Modeling methods, devices, storage media, and electronic equipment for geological lithology models

Abstract

This application provides a modeling method, apparatus, storage medium, and electronic device for a geological lithology model. The method includes: acquiring and interpreting three-dimensional seismic data of a target stratum to obtain first initial stratigraphic data of a first interface and second initial stratigraphic data of a second interface. The target stratum includes at least a bottom layer, a target layer, and a top layer arranged sequentially. The first interface is the interface between the top layer and the target layer, and the second interface is the interface between the bottom layer and the target layer. The first and second initial stratigraphic data are then corrected to obtain first and second target stratigraphic data, where the first target stratigraphic data corresponds to the first initial stratigraphic data, and the second target stratigraphic data corresponds to the second initial stratigraphic data. At least based on the first and second target stratigraphic data, a framework model of the target stratum is constructed. Finally, a geological lithology model is constructed based on the target data in the three-dimensional seismic data that corresponds to the framework model.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and more specifically, to a modeling method, apparatus, storage medium, and electronic device for a geological lithology model. Background Technology

[0002] In 3D geological modeling of coal mines, detailed and accurate characterization of coal seam thickness and roof and floor lithology is required. However, traditional geological models are mainly constructed using mathematical interpolation of borehole data, which results in significant uncertainties in stratigraphic information between boreholes and low precision, leading to low accuracy in the resulting lithological models and limiting the application of 3D geological models in coal mining. There is an urgent need for high-precision 3D geological lithology models that can completely and accurately represent the boundary conditions of complex geological phenomena. Summary of the Invention

[0003] The main objective of this application is to provide a modeling method, apparatus, storage medium, and electronic device for geological lithology models, so as to at least solve the problem of low accuracy of geological lithology models in the prior art.

[0004] To achieve the above objectives, according to one aspect of this application, a modeling method for a geological lithology model is provided. This modeling method includes: acquiring and interpreting three-dimensional seismic data of a target stratum to obtain first initial stratigraphic data of a first interface and second initial stratigraphic data of a second interface, wherein the target stratum includes at least a bottom layer, a target layer, and a top layer arranged sequentially; the first interface is the interface between the top layer and the target layer; and the second interface is the interface between the bottom layer and the target layer; correcting the first initial stratigraphic data and the second initial stratigraphic data to obtain first target stratigraphic data and second target stratigraphic data, wherein the first target stratigraphic data corresponds to the first initial stratigraphic data, and the second target stratigraphic data corresponds to the second initial stratigraphic data; constructing a framework model of the target stratum based at least on the first target stratigraphic data and the second target stratigraphic data; and constructing a geological lithology model based on the target data in the three-dimensional seismic data corresponding to the framework model.

[0005] Optionally, interpreting the three-dimensional seismic data to obtain first initial horizon data and second initial horizon data includes: selecting first data from the three-dimensional seismic data, wherein the first data is used for horizon interpretation; and interpreting the first data using seismic phase axes to obtain first initial horizon data and second initial horizon data.

[0006] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected to obtain the first target stratigraphic data and the second target stratigraphic data, including: correcting the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data.

[0007] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected using radial basis functions, including: selecting a target radial basis function; and performing a weighted summation based on the target radial basis function, the first initial stratigraphic data, and the target radial basis function and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data.

[0008] Optionally, it also includes: determining the stratigraphic thickness and spatial location of the target layer based on the first target stratigraphic data and the second target stratigraphic data.

[0009] Optionally, a geological lithology model is constructed based at least on the target data corresponding to the framework model in the 3D seismic data, including: acquiring well logging lithology data and P-wave impedance of the target formation; acquiring the target data from the 3D seismic data; and constructing a geological lithology model based on the target data, well logging lithology data, and P-wave impedance.

[0010] Optionally, a geological lithology model is constructed based on the target data, well logging lithology data, and P-wave impedance, including: determining a prior function and a likelihood function based on the target data, well logging lithology data, and P-wave impedance. The prior function is used to characterize the probability distribution of the lithology and P-wave impedance of the target layer, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data. The prior function and the likelihood function are fitted using Bayesian discriminant fitting to obtain the geological lithology model.

[0011] According to another aspect of this application, a modeling apparatus for a geological lithology model is provided. The modeling apparatus includes: an acquisition unit for acquiring and interpreting three-dimensional seismic data of a target stratum to obtain first initial stratigraphic data of a first interface and second initial stratigraphic data of a second interface, wherein the target stratum includes at least a bottom layer, a target layer, and a top layer arranged sequentially; the first interface is the interface between the top layer and the target layer; and the second interface is the interface between the bottom layer and the target layer; a correction unit for correcting the first initial stratigraphic data and the second initial stratigraphic data to obtain first target stratigraphic data and second target stratigraphic data, wherein the first target stratigraphic data corresponds to the first initial stratigraphic data, and the second target stratigraphic data corresponds to the second initial stratigraphic data; a first construction unit for constructing a framework model of the target stratum based at least on the first target stratigraphic data and the second target stratigraphic data; and a second construction unit for constructing a geological lithology model based on target data in the three-dimensional seismic data corresponding to the framework model.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is running, it controls the device where the computer-readable storage medium is located to execute the above-described modeling method for the geological lithology model.

[0013] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a modeling method for performing the above-described geological lithology model.

