Method, device and equipment for predicting fault by using stratigraphic gradient and storage medium

Abstract

The invention discloses a method, device and equipment for predicting a fault by using a stratum gradient, and a storage medium. The method comprises the following steps: acquiring seismic horizon data of a target reservoir in a depth domain; determining a stratum gradient corresponding to each common center point in the seismic horizon data; and determining fault distribution data in the target reservoir according to the stratum gradient corresponding to each common center point. According to the technical scheme provided by the embodiment of the invention, the technical effect of automatically and accurately determining the fault distribution data can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration, and in particular to a method, device, equipment and storage medium for predicting faults by using formation gradients. Background Art

[0002] In petroleum exploration, the important issue that affects the exploration and development of oil and gas fields is fault structure. The existing technology often uses conventional 3D seismic interpretation. Conventional 3D seismic interpretation is basically carried out on vertical seismic profiles. It requires interpreting each survey line in the work area one by one to determine the plane distribution of faults and stratigraphic features, so as to use manual analysis, judgment and interpretation on the plane. However, this method is cumbersome and requires manual analysis, and it is impossible to automatically and accurately determine the fault distribution data. Summary of the invention

[0003] The present invention provides a method, device, equipment and storage medium for predicting faults using formation gradients, so as to solve the problem that the prior art cannot automatically and accurately determine fault distribution data.

[0004] According to one aspect of the present invention, a method for predicting faults using formation gradients is provided, the method comprising:

[0005] Obtain seismic layer data of the target reservoir in the depth domain;

[0006] Determine the stratigraphic gradient corresponding to each common center point in the seismic horizon data;

[0007] The fault distribution data in the target reservoir is determined according to the formation gradients corresponding to the common center points.

[0008] According to another aspect of the present invention, there is provided a device for predicting faults using formation gradients, the device comprising:

[0009] A depth domain layer acquisition module is used to obtain seismic layer data of the target reservoir in the depth domain;

[0010] A stratigraphic gradient calculation module, used to determine the stratigraphic gradient corresponding to each common center point in the seismic horizon data;

[0011] The fault prediction module is used to determine the fault distribution data in the target reservoir according to the formation gradient corresponding to each of the common center points.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed 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 method of predicting faults using formation gradients as described in any embodiment of the present invention.

[0016] 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 enable a processor to implement the method of predicting faults using formation gradients as described in any embodiment of the present invention when executed.

[0017] The technical solution of the method for predicting faults using formation gradients provided in the embodiment of the present invention obtains seismic horizon data of the target reservoir in the depth domain; determines the formation gradients corresponding to each common center point in the seismic horizon data; and determines the fault distribution data in the target reservoir according to the formation gradients corresponding to each common center point. The technical effect of determining the fault distribution data in the target reservoir according to the formation gradients corresponding to each common center point in the seismic horizon data is achieved, which is simple and accurate.

[0018] 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

[0019] 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.

[0020] Figure 1 is a flow chart of a method for predicting faults using formation gradients provided according to an embodiment of the present invention;

[0021] Figure 2 is a time domain stratigraphic diagram of a target reservoir provided according to an embodiment of the present invention;

[0022] Figure 3 is a local enlarged schematic diagram of a depth domain layer provided according to an embodiment of the present invention;

[0023] Figure 4 is a 5×5 scattered point schematic diagram of a local layer provided according to an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of a local curved surface formation gradient provided according to an embodiment of the present invention;

[0025] Figure 6 is another flow chart of a method for predicting faults using formation gradients provided according to an embodiment of the present invention;

[0026] Figure 7 is a schematic diagram of a formation gradient image provided according to an embodiment of the present invention;

[0027] Figure 8 is a schematic diagram of connecting points with abnormally high values ​​of formation gradient according to an embodiment of the present invention;

[0028] Fig. 9 is a schematic diagram of fault centerline segment distribution provided according to an embodiment of the present invention;

[0029] Fig.10 is a schematic diagram of the structure of a device for predicting faults using formation gradients provided according to an embodiment of the present invention;

[0030] Fig.11 is another structural schematic diagram of a device for predicting faults using formation gradients provided according to an embodiment of the present invention;

[0031] Fig.12 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] It should be noted that the terms "purpose", "goal", etc. 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.

