A reservoir dispersion velocity inversion method, device, equipment and medium

Abstract

The application discloses a reservoir dispersion velocity inversion method, device, equipment and medium, and relates to the technical field of seismic exploration. The method comprises the following steps: obtaining the dispersion velocity of an actual work area core through a rock physical experiment; constructing a dispersion velocity model by using the dispersion velocity, and determining forward recording corresponding to the dispersion velocity model based on a propagation matrix forward method; deducing an inversion iteration formula based on the forward recording, and determining the parameter value corresponding to the maximum posterior probability of the inversion iteration formula as a target dispersion velocity in an inversion obtained dispersion velocity model according to the Bayes principle. Through the technical scheme of the application, the inversion result accurately describes the seismic wave dispersion velocity, solves the defect that a traditional frequency-variable AVO inversion only uses a dispersion gradient attribute to identify oil and gas fluid, and has very important reference value for predicting underground fluid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic exploration, in particular to a reservoir dispersion velocity inversion method, device, equipment and medium. BACKGROUND

[0002] With the deepening of oil and gas exploration, using seismic frequency information to predict underground reservoirs has unique advantages. The traditional dispersion AVO (Amplitude Versus Offset) inversion converts time-domain seismic data to time-frequency domain through time-frequency analysis technology, and then constructs an inversion equation to invert the dispersion gradient attribute of different offsets. This uses different seismic wave dispersion characteristics to qualitatively depict underground fluids. Since the dispersion gradient attribute cannot directly depict the real changes of seismic wave dispersion, it is necessary to reduce the accuracy in resolution. Generally, the prediction results need to be combined with other data for joint judgment, and the inversion results are also affected by the accuracy of spectral decomposition and the selection of inversion formula and a series of other factors.

[0003] Therefore, how to provide a solution to the above technical problems is a problem that those skilled in the art need to solve at present. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a reservoir dispersion velocity inversion method, device, equipment and storage medium, which solves the defect that the traditional frequency-variable AVO inversion only uses the dispersion gradient attribute to identify oil and gas fluids, and specifically inverts the reservoir dispersion velocity, providing a solid theoretical support for accurately quantifying the velocity dispersion characteristics of the fluid-bearing formation. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a reservoir dispersion velocity inversion method, comprising:

[0006] obtaining the dispersion velocity of the actual work area core through rock physical experiments;

[0007] constructing a dispersion velocity model using the dispersion velocity, and determining a forward record corresponding to the dispersion velocity model based on a propagation matrix forward method;

[0008] deducing an inversion iteration formula based on the forward record, and determining the parameter value corresponding to the maximum posterior probability of the inversion iteration formula as a target dispersion velocity in the dispersion velocity model obtained by inversion according to the Bayesian principle.

[0009] Optionally, the determination of the forward record corresponding to the dispersion velocity model based on the propagation matrix forward method comprises:

[0010] dividing the formation to obtain a top layer semi-elastic space part, a bottom layer semi-elastic space part and an intermediate dispersion layer part;

[0011] Determine frequency domain reflection coefficients using propagation matrices corresponding to the top semi-elastic space portion, the bottom semi-elastic space portion, and the intermediate dispersion layer portion, respectively;

[0012] Based on the seismic convolution model, the forward record corresponding to the dispersion velocity model is determined by convolving the frequency domain reflection coefficient with the seismic wavelet.

[0013] Optionally, after determining the forward record corresponding to the dispersion velocity model by convolving the frequency domain reflection coefficient with the seismic wavelet based on the seismic convolution model, the method further includes:

[0014] Based on the approximation principle, a linearized forward model is established using the forward modeling records to obtain actual observed seismic data;

[0015] Accordingly, the inversion iterative formula is derived based on the forward modeling record, and according to the Bayesian principle, a parameter value corresponding to the maximum posterior probability of the inversion iterative formula is determined as the target dispersion velocity in the dispersion velocity model obtained by inversion, including:

[0016] An inversion iteration formula is derived using the actual observed seismic data, and according to the Bayesian principle, a parameter value corresponding to the maximum posterior probability of the inversion iteration formula is determined as the target dispersion velocity in the dispersion velocity model obtained by inversion.

[0017] Optionally, determining the frequency domain reflection coefficient by using propagation matrices corresponding to the top semi-elastic space portion, the bottom semi-elastic space portion, and the intermediate dispersion layer portion, respectively, includes:

[0018] use Determine the frequency domain reflection coefficient;

[0019] Wherein, R is the frequency domain reflection coefficient; A1 is the propagation matrix corresponding to the top semi-elastic space part; A2 is the propagation matrix corresponding to the bottom semi-elastic space part; B α =T(0)T -1 (h α ), is the propagation matrix corresponding to the intermediate frequency dispersion layer, α is the sequence number of each horizontal layer contained in the intermediate frequency dispersion layer, α = 1, ..., N, h α is the thickness of the αth horizontal layer, T(0) represents the T matrix when the thickness is 0; i p represents the longitudinal wave incident vector.

