Micro logging interpretation data anomaly identification method and device

Abstract

This application provides a method and apparatus for identifying anomalies in micrologging interpretation data. The method includes: acquiring the first arrival wave from historical seismic observation data; acquiring the coordinate data of the micrologging well to be identified; performing tomographic inversion on the surface layer based on the coordinate data and the first arrival wave to obtain surface velocities and thicknesses for different surface layers; generating a first velocity-thickness curve based on the surface velocity and thickness; performing tomographic inversion on the micrologging well to obtain a second surface velocity and a second surface thickness; generating a second velocity-thickness curve based on the second surface velocity and the second surface thickness; and determining whether the second velocity-thickness curve is anomaly based on the first velocity-thickness curve. This application can effectively improve the accuracy of micrologging data, thereby improving the quality of single-shot seismic exploration acquisition and the accuracy of static correction calculations in data processing.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration acquisition and data processing technology, and more specifically, to a method and apparatus for identifying anomalies in micrologging interpretation data. Background Technology

[0002] With the efficient development of seismic exploration and acquisition and the increasing complexity of underground areas in unexplored regions, integrated quality control of acquisition and processing is playing an increasingly important role. At the same time, it is necessary to adapt to new challenges, including rapid quality control of large-scale acquired data, dealing with the complexity of acquired data sources, improving real-time monitoring capabilities, and actively exploring the application of new technologies such as artificial intelligence in quality control to ensure the efficiency, accuracy and reliability of seismic exploration and acquisition.

[0003] In the process of seismic exploration data acquisition, micrologging is a task that must be completed in advance. The interpretation results of micrologging can be used to predict the depth of the high-velocity layer top interface at each seismic excitation point, thereby designing the well depth for each excitation point. In the process of seismic exploration data processing, micrologging interpretation results can be used to constrain static correction calculations, improve the accuracy of near-surface velocity models and micro-tectonic imaging. Therefore, micrologging data plays an important role in seismic exploration data acquisition and processing. However, how to quickly control the quality of micrologging interpretation results, evaluate the reliability of the interpretation results, and promptly detect errors and anomalies in the micrologging interpretation results, so as to provide all accurate basic data for the smooth progress of seismic exploration data processing, has become one of the technical challenges of quality control of seismic exploration acquisition data.

[0004] Currently, real-time monitoring of seismic exploration data is achieved using processing technologies. This primarily includes qualitative and quantitative analysis of raw single-shot data, analysis of source-receiver deviations, excitation delay analysis, and analysis of background noise interference and Q-value survey results. However, there are no quality control measures for analyzing the accuracy of micrologging data interpretation results. In most cases, the near-surface survey results interpreted by the acquisition team are directly given to the data processing team without evaluation by a third-party data monitoring group. With the increasing density of micrologging data acquisition, from low-density acquisition and interpretation of one micrologging well ten years ago to high-density acquisition and interpretation of one micrologging well currently at 500m×500m, and potentially even higher density acquisition and interpretation in the future.

[0005] Therefore, how to solve the above problems is an urgent issue that needs to be addressed. Summary of the Invention

[0006] This application provides a method and apparatus for identifying anomalies in micro-logging interpretation data, aiming to improve the above-mentioned problems.

[0007] Firstly, this application provides a method for identifying anomalies in micro-logging interpretation data, the method comprising:

[0008] Obtain the first arrival wave from historical earthquake observation data;

[0009] Obtain the coordinate data of the micro-logging well to be identified;

[0010] Based on the coordinate data and the first arrival wave, a tomographic inversion is performed on the surface layer to obtain the surface velocity and surface thickness of different surface layers.

[0011] A first velocity-thickness curve is generated based on the surface velocity and the surface thickness;

[0012] The micrologging was subjected to tomographic inversion to obtain the second surface velocity and the second surface thickness.

[0013] A second velocity-thickness curve is generated based on the second surface velocity and the second surface thickness;

[0014] Determine whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve.

[0015] In one possible embodiment, before determining whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve, the method further includes:

[0016] The first velocity-thickness curve is superimposed with the second velocity-thickness curve.

[0017] In one possible embodiment, tomographic inversion is performed on the surface layer based on the coordinate data and the first arrival wave to obtain the surface velocity and surface thickness of different surface layers, including:

[0018] Obtain the travel time of the first arrival wave;

[0019] The surface velocity of different surface layers is obtained based on the travel time and the coordinate data.

[0020] The surface thickness is obtained based on the surface velocity.

[0021] In one possible embodiment, obtaining the surface velocity of different surface layers based on the travel time and the coordinate data includes:

[0022] The travel time is spatially decomposed to model the travel time picking from the excitation point to the receiver point in the coordinate data;

[0023] The model is solved to obtain the surface velocities of different surface layers.

