Stratum model construction method and device, electronic equipment and storage medium

Abstract

The invention provides a stratum model construction method and device, electronic equipment and a storage medium. The method comprises the following steps: estimating random medium statistical characteristic parameters of each unknown space point and each known space point according to seismic data; determining an anisotropic space correlation length according to the position coordinates of each unknown space point and each known space point and the random medium statistical characteristic parameters; substituting the determined anisotropic space correlation length into a space correlation function to obtain an anisotropic space covariance function; for each unknown space point, determining an anisotropic Kriging weight coefficient of each known space point according to the determined anisotropic space covariance function; and determining a predicted value of the unknown space point according to the anisotropic Kriging weight coefficient and the observed value of each known space point. The Kriging interpolation result which is smaller in error, more in line with the actual spatial structure characteristics of the underground medium and smaller in uncertainty can be established.

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] The Kriging interpolation method is a method that uses the spatial correlation information of spatial variables to assign certain weights to known points in space, and then estimates the unknown points in space by weighted averaging. First, the variogram is calculated from the logging data to obtain the spatial range of the underground medium to characterize the spatial correlation information of the underground medium. Then, the spatial range is substituted into the spatial correlation function to obtain the spatial covariance function, and the Kriging matrix and Kriging vector are established. Finally, the Kriging weight coefficients of the known points in space are calculated from the Kriging matrix and the Kriging vector, and the Kriging interpolation results are obtained by weighted averaging the known points in space according to their respective Kriging weight coefficients.

[0003] In the process of Kriging interpolation, in order to simplify the interpolation process, it is usually assumed that the spatial correlation information of all points in space in different directions is the same, and the spatial range is used to characterize the isotropic spatial structure of the underground medium. However, due to the influence of geological factors such as sedimentary environment, tectonic movement and overlying pressure, the spatial structure of the underground medium usually shows anisotropic characteristics, and the spatial correlation lengths in different directions are different. Therefore, the Kriging interpolation results obtained by the isotropic spatial structure Kriging interpolation method based on spatial range are quite different from the actual situation of the underground medium, and it is difficult to conform to the actual spatial structure of the underground 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, including: acquiring seismic data of a formation space and position coordinates of unknown space points and known space points in the formation space, as well as observation values ​​of known space points; estimating random medium statistical characteristic parameters of each unknown space point and each known space point based on the seismic data of the formation space; determining the anisotropic spatial correlation length of each known space point in the direction of the line connecting other known space points and each unknown space point based on the position coordinates of each unknown space point and each known space 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 space point with other known space points and each unknown space point; for each unknown space point: determining the anisotropic Kriging weight coefficient of each known space point based on the determined anisotropic spatial covariance function; and determining the predicted value of the unknown space point based on the determined anisotropic Kriging weight coefficient and the observation values ​​of each known space 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 as follows: φij :

[0007]

[0008] In the formula, a i is the transverse 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 horizontal and vertical coordinate differences between space point i and space point j respectively.

[0009] In some possible implementations, the statistical characteristic parameters of the random medium at each unknown spatial point and each known spatial point are estimated based on the seismic data in the formation 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-Khinchin 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 lateral 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.

[0010] According to another aspect of an embodiment of the present disclosure, a formation model construction device is provided, including: an acquisition module, used to acquire seismic data of a formation space and position coordinates of unknown space points and known space points in the formation space, and observation values ​​of known space points; a processing module, used to estimate random medium statistical characteristic parameters of each unknown space point and each known space point based on the seismic data of the formation space; determine the anisotropic spatial correlation length of each known space point in the direction of the line connecting other known space points and each unknown space point based on the position coordinates of each unknown space point and each known space point and the statistical characteristic parameters of the random medium; substitute the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known space point with other known space points and each unknown space point; for each unknown space point: determine the anisotropic Kriging weight coefficient of each known space point based on the determined anisotropic spatial covariance function; determine the predicted value of the unknown space point based on the determined anisotropic Kriging weight coefficient and the observation values ​​of each known space 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 used 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] In the formula, a i is the transverse 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 horizontal and vertical coordinate differences between space point i and space 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; 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-Hinchin 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 lateral 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, there is provided an electronic device, 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 executes 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, thereby overcoming 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 in line 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 the characterization of 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] Embodiments of the present disclosure will be described in more detail below 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 being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes 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". 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 "plurality" 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, it 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 terminal mentioned here may include but is not limited to: smart phones, tablet computers, laptops, desktop computers, smart watches, smart voice interaction devices, smart home appliances, vehicle 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 in related technologies, in order to simplify the interpolation process, it is usually assumed that the spatial correlation information of all points in space in different directions is the same, and the spatial range is used to characterize the isotropic spatial structure of the underground medium. However, due to the influence of geological factors such as sedimentary environment, tectonic movement and overlying pressure, the spatial structure of the underground medium usually shows anisotropic characteristics, and the spatial correlation lengths in different directions are different. Therefore, the kriging interpolation results obtained by the isotropic spatial structure kriging interpolation method based on spatial range are quite different from the actual situation of the underground medium, and it is difficult to conform to the actual spatial structure of the underground medium.

