A method, device, equipment and storage medium for phase-controlled elastic parameter modeling

Abstract

This article provides a method, device, equipment and storage medium for phase-controlled elastic parameter modeling, wherein the method includes: obtaining logging parameters in a work area, and determining the seismic lithofacies on a seismic well-connected section in the work area; determining a fitting formula for the elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters; calculating the elastic parameters of the seismic lithofacies on the seismic well-connected section by the fitting formula to obtain an elastic parameter model. The above method can improve the accuracy of the elastic parameter model, and improve the accuracy of subsequent seismic inversion by improving the accuracy of modeling.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration, and in particular to a method, device, equipment and storage medium for phase-controlled elastic parameter modeling. Background Art

[0002] With the continuous deepening of oil and gas exploration and development, reservoir prediction for complex exploration targets such as "small, scattered, and thin" is becoming increasingly difficult, and seismic inversion requires higher and higher accuracy of velocity models. However, traditional modeling methods are difficult to meet the modeling accuracy requirements of complex geological bodies. At present, my country's onshore exploration and development targets have shifted from structural oil and gas reservoirs to lithological oil and gas reservoirs. Reservoir prediction for geological targets such as thin reservoirs, vertical overlaps, and rapid lateral changes is becoming increasingly difficult. To obtain precise seismic inversion or depth migration results, it depends on the initial model constructed by modeling.

[0003] At the same time, lithofacies plays an important role in rock deposition, reservoir distribution, and rock elastic properties, while conventional seismic lithofacies identification methods mainly rely on wave impedance. The wave impedance seismic lithofacies identification method uses acoustic logging data to establish accurate well point thin layer or thin interlayer information, and interpolation and extrapolation are required outside the well point or between wells. When the lateral changes of lithology are stable, the results obtained are relatively reliable, but if there are lateral changes in lithology, lithofacies or structure, the constraints are difficult to construct accurately, so the method itself has difficult-to-overcome interpretation multiple solutions. Moreover, under the constraints of seismic horizons, the traditional method of simple linear interpolation and extrapolation of logging data is too random and difficult to control the inter-well area. The uncertainty has a great impact, which reduces the resolution and accuracy of seismic inversion. In order to improve the accuracy of seismic inversion, a method of phase-controlled elastic parameter modeling with high accuracy is urgently needed. Summary of the invention

[0004] The purpose of the embodiments of this document is to provide a method, device, equipment and storage medium for phase-controlled elastic parameter modeling, so as to improve the accuracy of subsequent seismic inversion by improving the accuracy of modeling.

[0005] To achieve the above objectives, on the one hand, an embodiment of the present invention provides a method for phase-controlled elastic parameter modeling, the method comprising:

[0006] Acquire well logging parameters in the work area, and determine the seismic lithofacies on the seismic well-connected section in the work area;

[0007] Determine a fitting formula in the time domain for elastic parameters of different lithofacies in the work area according to the logging parameters;

[0008] The elastic parameters of the seismic lithofacies on the seismic well-connected section are calculated by the fitting formula to obtain an elastic parameter model.

[0009] Preferably, the fitting formula for determining the elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters includes:

[0010] Converting the logging parameters from the depth domain to the time domain through well seismic calibration;

[0011] Draw a scatter plot of the elastic parameters of each lithofacies in the time domain based on the logging parameters in the time domain;

[0012] Polynomial fitting is performed on the scattered points in the scatter plot to generate a fitting curve of the elastic parameters of each lithofacies and obtain a fitting formula of the elastic parameters of each lithofacies in the time domain.

[0013] Preferably, the determining of the seismic lithofacies on the seismic well-connected section in the work area comprises:

[0014] Obtain seismic data within the work area;

[0015] Determining a logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters;

[0016] Converting the well logging lithofacies sequence from the depth domain to the time domain through well seismic calibration;

[0017] Coarsening the time-domain logging lithofacies sequence to the seismic scale to obtain the coarsened time-domain logging lithofacies sequence;

[0018] A discriminant relationship is established between the coarsened time-domain well logging lithofacies sequence and the seismic data, and the seismic lithofacies on the seismic well-connected section is determined according to the discriminant relationship.

[0019] Preferably, determining the logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters comprises:

[0020] Selecting well logging sensitive parameters from the well logging parameters and establishing a well logging sensitive parameter discrimination standard, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; determining the well logging lithofacies sequence according to the well logging sensitive parameter discrimination standard;

[0021] Or, well logging sensitive parameters are selected from the well logging parameters, and a well logging sensitive parameter discrimination standard is established, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; data of multiple depth ranges close to the well logging sensitive parameter discrimination standard are selected to establish a priori information clustering data set; the posterior probability is calculated by a Bayesian algorithm, and the maximum posterior probability solution is determined according to the posterior probability distribution, the maximum posterior probability solution is the well logging lithofacies sequence, and the posterior probability is the conditional probability of the occurrence of the lithofacies.

[0022] Preferably, establishing a discriminant relationship between the coarsened time-domain well logging lithofacies sequence and the seismic data, and determining the seismic lithofacies on the seismic well-connected section according to the discriminant relationship comprises:

[0023] The coarsened time-domain well logging lithofacies sequence and the seismic data form a priori matrix to serve as priori information for each clustering data set;

[0024] The posterior probability is obtained by using a Bayesian algorithm, and the maximum posterior probability solution on the seismic well-connected section is obtained according to the posterior probability distribution. The maximum posterior probability solution is the seismic lithofacies on the seismic well-connected section.

[0025] Preferably, the obtaining of seismic data in the work area includes:

[0026] Obtain pre-stack inversion elastic parameters of the seismic trace near the well as seismic data;

[0027] Alternatively, the seismic attributes of the seismic traces near the well are obtained, logging sensitive parameters are selected from the logging parameters, and the correlation coefficient between the logging sensitive parameters of each well and the seismic attributes of the seismic traces near the well is calculated using the correlation coefficient formula to obtain several correlation coefficient values, and the seismic attributes with large correlation coefficient values ​​are selected as seismic data.