[0014] By applying the technical solution of this application, after acquiring three-dimensional seismic data and interpreting the aforementioned three-dimensional seismic data, it is possible to first obtain the first initial stratigraphic data corresponding to the first interface between the target layer and the top layer, and the second initial stratigraphic data corresponding to the second interface between the target layer and the bottom layer. After correcting the first and second initial stratigraphic data, the corresponding first and second target stratigraphic data can be obtained. Then, a framework model of the target stratum can be constructed based on the corrected first and second target stratigraphic data. Since this framework model can play an important role in the prediction of the stratigraphic thickness and spatial location of the target layer, the geological lithology model obtained after analyzing and processing the three-dimensional seismic data based on this framework model has higher accuracy than the models in the prior art. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for performing a modeling method for a geological lithology model according to an embodiment of this application is shown.

[0017] Figure 2 A schematic flowchart of a modeling method for a geological lithology model according to an embodiment of this application is shown.

[0018] Figure 3 The diagram illustrates a contour map of the bottom layer before correction of the first initial stratigraphic data and the second initial stratigraphic data of the second interface in a modeling method for a geological lithology model provided according to an embodiment of this application.

[0019] Figure 4 The illustration shows a contour map of the bottom layer after correction of the first initial stratigraphic data and the second initial stratigraphic data of the second interface in a modeling method for a geological lithology model provided according to an embodiment of this application.

[0020] Figure 5 A three-dimensional schematic diagram of a geological lithology modeling method provided according to an embodiment of this application is shown.

[0021] Figure 6 A structural block diagram of a modeling apparatus for a geological lithology model provided according to an embodiment of this application is shown. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0026] Seismic inversion technology is one of the most effective lithology prediction methods. Based on high-resolution inversion methods, it is possible to accurately predict the spatial distribution characteristics of reservoirs and finely characterize the thickness of coal seams and the lithology of the roof and floor. This paper uses geostatistical inversion methods to predict the lithology and thickness of target strata. However, the accuracy of geostatistical inversion is affected by multiple parameters such as the stratigraphic framework model, prior functions, and signal-to-noise ratio. Among these, the stratigraphic framework model plays an important role in the prediction of stratigraphic thickness and its spatial location, and directly affects the accuracy of the final model.

[0027] As described in the background section, traditional geological models in the prior art are constructed primarily through mathematical interpolation of borehole data. This results in significant uncertainties in stratigraphic information between boreholes and a low level of precision, leading to low accuracy in the resulting lithological models and limiting the application of 3D geological models in coal mining. To address the issue of low accuracy in lithological models, embodiments of this application provide a method, apparatus, storage medium, processor, and electronic device for modeling geological lithology models.

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a geological lithology modeling method according to an embodiment of the present invention. For example... Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] This embodiment provides a modeling method for a geological lithology model that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] Figure 2 This is a flowchart of a modeling method for a geological lithology model according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0033] Step S201: Obtain and interpret the three-dimensional seismic data of the target stratum to obtain the first initial stratigraphic data of the first interface and the second initial stratigraphic data of the second interface. The target stratum includes at least the bottom layer, the target layer and the top layer set in sequence. The first interface is the interface between the top layer and the target layer, and the second interface is the interface between the bottom layer and the target layer.

[0034] Specifically, in the study of the aforementioned target strata, the three-dimensional seismic data, as a data volume covering the entire study area, contains a wealth of geological information, enabling stratigraphic interpretation of the target strata based on this data. After stratigraphic interpretation using the three-dimensional seismic data, the bottom layer, target layer, and top layer of the target strata can be distinguished, and corresponding initial stratigraphic data corresponding to the first interface between the top layer and the target layer, and second initial stratigraphic data corresponding to the second interface between the bottom layer and the target layer, can be obtained.

[0035] Step S202: Correct the first initial layer data and the second initial layer data to obtain the first target layer data and the second target layer data, wherein the first target layer data corresponds to the first initial layer data and the second target layer data corresponds to the second initial layer data.

[0036] Specifically, since the target strata may have unclear stratigraphic strata, faults, etc., in order to first correct the first initial stratigraphic data and the second initial stratigraphic data so that the first initial stratigraphic data and the second initial stratigraphic data can approximate the target data points, thereby obtaining the first target stratigraphic data and the second target stratigraphic data, making the stratigraphic interpretation results more accurate.

[0037] Step S203: Construct a framework model of the target strata based at least on the first target stratigraphic data and the second target stratigraphic data;

[0038] Specifically, after obtaining the first and second target stratigraphic data, since the framework model of the target strata plays an important role in the prediction of the stratigraphic thickness and spatial location of the target strata, a framework model of the target strata is first constructed based on the first and second target stratigraphic data. This framework model is used to constrain the stratigraphic thickness and spatial location of the target strata. That is, the framework model is equivalent to the computational grid unit for the subsequent formation of the geological lithology model. It is the basis for carrying out inversion calculations for the target strata and directly affects the accuracy of the final model.

[0039] Step S204: Construct a geological lithology model based on the target data corresponding to the frame model in the 3D seismic data.

[0040] Specifically, after obtaining the above-mentioned framework model, since the framework model can constrain the stratigraphic thickness and spatial location of the target layer, the portion of the three-dimensional seismic data corresponding to the framework model in the above-mentioned three-dimensional seismic data is directly used as the target data for constructing the geological lithology model. That is, the above-mentioned three-dimensional seismic data includes the target data, thereby making the geological lithology model more accurate.

[0041] In this embodiment, by acquiring three-dimensional seismic data and interpreting it, we can first obtain the first initial stratigraphic data corresponding to the first interface between the target layer and the top layer, and the second initial stratigraphic data corresponding to the second interface between the target layer and the bottom layer. After correcting the first and second initial stratigraphic data, we can obtain the corresponding first and second target stratigraphic data. Based on the corrected first and second target stratigraphic data, we can construct a framework model of the target strata. Since this framework model can play an important role in the prediction of the stratigraphic thickness and spatial location of the target layer, the geological lithology model obtained after analyzing and processing the three-dimensional seismic data based on this framework model has higher accuracy than the models in the prior art.

