Formation model construction method, device, electronic device and storage medium

Abstract

The present disclosure provides a method, device, electronic device, and storage medium for constructing a stratum model. The method comprises: estimating random medium statistical characteristic parameters for each unknown spatial point and each known spatial point based on seismic data; determining anisotropic spatial correlation lengths based on the position coordinates and random medium statistical characteristic parameters of each unknown spatial point and each known spatial point; substituting the determined anisotropic spatial correlation lengths into a spatial correlation function to obtain anisotropic spatial covariance function; for each unknown spatial point: determining anisotropic kriging weight coefficients for each known spatial point based on the determined anisotropic spatial covariance function; and determining predicted values ​​for each known spatial point based on the anisotropic kriging weight coefficients and observed values ​​of each known spatial point. This method can establish kriging interpolation results with smaller errors, more consistent with the actual spatial structural characteristics of the underground medium, and less uncertainty.

Description

Technical Field

[0001] The present disclosure relates to the field of geophysical technology, and in particular to a formation model construction method, device, electronic equipment, and storage medium. Background Art

[0002] Kriging interpolation uses spatially correlated information about spatial variables to assign weights to known points, then estimates unknown points using a weighted average. First, the variogram is calculated from well logging data to obtain the spatial range of the subsurface medium, representing the spatially correlated information of the subsurface medium. The spatial range is then substituted into the spatial correlation function to obtain the spatial covariance function, from which the kriging matrix and kriging vectors are constructed. Finally, the kriging weights for the known points are calculated from the kriging matrix and vectors. The kriging interpolation result is then obtained by taking the weighted average of the known points according to their respective kriging weights.

[0003] To simplify the interpolation process, kriging interpolation typically assumes that the spatial correlation information for all points in space is the same across all directions. Spatial ranges are then used to characterize the isotropic spatial structure of the subsurface medium. However, due to the influence of geological factors such as the sedimentary environment, tectonic movement, and overlying pressure, the spatial structure of the subsurface medium often exhibits anisotropic characteristics, with varying spatial correlation lengths in different directions. Therefore, the kriging interpolation results obtained using the isotropic spatial structure kriging method based on spatial ranges differ significantly from the actual subsurface structure and are difficult to reconcile with the actual spatial structure of the subsurface medium. Summary of the Invention

[0004] According to one aspect of an embodiment of the present disclosure, a method for constructing a formation model is provided, comprising: acquiring seismic data of a formation space and position coordinates of unknown spatial points and known spatial points in the formation space, as well as observation values ​​of known spatial points; estimating random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the formation space; determining the anisotropic spatial correlation length of each known spatial point in the direction of a line connecting the other known spatial points and each unknown spatial point based on the position coordinates and random medium statistical characteristic parameters of each unknown spatial point and each known spatial point; substituting the determined anisotropic spatial correlation length into a spatial correlation function to obtain an anisotropic spatial covariance function of each known spatial point with the other known spatial points and each unknown spatial point; for each unknown spatial point: determining an anisotropic kriging weight coefficient of each known spatial point based on the determined anisotropic spatial covariance function; and determining a predicted value of the unknown spatial point based on the determined anisotropic kriging weight coefficient and the observation values ​​of each known spatial point.

[0005] In some possible implementations, the statistical characteristic parameters of the random medium include: a transverse autocorrelation length, a longitudinal autocorrelation length, and an autocorrelation angle.

[0006] In some possible implementations, the anisotropic spatial correlation length l in the direction of the line connecting the spatial point i and the spatial point j is calculated as follows: φij :

[0007]

[0008] Where a i is the horizontal autocorrelation length of spatial point i, b i is the longitudinal autocorrelation length of spatial point i, θ i is the autocorrelation angle of spatial point i, Δx ij and Δy ij are the differences in the horizontal and vertical coordinates between spatial point i and spatial point j, respectively.

