077]

[0078] Since the value of formula (5) varies greatly with the incident angle, it is not conducive to comparing the elastic wave impedance values ​​at different angles. Therefore, formula (5) is further standardized, that is,

[0079]

[0080] Where α, β, and ρ are the longitudinal wave velocity, shear wave velocity, and density, respectively. are the average values ​​of the corresponding parameters in the logging curve, is the ratio of the average transverse and longitudinal wave velocities of the upper and lower layers. Formula (6) is the first elastic impedance formula in the present invention. By using the reflection coefficient approximation equation of the power series approximation to make an elastic impedance body, the problem of insufficient accuracy of the traditional elastic impedance equation can be well solved. The synthetic data test shows that by comparing the reflection coefficient calculated by the method of the present invention with the exact value, it can be found that this elastic impedance equation can effectively improve the accuracy of the reflection coefficient. Therefore, by using this formula to extract elastic parameters from the elastic impedance body, the elastic parameters obtained by inversion can be made more accurate and stable, and better describe the reservoir information. As shown in Table 1 and Figure 2-Figure 7 As shown, the present invention calculates the reflection coefficient (El-Quadratic) and compares it with the accurate Zoeppritz reflection coefficient (Zoe) and the reflection coefficient (Connally) calculated by the classic Connolly elastic impedance equation, showing the significant advantages of the method of the present invention in terms of calculation accuracy. In particular, in the calculation of the AVO model, the method of the present invention performs particularly well in the first and second types of AVO models. The calculation results are not only closer to the exact value, but also have a smaller deviation from the exact value when the incident angle increases, which further proves the effectiveness of the present invention in improving the calculation accuracy. In the third type of AVO model, the accuracy of the reflection coefficient of the present invention and the Connolly elastic impedance equation is not much different.

[0081] Table 1 Formation model parameters

[0082]

[0083] In some embodiments, the seismic data is preprocessed to convert the offset data volume of the seismic data into an angle gather data volume, which then includes: obtaining well logging data through well seismic calibration, extracting angle wavelets from the angle gather data volume of the seismic data and the well logging data;

[0084] Calculating the angle gather data body according to the first elastic impedance formula to obtain the first elastic impedance includes: using the first elastic impedance formula to calculate the angle gather data body of seismic data and logging data corresponding to the angle wavelet to obtain the first elastic impedance.

[0085] In some embodiments, the angle gather data body includes angle gathers at three different angles, specifically small, medium and large angle gathers, for example: 10° angle stacking gathers, 20° angle stacking gathers and 30° angle stacking gathers.

[0086] In some embodiments, the low-frequency model is introduced into the first elastic impedance to obtain the second elastic impedance, and the method further includes:

[0087] The low-frequency components in the logging data are weighted and calculated to establish a low-frequency model. Specifically, the logging data is parameter-fitted, the low-frequency components between wells are weighted according to the distance, and the low-frequency model of the entire inversion block is established through geological interpretation data. Among them, parameter fitting is to fit the missing parameters according to the known parameters. For example, if the shear wave velocity is missing, the shear wave velocity curve can be obtained by calculating and fitting the compressional wave velocity and density.

[0088] In some embodiments, extracting elastic parameters from the second elastic impedance according to weighted coefficients may also include: solving a coefficient matrix using a coefficient estimation method with spatial variation characteristics to obtain weighted coefficients.

[0089] In some embodiments, a coefficient estimation method with spatial variation characteristics is used to solve the coefficient matrix to obtain weighted coefficients, including: performing statistical analysis on the second elastic impedance, establishing a coefficient matrix for the second elastic impedance with bimodal or multimodal characteristics, and solving the weighted coefficients using singular value decomposition. The singular value decomposition is specifically as follows: Let the matrix D n×m The singular value decomposition of is:

[0090] D T =VΛU T (7)

[0091] Among them, r = rank (D), when r is much smaller than the number of rows and columns of the matrix D, the first r singular values ​​can be used to approximate the matrix, that is, partial singular value decomposition. That is, Λ is an r×r-order diagonal matrix, except for the elements on the main diagonal, all are 0, and each element on the main diagonal σ i are all called eigenvalues, and σ1≥σ2≥L≥σ i ≥0;D T represents the transpose of matrix D, U is an n×r matrix, U T To represent the transpose of matrix U, V T is an r×m-order matrix, V TTo represent the transpose of matrix V, when r is closer to m, the result of multiplication on the right side is closer to the left side. Because:

[0092] D T D=(VΛU T )(U T ΛV)=VΛ(U T U)ΛV T =VΛ 2 V T (8)

[0093] (D T D) -1 =(VΛ 2 V T ) -1 =VΛ -2 V T (9)

[0094] Among them, Λ 2 represents the square of each singular value in Λ, Λ -2 Represents Λ 2 The inverse matrix of , that is, the reciprocal of the square of each singular value.

