Earthquake forward modeling method and device, electronic equipment and storage medium

Abstract

The invention discloses an earthquake forward modeling method and device, electronic equipment and a storage medium. The method comprises the following steps: determining target depth domain logging data of a target logging; determining a frequency-slowness domain reflection coefficient based on the target depth domain logging data; determining a frequency domain wavelet in a target seismic record related to the target depth domain logging data, and obtaining a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet; and finally, obtaining a corresponding relation between the horizontal slowness and the incident angle of the propagation waveform in the target logging, and obtaining a time angle domain seismic record based on the corresponding relation and the time slowness domain seismic record. According to the technical scheme, the time angle domain seismic record is accurately obtained through the frequency slowness domain reflection coefficient, the frequency domain wavelet and the corresponding relation between the horizontal slowness and the incident angle, so that the seismic record is accurately obtained, and the problem that the seismic record obtained through simulation under the influence of vertical transverse isotropy is inaccurate is solved.

Description

Technical Field

[0001] The invention relates to the technical field of exploration geophysics, and in particular to a seismic forward modeling method, device, electronic equipment and storage medium. Background Art

[0002] Seismic forward modeling is a method and means to simulate the propagation of waves in a constructed underground medium model. In seismic exploration, seismic records synthesized by seismic forward modeling are often used for reservoir identification and hydrocarbon detection of actual seismic data, and are also helpful in evaluating the performance of seismic processing algorithms.

[0003] At present, seismic forward modeling methods generally include two solutions: analytical method and numerical method. The analytical method mainly involves solving partial differential equations (PDE) of seismic wave propagation. Different forms of partial differential equations can be used for different earth models. Numerical methods, such as brute force numerical methods such as finite difference and finite element, are used for seismic forward modeling. However, due to the influence of vertical and lateral isotropy caused by factors such as strong reservoir heterogeneity and tuning effect, its seismic response characteristic data is very complex. If the existing technology is directly applied to the seismic response characteristic data, the obtained seismic response pattern will cause large errors, resulting in the amplitude of the simulated data at deep locations not matching the actual amplitude, so that the simulated seismic records are not accurate. Summary of the invention

[0004] The present invention provides an earthquake forward modeling method, device, electronic equipment and storage medium to solve the problem of inaccurate earthquake records obtained by simulation under the influence of vertical transverse isotropy.

[0005] According to one aspect of the present invention, a seismic forward modeling method is provided, the method comprising:

[0006] Determine target depth domain logging data for target logging, wherein the target depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data;

[0007] Determine the frequency slowness domain reflection coefficient based on the target depth domain logging data and the composite matrix method, wherein the frequency slowness domain reflection coefficient includes the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the frequency slowness domain;

[0008] Determine a frequency domain wavelet in a target seismic record associated with the target depth domain logging data, and obtain a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, wherein the time slowness domain seismic record is a seismic record synthesized by a PP wave reflection coefficient, a PS wave reflection coefficient, and a SS wave reflection coefficient in the time slowness domain;

[0009] The correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target well logging is obtained, and the time angle domain seismic record is obtained based on the correspondence and the time slowness domain seismic record. The time angle domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the time angle domain.

[0010] According to another aspect of the present invention, there is provided a seismic forward modeling device, the device comprising:

[0011] A logging data determination module, used to determine target depth domain logging data of a target well logging, wherein the target depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data;

[0012] A reflection coefficient determination module, used to determine the frequency slowness domain reflection coefficient based on the target depth domain logging data and a composite matrix method, wherein the frequency slowness domain reflection coefficient includes a PP wave reflection coefficient, a PS wave reflection coefficient and a SS wave reflection coefficient in the frequency slowness domain;

[0013] A first seismic record determination module is used to determine a frequency domain wavelet in a target seismic record associated with the target depth domain well logging data, and obtain a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, wherein the time slowness domain seismic record is a seismic record synthesized by a PP wave reflection coefficient, a PS wave reflection coefficient, and a SS wave reflection coefficient in the time slowness domain;

[0014] The second seismic record determination module is used to obtain the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target well logging, and obtain the time angle domain seismic record based on the correspondence and the time slowness domain seismic record. The time angle domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the time angle domain.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the seismic forward modeling method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the earthquake forward modeling method described in any embodiment of the present invention when executed by a processor.

