A method and apparatus for well tomography inversion
By supplementing and resampling well velocity information using tomographic inversion methods, the problems of incomplete well velocity information and excessively high vertical sampling density were solved, thereby improving the accuracy of the mid-deep velocity model and the pre-stack depth migration processing effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-11-26
- Publication Date
- 2026-05-26
Smart Images
Figure CN122085341A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of seismic exploration and data processing technology, and in particular to a well tomographic inversion method and apparatus. Background Technology
[0002] Seismic inversion is an important method in geophysics. It is a means of inferring the internal structure and properties of the Earth by analyzing the propagation and interference phenomena of seismic waves. Seismic inversion methods are widely used in fields such as the study of the Earth's internal structure, oil and gas exploration, and earthquake monitoring. In recent years, with the deepening of research on seismic exploration technology, the demand for the accuracy of tomographic inversion has been increasing. However, due to the influence of the complex "dual surface and subsurface" structure in western China, the depth domain migration imaging effect is not outstanding.
[0003] To improve the quality of depth-domain migration imaging, various constrained tomographic inversion methods are typically used for shallow layers to enhance the modeling accuracy of low-velocity zones. However, the impact of velocity characteristics in the mid-to-deep layers below the high-velocity top interface on migration imaging results is rarely considered. In existing technologies, using large offset distances and well velocity information as constraints has become a novel approach to improve the velocity accuracy of mid-to-deep layer model tomographic inversion.
[0004] The above-mentioned well velocity constraint method has limitations, specifically in the following two aspects:
[0005] First, shallow data (within a few hundred meters) of previous logging velocity information is usually missing, resulting in incomplete well velocity information. This means that well constraints can easily lead to shallow distortion. Second, the vertical sampling density of logging velocity information is high (centimeter level), which means that there are often multiple velocity sampling points within a single vertical tomographic grid (5-10m level). This means that the constrained velocity changes significantly within a single grid. Summary of the Invention
[0006] This application discloses a well tomographic inversion method and apparatus.
[0007] In a first aspect, this application discloses a well tomographic inversion method, the method comprising:
[0008] An initial velocity model of the target well is established using the tomographic inversion method;
[0009] Based on the pre-established logging velocity-depth curve, determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well;
[0010] The velocity-depth simulation curve generated by the initial velocity model is used to supplement the velocity corresponding to the missing depth information in the logging velocity-depth curve;
[0011] While ensuring the integrity of the target well velocity information and under the constraint of a preset sampling interval, a large offset inversion is performed on the target well to generate an optimized constrained velocity model of the target well.
[0012] Optionally, the step of supplementing the velocity corresponding to the missing depth information in the logging velocity-depth curve includes:
[0013] In the velocity-depth simulation curve, the velocity information at the same depth as the well logging velocity-depth curve is corrected to eliminate the velocity difference;
[0014] The logging velocity-depth curve is supplemented with velocity information based on the velocity information that eliminates the velocity difference.
[0015] Optionally, after the step of supplementing the velocity corresponding to the missing depth information in the logging velocity-depth curve using the velocity-depth simulation curve generated by the initial velocity model, the method further includes:
[0016] Based on the vertical grid size of the tomographic inversion, the logging velocity-depth curve is resampled.
[0017] Optionally, the step of resampling the logging velocity-depth curve based on the tomographic inversion vertical grid size includes:
[0018] Obtain the original sampling interval of the logging velocity-depth curve;
[0019] Obtain the tomographic inversion vertical grid size of the velocity-depth simulation curve;
[0020] Based on the vertical grid size of the tomographic inversion, the logging velocity-depth curve is resampled at equal depths so that there are sampling points within a single tomographic vertical grid.
[0021] Optionally, the step of resampling the logging velocity-depth curve at equal depths based on the tomographic inversion vertical grid size, so that sampling points exist within a single tomographic vertical grid, includes:
[0022] Resample the logging velocity-depth curves within each grid according to the following expression.