[0020] Optionally, the determining of a forward record corresponding to the dispersion velocity model by convolution of the frequency domain reflection coefficient and the seismic wavelet based on the seismic convolution model includes:

[0021] based on a seismic convolution model, using determining a forward record corresponding to the dispersion velocity model; wherein S represents the frequency domain reflectivity and seismic wavelet convolution; w(t) represents the time domain Ricker wavelet; r(t) represents the time domain reflectivity; t is time; W(ω) represents the frequency domain Ricker wavelet; R(θ;ω) represents the frequency domain reflectivity of different incident angles obtained by the propagation matrix, and θ is the incident angle;

[0022] Optionally, the dispersion velocity model is obtained by the following steps: deriving an inversion iteration formula based on the forward record, and determining a parameter value corresponding to a maximum posterior probability of the inversion iteration formula as a target dispersion velocity in the dispersion velocity model obtained by inversion according to a Bayesian principle.

[0023] using deriving an inversion iteration formula, and determining a parameter value corresponding to a maximum posterior probability of the inversion iteration formula as a target dispersion velocity in the dispersion velocity model obtained by inversion according to a Bayesian principle.

[0024] wherein, is a likelihood function;

[0025] is a prior probability; d is the forward record, d=(d1, d2, d3...dn), d1, d2, d3...dn represent actual observed seismic data; P(d(θ)) is a constant; det represents the value of solving the corresponding determinant; T is a transpose; Δm is a model parameter perturbation; C Δm is a variance of the initial dispersion velocity; σ is a variance of a seismic residual; N represents a sample number of a reservoir feature parameter to be inverted; G represents a forward process; m is the initial dispersion velocity.

[0026] Optionally, after the dispersion velocity model is obtained by the above method, the method further comprises the following steps:

[0027] obtaining a real value of the dispersion velocity in a preset frequency range;

[0028] using the target dispersion velocity to determine an inversion result in the preset frequency range;

[0029] comparing the real value of the dispersion velocity with the inversion result to verify the accuracy of the target dispersion velocity.

[0030] In a second aspect, the application discloses a reservoir dispersion velocity inversion device, comprising:

[0031] a velocity acquisition module, configured to acquire a dispersion velocity of an actual work area core through a rock physics experiment;

[0032] a forward modeling module, configured to construct a dispersion velocity model by using the dispersion velocity, and determine a forward modeling record corresponding to the dispersion velocity model based on a propagation matrix forward modeling method;

[0033] an inversion module, configured to derive an inversion iterative formula based on the forward modeling record, and determine a parameter value corresponding to a maximum posterior probability of the inversion iterative formula as a target dispersion velocity in the dispersion velocity model obtained through inversion according to a Bayesian principle.

[0034] In a third aspect, the present application discloses an electronic device, comprising a processor and a memory; wherein the memory is configured to store a computer program, and the computer program is loaded and executed by the processor to implement the reservoir dispersion velocity inversion method as described above.

[0035] In a fourth aspect, the present application discloses a computer readable storage medium configured to store a computer program; wherein the computer program is executed by a processor to implement the reservoir dispersion velocity inversion method as described above.

[0036] The present application provides a reservoir dispersion velocity inversion method, comprising: acquiring a dispersion velocity of an actual work area core through a rock physics experiment; constructing a dispersion velocity model by using the dispersion velocity, and determining a forward modeling record corresponding to the dispersion velocity model based on a propagation matrix forward modeling method; deriving an inversion iterative formula based on the forward modeling record, and determining a parameter value corresponding to a maximum posterior probability of the inversion iterative formula as a target dispersion velocity in the dispersion velocity model obtained through inversion according to a Bayesian principle. It can be seen that the present application uses the propagation matrix forward modeling method on the constructed dispersion velocity model, and performs forward modeling on the dispersion velocity of the actual work area core obtained through the rock physics experiment to obtain a corresponding forward modeling synthetic seismogram. Since the propagation matrix forward modeling is derived based on the wave equation under the assumption of layered medium, compared with the traditional forward modeling formula and its approximation formula based on the single interface half-infinite space assumption, the propagation matrix considers the transmission loss and interlayer multiple wave effect in the seismic wave propagation process, and most importantly, it considers the harmonic effect between thin interbeds, which can better match the underground seismic wave propagation process and the acquired seismic data, and achieve higher inversion accuracy. Further, the Bayesian principle is introduced based on the obtained forward modeling record, and the parameter at the maximum posterior probability of the inversion iterative formula is determined as a prediction value according to the Bayesian principle, which corresponds to the target dispersion velocity in the dispersion velocity model obtained through inversion. In this way, the inversion result accurately describes the seismic wave dispersion velocity, provides support for quantifying the dispersion degree of the dispersion layer, and improves the accuracy of the entire inversion result.