[0020] The technical solution of the embodiment of the present invention is to determine the target depth domain logging data of the target logging, calibrate the target depth domain logging data according to the correlation between the time domain seismic record and the depth domain logging data, and then determine the frequency slowness domain reflection coefficient based on the target depth domain logging data, further determine the frequency domain wavelet in the target seismic record related to the target depth domain logging data, and obtain the time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, and finally obtain the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target logging, and obtain the time angle domain seismic record based on the correspondence and the time slowness domain seismic record, thereby accurately obtaining the seismic record, and solving the problem of inaccurate seismic records obtained by simulation under the influence of vertical lateral isotropy.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of an earthquake forward modeling method provided according to an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of target depth domain logging data applicable to an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of a time domain wavelet applicable to an embodiment of the present invention;

[0026] Figure 4 is a schematic diagram of a seismic record obtained based on an isotropic medium assumption applicable to an embodiment of the present invention;

[0027] Figure 5 is a schematic diagram of a seismic record obtained based on the seismic forward modeling method of the present application applicable to an embodiment of the present invention;

[0028] Figure 6 It is a schematic diagram of subtracting a seismic record obtained based on an isotropic medium assumption and a seismic record obtained by the seismic forward modeling method of the present application according to an embodiment of the present invention;

[0029] Figure 7 is a structural schematic diagram of an earthquake forward modeling device provided according to an embodiment of the present invention;

[0030] Figure 8 It is a schematic diagram of the structure of an electronic device for implementing the earthquake forward modeling method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second" and "target" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 The flowchart of a seismic forward modeling method provided by an embodiment of the present invention is applicable to the case where a seismic record is obtained by forward modeling well logging data under the influence of vertical transverse isotropy. The method can be executed by a seismic forward modeling device, which can be implemented in the form of hardware and / or software. The seismic forward modeling device can be configured in an electronic device having a seismic forward modeling method. Figure 1 As shown, the method includes:

[0035] S110, determining target depth domain logging data of target well logging, and calibrating the target depth domain logging data according to the correlation relationship between the time domain seismic record and the depth domain logging data.

[0036] Among them, the target depth domain logging data includes compressional wave velocity, shear wave velocity, density and Thomsen anisotropy parameters, such as Figure 2 As shown in FIG. 1 , the Thomsen anisotropy parameters include the Thomsen anisotropy parameter ε and the Thomsen anisotropy parameter δ. Thomsen anisotropy parameter ε: P-wave anisotropy parameter. Thomsen anisotropy parameter δ: defines the second derivative of the P-wave phase velocity function at vertical incidence, characterizing the angular dependence of the P-wave velocity near the vertical (symmetry) axis.

[0037] Specifically, the correlation between the time-domain seismic records and the depth-domain well logging data has been set in advance. Therefore, after obtaining the depth-domain well logging data of the target well, the depth-domain well logging data is calibrated using the correlation between the time-domain seismic records and the depth-domain well logging data to obtain the target depth-domain well logging data. That is, the target depth-domain well logging data is calibrated with the content related to the time-domain seismic records, thereby facilitating the subsequent synthesis of the seismic records.

[0038] Optionally, determining the target depth domain logging data of the target logging also includes: acquiring the depth domain logging data and time domain seismic data of the target logging; obtaining the time domain logging data by time-to-depth conversion processing of the depth domain logging data, and then determining the correlation between the time domain seismic records and the depth domain logging data based on the correlation between the time domain logging data and the time domain seismic data; and finally calibrating the depth domain logging data based on the correlation between the time domain seismic records and the depth domain logging data to obtain the target depth domain logging data, thereby facilitating the subsequent synthesis of the seismic records.

[0039] S120. Determine the frequency slowness domain reflection coefficient based on the target depth domain logging data and the composite matrix method. The frequency slowness domain reflection coefficient includes the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the frequency slowness domain.

[0040] Specifically, the target depth domain logging data is used to determine the frequency slowness domain reflection coefficient according to the composite matrix method.

[0041] The composite matrix method is a method used to obtain characteristic functions in a system of differential equations with boundary conditions and boundary layers. That is, the composite matrix method is implemented for N (arbitrary value) systems of equations, that is, N layers of medium, which are connected by (N-1) sets of interface boundaries. Therefore, the integral of the composite differential matrix can be used to express the interface conditions of the composite differential matrix of the next domain until it is passed to the last layer of medium.