[0042] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the geological lithology modeling method of this application will be described in detail below with reference to specific embodiments.

[0043] In specific implementation, in some optional embodiments, the interpretation of three-dimensional seismic data in step S201 above to obtain first initial layer data and second initial layer data can be achieved through the following steps: selecting first data from the three-dimensional seismic data, wherein the first data is used for layer interpretation; interpreting the first data using seismic phase axes to obtain first initial layer data and second initial layer data.

[0044] In the above embodiments, since the 3D seismic data is a data volume covering the entire study area, including parts unrelated to stratigraphic interpretation, first data for stratigraphic interpretation is selected from the data volume. By interpreting this first data, first initial stratigraphic data representing the first interface between the top layer and the target layer in the target strata, and second initial stratigraphic data representing the second interface between the bottom layer and the target layer in the target strata, are obtained. Furthermore, since seismic phase axes can be identified during the 3D seismic data interpretation process based on the regularity of vibrations with similar shapes, they represent stratigraphic interfaces of different lithologies in the target strata, such as the first interface between the top layer and the target layer, and the second interface between the target layer and the bottom layer. Therefore, interpreting the first data using these seismic phase axes significantly improves the signal-to-noise ratio of the first and second interfaces, resulting in clear interface contours for the first interface corresponding to the first initial stratigraphic data and the second interface corresponding to the second initial stratigraphic data.

[0045] Specifically, such as Figure 3As shown, the numbers 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, and 222 represent the depth of the base plate. By using the seismic phase axis to interpret the top and bottom positions of the first and second interfaces, the model can be based on three-dimensional seismic data within the study area, thus effectively controlling the horizontal extension of the target strata in the study area.

[0046] In some optional implementations, the correction of the first initial stratigraphic data and the second initial stratigraphic data in step S202 above to obtain the first target stratigraphic data and the second target stratigraphic data can be achieved by the following steps: using radial basis functions to correct the first initial stratigraphic data and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data.

[0047] In the above implementation, since the first and second initial stratigraphic data of the 3D seismic data are stratigraphic data obtained by interpreting the 3D seismic data collected during the actual survey process, and if the data volume of the 3D seismic data used for stratigraphic interpretation is insufficient, it will directly affect the accuracy and correctness of the final results. Therefore, radial basis functions are used to process the first and second initial stratigraphic data, thereby enabling further interpolation based on the first and second initial stratigraphic data, making the data volume richer, and thus more realistically depicting the distribution pattern of the stratigraphic data of the target strata, such as... Figure 4 As shown, the numbers 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, and 222 represent the depth of the base plate.

[0048] In some optional implementations, radial basis functions are used to correct the first initial stratigraphic data and the second initial stratigraphic data, including: selecting a target radial basis function; and performing a weighted summation based on the target radial basis function and the first initial stratigraphic data, as well as the target radial basis function and the second initial stratigraphic data, to obtain the first target stratigraphic data and the second target stratigraphic data.

[0049] Specifically, radial basis functions (RBFs) are a commonly used global interpolation method. They can select appropriate RBFs based on finite sampled data, and then generate a first surface with minimum curvature and minimum distance to each sample point based on the RBFs and the first initial layer data. This first surface corresponds to the first interface. In addition, a second surface with small curvature and minimum distance to each sample point is generated based on the RBFs and the second initial layer data. This second surface corresponds to the second interface. This completes the fitting and approximation of the data points for constructing the above framework model, and calculates the Euclidean distance between the interpolation points and the known data. The known data are then linearly weighted and summed to correct the first and second initial layer data.

[0050] The interpolation function Z in the radial basis function interpolation method can be expressed by the following formula:

[0051]

[0052] Where n is the number of known data points, μ k These are the interpolation coefficients. Let λ be a radial basis function, and let λ be any point in space. k This represents the center point of the kernel function corresponding to the radial basis function.

[0053] In some optional implementations, the target radial basis function is selected as the Gaussian distribution function of the radial basis function Kriging method. It can be expressed by the following formula:

[0054]

[0055] Here, b is a shape parameter that controls the smoothing ability of the basis function. Thus, when the Gaussian distribution function assigns different weights according to the standard deviation range, the scattered data points are linearly weighted and summed to make the interpolation result more accurate.

[0056] In some optional implementations, the modeling method of the above-mentioned geological lithology model further includes: determining the stratigraphic thickness and spatial location of the target layer based on the first target stratigraphic data and the second target stratigraphic data.

[0057] In the above embodiments, since the first target stratigraphic data and the second target stratigraphic data can more realistically depict the distribution pattern of the stratigraphic data of the target strata, after determining the stratigraphic thickness and spatial location of the target strata, the distribution of the target strata in the geological lithology model can be further determined.

[0058] In some optional implementations, the step S204 above, which involves constructing a geological lithology model based at least on the target data corresponding to the frame model in the three-dimensional seismic data, can be achieved through the following steps: obtaining well logging lithology data and P-wave impedance of the target formation; obtaining the target data from the three-dimensional seismic data; and constructing a geological lithology model based on the target data, well logging lithology data, and P-wave impedance.

[0059] In the above embodiments, the well logging lithology data and P-wave impedance are obtained through actual surveys of the study area of ​​the target strata. In order to make the geological lithology model constructed based on the target data closer to the actual geological conditions, the target strata are surveyed to obtain the well logging lithology data and P-wave impedance. The target data, well logging lithology data and P-wave impedance are then combined to make the constructed geological lithology model closer to the actual geological conditions of the target strata.

