Plane constraint prestack inversion-based beach facies thin reservoir prediction method and device

Abstract

The invention relates to the field of seismic reservoir prediction, and particularly discloses a flat facies thin reservoir prediction method and device based on plane constraint pre-stack inversion, and the method comprises the steps: carrying out the rock physical analysis of logging information, and obtaining the pre-stack sensitive parameters of a reservoir; obtaining plane probabilities corresponding to different rocks based on logging information and geological knowledge; based on the seismic data, the logging information and the seismic interpretation horizon, pre-stack inversion is carried out with the plane probabilities corresponding to different rocks as constraints, and an inversion result is obtained; and calculating to obtain a pre-stack sensitive parameter body of the reservoir based on the inversion result and the pre-stack sensitive parameters of the reservoir. According to the method provided by the invention, geological cognition and geophysical prospecting technologies are combined, on the basis of determining reservoir sensitive pre-stack elastic parameters through logging rock physical analysis, the cognition of sedimentary facies is introduced to determine the plane probability conditions of different lithofacies, and then pre-stack geostatistical inversion of plane constraint is carried out; the multiplicity of solutions of longitudinal and transverse prediction of the beach facies carbonate reservoir is reduced, and the inversion efficiency is high.

Description

Technical Field

[0001] The present invention relates to the field of seismic reservoir prediction, and particularly to a method and device for predicting beach facies thin reservoirs by plane-constrained prestack inversion. Background Art

[0002] The beach facies reservoirs inside marine carbonate platforms are a main type of reservoirs in lithologic oil and gas reservoirs. However, their lithology is complex, the heterogeneity is extremely strong, the thickness is thin, and it is difficult to accurately predict the reservoirs. Many scholars at home and abroad have done a lot of research on quantitatively predicting reservoirs by using seismic inversion. According to the type of input seismic data, it can be divided into post-stack inversion and prestack inversion. The post-stack inversion method is more commonly used, but the number of parameters obtained by inversion is small; prestack inversion can obtain P-wave velocity, S-wave velocity, density and other elastic parameters, providing more options for reservoir prediction and can be applied to reservoirs with complex lithology and complex structures. According to the method used in inversion, seismic inversion can also be divided into sparse pulse inversion, model-constrained inversion, geostatistical inversion, etc. Geostatistical inversion combines geostatistics and inversion, has a high vertical resolution, and is often used for predicting thin reservoirs. However, the lateral trend often has strong randomness and it is difficult to obtain a relatively stable inversion result.

[0003] Since the development of beach facies carbonate reservoirs is controlled by the development of beach bodies, with strong heterogeneity and thin thickness, the conventional post-stack inversion for reservoir prediction cannot be applied to such complex situations, and it is difficult to solve the problems of vertical resolution and lateral prediction accuracy of inversion, and the results are not ideal.

[0004] Carrying out prestack geostatistical inversion and using the existing understanding of sedimentary facies for constraint can improve the prediction effect and identification accuracy of reservoirs.

[0005] Based on this technical background, the present invention studies a method and device for predicting beach facies thin reservoirs by plane-constrained prestack inversion. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a method and device for predicting beach facies thin reservoirs by plane-constrained prestack inversion. This method combines geological understanding and geophysical exploration technology. On the basis of clearly defining the sensitive prestack elastic parameters of the reservoir through well logging rock physics analysis, it introduces the understanding of sedimentary facies to determine the plane probability of different lithofacies, and then conducts plane-constrained prestack geostatistical inversion. Compared with the prior art, the inversion result reduces the multi-solution of vertical and lateral prediction of beach facies carbonate reservoirs, and at the same time, the plane constraint is more efficient than the three-dimensional constraint inversion for thin reservoirs.

[0007] To achieve the above object, the first aspect of the present invention provides a method for predicting beach facies thin reservoirs by plane-constrained prestack inversion, including:

[0008] Perform petrophysical analysis on logging data to obtain pre-stack sensitive parameters of the reservoir;

[0009] Based on logging data and geological understanding, obtain the plane probabilities corresponding to different lithofacies;

[0010] Based on seismic data, logging data, and seismic interpretation horizons, perform pre-stack inversion with the plane probabilities corresponding to different lithofacies as constraints to obtain the inversion results;

[0011] Based on the inversion results and the pre-stack sensitive parameters of the reservoir, calculate the pre-stack sensitive parameter volume of the reservoir.