[0034] Figure 1The flowchart of the method for predicting faults using formation gradients provided in an embodiment of the present invention is applicable to the case where fault distribution data is automatically determined based on seismic horizon data in the depth domain. The method can be executed by a device for predicting faults using formation gradients. The device for predicting faults using formation gradients can be implemented in the form of hardware and / or software. The device for predicting faults using formation gradients can be configured in a processor of an electronic device. Figure 1 As shown, the method includes:

[0035] S110, obtaining seismic layer data of the target reservoir in the depth domain.

[0036] Figure 2 A schematic diagram of the time domain horizon of a target reservoir provided by an embodiment of the present invention, wherein the target reservoir refers to a target horizon, i.e., a horizon of interest, which includes a fault.

[0037] The seismic horizon data of the target reservoir in the depth domain can be determined based on the seismic horizon data of the target reservoir in the time domain. In one embodiment, the seismic horizon data of the target reservoir in the time domain is obtained; based on the seismic horizon data of the target reservoir in the time domain and the longitudinal wave velocity of the target reservoir, the seismic horizon data of the target reservoir in the depth domain is determined. It can be understood that the seismic horizon data of the target reservoir in the depth domain is three-dimensional data, and each coordinate point (common center point) includes a row number, a channel number, and a horizon depth data. Therefore, the seismic horizon data of the target reservoir in the depth domain is mapped to a three-dimensional coordinate system to obtain a three-dimensional model of the target reservoir.

[0038] Specifically, the depth domain horizon of the target reservoir is determined by the following formula:

[0039]

[0040] Among them, time is the time horizon, and the one-way propagation time of seismic waves is v is the P-wave velocity of the target reservoir. According to the above formula, the depth domain layer z of the target reservoir can be determined.

[0041] S120, determining the formation gradient corresponding to each common center point in the seismic layer data.

[0042] Among them, the common center point is also called Common Middle Point, abbreviated as CMP, which can be understood as a grid point in a plane coordinate system.

[0043] In one embodiment, the formation gradient corresponding to each common center point in the seismic horizon data is determined by the following steps, including:

[0044] Step a1, determining the local surface expression corresponding to each common center point in the seismic layer data.

[0045] In one embodiment, a two-variable binomial equation is used to construct the local surface expression corresponding to each common center point. For example, Figure 3 As shown in FIG. 1 , the seismic layer data includes common center point A, common center point B and common center point C. Taking common center point B as the current common center point as an example, determine the face element where the current common center point is located (see Figure 4 ), the face element includes 5×5 common center points including the current common center point, and the current common center point is in the middle of the 5×5 common center points. The data of the 25 common center points are input into the binary binomial equation, and the specific values ​​of the polynomial coefficients a, b, c, d, e, and f in the equation can be obtained, thereby obtaining the local surface expression corresponding to the current common center point, that is, the local surface corresponding to the current common center point. Figure 5 The local surface corresponding to the common center point B is shown.

[0046] The two-variable binomial equation is as follows:

[0047] z(x,y)=ax 2 +by 2 +cxy+dx+ey+f

[0048] Step a2: determine the formation gradient corresponding to each of the common center points according to the local surface expression corresponding to each of the common center points.

[0049] Determine the gradient vector of each local surface expression at the corresponding common center point, and the formation gradient corresponding to the gradient vector, wherein the direction of the gradient vector is the formation dip, and the formation dip is perpendicular to the strike of the fault; and use the formation gradient corresponding to the gradient vector as the formation gradient corresponding to the corresponding common center point.