[0023]
[0024] Among them, v i h represents the velocity value after resampling within the i-th grid. nij t represents the depth information corresponding to the j-th sampling point within the i-th grid. ij This represents the sampling time of the j-th sampling point within the i-th grid.
[0025] Optionally, the step of establishing the initial velocity model of the target well using the tomographic inversion method includes:
[0026] Under the constraints of refraction stratification velocity at the common center point and micrologging, the original velocity information of the target well is obtained, and the initial velocity model is established based on the original velocity information.
[0027] Secondly, this application discloses a well tomographic inversion apparatus, the apparatus comprising:
[0028] The velocity model building module is used to build the initial velocity model of the target well using the tomographic inversion method;
[0029] The missing information determination module is used to determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well based on the pre-established logging velocity-depth curve;
[0030] The missing information supplementation module is used to supplement the velocity corresponding to the missing depth information in the logging velocity-depth curve using the velocity-depth simulation curve generated by the initial velocity model.
[0031] The velocity model inversion module is used to perform large offset inversion on the target well while ensuring the integrity of the target well velocity information and under the constraint of a preset sampling interval, to generate an optimized constrained velocity model for the target well.
[0032] Optionally, the missing information supplementation module is specifically used to correct the velocity information in the velocity-depth simulation curve that is at the same depth as the logging velocity-depth curve, so as to eliminate the velocity difference; and to supplement the logging velocity-depth curve with velocity based on the velocity information that has eliminated the velocity difference.
[0033] Optionally, the device further includes a resampling module, used to resample the logging velocity-depth curve based on the vertical grid size of the tomographic inversion after supplementing the velocity corresponding to the missing depth information in the logging velocity-depth curve.
[0034] Optionally, the resampling module is specifically used to obtain the original sampling interval of the logging velocity-depth curve; obtain the tomographic inversion vertical grid size of the velocity-depth simulation curve; and perform equal-depth resampling on the logging velocity-depth curve based on the tomographic inversion vertical grid size, so that there are sampling points within a single tomographic vertical grid.
[0035] Optionally, the resampling module is specifically used to resample the logging velocity-depth curves within each grid according to the following expression:
[0036]
[0037] Among them, v i This represents the velocity value after resampling within the i-th grid. t represents the depth information corresponding to the j-th sampling point within the i-th grid. ij This represents the sampling time of the j-th sampling point within the i-th grid.
[0038] Optionally, the velocity model establishment module is specifically used to obtain the original velocity information of the target well under the constraints of the common center point refraction stratification velocity and micro-logging, and to establish the initial velocity model based on the original velocity information.
[0039] Thirdly, this application discloses an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to perform the method as described in any of the preceding aspects.
[0040] Fourthly, this application discloses a non-transitory computer-readable storage medium in which, when the instructions in the storage medium are executed by a processor of an electronic device, enable the electronic device to perform the methods described in any of the preceding aspects.
[0041] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.
[0042] The technical solution provided in this application may include the following beneficial effects:
[0043] In the well tomographic inversion scheme provided in this application, an initial velocity model is first obtained through conventional tomographic inversion iteration. Then, the missing velocity information in the shallow region of the target well's logging velocity-depth curve is supplemented to obtain a complete logging velocity-depth curve, thus avoiding constraint distortion. Then, the logging velocity-depth curve is resampled based on tomographic vertical grid size matching to ensure that there is a velocity sampling point within a single tomographic vertical grid, thereby achieving the uniqueness of the constrained velocity within a single grid.