[0012] The second aspect of the present invention provides a beach facies thin reservoir prediction device for plane-constrained pre-stack inversion, including:

[0013] A sensitive parameter acquisition module, configured to perform petrophysical analysis on logging data to obtain pre-stack sensitive parameters of the reservoir;

[0014] A probability acquisition module, configured to obtain the plane probabilities corresponding to different lithofacies based on logging data and geological understanding;

[0015] An inversion module, configured to perform pre-stack inversion with the plane probabilities corresponding to different lithofacies as constraints based on seismic data, logging data, and seismic interpretation horizons to obtain the inversion results;

[0016] A parameter volume acquisition module, configured to calculate the pre-stack sensitive parameter volume of the reservoir based on the inversion results and the pre-stack sensitive parameters of the reservoir.

[0017] The third aspect of the present invention provides an electronic device, and the electronic device includes:

[0018] A memory storing executable instructions;

[0019] A processor, the processor running the executable instructions in the memory to implement the beach facies thin reservoir prediction method for plane-constrained pre-stack inversion described in the first aspect.

[0020] The fourth aspect of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the beach facies thin reservoir prediction method for plane-constrained pre-stack inversion described in the first aspect.

[0021] The beneficial effects of the present invention include:

[0022] (1) The proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion combines geological understanding and geophysical exploration techniques. Based on the clear identification of reservoir-sensitive prestack elastic parameters through well logging rock physics analysis, the understanding of sedimentary facies is introduced to determine the plane probability of different lithofacies, and then plane-constrained prestack geostatistical inversion is carried out. Compared with the existing technology, the inversion results reduce the multi-solution of vertical and horizontal prediction of beach facies carbonate reservoirs. At the same time, for thin reservoirs, plane constraint is more efficient than 3D constraint inversion.

[0023] (2) The proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion can be applied to the prediction of relatively complex thin carbonate reservoirs, improving the accuracy of inversion and reservoir prediction. The results are more in line with geological understanding and have strong practicability.

[0024] (3) The proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion conducts statistics and uncertainty analysis on a large number of equally probable realization results obtained by stochastic inversion to predict the possible distribution range of reservoirs. At the same time, prior information constraint is imposed on the stochastic inversion, which can obtain relatively reliable results and reduce the uncertainty of inversion.

[0025] Other features and advantages of the present invention will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings.

[0027] Figure 1 It is a schematic flow chart of the proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion.

[0028] Figure 2 It is a schematic flow chart of predicting thin prestack beach facies carbonate reservoirs in a specific implementation of the proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion.

[0029] Figure 3 It is a schematic diagram of the intersection of prestack elastic parameters in a specific implementation of the proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion.

[0030] Figure 4 It is a schematic diagram of the probability distribution of different lithofacies of wellhead samples in a specific implementation of the proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion.

[0031] Figure 5 It is a schematic diagram of the plane distribution of probabilities of different lithofacies in a specific implementation of the proposed method for predicting thin beach facies reservoirs by plane-constrained prestack inversion.

[0032] Figure 6 Schematic diagram of reservoir sensitive parameter profile in a specific embodiment of the beach facies thin reservoir prediction method based on plane-constrained prestack inversion proposed by the present invention.

[0033] Figure 7 Schematic diagram of reservoir plane prediction in a specific embodiment of the beach facies thin reservoir prediction method based on plane-constrained prestack inversion proposed by the present invention. Specific embodiment

[0034] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0035] The present invention provides a beach facies thin reservoir prediction method based on plane-constrained prestack inversion, as Figure 1 shown, including:

[0036] Performing petrophysical analysis on well logging data to obtain prestack sensitive parameters of the reservoir;

[0037] Based on well logging data and geological understanding, obtaining the plane probabilities corresponding to different lithofacies;

[0038] Based on seismic data, well logging data, and seismic interpretation horizons, performing prestack inversion with the plane probabilities corresponding to different lithofacies as constraints to obtain an inversion result;

[0039] Calculating a prestack sensitive parameter volume of the reservoir based on the inversion result and the prestack sensitive parameters of the reservoir.