[0060] In some optional implementations, the construction of the geological lithology model based on the target data, well logging lithology data, and P-wave impedance in step S204 above can be achieved through the following steps: determining the prior function and the likelihood function based on the target data, well logging lithology data, and P-wave impedance. The prior function is used to characterize the probability distribution of the lithology and P-wave impedance of the target layer, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data. The prior function and the likelihood function are fitted using Bayesian discriminant fitting to obtain the geological lithology model.

[0061] In the above implementation method, a high-resolution inversion method is employed. Based on the aforementioned framework model, analysis is performed using the target data, well logging lithology data of the target strata, and P-wave impedance to determine the probability distribution of the lithology and P-wave impedance of the target strata. The function corresponding to this probability distribution is the prior function. Furthermore, the likelihood probabilities representing the geological lithology model and actual seismic data are determined, and the function corresponding to these likelihood probabilities is the likelihood function. This allows for the inversion and construction of a geological model in Jiangxi Province, based on Bayesian theory, to characterize the thickness of the target strata and the lithology of the top layer and strata. Specifically, after fitting the prior function and likelihood function using Bayesian discriminant fitting, a posterior function is obtained. This posterior function can determine the probability of determining the lithology and P-wave impedance of the target strata based on actual seismic data, thus obtaining the geological lithology model. Figure 5 As shown, the geological lithology model includes cubic strata 110.

[0062] Specifically, the above prior function is expressed by the following formula:

[0063]

[0064] Where m represents P-wave impedance; f represents lithology; i is the sampling point of the 3D seismic data; P(f) is the probability of lithology; P(m1|f1) is the probability distribution of impedance corresponding to the lithology of the top-level 3D seismic data sample point; P(m i |m (i-1) ,f (i-1) ,f i ) represents the probability distribution of impedance corresponding to the lithology of the i-th three-dimensional seismic data sample.

[0065] Bayesian discriminant analysis is a method that integrates and discriminates multiple types of information using probability and statistics principles. It can fairly analyze and judge multiple uncertain information sources in a unified manner. The Bayesian formula is as follows:

[0066]

[0067] Wherein, P(X|H,E) is the posterior function (posterior probability distribution) to be solved, representing the obtained geological lithology model; P(X|H) is the prior function (prior probability distribution) of event X under the known assumptions (H), representing the probability distribution of the lithology and P-wave impedance of the target layer; P(E|X) is the likelihood function; P(H|E) can be approximated as a total probability model, representing the likelihood probability of the obtained geological lithology model and the actual seismic data, with a value of 1.

[0068] This application also provides a modeling apparatus for geological lithology models. It should be noted that the modeling apparatus for geological lithology models in this application can be used to execute the modeling method for geological lithology models provided in this application. This apparatus is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0069] The following describes the modeling apparatus for the geological lithology model provided in the embodiments of this application.

[0070] Figure 6 This is a schematic diagram of a modeling apparatus for a geological lithology model according to an embodiment of this application. Figure 6 As shown, the device includes:

[0071] The acquisition unit 301 is used to acquire and interpret the three-dimensional seismic data of the target stratum to obtain the first initial stratigraphic data of the first interface and the second initial stratigraphic data of the second interface. The target stratum includes at least the bottom layer, the target layer and the top layer set in sequence. The first interface is the interface between the top layer and the target layer, and the second interface is the interface between the bottom layer and the target layer.

[0072] Specifically, in the study of the aforementioned target strata, the three-dimensional seismic data, as a data volume covering the entire study area, contains a wealth of geological information, enabling stratigraphic interpretation of the target strata based on this data. After stratigraphic interpretation using the three-dimensional seismic data, the bottom layer, target layer, and top layer of the target strata can be distinguished, and corresponding initial stratigraphic data corresponding to the first interface between the top layer and the target layer, and second initial stratigraphic data corresponding to the second interface between the bottom layer and the target layer, can be obtained.

[0073] The correction unit 302 is used to correct the first initial layer data and the second initial layer data to obtain the first target layer data and the second target layer data, wherein the first target layer data corresponds to the first initial layer data and the second target layer data corresponds to the second initial layer data.

[0074] Specifically, since the target strata may have unclear stratigraphic strata, faults, etc., in order to first correct the first initial stratigraphic data and the second initial stratigraphic data so that the first initial stratigraphic data and the second initial stratigraphic data can approximate the target data points, thereby obtaining the first target stratigraphic data and the second target stratigraphic data, making the stratigraphic interpretation results more accurate.

[0075] The first building unit 303 is used to build a framework model of the target strata based at least on the first target stratigraphic data and the second target stratigraphic data;

[0076] Specifically, after obtaining the first and second target stratigraphic data, since the framework model of the target strata plays an important role in the prediction of the stratigraphic thickness and spatial location of the target strata, a framework model of the target strata is first constructed based on the first and second target stratigraphic data. This framework model is used to constrain the stratigraphic thickness and spatial location of the target strata. That is, the framework model is equivalent to the computational grid unit for the subsequent formation of the geological lithology model. It is the basis for carrying out inversion calculations for the target strata and directly affects the accuracy of the final model.

[0077] The second building unit 304 is used to build a geological lithology model based on the target data corresponding to the frame model in the three-dimensional seismic data.

[0078] Specifically, after obtaining the above-mentioned framework model, since the framework model can constrain the stratigraphic thickness and spatial location of the target layer, the portion of the three-dimensional seismic data corresponding to the framework model in the above-mentioned three-dimensional seismic data is directly used as the target data for constructing the geological lithology model. That is, the above-mentioned three-dimensional seismic data includes the target data, thereby making the geological lithology model more accurate.