[0044] Furthermore, to verify the effectiveness of the proposed solution, practical explorations and experiments were conducted in the processing of 2D seismic survey lines for a certain project and in the processing of 3D seismic depth migration data for a well area in a certain basin. The experimental results show that in a certain region with well-developed underground faults, strong tectonic alteration, rapid lateral changes, and extremely low signal-to-noise ratio in seismic data, the method provided in this application can further characterize the accuracy of the intermediate-deep velocity model and optimize the effect of pre-stack depth migration processing. In the processing of 3D data in a well area of a certain basin, the method provided in this application improved the accuracy of velocity inversion in complex dual-structure systems and enhanced the accuracy of pre-stack depth migration processing in this area. Attached Figure Description
[0045] Figure 1 A flowchart of a well torsion inversion method provided in this application.
[0046] Figure 2 Another structural diagram of the well tomographic inversion method provided in this application.
[0047] Figure 3 A structural diagram of a well torsion inversion device provided in this application.
[0048] Figure 4 Another structural diagram of the tomographic inversion apparatus for the well provided in this application.
[0049] Figure 5 A block diagram of an electronic device provided in this application.
[0050] Figure 6 A block diagram of another electronic device provided in this application. Detailed Implementation
[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0052] Existing well velocity constraint methods have limitations. First, shallow data (within a few hundred meters) of well logging velocity information is usually missing, resulting in incomplete well velocity information. This means that well constraints can easily lead to shallow distortion. Second, the vertical sampling density of well logging velocity information is high (on the centimeter level), which often results in multiple velocity sampling points within a single vertical tomographic grid (5-10m level). This means that the constrained velocity varies greatly within a single grid.
[0053] To address the problems in the prior art, this application provides a well tomographic inversion method and apparatus. The well tomographic inversion method provided in this application will be described in detail below.
[0054] Example 1
[0055] Reference Figure 1 This is a flowchart of a well torsion inversion method provided in this application, the method may include the following steps:
[0056] Step S101: Establish the initial velocity model of the target well using the tomographic inversion method. It should be noted that the initial velocity model obtained here has high accuracy in velocity information above the low-dropout zone. However, due to the lack of constraints on the velocity in the middle and deep layers, the velocity information below the low-dropout zone is not sufficiently detailed, and the accuracy of the velocity information in the deeper layers is insufficient. In other words, this initial velocity model is only a relatively accurate tomographic inversion velocity model, and its accuracy is not ideal, requiring improvement through subsequent methods.
[0057] In one scenario, the original velocity information of the target well can be obtained under the constraints of CMP (Common Middle Point) refraction stratification velocity and micrologging, and the initial velocity model can be established based on the original velocity information.
[0058] Step S102: Based on the pre-established logging velocity-depth curve, determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well.
[0059] Step S103: Use the velocity-depth simulation curve generated by the initial velocity model to supplement the velocity corresponding to the missing depth information in the logging velocity-depth curve.
[0060] In one scenario, the velocity corresponding to the missing depth information in the logging velocity-depth curve can be supplemented in the following manner: the velocity information in the velocity-depth simulation curve that is at the same depth as the logging velocity-depth curve is corrected to eliminate the velocity difference; the logging velocity-depth curve is supplemented based on the velocity information with the eliminated velocity difference.
[0061] Specifically, step S102 supplements the shallow missing velocity in the logging velocity-depth curve based on the initial velocity model established by the tomographic inversion method in step S101, thereby determining the missing depth information of the logging velocity; step S103 supplements the velocity-depth simulation curve generated by the tomographic inversion model, but at the same depth, there is a certain velocity difference (Δv) between the two. Through velocity correction, the velocity difference between the two is eliminated, thereby obtaining a complete logging velocity-depth curve.
[0062] Step S104: Under the constraints of ensuring the integrity of the target well velocity information and following the preset sampling interval, perform large offset inversion on the target well to generate the optimized constrained velocity model of the target well.
[0063] It should be noted that the “preset sampling interval” mentioned here can be determined according to the desired level of detail in the velocity-depth simulation curve to be generated. Those skilled in the art can set the value of this sampling interval reasonably according to the actual needs of seismic exploration.