[0040] The method proposed by the present invention combines geological understanding and geophysical exploration technology. On the basis of clarifying the sensitive prestack elastic parameters of the reservoir through petrophysical analysis of well logging data, the understanding of sedimentary facies is introduced to determine the plane probability situation of different lithofacies, and then plane-constrained prestack geostatistical inversion is carried out. Compared with the prior art, the inversion result reduces the multi-solutionness of longitudinal and lateral prediction of beach facies carbonate rock reservoirs, and at the same time, the plane constraint for thin reservoirs is more efficient than three-dimensional constrained inversion.

[0041] According to the present invention, performing petrophysical analysis on well logging data to obtain prestack sensitive parameters of the reservoir includes:

[0042] Obtaining a lithofacies curve based on well logging data and performing petrophysical analysis on the lithofacies curve to obtain prestack sensitive parameters of the reservoir.

[0043] According to the present invention, obtaining the plane probabilities corresponding to different lithofacies based on well logging data and geological understanding includes:

[0044] Performing probability statistical analysis on the parameter variables of the well logging data at wellhead sampling points to establish a probability density distribution corresponding to different lithofacies;

[0045] Using the probability density distribution corresponding to geological understanding and different lithofacies, the sedimentary facies is converted into the planar distribution of different lithofacies, and the planar probability corresponding to different lithofacies is obtained.

[0046] Preferably, the probability density distribution corresponding to different lithofacies is established by adjusting the parameters of the probability density function to change the conditional probability distribution of each attribute of the wellhead sample points, so that it is consistent with the distribution form of the wellhead sample points.

[0047] The method proposed by the present invention performs statistics and uncertainty analysis on a large number of equally probable realization results obtained by stochastic inversion, predicts the possible distribution range of the reservoir, and at the same time performs prior information constraint on the stochastic inversion, so as to obtain relatively reliable results and reduce the uncertainty of the inversion.

[0048] According to the present invention, the parameters are the P-wave impedance and the P-S wave velocity ratio.

[0049] According to the present invention, the probability density distribution is calculated through the following Bayesian formula;

[0050]

[0051] Wherein, x is the variable attribute parameter, yi is different lithofacies categories, and P(y i |x) is the posterior probability of belonging to a certain lithofacies, that is, the probability that the sample point of x belongs to a certain lithofacies under certain attribute conditions, P(x|y i ) is the conditional probability density function, P(y i ) is the prior probability of a certain lithofacies, which is obtained based on well logging data statistics, and P(x) is a constant proportionality factor used to control the sum of the probabilities of different lithofacies classifications to be 1.

[0052] Preferably, the inversion results are the P-wave impedance body, the P-S wave velocity body and the lithofacies data body.

[0053] In the present invention, based on the probability density function representing the probability distribution of elastic parameters corresponding to different lithofacies, the probability density functions of different lithofacies and prior geological information can be fused into the Bayesian classifier based on well logging data sample point statistics, and the lithofacies classification body and the probability body of different lithofacies are generated by using the elastic parameter body. The Bayesian classification method is based on the Bayesian theory, calculates the posterior probability of each sample belonging to each category according to the distribution of each category sample in the training set, and then judges the category of the sample as the category corresponding to the maximum posterior probability.

[0054] The method proposed by the present invention can be applied to the prediction of relatively complex carbonate thin reservoirs, improve the accuracy of inversion and reservoir prediction, the results are more in line with geological understanding, and it has strong practicability.