[0079] In this embodiment, after acquiring three-dimensional seismic data through the acquisition unit 301, by interpreting the three-dimensional seismic data, the first initial stratigraphic data corresponding to the first interface between the target layer and the top layer, and the second initial stratigraphic data corresponding to the second interface between the target layer and the bottom layer can be obtained. Then, after the correction unit 302 corrects the first initial stratigraphic data and the second initial stratigraphic data, the corresponding first target stratigraphic data and the second target stratigraphic data can be obtained. Then, the first construction unit 303 can construct a framework model of the target stratum based on the corrected first target stratigraphic data and the second target stratigraphic data. Since the framework model can play an important role in the prediction of the stratum thickness and spatial location of the target layer, after the second construction unit 304 analyzes and processes the three-dimensional seismic data in the framework model, the resulting geological lithology model has higher accuracy than the models in the prior art.

[0080] In specific implementation, in some optional embodiments, the interpretation of three-dimensional seismic data in the acquisition unit 301 to obtain first initial layer data and second initial layer data can be achieved by the following modules: a first processing module, used to select first data from the three-dimensional seismic data, wherein the first data is used for layer interpretation; and a second processing module, used to interpret the first data using seismic phase axes to obtain first initial layer data and second initial layer data.

[0081] In the above embodiments, since the 3D seismic data is a data volume covering the entire study area, including parts unrelated to stratigraphic interpretation, first data for stratigraphic interpretation is selected from the data volume. By interpreting this first data, first initial stratigraphic data representing the first interface between the top layer and the target layer in the target strata, and second initial stratigraphic data representing the second interface between the bottom layer and the target layer in the target strata, are obtained. Furthermore, since seismic phase axes can be identified during the 3D seismic data interpretation process based on the regularity of vibrations with similar shapes, they represent stratigraphic interfaces of different lithologies in the target strata, such as the first interface between the top layer and the target layer, and the second interface between the target layer and the bottom layer. Therefore, interpreting the first data using these seismic phase axes significantly improves the signal-to-noise ratio of the first and second interfaces, resulting in clear interface contours for the first interface corresponding to the first initial stratigraphic data and the second interface corresponding to the second initial stratigraphic data.

[0082] Specifically, such as Figure 3As shown, the numbers 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, and 222 represent the depth of the base plate. By using the seismic phase axis to interpret the top and bottom positions of the first and second interfaces, the model can be based on three-dimensional seismic data within the study area, thus effectively controlling the horizontal extension of the target strata in the study area.

[0083] In some optional embodiments, the correction unit 302 described above, which corrects the first initial stratigraphic data and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data, can be implemented by the following module: a third processing module, used to correct the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data.

[0084] In the above implementation, since the first and second initial stratigraphic data of the 3D seismic data are stratigraphic data obtained by interpreting the 3D seismic data collected during the actual survey process, and if the data volume of the 3D seismic data used for stratigraphic interpretation is insufficient, it will directly affect the accuracy and correctness of the final results. Therefore, radial basis functions are used to process the first and second initial stratigraphic data, thereby enabling further interpolation based on the first and second initial stratigraphic data, making the data volume richer, and thus more realistically depicting the distribution pattern of the stratigraphic data of the target strata, such as... Figure 4 As shown, the numbers 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, and 222 represent the depth of the base plate.

[0085] In some optional implementations, the third processing module described above can be implemented by the following modules: a first selection module for selecting a target radial basis function; and a summation module for performing a weighted summation based on the target radial basis function and the first initial stratigraphic data, as well as the target radial basis function and the second initial stratigraphic data, to obtain the first target stratigraphic data and the second target stratigraphic data.

[0086] Specifically, radial basis functions (RBFs) are a commonly used global interpolation method. They can select appropriate RBFs based on finite sampled data, and then generate a first surface with minimum curvature and minimum distance to each sample point based on the RBFs and the first target layer data. This first surface corresponds to the first interface. In addition, a second surface with minimum curvature and minimum distance to each sample point is generated based on the RBFs and the second target layer data. This second surface corresponds to the second interface. This completes the fitting and approximation of the data points for constructing the above framework model, calculates the Euclidean distance between the interpolation points and the known data, and performs a linear weighted summation of the known data to correct the first and second initial layer data.

[0087] The interpolation function for radial basis function interpolation can be:

[0088]

[0089] Where n is the number of known data points, μ k These are the interpolation coefficients. These are radial basis functions.

[0090] In some optional implementations, the target radial basis function is selected as the Gaussian distribution function of the radial basis function Kriging method: Here, b is a shape parameter that controls the smoothing ability of the basis function. Thus, when the Gaussian distribution function assigns different weights according to the standard deviation range, the scattered data points are linearly weighted and summed to make the interpolation result more accurate.

[0091] In some optional embodiments, the modeling apparatus for the geological lithology model further includes: a first determining unit, used to determine the stratigraphic thickness and spatial location of the target layer based on the first target stratigraphic data and the second target stratigraphic data.

[0092] In the above embodiments, since the first target stratigraphic data and the second target stratigraphic data can more realistically depict the distribution pattern of the stratigraphic data of the target strata, after determining the stratigraphic thickness and spatial location of the target strata, the distribution of the target strata in the geological lithology model can be further determined.

[0093] In some optional implementations, the geological lithology model constructed in the second construction unit 304 above, based at least on the target data corresponding to the framework model in the three-dimensional seismic data, can be implemented by the following modules: a first acquisition module for acquiring well logging lithology data and P-wave impedance of the target formation; a second acquisition module for acquiring the target data from the three-dimensional seismic data; and a first construction module for constructing the geological lithology model based on the target data, well logging lithology data, and P-wave impedance.