[0037] In addition, the present application provides a reservoir dispersion velocity inversion device, equipment, and storage medium, which correspond to the above-mentioned reservoir dispersion velocity inversion method and have the same effect as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0039] Figure 1 This is a flow chart of a reservoir dispersion velocity inversion method disclosed in this application;

[0040] Figure 2 This is a schematic diagram of rock physics experimental results disclosed in this application;

[0041] Figure 3 A schematic diagram of stratigraphic division disclosed in this application;

[0042] Figure 4 This is a schematic diagram of a preset dispersion velocity model based on a propagation matrix disclosed in this application;

[0043] Figure 5 A schematic diagram of a forward recording disclosed in this application;

[0044] Figure 6 This is a schematic diagram of the overall process of reservoir dispersion velocity inversion disclosed in this application;

[0045] Figure 7 This is a schematic diagram of the first dispersion layer inversion result disclosed in this application;

[0046] Figure 8 This is a schematic diagram of a second dispersion layer inversion result disclosed in this application;

[0047] Figure 9 This is a schematic structural diagram of a reservoir dispersion velocity inversion device disclosed in this application;

[0048] Figure 10 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0049] Clearly, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0050] Currently, with the deepening of oil and gas exploration, the inversion technology of predicting reservoir elastic parameters and then predicting fluid by using the amplitude information of seismic records ignores the seismic frequency information. However, using the seismic frequency information to predict the underground reservoir has unique advantages. Domestic and foreign researches show that when the seismic wave meets the underground fracture, fault and the like, the fluid exchange between the pore fluid and the background medium or between the pores will cause the amplitude attenuation and velocity dispersion of the seismic wave. Therefore, the P-wave velocity dispersion under the seismic frequency is usually related to oil and gas, which leads to the frequency-dependent P-wave reflection coefficient. This influence is ignored in the traditional Amplitude versus angle (AVA) inversion. The traditional frequency-dependent AVO inversion converts the time-domain seismic data to the time-frequency domain through the time-frequency analysis technology, and then constructs the inversion equation to invert the dispersion gradient attribute of different offsets, which qualitatively describes the underground fluid by using different seismic wave dispersion characteristics. Since the dispersion gradient attribute cannot directly describe the real change of the seismic wave dispersion, the resolution has to be reduced in accuracy. Generally, the prediction result has to be combined with other data to jointly judge, and the inversion result is affected by the accuracy of spectral decomposition, the selection of the inversion formula and a series of other factors.

[0051] Therefore, the present application provides a reservoir dispersion velocity inversion scheme, which can improve the inversion accuracy of the traditional dispersion AVO and then reduce the exploration and development risk of the oil and gas field.

[0052] The embodiment of the present application discloses a reservoir dispersion velocity inversion method, referring to Figure 1 The method comprises the following steps.

[0053] Step S11: Obtain the dispersion velocity of the actual work area core through the rock physical experiment.

[0054] The seismic data has amplitude, frequency, phase and the like information, and the frequency-dependent seismic attribute has very important significance for oil and gas prediction, and has special advantages that the amplitude and phase do not have. The inherent frequency information of the seismic data is considered to be closely related to the non-homogeneous scale length of the underground geological body, the underground rock permeability and the saturated fluid. When the seismic wave meets the underground fluid, the fluid exchange will occur between the pore fluid and the background medium and between the pores, and this fluid exchange will cause the amplitude attenuation and the velocity dispersion of the seismic wave.

[0055] In the embodiments of the present application, the dispersion velocity of the actual core in the working area is first measured through rock physical experiment, and the dispersion velocity is taken as the dispersion velocity for constructing the dispersion velocity model. As shown in the following table, the dispersion velocity measured through rock physical experiment is shown as an example, wherein the results measured at 0Mpa and 8Mpa are taken as the dispersion velocity for constructing the dispersion velocity model, and the abscissa is the frequency and the ordinate is the P-wave modulus. Figure 2 As shown in the following table, the dispersion velocity measured through rock physical experiment is shown as an example, wherein the results measured at 0Mpa and 8Mpa are taken as the dispersion velocity for constructing the dispersion velocity model, and the abscissa is the frequency and the ordinate is the P-wave modulus.