[0064] This application provides a well tomographic inversion method, which relates to pre-stack depth migration modeling technology, specifically a well-constrained tomographic inversion method. In the well tomographic inversion scheme provided in this application, an initial velocity model is first obtained through conventional tomographic inversion iteration, and then the velocity information missing in the shallow region of the target well's logging velocity-depth curve is supplemented, thereby obtaining a complete logging velocity-depth curve and avoiding constraint distortion.
[0065] Example 2
[0066] Reference Figure 2 Here is another flowchart of the well tomographic inversion method provided in this application, which may specifically include the following steps:
[0067] Step S201: Establish the initial velocity model of the target well using the tomographic inversion method.
[0068] Step S202: Based on the pre-established logging velocity-depth curve, determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well.
[0069] Step S203: Use the velocity-depth simulation curve generated by the initial velocity model to supplement the velocity corresponding to the missing depth information in the logging velocity-depth curve.
[0070] Step S204: Based on the vertical grid size of the tomographic inversion, resample the logging velocity-depth curve.
[0071] In one scenario, the logging velocity-depth curve can be resampled as follows: obtain the original sampling interval of the logging velocity-depth curve; obtain the tomographic inversion vertical grid size of the velocity-depth simulation curve; and resample the logging velocity-depth curve at equal depths based on the tomographic inversion vertical grid size, so that there are sampling points within a single tomographic vertical grid.
[0072] Specifically, the logging velocity-depth curves within each grid can be resampled according to the following expression.
[0073]
[0074] Among them, v i This represents the velocity value after resampling within the i-th grid. t represents the depth information corresponding to the j-th sampling point within the i-th grid. ij This represents the sampling time of the j-th sampling point within the i-th grid.
[0075] Step S205: Under the constraints of ensuring the integrity of the target well velocity information and following the preset sampling interval, perform large offset inversion on the target well to generate the optimized constrained velocity model of the target well.
[0076] It should be noted that, Figure 2 Steps S201 to S203, and step S205 in the method embodiment shown are Figure 1 Steps S101 to S104 in the method embodiment shown are similar, and relevant details can be found by referring to [reference needed]. Figure 1 The specific details of the method embodiment shown will not be repeated here.
[0077] It should be noted that, Figure 2 The method embodiment shown is in Figure 1 Based on the method embodiment shown, the logging velocity-depth curve is resampled based on tomographic vertical grid size matching. It can be seen that... Figure 2 The method embodiments shown have Figure 1 All the beneficial effects of the method embodiment shown not only avoid constraint evenness, but also ensure that there is a velocity sampling point within a single tomographic vertical grid, thus achieving the singleness of the constraint velocity within a single grid.
[0078] Furthermore, to verify the effectiveness of the proposed solution, practical explorations and experiments were conducted in the processing of 2D seismic survey lines for a certain project and in the processing of 3D seismic depth migration data for a well area in a certain basin. The experimental results show that in a certain region with well-developed underground faults, strong tectonic alteration, rapid lateral changes, and extremely low signal-to-noise ratio in seismic data, the method provided in this application can further characterize the accuracy of the intermediate-deep velocity model and optimize the effect of pre-stack depth migration processing. In the processing of 3D data in a well area of a certain basin, the method provided in this application improved the accuracy of velocity inversion in complex dual-structure systems and enhanced the accuracy of pre-stack depth migration processing in this area.
[0079] It should be noted that the scheme provided in this application is suitable for establishing a high-precision velocity field in pre-stack depth migration processing. It can be applied to pre-stack depth migration imaging processing in the extremely low signal-to-noise ratio area of western China and other complex tectonic areas in China. It can effectively improve imaging quality and has broad application prospects.
[0080] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by this application.
[0081] Example 3
[0082] Reference Figure 3 This is a structural diagram of a well tomography inversion device provided in this application. The well tomography inversion device provided in this application may include the following modules: velocity model establishment module 310, missing information determination module 320, missing information supplementation module 330, and velocity model inversion module 340.