[0055] In the present invention, by using the geostatistical inversion method, information such as seismic lithofacies bodies, logging curves, and probability density functions is combined to obtain a probability distribution model. The Markov chain-Monte Carlo algorithm is used to obtain a correct set of sample points according to the probability distribution function. Finally, statistical and uncertainty analyses are performed on a large number of equally probable realization results obtained by stochastic inversion to predict the possible distribution range of the reservoir. If prior information constraints are imposed on the stochastic inversion, relatively reliable results can be obtained, reducing the uncertainty of the inversion.

[0056] The present invention will be described in more detail below through embodiments.

[0057] Embodiment 1:

[0058] This embodiment provides a method for predicting thin beach facies reservoirs with plane constraints in pre-stack inversion. The specific process is as Figure 2 shown. The geological understanding of the study area shows that the reservoir is affected by the development of beach bodies and is mainly dolomite. Logging data and interpretation results are used to define different lithofacies, which are divided into two lithofacies types: dolomite and limestone.

[0059] The characteristics of dolomite reservoirs are analyzed using logging curves. As Figure 3 shown, the cross-plot results of multiple elastic parameters show that the reservoir has characteristics such as high shear wave impedance, low P-wave to S-wave velocity ratio, and low Poisson's ratio. However, there is a certain superposition phenomenon in single parameters. The L lithology-sensitive parameter constructed by the first coordinate rotation of P-wave impedance and velocity ratio can better distinguish dolomite within large intervals of limestone when L>0.

[0060] To reduce the non-uniqueness of reservoir prediction, a probability statistical analysis method is used. The probability distributions of different lithofacies are established using P-wave impedance and P-wave to S-wave velocity ratio. According to the probability distribution characteristics, the corresponding function type is selected. In this case, the Bayesian function is selected. By adjusting the main parameters of the function, including the mean and variance, etc., the interval described by the function is made to be basically consistent with the distribution of logging scatter points, as Figure 4 shown.

[0061] Combined with the logging statistical results, the existing plane distribution of beach facies is converted into the probability plane distribution of different lithofacies as Figure 5 shown, and the lateral variation is controlled by sedimentary facies. Geological statistical inversion is performed using the original seismic data, logging data, seismic interpretation horizons, wavelets, and low-frequency models, and at the same time, the probability plane distributions of different lithofacies mentioned above are used as constraints to obtain high-resolution P-wave impedance bodies, P-wave to S-wave velocity bodies, and lithofacies data bodies. Combining the previous analysis of rock physics sensitive parameters, the reservoir sensitive parameter L body is calculated as Figure 6 shown. When L>0, the predicted reservoir plane results are as Figure 7 shown.

[0062] Embodiment 2:

[0063] This embodiment provides a prediction method for beach facies thin reservoirs using plane-constrained prestack inversion, as Figure 1 shown, including:

[0064] Performing petrophysical analysis on well logging data to obtain prestack sensitive parameters of the reservoir;

[0065] Based on well logging data and geological understanding, obtaining the plane probabilities corresponding to different lithofacies;

[0066] Based on seismic data, well logging data, and seismic interpretation horizons, performing prestack inversion with the plane probabilities corresponding to different lithofacies as constraints to obtain the inversion results;

[0067] Based on the inversion results and the prestack sensitive parameters of the reservoir, calculating to obtain a prestack sensitive parameter volume of the reservoir;

[0068] Performing petrophysical analysis on well logging data to obtain prestack sensitive parameters of the reservoir includes:

[0069] Based on well logging data, obtaining a lithofacies curve, and performing petrophysical analysis on the lithofacies curve to obtain prestack sensitive parameters of the reservoir;

[0070] Based on well logging data and geological understanding, obtaining the plane probabilities corresponding to different lithofacies includes:

[0071] Performing probability statistical analysis on the parameter variables of the well logging data at wellhead sampling points to establish a probability density distribution corresponding to different lithofacies;

[0072] Using geological understanding and the probability density distribution corresponding to different lithofacies, converting the sedimentary facies into a plane distribution of different lithofacies to obtain the plane probabilities corresponding to different lithofacies;

[0073] Establishing the probability density distribution corresponding to different lithofacies is to change the conditional probability distribution of each attribute of the wellhead sampling points by adjusting the parameters of the probability density function to make it consistent with the distribution form of the wellhead sampling points;