[0050] Specifically, after the local surface expressions corresponding to each common center point are determined, the gradient vector of the corresponding local surface at the common center point can be obtained based on the local surface expressions corresponding to each common center point, as follows:

[0051]

[0052] in, are the first-order partial derivatives of the local surface with respect to x and y, respectively, which can be expressed as:

[0053]

[0054]

[0055] The modulus of the gradient vector is defined as the formation gradient grad, which can be expressed as:

[0056]

[0057] The direction of the gradient vector is defined as the formation dip φ dip , which can be expressed as:

[0058]

[0059] Among them, atan2 is the four-quadrant inverse tangent function.

[0060] The strata dip is perpendicular to the fault strike. Figure 5 The relationship between the stratigraphic dip, fault strike and stratigraphic gradient corresponding to the common center point B is shown.

[0061] S130. Determine fault distribution data in the target reservoir according to the formation gradients corresponding to the common center points.

[0062] After the formation gradients corresponding to the common center points are determined, the fault distribution data in the target reservoir are determined according to the formation gradients corresponding to the common center points.

[0063] The technical solution of the method for predicting faults using formation gradients provided in the embodiment of the present invention obtains seismic horizon data of the target reservoir in the depth domain; determines the formation gradients corresponding to each common center point in the seismic horizon data; and determines the fault distribution data in the target reservoir according to the formation gradients corresponding to each common center point. The technical effect of determining the fault distribution data in the target reservoir according to the formation gradients corresponding to each common center point in the seismic horizon data is achieved, which is simple and accurate.

[0064] Figure 6 Another flow chart of the method for predicting faults using formation gradients provided in an embodiment of the present invention. This embodiment refines the steps of determining fault distribution data in the target reservoir based on the formation gradients corresponding to each common center point on the basis of the above embodiment. Figure 6 As shown, the method includes:

[0065] S210: Acquire seismic layer data of the target reservoir in the depth domain.

[0066] S220, determining the formation gradient corresponding to each common center point in the seismic layer data.

[0067] S2301. Determine a stratigraphic gradient image according to the stratigraphic gradients corresponding to the common center points, wherein each coordinate data in the stratigraphic gradient image includes track number data, line number data, layer depth data, and gradient values ​​corresponding to the track number data and the line number data.

[0068] After the stratigraphic gradients corresponding to the common center points are determined, the stratigraphic gradients corresponding to the common center points are added to the coordinate data of the common center points to update the seismic layer data, so that the coordinate data of the common center points in the updated seismic layer data include channel number data, line number data, layer depth data and stratigraphic gradients; then the stratigraphic gradient image corresponding to the target reservoir is determined based on the updated seismic layer data.

[0069] Figure 7 A schematic diagram of a formation gradient image provided by an embodiment of the present invention. Figure 7 The stratigraphic gradient image in is a stratigraphic gradient distribution image corresponding to the target reservoir. Figure 7 The stratigraphic gradient image in is the stratigraphic gradient distribution result formed by projecting the stratigraphic gradient distribution data onto the specified plane.

[0070] S2302. Determine an abnormally high gradient value area in the formation gradient image, where the abnormally high gradient value is a local maximum value where the gradient value is greater than a set threshold.

[0071] Since the formation gradient is larger at the fault, smaller at the non-fault, and reaches a maximum value at the center of the fault, the formation gradient gradually decreases from the center of the fault to the edge of the fault, and the set threshold can be configured as the formation gradient at the edge of the fault. Figure 7 The black areas in the figure are areas with abnormally high gradient values.

[0072] S2303. Determine fault distribution data based on the abnormally high gradient area.

[0073] In one embodiment, each connected domain of maximum points in the region with abnormally high gradient values ​​is used as a fault center line segment; and the fault distribution data is determined based on the fault center line segment. This embodiment uses each connected domain of maximum points in the region with abnormally high gradient values ​​as a fault center line segment, and can quickly and accurately complete the determination of the fault center line segment. It can be understood that the region with abnormally high gradient values ​​may include one connected domain of maximum points, or may include at least two connected domains of maximum points, so one or more groups of fault center line segments can be determined based on the region with abnormally high gradient values. Since the fault center line segment corresponds to the fault distribution, this embodiment uses the one or more groups of fault center line segments as one or more groups of fault distribution data.