[0024] In one possible implementation, travel time is picked up, satisfying:

[0025]

[0026] Where: X represents the distance from the excitation point to the receiver point; X sd =|R s ,R d |; Let τ be the surface velocity, and τ be the modeled time delay function. R represents the coordinate data.

[0027] In one possible embodiment, the surface layer thickness satisfies:

[0028]

[0029] Where: i represents the path corresponding to different coordinates in the coordinate data.

[0030] Secondly, this application provides a device for identifying anomalies in micro-logging interpretation data, the device comprising:

[0031] The first acquisition unit is used to acquire the first arrival wave from historical earthquake observation data;

[0032] The second acquisition unit is used to acquire the coordinate data of the micro-logging well to be identified;

[0033] The first inversion unit is used to perform tomographic inversion on the surface layer based on the coordinate data and the first arrival wave to obtain the surface velocity and surface thickness of different surface layers.

[0034] The first curve generation unit is used to generate a first velocity-thickness curve based on the surface velocity and the surface thickness.

[0035] The second inversion unit is used to perform tomographic inversion on the micro-logging to obtain the second surface velocity and the second surface thickness.

[0036] The second curve generation unit is used to generate a second velocity-thickness curve based on the second surface velocity and the second surface thickness.

[0037] An anomaly identification unit is used to determine whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve.

[0038] In one possible embodiment, the apparatus further includes: a stacking unit, the stacking unit being configured to:

[0039] The first velocity-thickness curve is superimposed with the second velocity-thickness curve.

[0040] In one possible embodiment, the first inversion unit is specifically configured to include:

[0041] Obtain the travel time of the first arrival wave;

[0042] The surface velocity of different surface layers is obtained based on the travel time and the coordinate data.

[0043] The surface thickness is obtained based on the surface velocity.

[0044] In one possible embodiment, obtaining the surface velocity of different surface layers based on the travel time and the coordinate data includes:

[0045] The travel time is spatially decomposed to model the travel time picking from the excitation point to the receiver point in the coordinate data;

[0046] The model is solved to obtain the surface velocities of different surface layers.

[0047] The present application provides a method and apparatus for identifying anomalies in micrologging interpretation data. This method involves: acquiring the first arrival wave from historical seismic observation data; acquiring the coordinate data of the micrologging to be identified; performing tomographic inversion on the surface layer based on the coordinate data and the first arrival wave to obtain the surface velocity and thickness of different surface layers; generating a first velocity-thickness curve based on the surface velocity and thickness; performing tomographic inversion on the micrologging to obtain a second surface velocity and a second surface thickness; generating a second velocity-thickness curve based on the second surface velocity and the second surface thickness; and determining whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve. This allows for the intuitive and rapid identification of micrologging results with anomalies. Problematic micrologging results are then fed back to the acquisition and interpretation team for reinterpretation or remeasurement, ultimately ensuring the accuracy of each micrologging data point and improving the quality of single-shot seismic exploration acquisition and the accuracy of static correction calculations in data processing. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of the structure of an electronic device provided in the first embodiment of this application;

[0050] Figure 2 A flowchart illustrating a method for identifying anomalies in micrologging interpretation data provided in the second embodiment of this application;

[0051] Figure 3 for Figure 2The diagram shows the first arrival wave pickup in a method for identifying anomalies in micrologging interpretation data.

[0052] Figure 4 for Figure 2 The diagram shows an integration path in a method for identifying anomalies in micrologging interpretation data.

[0053] Figure 5 for Figure 2 The diagram shows a refraction tomography static correction inversion of surface velocity and thickness in a micro-logging interpretation data anomaly identification method.

[0054] Figure 6 for Figure 2 The diagram shows the distribution of micrologging acquisition locations in a method for identifying anomalies in micrologging interpretation data.

[0055] Figure 7 for Figure 2 The diagram shows a first velocity thickness curve in a method for identifying anomalies in micrologging interpretation data.

[0056] Figure 8 for Figure 2 The diagram shows a second velocity thickness curve in a method for identifying anomalies in micrologging interpretation data.

[0057] Figure 9 for Figure 2 The diagram shows an overlay of all first velocity thickness curves in a method for identifying anomalies in micrologging interpretation data.

[0058] Figure 10 for Figure 2 The diagram shows an overlay of all second velocity thickness curves in a method for identifying anomalies in micrologging interpretation data.

[0059] Figure 11 for Figure 2 The diagram shows the superposition of all first velocity thickness curves and all second velocity thickness curves in a micro-logging interpretation data anomaly identification method.

[0060] Figure 12 This is a schematic diagram of the functional modules of a micro-logging interpretation data anomaly identification device provided in the third embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions 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.

[0062] First embodiment:

[0063] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. In this application, it can be... Figure 1 The schematic diagram shown illustrates an example of an electronic device 100 used to implement the micro-logging interpretation data anomaly identification method and apparatus of the present application embodiments.