[0028] Preferably, the method further comprises:

[0029] By analyzing and comparing the changing rules of the entropy value information of the posterior probability solution in the phase-controlled elastic parameter modeling process, the uncertainty transfer rule in the phase-controlled elastic parameter modeling process is quantitatively analyzed.

[0030] On the other hand, an embodiment of the present invention provides a device for phase-controlled elastic parameter modeling, the device comprising:

[0031] A lithofacies determination module, which obtains well logging parameters in a work area and determines the seismic lithofacies on a seismic well-connected section in the work area;

[0032] A formula determination module, which determines the fitting formula of the elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters;

[0033] The model building module calculates the elastic parameters of the seismic lithofacies on the seismic well-connected section through the fitting formula to obtain an elastic parameter model.

[0034] On the other hand, the embodiments of the present invention further provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, the instructions according to any one of the methods described above are executed.

[0035] On the other hand, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor of a computer device, the computer program executes instructions according to any one of the methods described above.

[0036] It can be seen from the technical solutions provided in the above embodiments of this invention that the embodiments of this invention obtain the fitting formula of the elastic parameters of each lithofacies in the time domain according to the logging parameters, which can reflect the changing law of the elastic parameters of different lithofacies in the time domain. After calculating the elastic parameters of the seismic lithofacies on the seismic well section through the fitting formula, the elastic parameter model is obtained, and the high-precision seismic inversion can be performed through the elastic parameter model. For the traditional method of inversion using wave impedance, since the wave impedance inversion is obtained by interpolation and extrapolation using the acoustic logging data and the stratigraphic data at the well point, for the work area with complex lithology, the wave impedance of various lithologies is seriously superimposed, resulting in the final inversion result having multiple solutions. The elastic parameter model under the constraint of seismic lithofacies in the embodiments of this invention is to interpret the preferred seismic attributes of the lithofacies by statistically analyzing the changing law of the elastic parameters of each lithofacies in the depth domain and the time domain, and to perform elastic parameter modeling on the seismic lithofacies identification results, which can more clearly depict the lithology and structure. When the accuracy of the elastic parameter model is high, the accuracy of seismic inversion using the elastic parameter model is higher, and the lithofacies and fluids can be more accurately identified. Therefore, the embodiments of this article can improve the accuracy of subsequent seismic inversion by improving the accuracy of modeling.

[0037] In order to make the above and other purposes, features and advantages of this article more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of this article or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of this article. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic flow chart of a method for phase-controlled elastic parameter modeling provided in an embodiment of this invention is shown;

[0040] Figure 2 A schematic diagram of a process for determining seismic lithofacies on a seismic well-connected section provided in an embodiment of the present invention is shown;

[0041] Figure 3 A schematic diagram of a process for calculating and obtaining seismic lithofacies on a seismic well-connected section after scale coarsening provided in an embodiment of this invention is shown;

[0042] Figure 4 A schematic diagram of a flow chart of obtaining a fitting formula of elastic parameters of each lithofacies in the time domain according to well logging parameters provided in an embodiment of the present invention is shown;

[0043] Figure 5 A schematic diagram of the well logging lithofacies sequence of Fudong 12 well provided in the embodiments of this article is shown;

[0044] Figure 6 A schematic diagram of the well logging lithofacies sequence of the Xi 1 well provided in the embodiments of this invention is shown;

[0045] Figure 7 A schematic diagram for reflecting the well seismic calibration results of Fudong 12 Well provided in the embodiments of this article is shown;

[0046] Figure 8 A schematic diagram for reflecting the well seismic calibration results of Xi 1 well provided in the embodiments of this article is shown;

[0047] Fig. 9 A schematic diagram for reflecting the well-seismic scale matching result of Fudong 12 well provided in the embodiment of this article is shown;

[0048] Fig.10 A schematic diagram showing the well seismic scale matching result of Xi 1 well provided in the embodiment of this article is shown;

[0049] Fig.11 A schematic diagram of an amplitude envelope provided by an embodiment of this invention is shown;

[0050] Fig.12 A schematic diagram of the amplitude weighted cosine phase provided in the embodiments of this document is shown;

[0051] Fig.13 A schematic diagram of the instantaneous phase provided by the embodiments of this article is shown;

[0052] Fig.14 A schematic diagram of amplitude weighted frequency provided in the embodiments of this invention is shown;

[0053] Fig.15 A schematic diagram of the instantaneous phase of the cosine provided in the embodiments of this invention is shown;

[0054] Fig.16 A schematic diagram showing the instantaneous frequency provided by the embodiments of this article is shown;

[0055] Fig.17 A schematic diagram for reflecting the posterior probability result of mudstone provided by the embodiment of this article is shown;

[0056] Fig.18 A schematic diagram for reflecting the posterior probability result of sandstone provided in the embodiment of this article is shown;

[0057] Fig.19 A schematic diagram for embodying a maximum a posteriori probability solution provided by an embodiment of this document is shown;

[0058] Fig. 20 A schematic diagram showing the well logging lithofacies sequence obtained by using Fudong 071 as a verification well provided in the embodiments of this article;

[0059] Fig.21 A schematic diagram for reflecting an entropy function profile provided in an embodiment of the present invention is shown;

[0060] Fig. 22 A schematic diagram showing the variation law of the longitudinal wave velocity of mudstone in the time domain provided by the embodiments of this article;

[0061] Fig.23 A schematic diagram showing the variation law of the shear wave velocity of mudstone in the time domain provided by the embodiments of this article;

[0062] Fig.24 A schematic diagram showing the variation law of mudstone density in the time domain provided by the embodiments of this article;

[0063] Fig.25 A schematic diagram showing the variation law of the longitudinal wave velocity of sandstone in the time domain provided by the embodiments of this article;