[0042] Among them, the PP wave reflection coefficient R in the frequency slowness domain is PP (ω,p), PS wave reflection coefficient R PS (ω,p) and SS wave reflection coefficient R SS (ω,p) is:

[0043]

[0044]

[0045]

[0046] Where ν0=Q0Q1…Q N-1 ν N , which is the waveform vector of the top layer medium. Q n is the transfer matrix of the nth layer of medium, i.e., the nth layer of interface boundary, ν N It represents the elastic wave in the last layer of the medium. There is only a transmitted wave but no reflected wave. Therefore, the reflectivity of all reflected waves is 0, that is, ν N =[1 0 0 0 0 0] T , which represents the initial vector. Therefore, the transfer matrix can be used to solve the seismic waves of each layer of the upper medium in turn. n Describes the effect of the nth layer of medium on the seismic wave phase, which can be expressed as:

[0047]

[0048] Where Δh is the thickness of the nth layer, are the vertical slownesses of longitudinal and transverse waves in the nth layer of medium, respectively;

[0049]

[0050]

[0051]

[0052] Among them, p is the horizontal slowness, also called the ray parameter, α represents the longitudinal wave velocity, and β represents the shear wave velocity.

[0053] matrix and They respectively describe the influence of the n-th layer interface on the amplitude of the downgoing wave and the upgoing wave. They are all 6×6 matrices, which can represent the contribution weight of the n-th layer interface to the total reflectivity. The matrix Contains 16 independent elements, represented as

[0054] t 11=-(p 2 +q p q s ) / μ=t 16 ,t 12 =-2pq p / μ,t 13 =-(p 2 -q p q s ) / μ=-t 14 ,

[0055] t 15 =-2pq s / μ,

[0056] t 21 =iq s / β 2 =-t 23 =-t 24 =-t 26 ,t 31 =-ip(Γ+2q p q s )=t 36 =t 41 =t 46 ,t 32 =-4ip 2 q p ,

[0057] t 33 =-ip(Γ-2q p q s )=t 43 =-t 34 =-t 44 ,

[0058] t 35 =-2iΓq s ,

[0059] t 42 =-2iΓq p ,

[0060] t 45 =-4ip 2 q s ,

[0061] t 51 =-iq p / μ 2 =t 53 =t 54 =-t 56 ,

[0062] t 61 =-μ(Γ 2+4p 2 q p q s ) = t 66 ,

[0063] t 62 =-4μΓpq p ,

[0064] t 63 = -μ(Γ 2 -4p 2 q p q s )=-t 64 ,

[0065] t 65 =-4μΓpq s ,

[0066] t 22 =t 25 =t 55 =t 52 =0,

[0067] Γ=2p 2 -1 / β 2 ,

[0068] μ=ρβ 2 is the shear wave modulus,

[0069] and can be represented as a rearrangement of these 16 elements:

[0070]

[0071] S130, determining a frequency domain wavelet in a target seismic record related to the target depth domain well logging data, and obtaining a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet.

[0072] The time slowness domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the time slowness domain.

[0073] Specifically, a target seismic record related to the target depth domain logging data is obtained, and a time domain wavelet is obtained from the target seismic record. For example, Figure 3 The time domain wavelet is shown, and the time domain wavelet is subjected to Fourier transform to determine the frequency domain wavelet. Then, the product of the frequency-slowness domain reflection coefficient and the frequency domain wavelet is integrated to obtain the time-slowness domain seismic record. The time-slowness domain seismic record can be expressed as:

[0074]

[0075]

[0076]

[0077] S140, obtaining the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target well logging, and obtaining the time angle domain seismic record based on the correspondence and the time slowness domain seismic record.

[0078] Among them, the time angle domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, PS wave reflection coefficient and SS wave reflection coefficient in the time angle domain. Specifically, after obtaining the time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, in order to observe the seismic record more accurately, it is necessary to further process the time slowness domain seismic record, specifically: the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target logging, the correspondence can be expressed as: Then, based on the corresponding relationship, the seismic records in the time slowness domain are resampled to obtain the seismic records in the time angle domain, which can be expressed as:

[0079] R PP (t,θ)=resample{G PP (t,p)};

[0080] R PS (t,θ)=resample{G PS (t,p)};

[0081] R SS (t,θ)=resample{G SS (t,p)};