[0074] In this embodiment, the parameter variables are P-wave impedance and P-S wave velocity ratio;

[0075] The probability density distribution is calculated through the following Bayesian formula;

[0076]

[0077] where x is the variable attribute parameter, yi is different lithofacies categories, P(y i |x) is the posterior probability of belonging to a certain lithofacies, that is, the probability that the sample point of x under certain attribute conditions belongs to a certain lithofacies, P(x|y i ) is the conditional probability density function, P(y i) is the prior probability of a certain lithofacies, which is obtained based on well logging data statistics. P(x) is a constant proportionality factor used to control the sum of probabilities of different lithofacies classifications to be 1;

[0078] The inversion results are the P-wave impedance body, the P-S wave velocity body, and the lithofacies data body.

[0079] Embodiment 3:

[0080] This embodiment provides a beach facies thin reservoir prediction device for plane-constrained prestack inversion, including:

[0081] A sensitive parameter acquisition module for performing rock physics analysis on well logging data to obtain prestack sensitive parameters of the reservoir;

[0082] A probability acquisition module for obtaining the plane probabilities corresponding to different lithofacies based on well logging data and geological understanding;

[0083] An inversion module for performing prestack inversion based on seismic data, well logging data, and seismic interpretation horizons, with the plane probabilities corresponding to different lithofacies as constraints to obtain inversion results;

[0084] A parameter body acquisition module for calculating the prestack sensitive parameter body of the reservoir based on the inversion results and the prestack sensitive parameters of the reservoir;

[0085] Performing rock physics analysis on well logging data to obtain prestack sensitive parameters of the reservoir includes:

[0086] Obtaining a lithofacies curve based on well logging data and performing rock physics analysis on the lithofacies curve to obtain prestack sensitive parameters of the reservoir;

[0087] Obtaining the plane probabilities corresponding to different lithofacies based on well logging data and geological understanding includes:

[0088] Performing probability statistical analysis on the parameter variables of the well logging data at wellhead sampling points to establish the probability density distribution corresponding to different lithofacies;

[0089] Using geological understanding and the probability density distribution corresponding to different lithofacies to convert the sedimentary facies into the plane distribution of different lithofacies to obtain the plane probabilities corresponding to different lithofacies;

[0090] Establishing the probability density distribution corresponding to different lithofacies is to change the conditional probability distribution of each attribute of the wellhead sampling points by adjusting the parameters of the probability density function to make it consistent with the distribution form of the wellhead sampling points;

[0091] In this embodiment, the parameter variables are P-wave impedance and P-S wave velocity ratio;

[0092] The probability density distribution is calculated through the following Bayesian formula;

[0093]

[0094] Among them, x is a variable attribute parameter, yi is different lithofacies categories, and P(y i |x) is the posterior probability of belonging to a certain lithofacies, that is, the probability that the sample points of x belong to a certain lithofacies under certain attribute conditions. P(x|y i ) is the conditional probability density function, P(y i ) is the prior probability of a certain lithofacies, which is obtained based on well logging data statistics. P(x) is a constant proportionality factor used to control the sum of probabilities of different lithofacies classifications to be 1;

[0095] The inversion results are the P-wave impedance body, the P-S wave velocity body, and the lithofacies data body.

[0096] Example 4:

[0097] An embodiment of the present invention provides an electronic device including a memory and a processor,

[0098] The memory stores executable instructions;

[0099] The processor runs the executable instructions in the memory to implement the prediction method of beach facies thin reservoirs for plane-constrained prestack inversion.

[0100] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and these computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. This volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. This non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0101] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0102] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses, interfaces, etc., and these well-known structures should also be included in the protection scope of the present invention.

[0103] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0104] Example 5:

[0105] An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, a beach facies thin reservoir prediction method for plane-constrained prestack inversion is implemented.

[0106] According to the computer-readable storage medium of the embodiments of the present invention, non-temporary computer-readable instructions are stored thereon. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present invention described above are executed.

[0107] The above computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROM and DVD), magneto-optical storage media (such as MO), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROM (such as ROM cartridges).