[0064] Fig.26 A schematic diagram showing the variation law of the shear wave velocity of sandstone in the time domain provided by the embodiments of this article;

[0065] Fig. 27 A schematic diagram showing the variation law of sandstone density in the time domain provided in the embodiments of this article;

[0066] Fig.28 A schematic diagram of seismic lithofacies based on seismic well-connected sections of Fudong 12 Well, Fudong 071 Well and Xi 1 Well provided in the embodiments of this article is shown;

[0067] Fig.29 A schematic diagram of a longitudinal wave velocity model provided in an embodiment of this invention is shown;

[0068] Fig.30 A schematic diagram of a shear wave velocity model provided in an embodiment of this invention is shown;

[0069] Fig.31 A schematic diagram of a density model provided in the embodiments of this invention is shown;

[0070] Fig.32 A schematic diagram of the module structure of a device for phase-controlled elastic parameter modeling provided in an embodiment of this invention is shown;

[0071] Fig.33 A schematic diagram of the structure of a computer device provided in an embodiment of this invention is shown.

[0072] Description of the accompanying symbols:

[0073] 100. Lithofacies determination module;

[0074] 200. formula determination module;

[0075] 300, model building module;

[0076] 3302. Computer equipment;

[0077] 3304, processor;

[0078] 3306, memory;

[0079] 3308, driving mechanism;

[0080] 3310, input / output module;

[0081] 3312. Input devices;

[0082] 3314, output device;

[0083] 3316, presentation equipment;

[0084] 3318. Graphical user interface;

[0085] 3320, network interface;

[0086] 3322, communication link;

[0087] 3324. Communication bus. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings in the embodiments of this article to clearly and completely describe the technical solutions in the embodiments of this article. Obviously, the described embodiments are only part of the embodiments of this article, not all of the embodiments. Based on the embodiments of this article, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this article.

[0089] At present, the onshore exploration and development targets in my country have shifted from structural oil and gas reservoirs to lithologic oil and gas reservoirs. The reservoir prediction of geological targets such as thin reservoirs, vertical overlaps, and rapid lateral changes is becoming increasingly difficult. To obtain precise seismic inversion or depth migration results, it depends on the initial model constructed by modeling. The reservoir prediction of complex exploration targets such as "small, scattered, and thin" is becoming increasingly difficult. Seismic inversion has higher and higher requirements for the accuracy of velocity models, and traditional modeling methods are difficult to meet the modeling accuracy requirements of complex geological bodies.

[0090] In order to solve the above problems, the embodiments of this article provide a method for phase-controlled elastic parameter modeling, which can improve the accuracy of modeling and further improve the accuracy of subsequent seismic inversion. Figure 1 It is a schematic diagram of the steps of a method for phase-controlled elastic parameter modeling provided in the embodiment of this article. This specification provides the method operation steps described in the embodiment or flowchart, but more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the order of many steps and does not represent the only execution order. When the system or device product is executed in practice, it can be executed in the order of the method shown in the embodiment or the drawings or in parallel.

[0091] Reference Figure 1 , the method may include the following steps:

[0092] S101: Acquire well logging parameters in a work area, and determine the seismic lithofacies on the seismic well-connected section in the work area.

[0093] S102: Determine a fitting formula of elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters.

[0094] S103: Calculating elastic parameters of the seismic lithofacies on the seismic well-connected section using the fitting formula to obtain an elastic parameter model.

[0095] The seismic lithofacies on the seismic well section can reflect the changing characteristics of the logging parameters to a certain extent, and effectively express the changes in geological conditions. As a constraint condition, the seismic lithofacies can reduce the uncertainty of modeling, improve the accuracy of the elastic parameter model, and more clearly describe the lithology and structure. When the accuracy of the elastic parameter model is high, the accuracy of seismic inversion using the elastic parameter model is higher, and the lithofacies and fluids can be identified more accurately. Among them, the elastic parameters include parameters such as P-wave velocity, S-wave velocity, and density.

[0096] Reference Figure 2 In the embodiment of this article, the determination of the seismic lithofacies on the seismic well-connected section in the work area includes the following steps:

[0097] S201: Acquire seismic data within the work area.

[0098] S202: Determine a logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters.

[0099] S203: Convert the well logging lithofacies sequence from the depth domain to the time domain through well seismic calibration.

[0100] S204: Coarsening the time-domain logging lithofacies sequence to the seismic scale to obtain a coarsened time-domain logging lithofacies sequence.

[0101] S205: Establishing a discriminant relationship between the coarsened time-domain well logging lithofacies sequence and the seismic data, and determining the seismic lithofacies on the seismic well-connected section according to the discriminant relationship.

[0102] In some embodiments, acquiring seismic data in the work area includes: acquiring pre-stack inversion elastic parameters of seismic traces near the well as seismic data.

[0103] Or, obtain preferred seismic attributes as seismic data. When obtaining preferred seismic attributes, first, well logging sensitive parameters can be selected from the well logging parameters. When selecting well logging sensitive parameters, well logging parameters that can identify different lithofacies can be selected as well logging sensitive parameters by performing intersection analysis on a variety of well logging parameters. The method for selecting well logging sensitive parameters is not specifically limited in this article. Then, the seismic attributes of the well-side seismic traces are extracted from the seismic results profile, and the correlation coefficient between the well logging sensitive parameters of each well and the seismic attributes of the well-side seismic traces is calculated using the correlation coefficient formula (1) to obtain several correlation coefficient values. The seismic attribute with the largest correlation coefficient value is selected as the preferred seismic attribute. The method for obtaining the preferred seismic attribute is not specifically limited in this article.

[0104]

[0105] Among them, r(Ew, Es) represents the correlation coefficient between the logging sensitive parameters and the seismic attributes of the seismic traces near the well; Cov(,) represents the covariance between the logging sensitive parameters and the seismic attributes of the seismic traces near the well; Var(.) is the variance of the parameter or seismic attribute.