[0009] In some possible implementations, the statistical characteristic parameters of a random medium at each unknown spatial point and each known spatial point are estimated based on the seismic data in the stratum space, including: for each spatial point: taking the spatial point as the center point of a sliding window, calculating the power spectrum of the seismic data and the power spectrum of the seismic wavelet within the sliding window; obtaining a power spectrum of the random medium based on the power spectrum method; performing an inverse Fourier transform on the power spectrum of the random medium based on the Wiener-Hinchin theorem to obtain an autocorrelation function of the random medium; binarizing the autocorrelation function of the random medium and fitting it into an ellipse; estimating the major axis of the ellipse to obtain a transverse autocorrelation length; estimating the minor axis of the ellipse to obtain a longitudinal autocorrelation length; and determining the angle formed by the major axis and the horizontal axis to obtain an autocorrelation angle.

[0010] According to another aspect of an embodiment of the present disclosure, a stratum model construction device is provided, including: an acquisition module for acquiring seismic data of a stratum space and the position coordinates of unknown spatial points and known spatial points in the stratum space, as well as the observation values ​​of the known spatial points; a processing module for estimating the random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the stratum space; determining the anisotropic spatial correlation length of each known spatial point in the direction of the line connecting the other known spatial points and each unknown spatial point based on the position coordinates and the random medium statistical characteristic parameters of each unknown spatial point and each known spatial point; substituting the determined anisotropic spatial correlation length into a spatial correlation function to obtain the anisotropic spatial covariance function of each known spatial point with the other known spatial points and each unknown spatial point; for each unknown spatial point: determining the anisotropic kriging weight coefficient of each known spatial point based on the determined anisotropic spatial covariance function; and determining the predicted value of the unknown spatial point based on the determined anisotropic kriging weight coefficient and the observation values ​​of each known spatial point.

[0011] In some possible implementations, the statistical characteristic parameters of the random medium include: a transverse autocorrelation length, a longitudinal autocorrelation length, and an autocorrelation angle.

[0012] In some possible implementations, the processing module is configured to calculate the anisotropic spatial correlation length l in the direction of the line connecting the spatial point i and the spatial point j according to the following formula: φij :

[0013]

[0014] Where a i is the horizontal autocorrelation length of spatial point i, b i is the longitudinal autocorrelation length of spatial point i, θ i is the autocorrelation angle of spatial point i, Δx ij and Δy ij are the differences in the horizontal and vertical coordinates between spatial point i and spatial point j, respectively.

[0015] In some possible implementations, a processing module is used to: for each spatial point: take the spatial point as the center point of the sliding window, calculate the power spectrum of the seismic data and the power spectrum of the seismic wavelet within the sliding window range; obtain the power spectrum of the random medium based on the power spectrum method; perform inverse Fourier transform on the power spectrum of the random medium based on the Wiener-Schinchin theorem to obtain the autocorrelation function of the random medium; binarize the autocorrelation function of the random medium and fit it into an ellipse; estimate the major axis of the ellipse to obtain the transverse autocorrelation length; estimate the minor axis of the ellipse to obtain the longitudinal autocorrelation length; determine the angle formed by the major axis and the horizontal axis to obtain the autocorrelation angle.

[0016] According to another aspect of an embodiment of the present disclosure, an electronic device is provided, including: a processor; and a memory storing a program, wherein the program includes instructions, and when the instructions are executed by the processor, the processor performs the aforementioned method.

[0017] According to another aspect of an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the aforementioned method.

[0018] One or more technical solutions provided in the embodiments of the present disclosure estimate random medium statistical characteristic parameters based on seismic data, characterize the anisotropic spatial structure characteristics of the underground medium through the random medium statistical characteristic parameters, and thereby calculate the spatial correlation lengths of spatial points in the underground medium in different directions, overcoming the traditional Kriging interpolation method's assumption that the underground medium spatial structure is isotropic, and can establish a Kriging interpolation result with smaller errors, more consistent with the actual spatial structure characteristics of the underground medium, and less uncertainty in the interpolation process. Moreover, in the case where the number of known spatial points is moderate or small, it still has better application effects. Especially in the case where the number of known spatial points is small, this method is excellent in depicting thin layers in the stratigraphic model, and has a good effect on both the boundaries of the thin layers and their continuity, and the thin layers are rich in detail information. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flow chart of a formation model building method according to an exemplary embodiment of the present disclosure is shown.