[0095] Then the least square generalized inverse can be transformed into the following form:

[0096] (D T D) -1 D T =(VΛ 2 V T ) -1 (VΛU T )=VΛ -1 U T (10)

[0097] So we can get the least square solution b of the inversion as:

[0098] b=(D T D) -1 D T m=VΛ -1 U T m(11)

[0099] Substituting the coefficient matrix D established by the second elastic impedance into formula (11) can obtain the weighted coefficient.

[0100] In some embodiments, the first elastic impedance and the second elastic impedance include longitudinal wave velocity, shear wave velocity, and density, respectively.

[0101] Some embodiments of the present invention further disclose a pre-stack density stabilization inversion device, the device comprising:

[0102] one or more processors;

[0103] The storage device is used to store one or more programs. When the one or more programs are executed by one or more processors, the one or more processors implement the pre-stack density stable inversion method as described in any of the above embodiments, which will not be repeated here.

[0104] The following are the effects in the embodiments of the present invention:

[0105] like Figure 8-Figure 10 As shown in the figure, the synthetic seismic data with signal-to-noise ratio (SNR) of 0, 10 and 5 are inverted by the present invention to obtain elastic impedance values ​​at three angles. The blue solid line is the elastic impedance value calculated by the well bypass curve, the orange solid line is the elastic impedance value inverted by the synthetic seismic record, and the black curve is the initial value. From the results, it can be found that the elastic impedance values ​​of small, medium and large angles can be accurately inverted.

[0106] like Figure 11-Figure 16 As shown in the figure, the reflection coefficient calculated by the method of the present invention is compared with the accurate Zoeppritz reflection coefficient and the reflection coefficient calculated by the classical Connolly elastic impedance equation. In the synthetic data test, we observed that the blue curve represents the true value of each elastic parameter, the orange curve represents the inversion value of each elastic parameter, and the black curve represents the initial value of each elastic parameter. Under different signal-to-noise ratio (SNR) conditions, including no noise (SNR=0), SNR of 10 and SNR of 5, the inversion results of the elastic impedance equation of the present invention and the conventional Connolly elastic impedance equation are compared. The results show that under noise-free conditions, the elastic parameter inversion results of the two elastic impedance equations are very accurate. When the SNR is 10, the inversion results of the shear wave velocity and density show a certain degree of deviation. However, when the SNR is reduced to 5, the elastic impedance equation proposed by the present invention shows better results than the conventional elastic impedance equation in the inversion of shear wave velocity and density, which proves its advantage in stability.

[0107] like Figure 17-Figure 19 As shown, in order to verify the effectiveness of the present invention in practical applications, the accuracy of the application effect of the well bypass channel was first tested. Using the logging data of the well bypass channel, the initial model and true value of the elastic impedance were calculated by applying the method of the present invention. Then, the elastic impedance was inverted for the three different angles of the data set. In the result display, the blue solid line represents the true value of the elastic impedance, the red solid line represents the result obtained by inversion, and the black solid line represents the initial model of the inversion process. Through this intuitive comparison, the accuracy and reliability of the method of the present invention in the elastic impedance inversion can be evaluated.

[0108] like Fig. 20As shown in the figure, the elastic impedance values ​​of the small, medium and large angle gathers obtained by inversion are used to successfully invert the elastic parameter values ​​of the wellside gathers. In the result display, the black solid line represents the true value of each elastic parameter, the red solid line represents the parameter value obtained by inversion, and the blue dotted line represents the initial value of the inversion. Fig. 20 As shown, the actual data of the well bypass channel and the inversion results have a high degree of match, which verifies the feasibility of the method of the present invention. Among the various parameters, the inversion accuracy of the longitudinal wave velocity is the highest, while the shear wave velocity and density can basically invert the trend of the entire curve. Specifically, the correlation coefficients between the inversion results of the longitudinal wave velocity, shear wave velocity and density and the true values ​​are 0.7239, 0.1775 and 0.3209, respectively, which shows that the inversion effect of the density is more ideal than that of the shear wave velocity. The coefficient matrix obtained based on the results of the well bypass channel can be further combined with the elastic impedance body obtained by inversion to obtain the inversion result of the entire profile.

[0109] like Figure 21-23 As shown, in order to verify the applicability of the algorithm in the inversion of actual seismic data, the present invention is applied to the inversion process of actual seismic data. First, based on the seismic data collected in the field, angle track gather data containing 400 sampling points and 500 channels were extracted. These angle track gathers are divided into three angle ranges of small, medium and large for superposition, so as to obtain the sub-waves of each partial stacked data body. Then, the well data model is established using logging data and stratigraphic interpretation, and the elastic impedance values ​​of different angle track gathers are obtained by conventional elastic impedance inversion methods. Then, combining the relationship between the matrix coefficients obtained from the wellside track and the angle track gathers, the longitudinal wave velocity, shear wave velocity and density of each track are inverted. During the inversion process, the parameters are continuously adjusted to make the inversion result closer to the true value. The final inversion effect is shown in the figure. Figure 21-23 As shown in , the P-wave velocity profile shows a good correspondence with the logging data, while the density profile inverted by the method of the present invention not only has clear lateral continuity, but also has a good correspondence with the logging data. This shows that the present invention has high applicability and accuracy in the inversion of actual seismic data.