[0094] In the above embodiments, the well logging lithology data and P-wave impedance are obtained through actual surveys of the study area of ​​the target strata. In order to make the geological lithology model constructed based on the target data closer to the actual geological conditions, the target strata are surveyed to obtain the well logging lithology data and P-wave impedance. The target data, well logging lithology data and P-wave impedance are then combined to make the constructed geological lithology model closer to the actual geological conditions of the target strata.

[0095] In some optional implementations, the geological lithology model constructed based on target data, well logging lithology data, and P-wave impedance in the second construction unit 304 described above can be implemented by the following modules: a first determination module, used to determine a prior function and a likelihood function based on the target data, well logging lithology data, and P-wave impedance. The prior function is used to characterize the probability distribution of the lithology and P-wave impedance of the target layer, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data; and a fourth processing module, used to fit the prior function and the likelihood function using Bayesian discriminant fitting to obtain the geological lithology model.

[0096] In the above implementation method, a high-resolution inversion method is employed. Based on the aforementioned framework model, analysis is performed using the target data, well logging lithology data of the target strata, and P-wave impedance to determine the probability distribution of the lithology and P-wave impedance of the target strata. The function corresponding to this probability distribution is the prior function. Furthermore, the likelihood probabilities representing the geological lithology model and actual seismic data are determined, and the function corresponding to these likelihood probabilities is the likelihood function. This allows for the inversion and construction of a geological model in Jiangxi Province, based on Bayesian theory, to characterize the thickness of the target strata and the lithology of the top layer and strata. Specifically, after fitting the prior function and likelihood function using Bayesian discriminant fitting, a posterior function is obtained. This posterior function can determine the probability of determining the lithology and P-wave impedance of the target strata based on actual seismic data, thus obtaining the geological lithology model. Figure 5 As shown.

[0097] Specifically, the formula for the prior function is as follows:

[0098]

[0099] Where m represents P-wave impedance; f represents lithology; i is the sampling point of the 3D seismic data; P(f) is the probability of lithology; P(m1|f1) is the probability distribution of impedance corresponding to the lithology of the top-level 3D seismic data sample point; P(m i |m (i-1) ,f (i-1) ,f i ) represents the probability distribution of impedance corresponding to the lithology of the i-th three-dimensional seismic data sample.

[0100] Bayesian discriminant analysis is a method that integrates and discriminates multiple types of information using probability and statistics principles. It can fairly analyze and judge multiple uncertain information sources in a unified manner. The Bayesian formula is as follows:

[0101]

[0102] Wherein, P(X|H, E) is the posterior function (posterior probability distribution) to be solved, representing the obtained geological lithology model; P(X|H) is the prior function (prior probability distribution) of event X under known assumptions (H), representing the probability distribution of the lithology and P-wave impedance of the target layer; P(E|X) is the likelihood function; P(H|E) can be approximated as a total probability model, representing the likelihood probability of the obtained geological lithology model and the actual seismic data, with a value of 1. The modeling device for the geological lithology model includes a processor and a memory. The aforementioned acquisition unit 301, correction unit 302, first construction unit 303, and second construction unit 304 are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0103] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and the model accuracy can be improved by adjusting the kernel parameters.

[0104] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0105] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is running, it controls the device where the computer-readable storage medium is located to execute a modeling method for a geological lithology model.

[0106] Specifically, the modeling methods for geological lithology models include:

[0107] Step S201: Obtain and interpret the three-dimensional seismic data of the target stratum to obtain the first initial stratigraphic data of the first interface and the second initial stratigraphic data of the second interface. The target stratum includes at least the bottom layer, the target layer and the top layer set in sequence. The first interface is the interface between the top layer and the target layer, and the second interface is the interface between the bottom layer and the target layer.

[0108] Specifically, in the study of the aforementioned target strata, the three-dimensional seismic data, as a data volume covering the entire study area, contains a wealth of geological information, enabling stratigraphic interpretation of the target strata based on this data. After stratigraphic interpretation using the three-dimensional seismic data, the bottom layer, target layer, and top layer of the target strata can be distinguished, and corresponding initial stratigraphic data corresponding to the first interface between the top layer and the target layer, and second initial stratigraphic data corresponding to the second interface between the bottom layer and the target layer, can be obtained.

[0109] Step S202: Correct the first initial layer data and the second initial layer data to obtain the first target layer data and the second target layer data, wherein the first target layer data corresponds to the first initial layer data and the second target layer data corresponds to the second initial layer data.

[0110] Specifically, since the target strata may have unclear stratigraphic strata, faults, etc., in order to first correct the first initial stratigraphic data and the second initial stratigraphic data so that the first initial stratigraphic data and the second initial stratigraphic data can approximate the target data points, thereby obtaining the first target stratigraphic data and the second target stratigraphic data, making the stratigraphic interpretation results more accurate.

[0111] Step S203: Construct a framework model of the target strata based at least on the first target stratigraphic data and the second target stratigraphic data;

[0112] Specifically, after obtaining the first and second target stratigraphic data, since the framework model of the target strata plays an important role in the prediction of the stratigraphic thickness and spatial location of the target strata, a framework model of the target strata is first constructed based on the first and second target stratigraphic data. This framework model is used to constrain the stratigraphic thickness and spatial location of the target strata. That is, the framework model is equivalent to the computational grid unit for the subsequent formation of the geological lithology model. It is the basis for carrying out inversion calculations for the target strata and directly affects the accuracy of the final model.

[0113] Step S204: Construct a geological lithology model based on the target data corresponding to the frame model in the 3D seismic data.

[0114] Specifically, after obtaining the above-mentioned framework model, since the framework model can constrain the stratigraphic thickness and spatial location of the target layer, the portion of the three-dimensional seismic data corresponding to the framework model in the above-mentioned three-dimensional seismic data is directly used as the target data for constructing the geological lithology model. That is, the above-mentioned three-dimensional seismic data includes the target data, thereby making the geological lithology model more accurate.