[0056] Step S12: Constructing the dispersion velocity model by using the dispersion velocity, and determining the forward record corresponding to the dispersion velocity model based on the propagation matrix forward method.

[0057] It should be noted that the existing frequency-variable AVO technology has certain problems, which often makes the P-wave dispersion and S-wave dispersion in the pre-stack data of the earthquake unable to be decoupled, resulting in a certain reduction in the inversion accuracy of the frequency-variable AVO technology. In view of this problem, the current research focus is on the frequency dependence of the time delay between the split S-wave and the azimuthal variation of the P-wave attenuation. Recently, laboratory studies show that the seismic velocity is related to the frequency in a relatively high frequency range, which seems to be caused by fluid mobility (Batzle et al., 2006), therefore, the fluid mobility is defined as the ratio of the rock permeability to the fluid viscosity based on the reflection coefficient during inversion. Theoretical studies (such as: Jakobsen and Chapman, 2009) also understand this frequency dependence, which will greatly help the fluid identification work if the frequency dependence of the velocity can be measured according to the reflection data. In addition, Chapman et al. (2005) theoretically studied the reflection of the formation showing fluid-related dispersion and attenuation, and showed that the AVO response is frequency-dependent in this case. The application of spectral decomposition technology can detect this behavior on the synthetic seismogram. Wilson et al. (2009) extended this analysis and introduced a frequency-dependent AVO inversion concept, which aims to allow direct measurement of dispersion from pre-stack data. The above conventional inversion technology lays a solid foundation for identifying underground fluids, and in the embodiments of the present application, compared with the existing conventional inversion technology, the dispersion assumption based on the reflection coefficient is not needed, and the spectral decomposition of the seismic data is not needed, and the underground fluid is identified from the specific dispersion, so that the inversion accuracy is higher, and the oil and gas exploration and development has great potential value.

[0058] Specifically, the velocity v p(ω) using the propagation matrix method to substitute and simulate the characteristics of seismic response, to obtain the corresponding forward record. Since the propagation matrix forward method is derived based on the wave equation under the assumption of layered medium, compared with the traditional forward formula based on the single interface half-infinite space assumption and its approximation, the propagation matrix considers the transmission loss and interlayer multiple wave effect in the process of seismic wave propagation, and most importantly, it considers the harmonic effect between thin interbeds, which can better match the process of underground seismic wave propagation and the acquired seismic data, and thus achieve higher inversion accuracy.

[0059] As shown in Figure 3 is a propagation matrix simulation diagram. Based on the continuity assumption of strain and stress, the propagation matrix method can calculate the frequency-dependent reflection coefficient in layered media. In the embodiments of the present application, the propagation matrix assumes that the horizontal multi-layer model of the stratum is divided into three parts: a top semi-elastic space part, a bottom semi-elastic space part and an intermediate frequency dispersion layer part, wherein the intermediate frequency dispersion layer part can include N horizontal layers. Each layer corresponds to the dispersion velocity of the corresponding P wave (Primary / Pressure, a kind of longitudinal wave) and S wave (Secondary / Shear, a kind of transverse wave). Further, since the dispersion velocity v p (ω) using the propagation matrix method to substitute can simulate the characteristics of seismic response, so that the results obtained from the rock physics experiment can be directly related to the characteristics of seismic response. That is, the frequency domain reflection coefficient is determined by using the propagation matrix corresponding to the top semi-elastic space part, the bottom semi-elastic space part and the intermediate frequency dispersion layer part, respectively; based on the seismic convolution model, the forward record corresponding to the dispersion velocity model is determined by using the frequency domain reflection coefficient and seismic wavelet convolution.

[0060] For P wave incidence, Carcione represents the reflection coefficient as R = [R PP , R PS , T PP , T PS ] T , R PP , R PS , T PP , T PS , respectively, represent the reflection coefficient and the transmission coefficient of P wave and S wave, and its calculation relationship is as follows:

[0061]

[0062] , R is the frequency domain reflection coefficient; A1 is the propagation matrix corresponding to the top semi-elastic space part; A2 is the propagation matrix corresponding to the bottom semi-elastic space part; B α =T(0)T -1(h α ), is a propagation matrix corresponding to the intermediate dispersive layer part, a is the index of each horizontal layer contained in the intermediate dispersive layer part, a = 1,..., N, h α is the thickness of the a-th horizontal layer, T(0) represents the T matrix when the thickness is 0; i p represents the incident vector of the longitudinal wave.

[0063] It should be noted that the propagation matrices A1, A2 and B α are determined by and .