[0106] In some embodiments, determining the logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters includes:

[0107] Selecting well logging sensitive parameters from the well logging parameters and establishing a well logging sensitive parameter discrimination standard, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; determining the well logging lithofacies sequence according to the well logging sensitive parameter discrimination standard;

[0108] Or, well logging sensitive parameters are selected from the well logging parameters, and a well logging sensitive parameter discrimination standard is established, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; data of multiple depth ranges close to the well logging sensitive parameter discrimination standard are selected to establish a priori information clustering data set; the posterior probability is calculated by a Bayesian algorithm, and the maximum posterior probability solution is determined according to the posterior probability distribution, the maximum posterior probability solution is the well logging lithofacies sequence, and the posterior probability is the conditional probability of the occurrence of the lithofacies.

[0109] When the geological distribution within the work area is relatively uniform and the lithofacies change relatively stably within the depth range, different lithofacies can be identified by selecting logging sensitive parameters and establishing a discrimination criterion for logging sensitive parameters. When selecting logging sensitive parameters, the selection method of the above-mentioned logging sensitive parameters can be used. After selecting the logging sensitive parameters, based on the fact that different lithofacies have different value magnitudes or deviation degrees with respect to the logging sensitive parameters as the discrimination criterion for logging sensitive parameters, different lithofacies can be identified accordingly, and then the logging lithofacies sequence in the depth domain can be obtained.

[0110] When the geological distribution within the work area is uneven and the lithofacies change rapidly within the depth range, after selecting the logging sensitive parameters and establishing a discrimination criterion for logging sensitive parameters by the above method, the distance between the value of the logging sensitive parameter at the current depth and the discrimination criterion for logging sensitive parameters is used as the basis for data selection, and the data of multiple depth ranges close to the logging sensitive parameters are selected as the prior information clustering data set, and the maximum a posteriori probability solution is obtained through the Bayesian algorithm.

[0111] Let E w be the logging sensitive parameter, and its prior probability distribution P(E w ) satisfies the Gaussian mixture model. f j (j = 1, …, N) represents N different lithofacies categories, and the point to be estimated x can be represented by a single logging sensitive parameter or multiple logging sensitive parameters. π d (d = 1, …, D) represents the lithofacies at each depth d. The Gaussian mixture model is a linear weighted superposition of traditional Gaussian models, and its multi-peak characteristics are more in line with the statistical characteristics of the actual work area than those of traditional Gaussian models:

[0112] P(E w ) = ∑P(π d = f j )P(E w 丨π d = f j ) (2)

[0113] where π d = f j represents the lithofacies at a certain depth, and P(π d = f j ) is the prior information of the proportion of each lithofacies. The weighted sum of P(π d = f j ) is 1, and P(E w 丨π d = f j ) represents the probability distribution of the logging sensitive parameter within each lithofacies and satisfies the Gaussian model:

[0114] P(E w 丨π d = fj ) = N(E w ; μ Ew | π d = f j , ∑E w | π d = f j ) (3)

[0115] Among them, N(.) represents the Gaussian function, and the expression can be recorded as:

[0116]

[0117] Among them, x represents the values of each point to be estimated participating in the calculation; μ represents the prior sample mean; ∑ represents the covariance matrix of the prior samples.

[0118] According to Bayes' theorem, the posterior probability can be expressed as:

[0119] P(π d = f j | E w ) ∝ P(π d = f j ) P(E w | π d = f j ) (5)

[0120] The posterior probability is the probability of the lithofacies f appearing under the condition of the given logging sensitive parameter E w Under the condition, by selecting the lithofacies with the highest occurrence probability according to different values of the logging sensitive parameter in the depth domain, the logging lithofacies sequence in the depth domain can be determined.

[0121] In some embodiments, after determining the logging lithofacies sequence in the depth domain, first, the corresponding relationship between the logging depth and time is obtained through well-seismic calibration, and the logging sensitive parameter is converted from the depth domain to the time domain, and then the logging lithofacies sequence in the depth domain is converted to the time domain. Then, the values corresponding to the logging sensitive parameter are extracted according to the sampling time interval of the seismic data, so as to obtain the coarsened logging lithofacies sequence in the time domain.

[0122] Referring to Figure 3 , in some embodiments, step S205 includes the following sub-steps:

[0123] S2051: Combine the coarsened logging lithofacies sequence in the time domain with the seismic data to form a prior matrix as the prior information of each clustering data set.

[0124] S2052: Obtain the posterior probability through the Bayesian algorithm, and obtain the maximum a posteriori probability solution on the seismic cross-well section according to the posterior probability distribution, and the maximum a posteriori probability solution is the seismic lithofacies on the seismic cross-well section.

[0125] In some embodiments, an equal-scale mapping relationship is established between the prestack inversion elastic parameters or preferred seismic attributes of the seismic traces beside the well and the coarsened time-domain logging lithofacies sequence, a prior information matrix is constructed, and it participates in the Bayesian discrimination calculation to obtain the posterior probability. The maximum a posteriori probability solution on the seismic cross-well section is obtained according to the posterior probability distribution.