[0030] To this end, the disclosed embodiment provides a method for constructing a stratigraphic model, which estimates random medium statistical characteristic parameters based on seismic data, characterizes the anisotropic spatial structural characteristics of the underground medium through the random medium statistical characteristic parameters, and thereby calculates the spatial correlation lengths of spatial points in the underground medium in different directions, thereby overcoming 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 in line 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 a better application effect. Especially in the case where the number of known spatial points is small, this method is excellent in the characterization of thin layers in the stratigraphic model, and has a good effect on both the boundary of the thin layer and its continuity, and the thin layer details are rich in 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 building 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 the 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 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 called hole bottom depth (KB), indicating the vertical depth from the wellhead to the bottom of the well; 3) measurement depth: during the logging process, different measurement points will have different depth values, and these depth values ​​are used to record the specific location of the logging data; 4) well inclination data: for inclined wells or horizontal wells, in addition to depth information, well inclination data is also recorded, including well inclination angle, azimuth and well inclination distance; 5) true vertical depth: the true vertical distance from the wellhead to a certain point downhole, taking into account the influence of well inclination; 6) horizontal displacement: in a horizontal well or inclined well, the 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 logging data may include P-wave velocity, S-wave velocity, density, porosity, permeability, water saturation, etc. In a specific implementation, the required logging data may be selected according to the formation modeling and analysis requirements.

[0036] In the disclosed embodiments, seismic data generally include information such as amplitude, frequency, and propagation time of seismic waves. Seismic data can be obtained from field observations or from scientific research institutions, universities, or government data centers. In specific implementations, a suitable 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 heterogeneity intensity 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 the embodiment of the present application, the transverse autocorrelation length, the longitudinal autocorrelation length and the autocorrelation angle are selected as key parameters to describe the random medium. The autocorrelation length can describe the range of spatial correlation of the medium properties, and the autocorrelation angle can describe the directionality of the distribution of the 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-Khinchin 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 lateral 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 seismic data power spectrum, the seismic wavelet power spectrum and the random medium power spectrum is:

[0042]

[0043] In the formula, |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 according to 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 as follows: φij :

[0046]

[0047] In the formula, a i is the transverse 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] Step S104: Substitute the determined anisotropic spatial correlation length into the spatial correlation function to obtain anisotropic spatial covariance functions of each known spatial point and other known spatial points and each unknown spatial point.

[0049] In the disclosed embodiments, the types of autocorrelation functions may include Gaussian, exponential, mixed autocorrelation functions, power-law autocorrelation functions, etc. In a specific implementation, the type of autocorrelation function may be selected according to the spatial characteristics of the stratum. The Gaussian autocorrelation function is a 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 significant peak. Compared with the Gaussian type, the medium described by the exponential function has multi-scale and self-similar characteristics, which is suitable for simulating more complex medium conditions. This function has a slower decline rate near the origin and is suitable for simulating a medium 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, a medium 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, a suitable 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] In the formula, 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 anisotropic kriging weight coefficients may include: using the obtained anisotropic spatial covariance function to establish anisotropic kriging matrix and anisotropic kriging vectors, and using them to solve anisotropic kriging weight coefficients. The relationship between the kriging matrix, kriging vectors, and kriging weight coefficients is:

[0059]

[0060] Where u0 is the unknown space point, u1,u2,L,u n is a spatial point, λ1,λ2,L,λ n They are u1,u2,L,u n The weight coefficients of . C(u1,u2) is the anisotropic spatial covariance function between spatial points u1 and u2, C(u2,u1) is the anisotropic spatial covariance function between 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 , and so on.