[0074] It is understandable that after a stratum fracture occurs, the fault has upper and lower boundaries and a central part. In this embodiment, the center of the fracture is taken as the central part of the fault, specifically the center line of the fault, that is, the above-mentioned center line segment of the fault.

[0075] Figure 8 The embodiment of the present invention provides Figure 7 Schematic diagram of the connected domain of extreme points corresponding to the stratigraphic gradient image in FIG. Figure 8The black line in the middle is the connected domain of the extreme points, which is also the center line segment of the fault corresponding to the fault.

[0076] Fig. 9 A schematic diagram of the distribution of the center line segments of the faults provided in an embodiment of the present invention. The black lines in the figure are the center line segments of the faults. In the figure, the black lines with wider line widths intersecting with the narrower black lines have higher fault levels, and the higher the fault level, the greater the extension length and fault distance corresponding to the fault. Since the faults are distributed in the vicinity of the center line segments of the faults, the distribution positions of the faults can be determined by the distribution of the center line segments of the faults shown in the figure, which is simple and intuitive.

[0077] The embodiment of the present invention determines a formation gradient image based on the formation gradients corresponding to each common center point, determines an area with abnormally high gradient values ​​in the formation gradient image, and determines fault distribution data based on the area with abnormally high gradient values, thereby achieving the technical effect of determining the fault distribution data in the target reservoir based on the formation gradients corresponding to each common center point, and simplifies the process of determining the fault distribution data while ensuring the accuracy of fault determination based on the formation gradient.

[0078] Fig.10 A schematic diagram of the structure of a device for predicting faults using formation gradients provided in an embodiment of the present invention. Fig.10 As shown, the device comprises:

[0079] A depth domain layer acquisition module 110 is used to acquire seismic layer data of a target reservoir in the depth domain;

[0080] A stratigraphic gradient calculation module 120 is used to determine the stratigraphic gradient corresponding to each common center point in the seismic horizon data;

[0081] The fault prediction module 130 is used to determine the fault distribution data in the target reservoir according to the formation gradients corresponding to the common center points.

[0082] In one embodiment, the formation gradient calculation module 120 includes:

[0083] An expression unit, used to determine a local surface expression corresponding to each common center point in the target reservoir;

[0084] The formation gradient unit is used to determine the formation gradient corresponding to each of the common center points according to the local surface expression corresponding to each of the common center points.

[0085] In one embodiment, the formation gradient unit is used to:

[0086] Determine the gradient vector of each of the local surface expressions at the corresponding common center point, and the formation gradient corresponding to the gradient vector, wherein the direction of the gradient vector is the formation dip, and the formation dip is perpendicular to the strike of the fault;

[0087] The formation gradient is taken as the formation gradient corresponding to the common center point.

[0088] In one embodiment, the local surface expression is determined based on a two-variable quadratic polynomial equation.

[0089] In one embodiment, the fault prediction module 130 is used to:

[0090] Determine a stratigraphic gradient image according to the stratigraphic gradients corresponding to the common center points, wherein each coordinate data in the stratigraphic gradient image includes track number data, line number data, layer depth data, and gradient values ​​corresponding to the track number data and the line number data;

[0091] Determine an abnormally high gradient value area in the formation gradient image, wherein the abnormally high gradient value is a local maximum value where the gradient value is greater than a set threshold;

[0092] The fault distribution data is determined based on the abnormally high gradient value area.

[0093] In one embodiment, Fig.11 As shown, the device further includes a display module 140, which is used to:

[0094] At least one fault center line segment corresponding to the fault distribution data is displayed in a visual interactive interface.

[0095] In one embodiment, the depth domain layer acquisition module 110 is used to:

[0096] Obtain seismic horizon data of the target reservoir in the time domain;

[0097] The seismic layer data of the target reservoir in the depth domain are determined according to the seismic layer data of the target reservoir in the time domain and the longitudinal wave velocity of the target reservoir.