[0108] The beach facies thin reservoir prediction method for plane-constrained prestack inversion proposed by the embodiments of the present invention combines geological understanding and geophysical exploration techniques. On the basis of clearly identifying the sensitive prestack elastic parameters of the reservoir through well logging petrophysical analysis, the understanding of sedimentary facies is introduced to determine the plane probability of different lithofacies, and then plane-constrained prestack geostatistical inversion is carried out. Compared with the prior art, the inversion results reduce the multi-solution of vertical and horizontal prediction of beach facies carbonate reservoirs. At the same time, for thin reservoirs, plane constraints are more efficient than three-dimensional constrained inversion.

[0109] The various embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for predicting thin beach facies reservoirs by plane-constrained prestack inversion, characterized in that, Including: Performing petrophysical analysis on logging data to obtain pre-stack sensitive parameters of the reservoir; Obtaining the planar probabilities corresponding to different lithofacies based on logging data and geological understanding; Performing pre-stack inversion based on seismic data, logging data, and seismic interpretation horizons, with the planar probabilities corresponding to different lithofacies as constraints, to obtain the inversion results; Calculating a pre-stack sensitive parameter volume of the reservoir based on the inversion results and the pre-stack sensitive parameters of the reservoir.

2. The method according to claim 1, wherein Performing petrophysical analysis on logging data to obtain pre-stack sensitive parameters of the reservoir includes: Obtaining a lithofacies curve based on logging data, and performing petrophysical analysis on the lithofacies curve to obtain pre-stack sensitive parameters of the reservoir.

3. The method according to claim 1, wherein Obtaining the planar probabilities corresponding to different lithofacies based on logging data and geological understanding includes: Performing probability statistical analysis on the parameter variables of the well logging sample points in the logging data to establish a probability density distribution corresponding to different lithofacies; Using the geological understanding and the probability density distribution corresponding to different lithofacies to convert the sedimentary facies into a planar distribution of different lithofacies, and obtaining the planar probabilities corresponding to different lithofacies.

4. The method according to claim 3, wherein Establishing the probability density distribution corresponding to different lithofacies is to change the conditional probability distribution of each attribute of the well logging sample points by adjusting the parameters of the probability density function to make it consistent with the distribution pattern of the well logging sample points.

5. The method according to claim 4, wherein The parameter variables are longitudinal wave impedance and longitudinal-to-transverse wave velocity ratio.

6. The method according to claim 5, wherein The probability density distribution is calculated through the following Bayesian formula; where x is a variable attribute parameter, yi is different lithofacies categories, and P(y i |x) is the posterior probability of belonging to a certain lithofacies, that is, the probability that the sample point of x belongs to a certain lithofacies under certain attribute conditions. P(x|y i ) is the conditional probability density function, P(y i ) is the prior probability of a certain lithofacies, which is obtained based on well logging data statistics. P(x) is a constant proportionality factor used to control the sum of probabilities of different lithofacies classifications to be 1.

7. The method according to claim 1, wherein The inversion results are a longitudinal wave impedance volume, a longitudinal-to-transverse wave velocity volume, and a lithofacies data volume.

8. A beach facies thin reservoir prediction device for plane-constrained prestack inversion, characterized in that, Including: A sensitive parameter acquisition module for performing petrophysical analysis on logging data to obtain pre-stack sensitive parameters of the reservoir; A probability acquisition module for obtaining the planar probabilities corresponding to different lithofacies based on logging data and geological understanding; An inversion module for performing pre-stack inversion based on seismic data, logging data, and seismic interpretation horizons, with the planar probabilities corresponding to different lithofacies as constraints, to obtain the inversion results; A parameter volume acquisition module for calculating a pre-stack sensitive parameter volume of the reservoir based on the inversion results and the pre-stack sensitive parameters of the reservoir.

9. An electronic device, characterized in that, The electronic device includes: A memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the planar constraint pre-stack inversion method for predicting beach facies thin reservoirs according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the planar constraint pre-stack inversion method for predicting beach facies thin reservoirs according to any one of claims 1-7.

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