[0126] Since the lithofacies categories as the research object are discrete variables, let f j (j = 1, …, N) represent N different lithofacies categories. The point x to be estimated is generally represented by multiple parameters, representing the prestack inversion elastic parameter longitudinal wave velocity V p , shear wave velocity Vs and density ρ, or various preferred seismic attributes. Let π t (t = 1, …, T) represent the lithofacies at each moment t. Using Bayes' formula, the posterior probability p(π j = f t | x) of the lithofacies category being f j is as follows:

[0127]

[0128] Expanding the numerator part gives:

[0129]

[0130] Among them, p(π t = f j , x) represents the joint probability of π t = f j and x; p(x | π t = f j ) represents the probability density function of the point x to be estimated corresponding to the lithofacies f j ; p(π t = f j ) represents the probability of the lithofacies type being f j , which is the prior probability density function, and its prior information is obtained from the logging lithofacies definition step; p(x) is the unconditional probability density function. For N lithofacies categories, it can be expressed as:

[0131]

[0132] Therefore, p(x) is just a scaling factor and plays no role in the lithofacies discrimination criterion for Bayes. During the lithofacies discrimination process, by observing the value of x, the prior probability p(π t = f j ) is converted into the posterior probability p(π t = f j | x). Among the N calculated posterior probabilities, if x belongs to fj If the posterior probability of (j = 1, …, N) is the largest, then the point x to be estimated belongs to f j Lithofacies. Let A' denote the maximum a posteriori probability solution of the lithofacies, that is:

[0133] A' = arg Max{p(π t = f j |x)} (9)

[0134] The maximum a posteriori probability solutions of each lithofacies on the seismic cross-well section can be obtained, and then the seismic lithofacies distribution on the seismic cross-well section can be obtained.

[0135] Referring to Figure 4 , in the embodiment of the present invention, step S102 includes the following sub-steps:

[0136] S1021: Convert the logging parameters from the depth domain to the time domain through well-seismic calibration.

[0137] S1022: Draw a scatter plot of the elastic parameters of each lithofacies in the time domain based on the logging parameters in the time domain.

[0138] S1023: Perform polynomial fitting on the scatter points in the scatter plot, generate a fitting curve of the elastic parameters of each lithofacies, and obtain a fitting formula of the elastic parameters of each lithofacies in the time domain.

[0139] In some embodiments, different elastic parameters (parameters such as P-wave velocity, S-wave velocity, density, etc.) can be obtained by performing simple multiplication and division calculations on the corresponding logging parameters. The logging parameters in the depth domain are converted to the time domain through well-seismic calibration, thereby converting the elastic parameters corresponding to the logging parameters to the time domain, and comprehensively plotting the elastic parameters calculated for each well in the work area into a single scatter plot.

[0140] In some embodiments, the scatter points in the scatter plot are fitted by the method of polynomial fitting to generate a fitting curve and obtain a fitting formula (11), that is, to statistically analyze the law of change of each lithofacies with depth. Among them, polynomial fitting is a method of using a polynomial expansion to fit all the observation points in a small analysis area to obtain an objective analysis field of the observed data. Polynomial fitting determines the expansion coefficients by the least squares method, that is, a method that uses a binomial equation and minimizes the sum of the squares of the errors.

[0141] Given data points p i (x i , y i ), where i = 1, 2, …, m. Find the approximate curve and make the deviation between the approximate curve and y = f(x) the smallest, that is:

[0142]

[0143] The general form of a polynomial function is:

[0144] y(x,W)=w0+w1x+w2x 2 +…+w m x m (11)

[0145] Among them, x m represents the highest degree of the polynomial, m represents the highest degree of the polynomial, x i represents x to the power of i, w i For x i The coefficient of .

[0146] If you

[0147]

[0148] Then there is

[0149] y(x,W)=XW (13)

[0150] It can be seen from the above formula that polynomial functions are also linear functions in an essential sense.

[0151] The obtained fitting formula is the variation law of the elastic parameters corresponding to each rock in the time domain. Therefore, after the type and time of the lithofacies are given, the corresponding elastic parameters can be obtained by substituting the fitting formula of the corresponding lithofacies. After knowing the elastic parameters corresponding to a given lithofacies at any time, the fitting formula is substituted into the moment when the seismic lithofacies on the seismic well section match the time, and the elastic parameter model based on lithofacies control can be obtained. When performing seismic inversion through this elastic parameter model, the accuracy of the inversion can be improved. The accuracy of lithofacies identification can be improved through high-precision seismic inversion. Lithofacies plays an important role in rock deposition, reservoir distribution, and rock elastic properties. Accurate lithofacies identification can better help rock deposition analysis, reservoir prediction, etc.

[0152] In the embodiment of this article, the method further includes: quantitatively analyzing the uncertainty transfer law in the phase-controlled elastic parameter modeling process by analyzing and comparing the change law of the entropy value information of the posterior probability solution in the phase-controlled elastic parameter modeling process.

[0153] In some embodiments, the entropy function reflects the order of the system: the more ordered the state, the smaller the entropy value. The entropy function can be used to quantitatively characterize the uncertainty of lithofacies probability information in the process of phase-controlled elastic parameter modeling. By analyzing and comparing the change law of the entropy value information of the posterior probability when determining the well logging lithofacies sequence and identifying the seismic lithofacies on the seismic well-connected profile, the transmission law of the uncertainty can be quantitatively analyzed. The probability vector P(π t =f jThe information entropy of P(π = f|R) can be defined as:

[0154]

[0155] where R represents physical property parameters, and Epy(π t = f j |R) represents the entropy value information of P(π t = f j |R). Epy(π t = f j |R) ∈ [0, 1], N f is the number of lithofacies categories, and P(π t = f j |R) is the conditional probability of the j-th lithofacies and satisfies P(π t = f j |R) and the probability mean 1 / N f The greater the distance, the smaller the uncertainty. The entropy function value Epy(π t = f j |R) is smaller, and vice versa. Therefore, Epy(π t = f j |R) can quantitatively characterize the uncertainty in the process of modeling facies-controlled elastic parameters.

[0156] After quantitatively evaluating the level of uncertainty, if the uncertainty of the posterior probability is high, it proves that the result is inaccurate, and the corresponding calculation result will be discarded, thereby ensuring the accuracy of subsequent calculations and improving the accuracy of the elastic parameter model.