[0098] The technical solution of the device for predicting faults using formation gradients provided in the embodiment of the present invention obtains seismic horizon data of the target reservoir in the depth domain; determines the formation gradients corresponding to each common center point in the seismic horizon data; and determines the fault distribution data in the target reservoir according to the formation gradients corresponding to each common center point. The technical effect of determining the fault distribution data in the target reservoir according to the formation gradients corresponding to each common center point in the seismic horizon data is achieved, which is simple and accurate.

[0099] The device for predicting faults using formation gradients provided in an embodiment of the present invention can execute the method for predicting faults using formation gradients provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0100] Fig.12A schematic diagram of the structure of an electronic device 10 that can be used to implement 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, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (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.

[0101] like Fig.12 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.

[0102] 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.

[0103] 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 dedicated 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 method for predicting faults using formation gradients.

[0104] In some embodiments, the method of predicting faults using formation gradients 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 method of predicting faults using formation gradients described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method of predicting faults using formation gradients in any other appropriate manner (e.g., by means of firmware).

[0105] 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), load 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 method for predicting faults using formation gradients, characterized in that: include: Obtain seismic layer data of the target reservoir in the depth domain; Determine the stratigraphic gradient corresponding to each common center point in the seismic horizon data; The fault distribution data in the target reservoir is determined according to the formation gradients corresponding to the common center points.

2. The method according to claim 1, characterized in that: Determining the formation gradient corresponding to each common center point in the seismic horizon data includes: Determine the local surface expression corresponding to each common center point in the target reservoir; The formation gradient corresponding to each of the common center points is determined according to the local surface expression corresponding to each of the common center points.

3. The method according to claim 2, characterized in that Determining the formation gradient corresponding to each of the common center points according to the local surface expressions corresponding to each of the common center points includes: Determine the gradient vector of each of the local surface expressions at the corresponding common center point, and the formation gradient corresponding to the gradient vector, wherein the direction of the gradient vector is the formation dip, and the formation dip is perpendicular to the strike of the fault; The formation gradient corresponding to the gradient vector is taken as the formation gradient corresponding to the common center point.

4. The method according to claim 2, characterized in that: The local surface expression is determined based on a two-variable quadratic polynomial equation.

5. The method according to claim 1, characterized in that Determining the fault distribution data in the target reservoir according to the formation gradients corresponding to the common center points includes: Determine a stratigraphic gradient image according to the stratigraphic gradients corresponding to the common center points, wherein each coordinate data in the stratigraphic gradient image includes track number data, line number data, layer depth data, and gradient values ​​corresponding to the track number data and the line number data; Determine an abnormally high gradient value area in the formation gradient image, wherein the abnormally high gradient value is a local maximum value where the gradient value is greater than a set threshold; The fault distribution data is determined based on the abnormally high gradient value area.

6. The method according to claim 1, characterized in that The fault distribution data is fault centerline segment distribution data, and the method further includes: At least one fault center line segment corresponding to the fault distribution data is displayed in a visual interactive interface.

7. The method according to claim 1, characterized in that The method of obtaining seismic horizon data of the target reservoir in the depth domain includes: Obtain seismic horizon data of the target reservoir in the time domain; The seismic layer data of the target reservoir in the depth domain are determined according to the seismic layer data of the target reservoir in the time domain and the longitudinal wave velocity of the target reservoir.

8. A fault determination device based on formation gradient, characterized in that: include: A depth domain layer acquisition module is used to obtain seismic layer data of the target reservoir in the depth domain; A stratigraphic gradient calculation module, used to determine the stratigraphic gradient corresponding to each common center point in the seismic horizon data; The fault prediction module is used to determine the fault distribution data in the target reservoir according to the formation gradient corresponding to each of the common center points.

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 execute the method for predicting faults using formation gradients as described in any one of claims 1-7.

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 method for predicting faults using formation gradients as described in any one of claims 1-7 when executed.

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