[0021] Figure 2 A structural block diagram of a formation model building device according to an exemplary embodiment of the present disclosure is shown.

[0022] Figure 3 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0024] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0025] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] It should be noted that the execution subject of the technical solution provided in the embodiment of the present disclosure may be one or more electronic devices, and the present disclosure does not limit this; wherein, the electronic device may be a terminal (i.e., a client) or a server. Then, when the execution subject includes multiple electronic devices, and the multiple electronic devices include at least one terminal and at least one server, the technical solution provided in the embodiment of the present disclosure may be jointly executed by the terminal and the server. Accordingly, the terminals mentioned here may include but are not limited to: smart phones, tablet computers, laptops, desktop computers, smart watches, smart voice interaction devices, smart home appliances, car terminals, and the like. The server mentioned here may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing (cloud computing), cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms, and the like.

[0029] In the kriging interpolation process used in related technologies, to simplify the interpolation process, it is often assumed that the spatial correlation information for all points in space is the same in different directions. Spatial range is then used to characterize the isotropic spatial structure of the subsurface medium. However, due to the influence of geological factors such as the sedimentary environment, tectonic movement, and overlying pressure, the spatial structure of the subsurface medium often exhibits anisotropic characteristics, with different spatial correlation lengths in different directions. Therefore, the kriging interpolation results obtained using the isotropic spatial structure kriging interpolation method based on spatial range differ significantly from the actual situation of the subsurface medium and are difficult to match the actual spatial structure of the subsurface medium.

[0030] To this end, the embodiment of the present disclosure provides a method for constructing a stratigraphic model. The method estimates the statistical characteristic parameters of a random medium based on seismic data, characterizes the anisotropic spatial structural characteristics of the underground medium through the statistical characteristic parameters of the random medium, and thereby calculates the spatial correlation lengths of spatial points in the underground medium in different directions. This overcomes the assumption of the traditional Kriging interpolation method that the spatial structure of the underground medium is isotropic, and can establish a Kriging interpolation result with smaller errors, more consistent with the actual spatial structural characteristics of the underground medium, and less uncertainty in the interpolation process. Moreover, in the case where the number of known spatial points is moderate or small, it still has better application effects. In particular, in the case where the number of known spatial points is small, this method is excellent in depicting thin layers in the stratigraphic model, and has a good effect on both the boundaries of the thin layers and their continuity, and the thin layers are rich in detail information.

[0031] Figure 1 A flow chart of a formation model construction method according to an exemplary embodiment of the present disclosure is shown. Figure 1 As shown, the formation model construction method includes steps S101 to S106.

[0032] Step S101 , obtaining seismic data of the stratum space and the position coordinates of unknown spatial points and known spatial points in the stratum space, as well as observation values ​​of the known spatial points.

[0033] In the embodiment of the present disclosure, the position coordinates and observation values ​​of the known spatial points in the formation space can be obtained by means such as well logging. Well logging data can be obtained by well logging means, and the position coordinates and observation values ​​of the known spatial points in the embodiment of the present disclosure can be determined based on the well logging data.

[0034] Specifically, the well logging data may include coordinate information, specifically, the coordinate information includes: 1) wellhead position coordinates: usually including X coordinates and Y coordinates, indicating the geographical location of the wellhead on the surface; 2) bottom hole depth: also known as bottom hole depth (KB), indicating the vertical depth from the wellhead to the bottom of the well; 3) measured depth: during the logging process, different measurement points will have different depth values, which are used to record the specific location of the logging data; 4) wellbore inclination data: for deviated or horizontal wells, in addition to depth information, wellbore inclination data is also recorded, including wellbore inclination angle, azimuth and wellbore inclination distance; 5) true vertical depth: the true vertical distance from the wellhead to a certain point downhole, taking into account the influence of wellbore inclination; 6) horizontal displacement: in horizontal or deviated wells, horizontal displacement data indicates the displacement of the wellbore on the horizontal plane; 7) wellbore trajectory: detailed wellbore trajectory data, including the depth, inclination angle and azimuth of each section. The measurement point is a known spatial point in the embodiment of the present disclosure, and the position coordinates of the measurement point in the formation space can be determined based on the coordinate information.