[0115] Optionally, interpreting the three-dimensional seismic data to obtain first initial horizon data and second initial horizon data includes: selecting first data from the three-dimensional seismic data, wherein the first data is used for horizon interpretation; and interpreting the first data using seismic phase axes to obtain first initial horizon data and second initial horizon data.

[0116] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected to obtain the first target stratigraphic data and the second target stratigraphic data, including: correcting the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data.

[0117] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected using radial basis functions, including: selecting a target radial basis function; and performing a weighted summation based on the target radial basis function, the first initial stratigraphic data, and the target radial basis function and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data.

[0118] Optionally, it also includes: determining the stratigraphic thickness and spatial location of the target layer based on the first target stratigraphic data and the second target stratigraphic data.

[0119] Optionally, a geological lithology model is constructed based at least on the target data corresponding to the framework model in the 3D seismic data, including: acquiring well logging lithology data and P-wave impedance of the target formation; acquiring the target data from the 3D seismic data; and constructing a geological lithology model based on the target data, well logging lithology data, and P-wave impedance.

[0120] Optionally, a geological lithology model is constructed based on the target data, well logging lithology data, and P-wave impedance, including: determining a prior function and a likelihood function based on the target data, well logging lithology data, and P-wave impedance. The prior function is used to characterize the probability distribution of the lithology and P-wave impedance of the target layer, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data. The prior function and the likelihood function are fitted using Bayesian discriminant fitting to obtain the geological lithology model.

[0121] This invention provides an electronic device, including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: acquiring and interpreting three-dimensional seismic data of a target stratum to obtain first initial stratigraphic data of a first interface and second initial stratigraphic data of a second interface, wherein the target stratum includes at least a bottom layer, a target layer, and a top layer arranged sequentially; the first interface is the interface between the top layer and the target layer; and the second interface is the interface between the bottom layer and the target layer; correcting the first and second initial stratigraphic data to obtain first and second target stratigraphic data, wherein the first target stratigraphic data corresponds to the first initial stratigraphic data, and the second target stratigraphic data corresponds to the second initial stratigraphic data; constructing a framework model of the target stratum based at least on the first and second target stratigraphic data; and constructing a geological lithology model based on the target data in the three-dimensional seismic data corresponding to the framework model. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0122] Optionally, interpreting the three-dimensional seismic data to obtain first initial horizon data and second initial horizon data includes: selecting first data from the three-dimensional seismic data, wherein the first data is used for horizon interpretation; and interpreting the first data using seismic phase axes to obtain first initial horizon data and second initial horizon data.

[0123] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected to obtain the first target stratigraphic data and the second target stratigraphic data, including: correcting the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data.

[0124] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected using radial basis functions, including: selecting a target radial basis function; and performing a weighted summation based on the target radial basis function, the first initial stratigraphic data, and the target radial basis function and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data.

[0125] Optionally, it also includes: determining the stratigraphic thickness and spatial location of the target layer based on the first target stratigraphic data and the second target stratigraphic data.

[0126] Optionally, a geological lithology model is constructed based at least on the target data corresponding to the framework model in the 3D seismic data, including: acquiring well logging lithology data and P-wave impedance of the target formation; acquiring the target data from the 3D seismic data; and constructing a geological lithology model based on the target data, well logging lithology data, and P-wave impedance.

[0127] Optionally, a geological lithology model is constructed based on the target data, well logging lithology data, and P-wave impedance, including: determining a prior function and a likelihood function based on the target data, well logging lithology data, and P-wave impedance. The prior function is used to characterize the probability distribution of the lithology and P-wave impedance of the target layer, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data. The prior function and the likelihood function are fitted using Bayesian discriminant fitting to obtain the geological lithology model.

[0128] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: acquiring and interpreting three-dimensional seismic data of a target stratum to obtain first initial stratigraphic data of a first interface and second initial stratigraphic data of a second interface, wherein the target stratum includes at least a bottom layer, a target layer, and a top layer arranged in sequence, the first interface being the interface between the top layer and the target layer, and the second interface being the interface between the bottom layer and the target layer; correcting the first initial stratigraphic data and the second initial stratigraphic data to obtain first target stratigraphic data and second target stratigraphic data, wherein the first target stratigraphic data corresponds to the first initial stratigraphic data, and the second target stratigraphic data corresponds to the second initial stratigraphic data; constructing a framework model of the target stratum based at least on the first target stratigraphic data and the second target stratigraphic data; and constructing a geological lithology model based on the target data in the three-dimensional seismic data corresponding to the framework model.

[0129] Optionally, interpreting the three-dimensional seismic data to obtain first initial horizon data and second initial horizon data includes: selecting first data from the three-dimensional seismic data, wherein the first data is used for horizon interpretation; and interpreting the first data using seismic phase axes to obtain first initial horizon data and second initial horizon data.

[0130] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected to obtain the first target stratigraphic data and the second target stratigraphic data, including: correcting the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data.

[0131] Optionally, the first initial stratigraphic data and the second initial stratigraphic data are corrected using radial basis functions, including: selecting a target radial basis function; and performing a weighted summation based on the target radial basis function, the first initial stratigraphic data, and the target radial basis function and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data.

[0132] Optionally, it also includes: determining the stratigraphic thickness and spatial location of the target layer based on the first target stratigraphic data and the second target stratigraphic data.

[0133] Optionally, a geological lithology model is constructed based at least on the target data corresponding to the framework model in the 3D seismic data, including: acquiring well logging lithology data and P-wave impedance of the target formation; acquiring the target data from the 3D seismic data; and constructing a geological lithology model based on the target data, well logging lithology data, and P-wave impedance.