[0064] According to the obtained frequency domain reflection coefficient, it can be seen that the frequency domain reflection coefficient is related to the elastic properties of the medium, the thin layer structure and thickness, the incident wave frequency, the incident angle, etc. Combined with the convolution model of the earthquake, it can be known that the seismic record is equal to the reflection coefficient and the convolution of the seismic wavelet, and the synthetic seismic forward record is determined:

[0065]

[0066] , wherein S represents the convolution of the frequency domain reflection coefficient and the seismic wavelet; w(t) represents the time domain Ricker wavelet; r(t) represents the time domain reflection coefficient; t is time; W(ω) represents the frequency domain Ricker wavelet; R(θ;ω) represents the frequency domain reflection coefficient at different incident angles obtained by the propagation matrix, and θ is the incident angle.

[0067] Further, based on the approximation principle, a linearized forward model is established by using the forward record to obtain the actual observed seismic data. Correspondingly, under the assumption of linear forward model, the inversion iteration formula is derived, and the parameter at the maximum posterior probability is determined as the predicted value according to the Bayesian principle. Specifically, the linearized forward model can be obtained by using d = G(m) + e. Wherein d is the forward record, d = (d1, d2, d3...dn), d1, d2, d3...dn represents the actual observed seismic data; e is a Gaussian distributed noise; G represents the forward process; m is the initial dispersion velocity.

[0068] As Figure 4 shown is a dispersion velocity model constructed by using the dispersion velocity measured by the experiment, and the propagation matrix method in Figure 3 is used to substitute the simulated seismic response characteristics, wherein the upper and lower are semi-infinite homogeneous isotropic media, and the middle two layers are dispersive layers. Both of the two layers of dispersive layers are longitudinal wave dispersion, and the dispersion velocity values are measured by rock physics experiment. The longitudinal wave velocity in the seismic frequency band is 1 to 100 Hz. Based on the dispersion velocity model, the reflection coefficient is obtained by the propagation matrix, and the theoretical forward record is obtained by multiplying the reflection coefficient and the Ricker wavelet with a main frequency of 30 Hz, as shown in Figure 5is shown, where the longitudinal direction is time and the transverse direction is the angle of incidence.

[0069] Step S13: Deriving an inversion iteration formula based on the forward record, and determining a parameter value corresponding to a maximum posterior probability of the inversion iteration formula as a target dispersion velocity in the inversion obtained dispersion velocity model according to the Bayesian principle.

[0070] In the embodiments of the present application, the Bayesian principle is introduced on the basis of obtaining the forward record, the inversion iteration formula is derived under the assumption of a linear forward model, and the inversion of the seismic dispersion velocity is performed according to the Bayesian framework, the dispersion velocity is taken as a parameter to be inverted, and the stratigraphic dispersion velocity is estimated through the probabilistic inversion strategy. Specifically, through the Bayesian principle, the inversion value of the parameter to be solved is the velocity value corresponding to the maximum value of the maximum likelihood function under the assumption that the prior parameter distribution and the seismic noise are both Gaussian distributions.

[0071] In the embodiments of the present application, the Bayesian principle is introduced on the basis of obtaining the forward record, the inversion iteration formula is derived under the assumption of a linear forward model, and the inversion of the seismic dispersion velocity is performed according to the Bayesian framework, the dispersion velocity is taken as a parameter to be inverted, and the stratigraphic dispersion velocity is estimated through the probabilistic inversion strategy. Specifically, through the Bayesian principle, the inversion value of the parameter to be solved is the velocity value corresponding to the maximum value of the maximum likelihood function under the assumption that the prior parameter distribution and the seismic noise are both Gaussian distributions. Deriving an inversion iteration formula, and determining a parameter value corresponding to a maximum posterior probability of the inversion iteration formula as a target dispersion velocity in the inversion obtained dispersion velocity model according to the Bayesian principle.

[0072] wherein, is a likelihood function;

[0073] is a prior probability from measurement data and other information, which can narrow the range of the parameter to be inverted, i.e., v p (ω), and can also narrow the solution space range of v p (ω), so as to obtain a more stable and reliable inversion result; d is the forward record, d=(d1, d2, d3...dn), d1, d2, d3...dn represent actual observed seismic data; P(d(θ)) is a constant; det represents the value of the corresponding determinant; T is a transpose; Δm is a model parameter perturbation; C Δm is the variance of the initial dispersion velocity; σ is the variance of the seismic residual; N represents the sample number of the reservoir feature parameter to be inverted; G represents a forward process; and m is the initial dispersion velocity. In the above formula, it is assumed that the velocity of each frequency component is independent, the maximum posterior probability of the likelihood function is solved here, and the velocity value corresponding to the maximum posterior probability according to the Bayesian principle is the target dispersion velocity obtained by AVO inversion at the frequency.