[0035] In the disclosed embodiment, the well logging data may include compressional wave velocity, shear wave velocity, density, porosity, permeability, water saturation, etc. In a specific implementation, the required well logging data may be selected according to formation modeling and analysis requirements.

[0036] In the disclosed embodiments, seismic data typically includes information such as the amplitude, frequency, and propagation time of seismic waves. Seismic data can be obtained from field observations or from research institutions, universities, or government data centers. In practice, the appropriate data acquisition method can be selected as needed.

[0037] Step S102 : estimating the random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the stratum space.

[0038] The statistical characteristic parameters of random media mainly include mean, standard deviation, autocorrelation function, etc. In seismic exploration, these parameters are very important for describing the heterogeneity of underground media. The following are some key statistical characteristic parameters: 1) Mean: describes the average characteristics of the medium; 2) Standard deviation: describes the intensity of the heterogeneity of the medium, that is, the degree of fluctuation of the medium properties around the mean; 3) Autocorrelation function: describes the spatial correlation of the medium properties; 4) Autocorrelation length: divided into horizontal autocorrelation length and vertical autocorrelation length, which describe the average scale of the heterogeneous anomalies of the medium in the horizontal and vertical directions respectively; 5) Autocorrelation angle: describes the spatial distribution direction of the heterogeneity of the medium.

[0039] In an embodiment of the present application, the transverse autocorrelation length, the longitudinal autocorrelation length and the autocorrelation angle are selected as key parameters to describe random media. The autocorrelation length can describe the range of spatial correlation of medium properties, and the autocorrelation angle can describe the directionality of the distribution of medium properties.

[0040] In one embodiment, the statistical characteristic parameters of the random medium at each spatial point are estimated based on the seismic data in the stratum space, including: for each spatial point: taking the spatial point as the center point of the sliding window, calculating the power spectrum of the seismic data and the power spectrum of the seismic wavelet within the sliding window; obtaining the power spectrum of the random medium based on the power spectrum method; performing an inverse Fourier transform on the power spectrum of the random medium based on the Wiener-Schinchin theorem to obtain the autocorrelation function of the random medium; binarizing the autocorrelation function of the random medium and fitting it into an ellipse; estimating the major axis of the ellipse to obtain the transverse autocorrelation length; estimating the minor axis of the ellipse to obtain the longitudinal autocorrelation length; determining the angle formed by the major axis and the horizontal axis to obtain the autocorrelation angle.

[0041] Specifically, the relationship between the power spectrum of seismic data, the power spectrum of seismic wavelet and the power spectrum of random medium is:

[0042]

[0043] Where, |S s (k x ,w) 2 is the power spectrum of the seismic data, is the power spectrum of the random medium, |S ψ (w) 2 is the power spectrum of ψ, w(t) is the seismic wavelet.

[0044] Step S103, determining the anisotropic spatial correlation length of each known spatial point in the direction of the line connecting other known spatial points and each unknown spatial point based on the position coordinates of each unknown spatial point and each known spatial point and the statistical characteristic parameters of the random medium.

[0045] In some possible implementations, the anisotropic spatial correlation length l in the direction of the line connecting the spatial point i and the spatial point j is calculated as follows: φij :

[0046]

[0047] Where a i is the horizontal autocorrelation length of spatial point i, b i is the longitudinal autocorrelation length of spatial point i, θ i is the autocorrelation angle of spatial point i, Δx ij and Δy ij are the differences in the horizontal and vertical coordinates between space point i and space point j, respectively, and i and j are positive integers.

[0048] In step S104, the determined anisotropic spatial correlation length is substituted into the spatial correlation function to obtain anisotropic spatial covariance functions of each known spatial point, other known spatial points, and each unknown spatial point.