[0082] Taking the reflected longitudinal wave simulation synthetic record as an example, Figure 4 Schematic diagram of seismic records obtained based on the assumption of isotropic medium. Figure 5 is a schematic diagram of earthquake records obtained based on the earthquake forward modeling method of this application. Figure 4 and Figure 5 Compare and do differential analysis to get Figure 6 The schematic diagram of subtracting the seismic records obtained based on the isotropic medium assumption and the seismic records obtained by the seismic forward modeling method of this application. By comparing the residuals, it can be found that Figure 4 and Figure 5The three black arrows in the figure indicate the primary reflections of the three interfaces. Due to the influence of the vertical transverse isotropy, the vertical transverse isotropic amplitude is different from the isotropic amplitude at the far offset. According to the principle of equal time difference, it can be inferred that the remaining phase axes are all layered multiple waves. In the case of multi-layer media, the energy of these inter-layer multiple waves is mixed with the energy of the primary wave, which is affected by the vertical transverse isotropy, reducing the resolution of the seismic record and causing structural illusions. If it is not analyzed and suppressed, it may mislead the subsequent interpretation and inversion work. Figure 6 It can be seen that the forward modeling method based on the isotropic assumption has errors under complex media conditions. In summary, the seismic forward modeling method based on the vertical transverse isotropic characteristics of this application can simulate all interlayer multiple wave responses and provide a theoretical basis for subsequent seismic interpretation and the use of interlayer multiple waves rather than suppression in the inversion process.

[0083] The technical solution of the embodiment of the present invention is to determine the target depth domain logging data of the target logging, calibrate the target depth domain logging data according to the correlation between the time domain seismic record and the depth domain logging data, and then determine the frequency-slowness domain reflection coefficient based on the target depth domain logging data, further determine the frequency domain wavelet in the target seismic record related to the target depth domain logging data, and obtain the time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, and finally obtain the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target logging, and obtain the time angle domain seismic record based on the correspondence and the time slowness domain seismic record, so as to accurately obtain the seismic record, and solve the problem of inaccurate seismic records obtained by simulation under the influence of vertical lateral isotropy.

[0084] Embodiment 2

[0085] Figure 7 The schematic diagram of the structure of an earthquake forward modeling device provided by an embodiment of the present invention is as follows. Figure 7 As shown, the device comprises:

[0086] The logging data determination module 210 is used to determine the target depth domain logging data of the target logging, wherein the target depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data;

[0087] A reflection coefficient determination module 220 is used to determine the frequency slowness domain reflection coefficient based on the target depth domain logging data and a composite matrix method, wherein the frequency slowness domain reflection coefficient includes a PP wave reflection coefficient, a PS wave reflection coefficient, and a SS wave reflection coefficient in the frequency slowness domain;

[0088] A first seismic record determination module 230 is used to determine a frequency domain wavelet in a target seismic record associated with the target depth domain well logging data, and obtain a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, wherein the time slowness domain seismic record is a seismic record synthesized by a PP wave reflection coefficient, a PS wave reflection coefficient, and a SS wave reflection coefficient in the time slowness domain;

[0089] The second seismic record determination module 240 is used to obtain the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target well logging, and obtain the time angle domain seismic record based on the correspondence and the time slowness domain seismic record. The time angle domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the time angle domain.

[0090] Optionally, a logging data determination module is used to:

[0091] Acquire depth domain logging data and time domain seismic data of target well logging;

[0092] Processing the depth domain logging data by time-depth conversion to obtain time domain logging data;

[0093] Determining the correlation between the time-domain seismic record and the depth-domain well logging data based on the correlation between the time-domain well logging data and the time-domain seismic data;

[0094] The depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data to obtain the target depth domain logging data, wherein the target depth domain logging data includes the longitudinal wave velocity, the shear wave velocity, the density and the Thomsen anisotropy parameter.

[0095] Optionally, a reflection coefficient determination module is used to:

[0096] The target depth domain logging data is used to determine the frequency slowness domain reflection coefficient according to the composite matrix method.

[0097] Optionally, the earthquake record determination module includes a frequency domain wavelet determination unit, which is used to:

[0098] Acquiring target seismic records related to the target depth domain logging data;

[0099] A time domain wavelet is obtained from the target seismic record, and the time domain wavelet is subjected to Fourier transformation to determine the frequency domain wavelet.

[0100] Optionally, the first earthquake record determination module is used to:

[0101] The time slowness domain seismic record is obtained by integrating the product of the frequency slowness domain reflection coefficient and the frequency domain wavelet.

[0102] Optionally, the second earthquake record determination module is used to:

[0103] The time slowness domain seismic record is resampled based on the corresponding relationship to obtain the time angle domain seismic record.

[0104] The earthquake forward simulation device provided in the embodiment of the present invention can execute the earthquake forward simulation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0105] Embodiment 3

[0106] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0107] Figure 8 A schematic diagram of the structure of an electronic device that can be used to implement the earthquake forward modeling method of an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, 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 (such as helmets, glasses, watches, etc.) 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 invention described and / or required herein.

[0108] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0109] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0110] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a seismic forward modeling method.

[0111] In some embodiments, the seismic forward modeling method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the seismic forward modeling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the seismic forward modeling method in any other appropriate manner (e.g., by means of firmware).