[0083] Among them, the velocity model establishment module 310 is used to establish the initial velocity model of the target well through the tomographic inversion method;
[0084] The missing information determination module 320 is used to determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well based on the pre-established logging velocity-depth curve;
[0085] The missing information supplementation module 330 is used to supplement the velocity corresponding to the missing depth information in the logging velocity depth curve using the velocity depth simulation curve generated by the initial velocity model.
[0086] The velocity model inversion module 340 is used to perform large offset inversion on the target well while ensuring the integrity of the target well velocity information and under the constraint of a preset sampling interval, to generate an optimized constrained velocity model for the target well.
[0087] In one scenario, the velocity model building module 310 is specifically used to obtain the original velocity information of the target well under the constraints of the common center point refraction stratification velocity and micro-logging, and to build the initial velocity model based on the original velocity information.
[0088] In one scenario, the missing information supplementation module 330 is specifically used to correct the velocity information in the velocity-depth simulation curve that is at the same depth as the logging velocity-depth curve, so as to eliminate the velocity difference; and to supplement the logging velocity-depth curve with velocity based on the velocity information that has eliminated the velocity difference.
[0089] In the well tomographic inversion scheme provided in this application, an initial velocity model is first obtained through conventional tomographic inversion iteration, and then the velocity information missing in the shallow region of the target well's logging velocity-depth curve is supplemented to obtain a complete logging velocity-depth curve, thus avoiding constraint distortion.
[0090] Example 4
[0091] Reference Figure 4 This is another structural diagram of the tomographic inversion apparatus for the well provided in this application. Figure 3Based on the illustrated embodiment, in addition to the velocity model establishment module 310, the missing information determination module 320, the missing information supplementation module 330, and the velocity model inversion module 340, the device also includes a resampling module 350. This resampling module 350 is used to resample the logging velocity-depth curve based on the tomographic inversion vertical grid size after supplementing the velocity corresponding to the missing depth information in the logging velocity-depth curve.
[0092] In one scenario, the resampling module 350 is specifically used to obtain the original sampling interval of the logging velocity-depth curve; obtain the tomographic inversion vertical grid size of the velocity-depth simulation curve; and perform equal-depth resampling on the logging velocity-depth curve based on the tomographic inversion vertical grid size, so that there are sampling points within a single tomographic vertical grid.
[0093] In one specific implementation of this application, the resampling module 350 is used to resample the logging velocity-depth curves within each grid according to the following expression:
[0094]
[0095] Among them, v i This represents the velocity value after resampling within the i-th grid. t represents the depth information corresponding to the j-th sampling point within the i-th grid. ij This represents the sampling time of the j-th sampling point within the i-th grid.
[0096] In the well tomographic inversion scheme provided in this application, an initial velocity model is first obtained through conventional tomographic inversion iteration. Then, the missing velocity information in the shallow region of the target well's logging velocity-depth curve is supplemented to obtain a complete logging velocity-depth curve, thus avoiding constraint distortion. Then, the logging velocity-depth curve is resampled based on tomographic vertical grid size matching to ensure that there is a velocity sampling point within a single tomographic vertical grid, thereby achieving the uniqueness of the constrained velocity within a single grid.
[0097] Furthermore, to verify the effectiveness of the proposed solution, practical explorations and experiments were conducted in the processing of 2D seismic survey lines for a certain project and in the processing of 3D seismic depth migration data for a well area in a certain basin. The experimental results show that in a certain region with well-developed underground faults, strong tectonic alteration, rapid lateral changes, and extremely low signal-to-noise ratio in seismic data, the method provided in this application can further characterize the accuracy of the intermediate-deep velocity model and optimize the effect of pre-stack depth migration processing. In the processing of 3D data in a well area of a certain basin, the method provided in this application improved the accuracy of velocity inversion in complex dual-structure systems and enhanced the accuracy of pre-stack depth migration processing in this area.
[0098] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.
[0099] Example 5
[0100] Optionally, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.