[0157] In the embodiment of this article, taking the distinction between sandstone and sandstone in the work area as an example:

[0158] Firstly, the logging parameters of Fudong 12 Well and Xi 1 Well in the work area were obtained, and the natural gamma curve and natural potential curve were selected as sensitive logging parameters. The discrimination criteria of the natural gamma curve and the natural potential curve were established, that is, the natural gamma value above 91 was judged as mudstone, and below 91 was judged as sandstone; the natural potential curve showed a relatively stable trend when drilling into mudstone layer, which was called “mudstone baseline”, and deviated from the “mudstone baseline” when drilling into sandstone layer. Then, determine the logging lithofacies sequence, define it through the natural gamma curve or natural potential curve, and the values ​​greater than a certain discrimination standard are mudstones, and values ​​less than the discrimination standard are sandstones, thereby determining the logging lithofacies sequence; or, when the geological distribution within the work area is uneven and the lithofacies change rapidly within the depth range, it will affect the establishment of the discrimination standard based on the natural gamma curve and the natural potential curve, resulting in greater uncertainty in lithofacies discrimination within certain depth ranges. Data from multiple depth ranges can be used, where multiple depth ranges can be determined based on the degree to which the current depth natural gamma value deviates from the discrimination standard based on the natural gamma logging curve and the degree to which the natural potential value deviates from the mudstone baseline, and the data with the smallest deviation is the optional depth range. Obtain data from multiple depth ranges to establish a priori information clustering data set, and calculate the maximum posterior probability based on the Bayesian discriminant method to determine the logging lithofacies sequence.

[0159] Reference Figure 5 and Figure 6 These are the logging lithofacies definition results of Fudong 12 Well and Xi 1 Well, respectively. Figure 5 and Figure 6 The vertical direction in the middle represents the depth change, from left to right: natural gamma curve, natural potential curve, lithofacies sequence determined based on natural gamma, Bayesian sandstone posterior probability curve, well logging lithofacies sequence determined based on Bayesian discriminant method, and entropy function curve. Among them: the dark part of the lithofacies sequence determined based on natural gamma and the well logging lithofacies sequence determined based on Bayesian discriminant method represents mudstone, and the light part represents sandstone. The dotted line in the natural gamma curve represents the standard value of lithofacies discrimination, that is, above 91 is judged as mudstone, and below 91 is judged as sandstone; the dotted line in the natural potential curve represents the "mudstone baseline".

[0160] The well logging lithofacies in the work area were divided through well seismic calibration, and the well logging lithofacies sequence in the depth domain was converted to the time domain according to the time-depth relationship. Figure 7 and Figure 8 As shown, the vertical direction represents the change in the time domain. Figure 7 The calibration correlation coefficient is 0.861. Figure 8 The calibration correlation coefficient is 0.741. Since the obtained time-domain logging lithofacies sequence is still a lithofacies sequence at the logging scale, it needs to be coarsened to the seismic scale for seismic lithofacies identification.

[0161] Reference Fig. 9 and Fig.10 They are the results of seismic scale matching of Fudong 12 well and Xi 1 well, respectively. The vertical direction represents the depth change. From left to right, they are: natural gamma curve in depth domain, natural potential curve in depth domain, natural gamma curve in time domain, natural potential curve in time domain, lithofacies sequence determined based on natural gamma, and well logging lithofacies sequence determined based on Bayesian lithofacies discrimination method. The dark part in the lithofacies sequence determined based on natural gamma and the well logging lithofacies sequence determined based on Bayesian lithofacies discrimination method represents mudstone, and the light part represents sandstone.

[0162] The seismic lithofacies on the seismic well-connected section are identified by using the discriminant relationship established between the pre-stack inversion elastic parameters or the preferred seismic attributes and the well logging lithofacies sequence. Figures 11 to 16 , the vertical direction represents the change of depth, and the various types of preferred seismic attributes are extracted, and their seismic attributes are amplitude envelope, amplitude-weighted cosine phase, amplitude-weighted frequency, cosine instantaneous phase, instantaneous frequency and instantaneous phase. Fig.17 and Fig.18 They are the posterior probability results of mudstone and sandstone obtained by Bayesian discriminant method, and the vertical direction represents the change in time domain. By determining the maximum posterior probability of each lithofacies, we can get Fig.19 The maximum a posteriori probability solution for seismic lithofacies identification shown in the figure is the seismic lithofacies on the seismic well section. The vertical direction represents the changes in the time domain. The dark part is mudstone and the light part is sandstone. Fig.19 In the identification of seismic lithofacies on the seismic well section, the prior probability information established by Fudong 12 and Xi 1 wells was used to participate in the Bayesian discriminant calculation, and Fudong 071 well was used as a test well. From left to right, they are Fudong 12, Fudong 071 and Xi 1 wells. Fig. 20 To verify the results, from left to right are the well logging lithofacies sequence based on the natural gamma curve at the seismic scale, the well logging lithofacies sequence obtained based on the Bayesian discriminant method, and the lithofacies sequence of the seismic trace near the well. The vertical direction represents the changes in the time domain. The dark part is mudstone and the light part is sandstone.

[0163] Reference Fig.21 , which is the entropy function profile for identifying seismic lithofacies on the seismic well-connected profile. The vertical direction represents the change in the time domain. The closer to white, the higher the entropy value, reflecting the higher uncertainty; the closer to black, the lower the entropy value, reflecting the lower uncertainty. From the overall perspective of the entropy function profile, there are many areas with medium and low entropy function values, especially at locations with high posterior probability of sandstone, that is, the entropy value of the layers that may have sandstone is low.