[0074] As Figure 6The whole reservoir dispersion velocity inversion process diagram is shown, based on the propagation matrix forward, directly from the seismic data inversion seismic dispersion velocity, for the quantification of dispersion of stratum dispersion degree to provide support. The seismic wave dispersion velocity obtained by the above method verifies the dispersion characteristics of the seismic wave when the medium contains fluid, based on the propagation matrix forward, considering the harmonic effect between the strata, and the transmission loss and multiple waves in the process of seismic wave propagation, such a forward model can better match the actual seismic data, and more accurate inversion velocity is obtained. The inversion result accurately describes the seismic wave dispersion velocity, which has very important reference value for predicting underground fluid, can be combined with the measured rock physical dispersion data to interpret the reservoir characteristics, and provides a solid theoretical support for accurately quantifying the velocity dispersion characteristics of fluid-containing strata.

[0075] In the embodiments of the present application, based on the Bayesian principle, the accuracy of the whole inversion result is improved by the statistical probability inversion method, and further, the uncertainty of the inversion result can be evaluated. The inversion result is compared with the true value to verify the accuracy of the inversion result. Specifically, the dispersion velocity true value in the preset frequency range is obtained; the inversion result in the preset frequency range is determined by using the target dispersion velocity; and the dispersion velocity true value and the inversion result are compared to verify the accuracy of the target dispersion velocity.

[0076] As shown in Figure 7 , Figure 8 The inversion velocities of two layers obtained by inversion based on forward records in the embodiments of the present application are respectively shown in Figure 7 The inversion result of the first dispersion layer is Figure 8 The inversion result of the second dispersion layer is. The longitudinal direction is the dispersion velocity value, and the transverse direction is the frequency corresponding to the dispersion velocity, which is inverted point by point from 1 Hz to 100 Hz, and finally the dispersion velocity inversion result is obtained. It can be found that the inversion result is very close to the true value, and therefore the accuracy of the inversion result is further verified. In addition, the stratum dispersion inversion method established in the embodiments of the present application starts from the target of serving the actual production of oilfields, considers the structure characteristics and medium characteristics of underground strata, and the target dispersion velocity obtained by inversion plays an important role in quantifying the stratum dispersion characteristics, and therefore the inversion result can also be used as the data input of other related attribute parameters.

[0077] The application provides a reservoir dispersion velocity inversion method, comprising the following steps: obtaining the dispersion velocity of the actual work area core through rock physical experiment; constructing a dispersion velocity model by using the dispersion velocity, and determining the forward record corresponding to the dispersion velocity model based on the propagation matrix forward method; deriving an inversion iteration formula based on the forward record, and determining the parameter value corresponding to the maximum posterior probability of the inversion iteration formula as the target dispersion velocity in the inversion dispersion velocity model according to the Bayesian principle. It can be seen that the propagation matrix forward method is used for the dispersion velocity model, the dispersion velocity of the actual work area core obtained through rock physical experiment is forward calculated, and the corresponding forward synthetic seismic record is obtained. Since the propagation matrix forward is derived based on the wave equation under the assumption of layered medium, compared with the traditional forward formula and its approximate formula based on the single interface half-infinite space assumption, the propagation matrix considers the transmission loss and interlayer multiple wave effect in the seismic wave propagation process, and most importantly, it considers the harmonic effect between thin interbeds, so that it can better match the underground seismic wave propagation process and the acquired seismic data, and higher inversion accuracy is achieved. Further, the Bayesian principle is introduced based on the obtained forward record, and the parameter at the maximum posterior probability of the inversion iteration formula is determined as the prediction value according to the Bayesian principle, and the prediction value corresponds to the target dispersion velocity in the inversion dispersion velocity model. In this way, the inversion result accurately describes the seismic wave dispersion velocity, provides support for quantifying the dispersion degree of the dispersion stratum, and improves the accuracy of the entire inversion result.

[0078] Correspondingly, the application also discloses a reservoir dispersion velocity inversion device, as shown in Figure 9 The device comprises:

[0079] The velocity acquisition module 11 is used for obtaining the dispersion velocity of the actual work area core through rock physical experiment.

[0080] The forward module 12 is used for constructing a dispersion velocity model by using the dispersion velocity, and determining the forward record corresponding to the dispersion velocity model based on the propagation matrix forward method.