[0110] like Figure 24-26 As shown, the inversion results of the entire profile show that the inversion results of P-wave velocity, S-wave velocity and density have significant continuity. Among these parameters, the inversion effect of P-wave velocity is the best, and the matching degree with the well data is the highest, which shows the efficiency and accuracy of the inversion method in P-wave velocity. At the same time, the density data obtained by inversion also shows good continuity and a high degree of matching with the well data, which further confirms the effectiveness and high stability of this method in inverting density. These results show that the inversion method of the present invention can provide reliable and consistent underground medium parameter information in seismic data interpretation.

[0111] By implementing the present invention, the following beneficial effects are achieved: by comparing the calculated reflection coefficient with the precise value, it is found that the elastic impedance equation of the present invention can significantly improve the accuracy of the reflection coefficient calculation. In the comparative analysis of the reflection coefficient, the calculation results of the Zoeppritz equation, the Connolly elastic impedance equation and the present invention in four types of AVO models are shown. In the first and second types of AVO models, the method of the present invention is closer to the precise value than the Connolly equation, especially when the incident angle increases, the deviation from the precise value is smaller, which highlights the superiority of the present invention in improving accuracy. In the third type of AVO model, the accuracy of the reflection coefficients obtained by different methods is not much different.

[0112] In addition, the effectiveness and applicability of the proposed method are verified by the elastic parameter inversion results of synthetic data. Compared with the results of conventional inversion methods, the proposed method shows higher accuracy, which further proves its potential in practical applications.

[0113] Secondly, elastic impedance inversion generally has better stability and noise resistance than traditional AVO inversion. This means that the elastic parameters extracted from elastic impedance will be more stable and accurate, which is crucial for accurately characterizing reservoir information. Therefore, the present invention not only improves the calculation accuracy of the reflection coefficient, but also helps to more accurately describe and understand reservoir characteristics by providing more stable elastic parameters.

[0114] It is understandable that the above embodiments only express some implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, the above embodiments or technical features can be freely combined, and several deformations and improvements can be made, which all belong to the protection scope of the present invention, that is, the embodiments described in "some embodiments" can be freely combined with any of the above and below embodiments. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should belong to the scope of the claims of the present invention.

Claims

1. A prestack density stability inversion method, characterized in that: The method comprises the following steps: Preprocessing the seismic data, converting the offset data volume of the seismic data into an angle gather data volume; Calculate the angle gather data volume according to the first elastic impedance formula to obtain the first elastic impedance; Introducing the low-frequency model into the first elastic impedance to obtain the second elastic impedance; The elastic parameter is extracted from the second elastic impedance according to the weighting coefficient.

2. The prestack density stable inversion method according to claim 1, characterized in that: Preprocessing the seismic data, converting the offset data volume of the seismic data into an angle gather data volume, and then including: obtaining well logging data through well seismic calibration, extracting angle wavelets from the angle gather data volume of the seismic data and the well logging data; Calculating the angle gather data body according to the first elastic impedance formula to obtain the first elastic impedance includes: using the first elastic impedance formula to calculate the angle gather data body of seismic data and logging data corresponding to the angle wavelet to obtain the first elastic impedance.

3. The prestack density stable inversion method according to claim 1, characterized in that: The angle gather data volume includes three angle gathers at different angles.

4. The prestack density stable inversion method according to claim 2, characterized in that: The low-frequency model is introduced into the first elastic impedance to obtain the second elastic impedance, which also includes: The low-frequency components in the logging data are weighted and calculated to establish a low-frequency model.

5. The prestack density stable inversion method according to claim 1, characterized in that: Extracting elastic parameters from the second elastic impedance according to the weighted coefficients also includes: solving the coefficient matrix using a coefficient estimation method with spatial variation characteristics to obtain the weighted coefficients.

6. The prestack density stable inversion method according to claim 5, characterized in that: The coefficient matrix is ​​solved by using a coefficient estimation method with spatial variation characteristics to obtain weighted coefficients, including: statistical analysis of the second elastic impedance, establishing a coefficient matrix for the second elastic impedance with double-peak or multi-peak characteristics, and solving the weighted coefficients by singular value decomposition.

7. The prestack density stable inversion method according to claim 1, characterized in that: The first elastic impedance and the second elastic impedance include longitudinal wave velocity, shear wave velocity and density, respectively.

8. The prestack density stable inversion method according to claim 1, characterized in that: The first elastic impedance formula is: an elastic impedance formula based on power series approximation.

9. The prestack density stable inversion method according to claim 1, characterized in that: Preprocess the seismic data, including fitting the missing data based on the existing data.

10. A pre-stack density stable inversion device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the pre-stack density stable inversion method as described in any one of claims 1 to 9.

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