[0134] Optionally, a geological lithology model is constructed based on the target data, well logging lithology data, and P-wave impedance, including: determining a prior function and a likelihood function based on the target data, well logging lithology data, and P-wave impedance. The prior function is used to characterize the probability distribution of the lithology and P-wave impedance of the target layer, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data. The prior function and the likelihood function are fitted using Bayesian discriminant fitting to obtain the geological lithology model.

[0135] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0136] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0137] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0138] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0140] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0141] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0142] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0144] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0145] By acquiring 3D seismic data and interpreting it, we can first obtain the first initial stratigraphic data corresponding to the first interface between the target layer and the top layer, and the second initial stratigraphic data corresponding to the second interface between the target layer and the bottom layer. After correcting the first and second initial stratigraphic data, we can obtain the corresponding first and second target stratigraphic data. Based on the corrected first and second target stratigraphic data, we can construct a framework model of the target strata. Since this framework model can play an important role in the prediction of the thickness and spatial location of the target layer, the geological lithology model obtained after analyzing and processing the 3D seismic data based on this framework model has higher accuracy than the models in the existing technology.

[0146] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A modeling method of a geological lithology model, characterized by, The modeling method includes: The three-dimensional seismic data of the target stratum are acquired and interpreted to obtain the first initial stratigraphic data of the first interface and the second initial stratigraphic data of the second interface. The target stratum includes at least a bottom layer, a target layer and a top layer arranged in sequence. The first interface is the interface between the top layer and the target layer, and the second interface is the interface between the bottom layer and the target layer. The first initial stratigraphic data and the second initial stratigraphic data are corrected to obtain the first target stratigraphic data and the second target stratigraphic data, wherein the first target stratigraphic data corresponds to the first initial stratigraphic data and the second target stratigraphic data corresponds to the second initial stratigraphic data. A framework model of the target stratum is constructed based at least on the first target stratigraphic data and the second target stratigraphic data; A geological lithology model is constructed based on the target data corresponding to the framework model in the three-dimensional seismic data. Interpreting the three-dimensional seismic data to obtain the first initial layer data and the second initial layer data includes: selecting first data from the three-dimensional seismic data, wherein the first data is used for layer interpretation; and interpreting the first data using seismic phase axes to obtain the first initial layer data and the second initial layer data. The step of correcting the first initial stratigraphic data and the second initial stratigraphic data to obtain the first target stratigraphic data and the second target stratigraphic data includes: correcting the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data; The step of constructing a geological lithology model based at least on target data corresponding to the framework model in the three-dimensional seismic data includes: acquiring well logging lithology data and P-wave impedance of the target formation; acquiring the target data from the three-dimensional seismic data; and constructing the geological lithology model based on the target data, the well logging lithology data, and the P-wave impedance.

2. The modeling method of claim 1, wherein, The step of correcting the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions includes: Select the target radial basis function; The first target layer data and the second target layer data are obtained by weighted summation of the target radial basis function and the first initial layer data, and the target radial basis function and the second initial layer data.

3. The modeling method of claim 1, wherein, Also includes: Based on the first target stratigraphic data and the second target stratigraphic data, the stratigraphic thickness of the target layer and the spatial location of the target layer are determined.

4. The modeling method of claim 1, wherein, The step of constructing the geological lithology model based on the target data, the well logging lithology data, and the P-wave impedance includes: Based on the target data, the well logging lithology data, and the P-wave impedance, a prior function and a likelihood function are determined. The prior function is used to characterize the probability distribution of the lithology of the target layer and the P-wave impedance, and the likelihood function is used to characterize the likelihood probability of the geological lithology model and the actual seismic data. The geological lithology model is obtained by fitting the prior function and the likelihood function using Bayesian discriminant fitting.

5. A modeling device of a geological lithology model, characterized by, The modeling device includes: The acquisition unit is used to acquire and interpret three-dimensional seismic data of the target stratum to obtain first initial stratigraphic data of the first interface and second initial stratigraphic data of the second interface. The target stratum includes at least a bottom layer, a target layer and a top layer arranged in sequence. The first interface is the interface between the top layer and the target layer, and the second interface is the interface between the bottom layer and the target layer. A correction unit is used to correct the first initial layer data and the second initial layer data to obtain a first target layer data and a second target layer data, wherein the first target layer data corresponds to the first initial layer data and the second target layer data corresponds to the second initial layer data. The first construction unit is used to construct a framework model of the target stratum based at least on the first target stratigraphic data and the second target stratigraphic data; The second construction unit is used to construct a geological lithology model based on the target data in the three-dimensional seismic data that corresponds to the framework model; The acquisition unit includes a first processing module and a second processing module. The first processing module is used to select first data from the three-dimensional seismic data, wherein the first data is used for horizon interpretation. The second processing module interprets the first data using seismic phase axes to obtain the first initial horizon data and the second initial horizon data. The correction unit includes a third processing module, which is used to correct the first initial stratigraphic data and the second initial stratigraphic data using radial basis functions to obtain the first target stratigraphic data and the second target stratigraphic data. The second construction unit includes a first acquisition module, a second acquisition module, and a first construction module. The first acquisition module is used to acquire well logging lithology data and P-wave impedance of the target formation. The second acquisition module is used to acquire the target data from the three-dimensional seismic data. The first construction module is used to construct the geological lithology model based on the target data, the well logging lithology data, and the P-wave impedance.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the modeling method of the geological lithology model according to any one of claims 1 to 4.

7. An electronic device, comprising: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a modeling method for performing a geological lithology model according to any one of claims 1 to 4.

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