[0081] The inversion module 13 is used for deriving an inversion iteration formula based on the forward record, and determining the parameter value corresponding to the maximum posterior probability of the inversion iteration formula as the target dispersion velocity in the inversion dispersion velocity model according to the Bayesian principle.

[0082] The working processes of the above modules are more specifically described in the foregoing embodiments, and will not be repeated here.

[0083] It can be seen that by the above scheme of the embodiment, the dispersion velocity of the actual work area core is obtained through the rock physical experiment, the dispersion velocity model is constructed by using the dispersion velocity, the forward record corresponding to the dispersion velocity model is determined based on the propagation matrix forward method, the inversion iteration formula is derived based on the forward record, and the parameter value corresponding to the maximum posterior probability of the inversion iteration formula is determined as the target dispersion velocity in the inversion dispersion velocity model according to the Bayes principle. It can be seen that the dispersion velocity model constructed by the application is used to perform forward calculation on the dispersion velocity of the actual work area core obtained through the rock physical experiment by using the propagation matrix forward method, and the corresponding forward synthetic seismic record is obtained. Since the propagation matrix forward is derived based on the wave equation under the assumption of layered medium, compared with the traditional forward formula and its approximation formula based on the single interface half-infinite space assumption, the propagation matrix considers the transmission loss and interlayer multiple wave effect in the seismic wave propagation process, and most importantly, it considers the harmonic effect between thin interbeds, which can better match the underground seismic wave propagation process and the acquired seismic data, and higher inversion accuracy is achieved. Further, the Bayes principle is introduced based on the obtained forward record, and the parameter at the maximum posterior probability of the inversion iteration formula is determined as the prediction value according to the Bayes principle, and the prediction value corresponds to the target dispersion velocity in the inversion dispersion velocity model. In this way, the inversion result accurately describes the seismic wave dispersion velocity, provides support for quantifying the dispersion degree of the dispersion stratum, and improves the accuracy of the entire inversion result.

[0084] Further, the electronic device 20 provided by the embodiment of the application is shown in the structure diagram of the electronic device 20, and the content in the diagram cannot be considered as any limitation on the use range of the application. Figure 10

[0085] Figure 10 The structure diagram of the electronic device 20 provided by the embodiment of the application is shown. The electronic device 20 can specifically include at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25 and a communication bus 26. The memory 22 is used to store a computer program, the computer program is loaded and executed by the processor 21 to realize the related steps in the reservoir dispersion velocity inversion method disclosed in any of the preceding embodiments. In addition, the electronic device 20 in the embodiment can be a computer.

[0086] ​In this embodiment, the power supply 23 is configured to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 is configured to create a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which will not be limited here; the input and output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which will not be limited here.

[0087] In addition, the memory 22 as a carrier of resource storage can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222 and data 223, etc., and the data 223 can include various data. The storage mode can be temporary storage or permanent storage.

[0088] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the reservoir dispersion velocity inversion method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0089] Further, the present application also discloses a computer readable storage medium, which includes a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a magnetic disk or an optical disk, or any other form of storage medium known in the technical field. The computer program is executed by the processor to implement the foregoing reservoir dispersion velocity inversion method. For the specific steps of the method, refer to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

[0090] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. For the same or similar parts between each embodiment, refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part refers to the method part.

[0091] The steps of the reservoir dispersion velocity inversion method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0092] Finally, it should be noted that, in the present document, relational terms such as first and second, and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0093] The reservoir dispersion velocity inversion method, device, equipment and storage medium provided by the present application are described in detail above, and the principles and implementation manners of the present application are described by applying specific examples in the present document. The above description of the embodiments is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A reservoir dispersion velocity inversion method, characterized in that: include: Obtain the dispersion velocity of the core in the actual work area through rock physics experiments; constructing a dispersion velocity model using the dispersion velocity, and determining a forward modeling record corresponding to the dispersion velocity model based on a propagation matrix forward modeling method; An inversion iterative formula is derived based on the forward modeling record, and according to the Bayesian principle, a parameter value corresponding to the maximum posterior probability of the inversion iterative formula is determined as the target dispersion velocity in the dispersion velocity model obtained by inversion.

2. The reservoir dispersion velocity inversion method according to claim 1, characterized in that: The determining of the forward record corresponding to the dispersion velocity model based on the propagation matrix forward modeling method includes: The stratum is divided into a top semi-elastic space part, a bottom semi-elastic space part and an intermediate dispersion layer part; Determine frequency domain reflection coefficients using propagation matrices corresponding to the top semi-elastic space portion, the bottom semi-elastic space portion, and the intermediate dispersion layer portion, respectively; Based on the seismic convolution model, the forward record corresponding to the dispersion velocity model is determined by convolving the frequency domain reflection coefficient with the seismic wavelet.