[0061] Step S106, determining the predicted value of the unknown spatial point according to 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 the 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 of 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 random medium statistical characteristic parameters, estimates random medium statistical characteristic parameters from seismic data, and describes the anisotropic spatial structure characteristics of the underground medium; obtains the spatial coordinates and observation values ​​of the known points in space from the logging data, and then obtains the spatial correlation length of the underground medium in different directions based on the estimated random medium statistical characteristic parameters, and thereby 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 weighted averaging on the known points in space in the logging data to obtain the Kriging interpolation result that satisfies the anisotropic spatial structure characteristics of the underground medium. The Kriging interpolation result thus obtained 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 in this specification, for example Figure 1 The stratigraphic model construction method shown.

[0068] Figure 2 FIG. 4 shows a structural block diagram of a formation model building device according to an exemplary embodiment of the present disclosure. 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 space points and known space points in the formation space, as well as the observed values ​​of the known space points. The processing module 20 is used to estimate the random medium statistical characteristic parameters of each unknown space point and each known space point according to the seismic data of the formation space; determine the anisotropic spatial correlation length of each known space point in the direction of the line connecting other known space points and each unknown space point according to the position coordinates of each unknown space point and each known space point and the random medium statistical characteristic parameters; substitute the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known space point and other known space points and each unknown space point; for each unknown space point: determine the anisotropic kriging weight coefficient of each known space point according to the determined anisotropic spatial covariance function; determine the predicted value of the unknown space point according to the determined anisotropic kriging weight coefficient and the observed values ​​of each known space 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 used 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] In the formula, a i is the transverse 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 horizontal and vertical coordinate differences between space point i and space 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-Hinchin 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 lateral 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 embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0075] The exemplary embodiments of the present disclosure also 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 the method according to the embodiments of the present disclosure.

[0076] The 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, the computer is used to enable the computer to perform the method according to the embodiments of the present disclosure.

[0077] refer to Figure 3, a block diagram of an electronic device 300 that can be used 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, workbenches, 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. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. 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] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, an output unit 307, a storage unit 308, and a communication unit 309. The input unit 306 may be any type of device capable of inputting information to the electronic device 300, and the input unit 306 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 307 may 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. The storage unit 308 may include, but is not limited to, a disk, an optical disk. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through 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 special 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 running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, 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, which is tangibly included 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 may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0082] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may 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 may 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., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing 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 may 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 with 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 may 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] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

Claims

1. A method for constructing a formation model, characterized in that: include: Acquire seismic data of the 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 according to the seismic data of the stratum space; According to the position coordinates of each unknown space point and each known space point and the statistical characteristic parameters of the random medium, the anisotropic spatial correlation length of each known space point in the direction of the line connecting other known space points and each unknown space point is determined; Substituting the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known spatial point and other known spatial points and each unknown spatial point; For each unknown spatial point: According to the determined anisotropic spatial covariance function, anisotropic Kriging weight coefficients of each known spatial point are 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, characterized in that The random medium statistical characteristic parameters include: transverse autocorrelation length, longitudinal autocorrelation length and autocorrelation angle.

3. The method according to claim 2, characterized in that According to the following formula, the anisotropic spatial correlation length l in the direction of the line connecting the spatial point i and the spatial point j φij : In the formula, a i is the transverse 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 horizontal and vertical coordinate differences between space point i and space point j respectively.

4. The method according to claim 2, characterized in that The estimating of random medium statistical characteristic parameters of each unknown spatial point and each known spatial point according to 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 inverse Fourier transform on the random medium power spectrum based on the Wiener-Khinchin 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 formation model construction device, characterized in that: include: An acquisition module, used 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 modules for: estimating random medium statistical characteristic parameters of each unknown spatial point and each known spatial point according to the seismic data of the stratum space; According to the position coordinates of each unknown space point and each known space point and the statistical characteristic parameters of the random medium, the anisotropic spatial correlation length in the direction of the line connecting the known space point with other known space points and each unknown space point is determined; Substituting the determined anisotropic spatial correlation length into the spatial correlation function to obtain the anisotropic spatial covariance function of each known spatial point and other known spatial points and each unknown spatial point; For each unknown spatial point: According to the determined anisotropic spatial covariance function, anisotropic Kriging weight coefficients of each known spatial point are determined; The predicted value of the unknown spatial point is determined based on the determined anisotropic Kriging weight coefficient and the observed values ​​of each known spatial point.

6. The device according to claim 5, characterized in that The random medium statistical characteristic parameters 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 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 : In the formula, a i is the transverse 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 horizontal and vertical coordinate differences between space point i and space point j respectively.

8. The device according to claim 6, characterized in that 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 inverse Fourier transform on the random medium power spectrum based on the Wiener-Khinchin 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 make a computer execute the method according to any one of claims 1-4.

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