[0164] The fitting curve obtained by fitting the scattered points of the elastic parameters of mudstone and sandstone in the time domain can reflect the variation law of the longitudinal wave velocity, shear wave velocity and density of mudstone and sandstone in the time domain. Fig. 22 The figure shows the variation of the longitudinal wave velocity of mudstone in the time domain. The vertical direction represents the variation in the time domain, and the horizontal direction represents the variation in velocity. Fig.23 The figure shows the variation of the shear wave velocity of mudstone in the time domain. The vertical direction represents the variation in the time domain, and the horizontal direction represents the variation in velocity. Fig.24 The figure shows the variation of mudstone density in the time domain. The vertical direction represents the variation in the time domain, and the horizontal direction represents the variation in density. Fig.25 The figure shows the variation of the longitudinal wave velocity of sandstone in the time domain. The vertical direction represents the variation in the time domain, and the horizontal direction represents the variation in velocity. Fig.26 The figure shows the variation of the shear wave velocity of sandstone in the time domain. The vertical direction represents the variation in the time domain, and the horizontal direction represents the variation in velocity. Fig. 27 The figure shows the variation of sandstone density in the time domain. The vertical direction represents the variation in the time domain, and the horizontal direction represents the variation in density. The fitting formulas for the elastic parameters of mudstone and sandstone defined by the experiment are as follows:

[0165] The fitting formulas of the longitudinal and shear wave velocities and density of mudstone in the time domain are:

[0166] f p (x) = -1.263*10 -3 *x 2 +8.848*10 -4 *x-1.158*10 4 (15)

[0167] f s (x) = -8.863*10 -4 *x 2 +6.211*x-8.686*10 3 (16)

[0168] f ρ (x) = 2.306*10 -7 *x 2 +1.297*10 -3 *x+4.229 (17)

[0169] The fitting formulas of the longitudinal and shear wave velocities and density of sandstone in the time domain are:

[0170] f p (x) = -4.92*10 -4 *x 2 +3.874*x-3622 (18)

[0171] fs (x) = -3.453*10 -4 *x 2 +2.27*x-3096 (19)

[0172] f ρ (x) = -2.695*10 -7 *x 2 +1.987*10 -3 *x-1.182 (20)

[0173] In the above formula, x is the independent variable, representing time (ms), f(x) is the function value, representing the elastic parameters of the mudstone phase and the sandstone phase, and x and f(x) both satisfy a quadratic function relationship.

[0174] Reference Fig.28 , is the seismic lithofacies on the seismic well-connected section based on Well Fudong 12, Well Fudong 071 and Well Xi 1. From left to right, they are Well Fudong 12, Well Fudong 071 and Well Xi 1. Substituting the fitting formulas (15)–(20) obtained above into Fig.28 When the seismic lithofacies on the seismic well section shown in the figure match, we can get the following Fig.29 The P-wave velocity model based on lithofacies control is shown in Figure 2. Fig.30 The shear wave velocity model shown in Fig.31 The density model shown is an elastic parameter model, in which the longitudinal direction represents the change in the time domain, the transverse direction represents the common depth point gather, and the color code represents the corresponding P-wave velocity or S-wave velocity or density.

[0175] When seismic inversion is performed using this elastic parameter model, the accuracy of the inversion can be improved. High-precision seismic inversion can improve the accuracy of lithofacies identification. Lithofacies plays an important role in rock deposition, reservoir distribution, and rock elastic properties. Accurate lithofacies identification can better help rock deposition analysis, reservoir prediction, etc.

[0176] Based on the method of phase-controlled elastic parameter modeling described above, the embodiments of this article also provide a device for phase-controlled elastic parameter modeling. The device may include a system (including a distributed system), software (application), module, component, server, client, etc. using the method described in the embodiments of this article and a device combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided in the embodiments of this article is as described in the following embodiments. Since the implementation scheme of the device to solve the problem is similar to the method, the implementation of the specific device in the embodiments of this article can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0177] Specifically, Fig.32 is a schematic diagram of a module structure of an embodiment of a phase-controlled elastic parameter modeling device provided in the embodiments of this invention, referring to Fig.32 As shown, a phase-controlled elastic parameter modeling device provided in the embodiment of this invention includes: a lithology determination module 100, a formula determination module 200, and a model establishment module 300.

[0178] The lithofacies determination module 100 obtains well logging parameters in the work area and determines the seismic lithofacies on the seismic well-connected section in the work area.

[0179] The formula determination module 200 determines the fitting formula of the elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters.

[0180] The model building module 300 calculates the elastic parameters of the seismic lithofacies on the seismic well-connected section by using the fitting formula to obtain an elastic parameter model.

[0181] In one embodiment of this invention, reference is made to Fig.33As shown, a computer device 3302 is also provided. The computer device 3302 may include one or more processors 3304, such as one or more central processing units (CPUs) or graphics processing units (GPUs), and each processing unit may implement one or more hardware threads. The computer device 3302 may also include any memory 3306, which is used to store any kind of information such as code, settings, data, etc. In a specific embodiment, a computer program on the memory 3306 and that can be run on the processor 3304, when the computer program is run by the processor 3304, can execute instructions according to the above method. Non-limiting, for example, the memory 3306 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 3302. In one embodiment, when the processor 3304 executes the associated instructions stored in any memory or combination of memories, the computer device 3302 can perform any operation of the associated instructions. The computer device 3302 also includes one or more drive mechanisms 3308 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0182] The computer device 3302 may also include an input / output module 3310 (I / O) for receiving various inputs (via input device 3312) and for providing various outputs (via output device 3314). A specific output mechanism may include a presentation device 3316 and an associated graphical user interface 3318 (GUI). In other embodiments, the input / output module 3310 (I / O), input device 3312, and output device 3314 may not be included, and the computer device 3302 may be used as a computer device in a network. The computer device 3302 may also include one or more network interfaces 3320 for exchanging data with other devices via one or more communication links 3322. One or more communication buses 3324 couple the components described above together.

[0183] The communication link 3322 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 3322 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0184] Corresponds to Figures 1 to 4The method in this article also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0185] The embodiment of the present invention also provides a computer readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the following Figures 1 to 4 The method shown.

[0186] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0187] It should also be understood that in the embodiments of this article, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0188] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this article.

[0189] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0190] In the several embodiments provided herein, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this article.