[0049] In the embodiments of the present disclosure, the types of autocorrelation functions may include Gaussian, exponential, mixed autocorrelation functions, power-law autocorrelation functions, and the like. In a specific implementation, the type of autocorrelation function may be selected based on the spatial characteristics of the stratum. The Gaussian autocorrelation function is a relatively commonly used autocorrelation function, which describes the average scale of the heterogeneous anomalies of the medium in all directions. The Gaussian function is usually used to describe a single-scale smooth inhomogeneous medium, which is characterized by a rapid decline near the origin and a distinct peak. Compared with the Gaussian type, the medium described by the exponential function has multi-scale and self-similar characteristics, and is suitable for simulating more complex medium conditions. This function has a slower decline rate near the origin and is suitable for simulating media with a long correlation length. The mixed autocorrelation function combines the characteristics of the Gaussian and exponential types, and can more flexibly describe the actual medium. By selecting different parameters, media with different roughness and scale characteristics can be simulated. The power-law autocorrelation function is used under certain specific geological conditions, such as the simulation of sedimentary sequences.

[0050] In some embodiments, an exponential spatial correlation function is used. In practical applications, an appropriate spatial correlation function is selected according to the specific conditions of the actual medium. Commonly used spatial correlation functions include exponential, Gaussian, and spherical. The formula of the exponential spatial correlation function in the embodiment of the present disclosure is:

[0051]

[0052] Where, ρ exp (h) is an exponential spatial correlation function, h is the distance between two points in space, and l is the spatial correlation length between two points. l is specifically the aforementioned l φij .

[0053] The calculation formula of the spatial covariance function between two points is:

[0054] C(h)=σ 2 ·ρ(h)

[0055] Where C(h) is the spatial covariance function, σ 2 is the variance of the known data, and ρ(h) is the spatial correlation function.

[0056] For each unknown spatial point, steps S105 and S106 are executed.

[0057] Step S105 : determining the anisotropic kriging weight coefficient of each known spatial point according to the determined anisotropic spatial covariance function.

[0058] Specifically, obtaining the anisotropic kriging weight coefficient may include: using the obtained anisotropic spatial covariance function to establish an anisotropic kriging matrix and anisotropic kriging vectors, and using them to solve the anisotropic kriging weight coefficient. The relationship between the kriging matrix, kriging vectors, and kriging weight coefficients is:

[0059]

[0060] Where u0 is an unknown space point, u1,u2,L,u n is a spatial point, λ1,λ2,L,λ n u1, u2, L, u respectively n The weight coefficients of u1, u2 are the anisotropic spatial covariance function between the spatial points u1 and u2, C(u2, u1) is the anisotropic spatial covariance function between the spatial points u2 and u1, u1, u2, L, u n The anisotropic spatial covariance function between each spatial point in and every other spatial point in is deduced in this way.

[0061] Step S106: Determine the predicted value of the unknown spatial point based on the determined anisotropic Kriging weight coefficient and the observed value of each known spatial point.

[0062] Specifically, the anisotropic kriging weight coefficient can be used to perform weighted averaging on known spatial points in the stratum space to obtain the anisotropic spatial structure kriging interpolation result.

[0063] In specific implementation, the Kriging interpolation formula can be:

[0064]

[0065] Where, X * (u0) is the Kriging estimate at the unknown spatial point u0, X(u i ) is the observed value of a known spatial point. m is the mean of the observed data at known spatial points, λ i is the Kriging weight coefficient of the known spatial point. is the estimated variance at the unknown spatial point u0, and C represents the spatial covariance function.

[0066] The disclosed embodiment adopts an anisotropic spatial structure kriging interpolation method based on the statistical characteristic parameters of random media, estimates the statistical characteristic parameters of random media from seismic data, and uses them to describe the anisotropic spatial structure characteristics of the underground medium; obtains the spatial coordinates and observation values ​​of spatial known points from the well logging data, and then obtains the spatial correlation length of the underground medium in different directions based on the estimated statistical characteristic parameters of the random media, and thus obtains the anisotropic spatial covariance function, and then obtains the anisotropic kriging weight coefficient. Finally, the obtained anisotropic kriging weight coefficient is used to perform a weighted average on the spatial known points in the well logging data to obtain a kriging interpolation result that satisfies the anisotropic spatial structure characteristics of the underground medium. The kriging interpolation result obtained in this way is more consistent with the anisotropic spatial structure characteristics of the underground medium, has a smaller error with the actual situation of the underground medium, and has less uncertainty in the interpolation process.