[0101] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0102] Figure 5 This application provides a block diagram of an electronic device 800. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.
[0103] Reference Figure 5 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.
[0104] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.
[0105] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, images, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0106] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.
[0107] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
[0108] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
[0109] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.
[0110] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.
[0111] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast operation information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0112] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.
[0113] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0114] Example 6
[0115] Figure 6 A block diagram of another electronic device 1900 provided for this application. For example, electronic device 1900 may be provided as a server.
[0116] Reference Figure 6 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.
[0117] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.
[0118] Example 7
[0119] This application discloses a computer program product in which, when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device is able to perform the method described above.
[0120] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0122] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
[0123] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0124] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0125] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0127] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0128] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.
[0129] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A well tomographic inversion method, characterized in that, The method includes: An initial velocity model of the target well is established using the tomographic inversion method; Based on the pre-established logging velocity-depth curve, determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well; The velocity-depth simulation curve generated by the initial velocity model is used to supplement the velocity corresponding to the missing depth information in the logging velocity-depth curve; While ensuring the integrity of the target well velocity information and under the constraint of a preset sampling interval, a large offset inversion is performed on the target well to generate an optimized constrained velocity model of the target well.
2. The well tomographic inversion method according to claim 1, characterized in that, The step of supplementing the velocity corresponding to the missing depth information in the logging velocity-depth curve includes: In the velocity-depth simulation curve, the velocity information at the same depth as the well logging velocity-depth curve is corrected to eliminate the velocity difference; The logging velocity-depth curve is supplemented with velocity information based on the velocity information that eliminates the velocity difference.
3. The well tomographic inversion method according to claim 1 or 2, characterized in that, After the step of supplementing the velocity corresponding to the missing depth information in the logging velocity-depth curve using the velocity-depth simulation curve generated by the initial velocity model, the method further includes: Based on the vertical grid size of the tomographic inversion, the logging velocity-depth curve is resampled.
4. The well tomographic inversion method according to claim 3, characterized in that, The step of resampling the logging velocity-depth curve based on the vertical grid size determined by tomographic inversion includes: Obtain the original sampling interval of the logging velocity-depth curve; Obtain the tomographic inversion vertical grid size of the velocity-depth simulation curve; Based on the vertical grid size of the tomographic inversion, the logging velocity-depth curve is resampled at equal depths so that there are sampling points within a single tomographic vertical grid.
5. The well tomographic inversion method according to claim 4, characterized in that, The step of resampling the logging velocity-depth curve at equal depths based on the tomographic inversion vertical grid size, so that there are sampling points within a single tomographic vertical grid, includes: Resample the logging velocity-depth curves within each grid according to the following expression. Among them, v i This represents the velocity value after resampling within the i-th grid. t represents the depth information corresponding to the j-th sampling point within the i-th grid. ij This represents the sampling time of the j-th sampling point within the i-th grid.
6. The well tomographic inversion method according to claim 1, characterized in that, The step of establishing the initial velocity model of the target well using the tomographic inversion method includes: Under the constraints of refraction stratification velocity at the common center point and micrologging, the original velocity information of the target well is obtained, and the initial velocity model is established based on the original velocity information.
7. A well tomographic inversion apparatus, characterized in that, The device includes: The velocity model building module is used to build the initial velocity model of the target well using the tomographic inversion method; The missing information determination module is used to determine the missing depth information corresponding to the missing velocity in the initial velocity model of the target well based on the pre-established logging velocity-depth curve; The missing information supplementation module is used to supplement the velocity corresponding to the missing depth information in the logging velocity-depth curve using the velocity-depth simulation curve generated by the initial velocity model. The velocity model inversion module is used to perform large offset inversion on the target well while ensuring the integrity of the target well velocity information and under the constraint of a preset sampling interval, to generate an optimized constrained velocity model for the target well.
8. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device implements the method as described in any one of claims 1 to 6.