3. The reservoir dispersion velocity inversion method according to claim 2, characterized in that: After determining the forward record corresponding to the dispersion velocity model by convolving the frequency domain reflection coefficient with the seismic wavelet based on the seismic convolution model, the method further includes: Based on the approximation principle, a linearized forward model is established using the forward modeling records to obtain actual observed seismic data; Accordingly, the inversion iterative formula is derived based on the forward modeling record, and according to the Bayesian principle, a parameter value corresponding to the maximum posterior probability of the inversion iterative formula is determined as the target dispersion velocity in the dispersion velocity model obtained by inversion, including: An inversion iteration formula is derived using the actual observed seismic data, and according to the Bayesian principle, a parameter value corresponding to the maximum posterior probability of the inversion iteration formula is determined as the target dispersion velocity in the dispersion velocity model obtained by inversion.

4. The reservoir dispersion velocity inversion method according to claim 2, characterized in that: The determining of the frequency domain reflection coefficient by using the propagation matrices corresponding to the top semi-elastic space portion, the bottom semi-elastic space portion, and the intermediate dispersion layer portion, respectively, includes: use , determine the frequency domain reflection coefficient; in, is the frequency domain reflection coefficient; is the propagation matrix corresponding to the top semi-elastic space portion; is the propagation matrix corresponding to the underlying semi-elastic space portion; , is the propagation matrix corresponding to the intermediate dispersion layer, are the serial numbers of the horizontal layers contained in the intermediate dispersion layer, , For the The thickness of the horizontal layer, Indicates that the thickness is 0 matrix; represents the longitudinal wave incident vector.

5. The reservoir dispersion velocity inversion method according to claim 2, characterized in that: The method of determining a forward record corresponding to the dispersion velocity model by convolving the frequency domain reflection coefficient with the seismic wavelet based on the seismic convolution model includes: Based on the seismic convolution model, Determine a forward record corresponding to the dispersion velocity model; wherein, represents the convolution of the frequency domain reflection coefficient and the seismic wavelet; represents the Ricker wavelet in the time domain; represents the time domain reflection coefficient; For time; Represents the Ricker wavelet in the frequency domain; represents the frequency domain reflection coefficient at different incident angles obtained by the propagation matrix, is the angle of incidence.

6. The reservoir dispersion velocity inversion method according to claim 1, characterized in that: The method of deriving an inversion iterative formula based on the forward modeling record and determining, according to the Bayesian principle, a parameter value corresponding to the maximum a posteriori probability of the inversion iterative formula as a target dispersion velocity in the dispersion velocity model obtained by inversion includes: use An inversion iterative formula is derived, and according to the Bayesian principle, a parameter value corresponding to the maximum posterior probability of the inversion iterative formula is determined as the target dispersion velocity in the dispersion velocity model obtained by inversion; in, is the likelihood function; is the prior probability; For the forward record, , represents the actual observed earthquake data; is a constant; Represents the value of solving the corresponding determinant; is transposed; is the model parameter disturbance; is the variance of the initial dispersion velocity; is the variance of seismic residuals; N represents the number of samples of reservoir characteristic parameters to be inverted; represents the forward process; is the initial dispersion velocity.

7. The reservoir dispersion velocity inversion method according to any one of claims 1 to 6, characterized in that: After deriving an inversion iterative formula based on the forward modeling record and determining, according to the Bayesian principle, a parameter value corresponding to the maximum a posteriori probability of the inversion iterative formula as the target dispersion velocity in the dispersion velocity model obtained by inversion, the method further includes: Get the true value of the dispersion velocity within the preset frequency range; Determining an inversion result within the preset frequency range using the target dispersion velocity; The true value of the dispersion velocity is compared with the inversion result to verify the accuracy of the target dispersion velocity.

8. A reservoir dispersion velocity inversion device, characterized in that: include: Velocity acquisition module, used to obtain the dispersion velocity of the core in the actual work area through rock physics experiments; a forward modeling module, configured to construct a dispersion velocity model using the dispersion velocity, and determine a forward modeling record corresponding to the dispersion velocity model based on a propagation matrix forward modeling method; An inversion module is used to derive an inversion iterative formula based on the forward modeling record, and determine, according to the Bayesian principle, a parameter value corresponding to the maximum a posteriori probability of the inversion iterative formula as a target dispersion velocity in the dispersion velocity model obtained by inversion.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the reservoir dispersion velocity inversion method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store a computer program; wherein when the computer program is executed by a processor, the reservoir dispersion velocity inversion method according to any one of claims 1 to 7 is implemented.

Images