[0112] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program 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.

[0114] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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.

[0115] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein may be implemented in a computing system that includes backend 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 frontend 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 backend components, middleware components, or frontend 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), a blockchain network, and the Internet.

[0117] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0118] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0119] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A seismic forward modeling method, characterized in that: include: Determine target depth domain logging data for target logging, wherein the target depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data; Determine the frequency slowness domain reflection coefficient based on the target depth domain logging data and the composite matrix method, wherein the frequency slowness domain reflection coefficient includes the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the frequency slowness domain; Determine a frequency domain wavelet in a target seismic record associated with the target depth domain logging data, and obtain a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, wherein the time slowness domain seismic record is a seismic record synthesized by a PP wave reflection coefficient, a PS wave reflection coefficient, and a SS wave reflection coefficient in the time slowness domain; The correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target well logging is obtained, and the time angle domain seismic record is obtained based on the correspondence and the time slowness domain seismic record. The time angle domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the time angle domain.

2. The method according to claim 1, characterized in that: Determine the target depth domain logging data of the target logging, including: Acquire depth domain logging data and time domain seismic data of target well logging; Processing the depth domain logging data by time-depth conversion to obtain time domain logging data; Determining the correlation between the time-domain seismic record and the depth-domain well logging data based on the correlation between the time-domain well logging data and the time-domain seismic data; The depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data to obtain the target depth domain logging data, wherein the target depth domain logging data includes the longitudinal wave velocity, the shear wave velocity, the density and the Thomsen anisotropy parameter.

3. The method according to claim 1, characterized in that Determining the reflection coefficient in the frequency slowness domain based on the target depth domain logging data and the composite matrix method includes: The target depth domain logging data is used to determine the frequency-slowness domain reflection coefficient according to the composite matrix method.

4. The method according to claim 1, characterized in that: Determining a frequency domain wavelet in a target seismic record associated with the target depth domain well logging data comprises: Acquiring target seismic records related to the target depth domain logging data; A time domain wavelet is obtained from the target seismic record, and the time domain wavelet is subjected to Fourier transformation to determine the frequency domain wavelet.

5. The method according to claim 1, characterized in that: Obtaining a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet includes: The time slowness domain seismic record is obtained by integrating the product of the frequency slowness domain reflection coefficient and the frequency domain wavelet.

6. The method according to claim 1, characterized in that Obtaining a time angle domain seismic record based on the corresponding relationship and the time slowness domain seismic record includes: The time slowness domain seismic record is resampled based on the corresponding relationship to obtain the time angle domain seismic record.

7. An earthquake forward modeling device, characterized in that: include: A logging data determination module, used to determine target depth domain logging data of a target well logging, wherein the target depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data; A reflection coefficient determination module, used to determine the frequency slowness domain reflection coefficient based on the target depth domain logging data and a composite matrix method, wherein the frequency slowness domain reflection coefficient includes a PP wave reflection coefficient, a PS wave reflection coefficient and a SS wave reflection coefficient in the frequency slowness domain; A first seismic record determination module is used to determine a frequency domain wavelet in a target seismic record associated with the target depth domain well logging data, and obtain a time slowness domain seismic record based on the frequency slowness domain reflection coefficient and the frequency domain wavelet, wherein the time slowness domain seismic record is a seismic record synthesized by a PP wave reflection coefficient, a PS wave reflection coefficient, and a SS wave reflection coefficient in the time slowness domain; The second seismic record determination module is used to obtain the correspondence between the horizontal slowness and the incident angle of the propagation waveform in the target well logging, and obtain the time angle domain seismic record based on the correspondence and the time slowness domain seismic record. The time angle domain seismic record is a seismic record synthesized by the PP wave reflection coefficient, the PS wave reflection coefficient and the SS wave reflection coefficient in the time angle domain.

8. The method according to claim 7, characterized in that Well logging data determination module, used for: Acquire depth domain logging data and time domain seismic data of target well logging; Processing the depth domain logging data by time-depth conversion to obtain time domain logging data; Determining the correlation between the time-domain seismic record and the depth-domain well logging data based on the correlation between the time-domain well logging data and the time-domain seismic data; The depth domain logging data is calibrated according to the correlation between the time domain seismic record and the depth domain logging data to obtain the target depth domain logging data, wherein the target depth domain logging data includes the longitudinal wave velocity, the shear wave velocity, the density and the Thomsen anisotropy parameter.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the seismic forward modeling method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the earthquake forward modeling method according to any one of claims 1 to 6 when executed.

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