[0192] In addition, each functional unit in each embodiment of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0193] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0194] Specific embodiments are used in this article to illustrate the principles and implementation methods of this article. The description of the above embodiments is only used to help understand the methods and core ideas of this article. At the same time, for general technicians in this field, according to the ideas of this article, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on this article.

Claims

1. A method for phase-controlled elastic parameter modeling, characterized in that: The method comprises: Acquire well logging parameters in the work area, and determine the seismic lithofacies on the seismic well-connected section in the work area; Determine a fitting formula in the time domain for elastic parameters of different lithofacies in the work area according to the logging parameters; Calculating elastic parameters of the seismic lithofacies on the seismic well-connected section by using the fitting formula to obtain an elastic parameter model; The determining of the seismic lithofacies on the seismic well-connected section in the work area comprises: Obtain seismic data within the work area; Determining a logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters; Converting the well logging lithofacies sequence from the depth domain to the time domain through well seismic calibration; Coarsening the time-domain logging lithofacies sequence to the seismic scale to obtain the coarsened time-domain logging lithofacies sequence; Establishing a discriminant relationship between the coarsened time-domain well logging lithofacies sequence and the seismic data, and determining the seismic lithofacies on the seismic well-connected section according to the discriminant relationship; The step of determining the logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters comprises: Selecting well logging sensitive parameters from the well logging parameters and establishing a well logging sensitive parameter discrimination standard, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; determining the well logging lithofacies sequence according to the well logging sensitive parameter discrimination standard; Or, well logging sensitive parameters are selected from the well logging parameters, and a well logging sensitive parameter discrimination standard is established, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; data of multiple depth ranges close to the well logging sensitive parameter discrimination standard are selected to establish a priori information clustering data set; the posterior probability is calculated by a Bayesian algorithm, and the maximum posterior probability solution is determined according to the posterior probability distribution, the maximum posterior probability solution is the well logging lithofacies sequence, and the posterior probability is the conditional probability of the occurrence of the lithofacies.

2. The method according to claim 1, characterized in that The fitting formula for determining the elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters includes: Converting the logging parameters from the depth domain to the time domain through well seismic calibration; Draw a scatter plot of the elastic parameters of each lithofacies in the time domain based on the logging parameters in the time domain; Polynomial fitting is performed on the scattered points in the scatter plot to generate a fitting curve of the elastic parameters of each lithofacies and obtain a fitting formula of the elastic parameters of each lithofacies in the time domain.

3. The method according to claim 1, characterized in that The step of establishing a discriminant relationship between the coarsened time-domain well logging lithofacies sequence and the seismic data, and determining the seismic lithofacies on the seismic well-connected section according to the discriminant relationship comprises: The coarsened time-domain well logging lithofacies sequence and the seismic data form a priori matrix to serve as priori information for each clustering data set; The posterior probability is obtained by using a Bayesian algorithm, and the maximum posterior probability solution on the seismic well-connected section is obtained according to the posterior probability distribution. The maximum posterior probability solution is the seismic lithofacies on the seismic well-connected section.

4. The method according to claim 1, characterized in that: The step of obtaining seismic data in the work area includes: Obtain pre-stack inversion elastic parameters of the seismic trace near the well as seismic data; Alternatively, the seismic attributes of the seismic traces near the well are obtained, logging sensitive parameters are selected from the logging parameters, and the correlation coefficient between the logging sensitive parameters of each well and the seismic attributes of the seismic traces near the well is calculated using the correlation coefficient formula to obtain several correlation coefficient values, and the seismic attributes with large correlation coefficient values ​​are selected as seismic data.

5. The method according to claim 1, characterized in that The method further comprises: By analyzing and comparing the changing rules of the entropy value information of the posterior probability solution in the phase-controlled elastic parameter modeling process, the uncertainty transfer rule in the phase-controlled elastic parameter modeling process is quantitatively analyzed.

6. A device for phase-controlled elastic parameter modeling, characterized in that: The device comprises: A lithofacies determination module, which obtains well logging parameters in a work area and determines the seismic lithofacies on a seismic well-connected section in the work area; A formula determination module, which determines the fitting formula of the elastic parameters of different lithofacies in the work area in the time domain according to the logging parameters; A model building module calculates elastic parameters of the seismic lithofacies on the seismic well-connected section by using the fitting formula to obtain an elastic parameter model; The determining of the seismic lithofacies on the seismic well-connected section in the work area comprises: Obtain seismic data within the work area; Determining a logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters; Converting the well logging lithofacies sequence from the depth domain to the time domain through well seismic calibration; Coarsening the time-domain logging lithofacies sequence to the seismic scale to obtain the coarsened time-domain logging lithofacies sequence; Establishing a discriminant relationship between the coarsened time-domain well logging lithofacies sequence and the seismic data, and determining the seismic lithofacies on the seismic well-connected section according to the discriminant relationship; The step of determining the logging lithofacies sequence for distinguishing various lithofacies according to the logging parameters comprises: Selecting well logging sensitive parameters from the well logging parameters and establishing a well logging sensitive parameter discrimination standard, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; determining the well logging lithofacies sequence according to the well logging sensitive parameter discrimination standard; Or, well logging sensitive parameters are selected from the well logging parameters, and a well logging sensitive parameter discrimination standard is established, wherein the well logging sensitive parameter discrimination standard is used to identify each lithofacies; data of multiple depth ranges close to the well logging sensitive parameter discrimination standard are selected to establish a priori information clustering data set; the posterior probability is calculated by a Bayesian algorithm, and the maximum posterior probability solution is determined according to the posterior probability distribution, the maximum posterior probability solution is the well logging lithofacies sequence, and the posterior probability is the conditional probability of the occurrence of the lithofacies.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the computer program executes the instructions of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor of a computer device, the computer program executes instructions of the method according to any one of claims 1 to 5.

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