[0067] The present disclosure also provides a formation model construction device, which can implement the formation model construction method described above. For example, Figure 1 The method for constructing the stratigraphic model is shown.

[0068] Figure 2 FIG. 1 shows a structural block diagram of a formation model building device according to an exemplary embodiment of the present disclosure, as shown in FIG. Figure 2 As shown, the formation model construction device includes an acquisition module 10 and a processing module 20. The acquisition module 10 is used to acquire seismic data of the formation space and the position coordinates of unknown and known spatial points in the formation space, as well as the observed values ​​of the known spatial points. The processing module 20 is used to estimate the random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the formation space; determine the anisotropic spatial correlation length of each known spatial point in the direction of the line connecting the other known spatial points and each unknown spatial point based on the position coordinates and the random medium statistical characteristic parameters of each unknown spatial point and each known spatial point; substitute the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known spatial point with the other known spatial points and each unknown spatial point; for each unknown spatial point: determine the anisotropic kriging weight coefficient of each known spatial point based on the determined anisotropic spatial covariance function; and determine the predicted value of the unknown spatial point based on the determined anisotropic kriging weight coefficient and the observed values ​​of each known spatial point.

[0069] In some embodiments, the statistical characteristic parameters of the random medium include: a transverse autocorrelation length, a longitudinal autocorrelation length, and an autocorrelation angle.

[0070] In some embodiments, the processing module 20 is configured to calculate the anisotropic spatial correlation length l in the direction of the line connecting the spatial point i and the spatial point j according to the following formula:φij :

[0071]

[0072] Where a i is the horizontal autocorrelation length of spatial point i, b i is the longitudinal autocorrelation length of spatial point i, θ i is the autocorrelation angle of spatial point i, Δx ij and Δy ij are the differences in the horizontal and vertical coordinates between spatial point i and spatial point j, respectively.

[0073] In some possible implementations, the processing module 20 is used to: for each spatial point: take the spatial point as the center point of the sliding window, calculate the power spectrum of the seismic data and the power spectrum of the seismic wavelet within the sliding window range; obtain the power spectrum of the random medium based on the power spectrum method; perform inverse Fourier transform on the power spectrum of the random medium based on the Wiener-Schinchin theorem to obtain the autocorrelation function of the random medium; binarize the autocorrelation function of the random medium and fit it into an ellipse; estimate the major axis of the ellipse to obtain the transverse autocorrelation length; estimate the minor axis of the ellipse to obtain the longitudinal autocorrelation length; determine the angle formed by the major axis and the horizontal axis to obtain the autocorrelation angle.

[0074] The exemplary embodiments of the present disclosure further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being configured to cause the electronic device to perform a method according to an exemplary embodiment of the present disclosure when executed by the at least one processor.

[0075] Exemplary embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0076] Exemplary embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, it is used to cause the computer to perform the method according to the embodiment of the present disclosure.

[0077] refer to Figure 3, a block diagram of an electronic device 300 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0078] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0079] Multiple components within electronic device 300 are connected to I / O interface 305, including an input unit 306, an output unit 307, a storage unit 308, and a communication unit 309. Input unit 306 can be any type of device capable of inputting information into electronic device 300. Input unit 306 can receive input numeric or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 307 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 308 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 309 allows electronic device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0080] The computing unit 301 may be a variety of general and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above. For example, in some embodiments, the formation model construction method may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. In some embodiments, the computing unit 301 may be configured to execute the formation model construction method in any other appropriate manner (e.g., by means of firmware).

[0081] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0082] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0083] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0084] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0085] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0086] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. A method for constructing a stratum model, characterized in that: include: Acquiring seismic data of a stratum space, position coordinates of unknown spatial points and known spatial points in the stratum space, and observation values ​​of the known spatial points; estimating random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the stratum space; Determine the anisotropic spatial correlation length of each known spatial point in the direction of the line connecting other known spatial points and each unknown spatial point based on the position coordinates of each unknown spatial point and each known spatial point and the statistical characteristic parameters of the random medium; Substituting the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known spatial point with other known spatial points and each unknown spatial point; For each unknown spatial point: According to the determined anisotropic spatial covariance function, the anisotropic kriging weight coefficient of each known spatial point is determined; The predicted value of the unknown spatial point is determined based on the determined anisotropic kriging weight coefficient and the observed value.

2. The method according to claim 1, wherein The statistical characteristic parameters of the random medium include: transverse autocorrelation length, longitudinal autocorrelation length and autocorrelation angle.

3. The method according to claim 2, wherein Determine the spatial point according to the following formula With spatial point Anisotropic spatial correlation length along the line direction : ； Where, For spatial points The horizontal autocorrelation length, For spatial points The longitudinal autocorrelation length, For spatial points The autocorrelation angle, and Space points and spatial points The difference between the horizontal and vertical coordinates.

4. The method according to claim 2, wherein The estimating of random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the stratum space includes: For each spatial point: Taking the spatial point as the center point of the sliding window, the power spectrum of the seismic data and the power spectrum of the seismic wavelet within the sliding window are calculated; Obtaining a random medium power spectrum based on the power spectrum method; Performing an inverse Fourier transform on the random medium power spectrum based on the Wiener-Hinchin theorem to obtain a random medium autocorrelation function; Binarizing the random medium autocorrelation function and fitting it into an ellipse; estimating the major axis of the ellipse to obtain the transverse autocorrelation length; estimating the minor axis of the ellipse to obtain the longitudinal autocorrelation length; The angle formed by the major axis and the horizontal axis is determined to obtain the autocorrelation angle.

5. A stratum model construction device, characterized in that: include: an acquisition module, configured to acquire seismic data of a stratum space, position coordinates of unknown spatial points and known spatial points in the stratum space, and observation values ​​of known spatial points; Processing module for: estimating random medium statistical characteristic parameters of each unknown spatial point and each known spatial point based on the seismic data of the stratum space; Determine the anisotropic spatial correlation length of each known spatial point in the direction of the line connecting other known spatial points and each unknown spatial point based on the position coordinates of each unknown spatial point and each known spatial point and the statistical characteristic parameters of the random medium; Substituting the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known spatial point with other known spatial points and each unknown spatial point; For each unknown spatial point: According to the determined anisotropic spatial covariance function, the anisotropic kriging weight coefficient of each known spatial point is determined; The predicted value of the unknown spatial point is determined based on the determined anisotropic kriging weight coefficient and the observed value of each known spatial point.

6. The device according to claim 5, characterized in that The statistical characteristic parameters of the random medium include: transverse autocorrelation length, longitudinal autocorrelation length and autocorrelation angle.

7. The device according to claim 6, characterized in that The processing module is used to determine the spatial point according to the following formula With spatial point Anisotropic spatial correlation length along the line direction : ； Where, For spatial points The horizontal autocorrelation length, For spatial points The longitudinal autocorrelation length, For spatial points The autocorrelation angle, and Space points and spatial points The difference between the horizontal and vertical coordinates.

8. The device according to claim 6, wherein The processing module is used for each spatial point: Taking the spatial point as the center point of the sliding window, the power spectrum of the seismic data and the power spectrum of the seismic wavelet within the sliding window are calculated; Obtaining a random medium power spectrum based on the power spectrum method; Performing an inverse Fourier transform on the random medium power spectrum based on the Wiener-Hinchin theorem to obtain a random medium autocorrelation function; Binarizing the random medium autocorrelation function and fitting it into an ellipse; estimating the major axis of the ellipse to obtain the transverse autocorrelation length; estimating the minor axis of the ellipse to obtain the longitudinal autocorrelation length; The angle formed by the major axis and the horizontal axis is determined to obtain the autocorrelation angle.

9. An electronic device comprising: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 4.

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