[0064] like Figure 1 The diagram shows the structure of an electronic device 100. The electronic device 100 includes one or more processors 102, one or more storage devices 104, input devices 106, and output devices 108. These components are interconnected via a bus system and / or other forms of connection mechanisms (not shown). It should be noted that... Figure 1 The components and structure of the electronic device 100 shown are merely exemplary and not limiting; the electronic device may have, as needed. Figure 1 The components shown may also have Figure 1 Other components and structures not shown.

[0065] The processor 102 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.

[0066] It should be understood that the processor 102 in the embodiments of this application may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0067] The storage device 104 may include one or more computer program products, which may include various forms of computer-readable storage media.

[0068] It should be understood that the storage device 104 in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0069] The computer-readable storage medium may store one or more computer program instructions, which the processor 102 may execute to implement the client functions (implemented by the processor) in the embodiments of this application described below, and / or other desired functions. Various applications and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the applications.

[0070] The input device 106 may be a device used by a user to input commands, and may include one or more of the following: keyboard, mouse, microphone, and touch screen.

[0071] Second embodiment:

[0072] Reference Figure 2 The flowchart shown is a method for identifying anomalies in micro-logging interpretation data. The method specifically includes the following steps:

[0073] Step S201: Obtain the first arrival wave from the seismic observation data.

[0074] Seismic observation data refers to seismic data collected at the engineering site. This data can include the spatiotemporal attributes of seismic events, the propagation characteristics of seismic waves, and historical records of seismic activity.

[0075] It should be noted that this application does not specify the particular method for picking up the initial wave. Techniques known in the art can be used for picking.

[0076] For example, the first wave picked up is like Figure 3 As shown.

[0077] It should be noted that, Figure 3 This is only for illustrating the initial wave and is not intended to limit the protection scheme of this application.

[0078] Step S202: Obtain the coordinate data of the micro-logging well to be identified.

[0079] The micro-logging well can be a well that is currently being explored.

[0080] The coordinate data of the micro-logging can be obtained through real-time transmission, and no specific limitations are made here.

[0081] Step S203: Perform tomographic inversion on the surface layer based on the coordinate data and the first arrival wave to obtain the surface velocity and surface thickness of different surface layers.

[0082] As one implementation, step S203 includes: obtaining the travel time of the first arrival wave; obtaining the surface velocity of different surface layers based on the travel time and the coordinate data; and obtaining the surface thickness based on the surface velocity.

[0083] Optionally, obtaining the surface velocities of different surfaces based on the travel time and the coordinate data includes: spatially decomposing the travel time to model the travel time picking from the excitation point to the receiver point in the coordinate data; and solving the modeled data to obtain the surface velocities of different surfaces.

[0084] In other words, travel time is spatially decomposed so that for coordinates R s and R d Picking up the travel time between the trigger point and the receiver point The model is constructed by tomographic integration of the apparent velocity along the line connecting the excitation and receiver points. This is an iterative model update process, involving multiple iterations to refine the model. Mapped to and τ.

[0085] Optionally, travel time can be picked up to satisfy:

[0086]

[0087] Where: X represents the distance from the excitation point to the receiver point; X sd =|R s ,R d |; Let τ be the surface velocity, and τ be the modeling time delay function. R represents the coordinate data. Coordinates R1 and R2 correspond to paths 1 and 2, respectively, as shown below. Figure 4 As shown.

[0088] Optionally, the surface layer thickness satisfies:

[0089]

[0090] Where: i represents the path (integral path) corresponding to different coordinates in the coordinate data. Surface velocity (apparent velocity) represents the instantaneous layer velocity corresponding to thickness Z. The velocity and depth functions form a near-surface model estimate, from which the static correction can be calculated. The instantaneous layer velocity can estimate the thickness inverted from the near-surface, and the depth is obtained by adding the thicknesses of each layer.

[0091] For example, with Figure 3 For example, for Figure 3 The first-arrival wave shown is obtained using the static correction method of refraction tomography. Based on the travel time of the first-arrival wave, tomographic inversion is performed on the surface layer to obtain different surface velocities V. n and its thickness H n (like Figure 5 As shown), the static correction value is obtained, where n represents the inversion layer.

[0092] In other words, the coordinates of the micro-logging points that have already been collected (such as...) Figure 6 As shown), the velocity V inverted at the selected location can be extracted. n and thickness H n This forms a list.

[0093] Step S204: Generate a first velocity-thickness curve based on the surface velocity and the surface thickness.

[0094] like Figure 7 As shown, as one implementation method, the first velocity-thickness curve of the tomographic inversion is plotted using MATLAB software.

[0095] Step S205: Perform tomographic inversion on the micro-logging to obtain the second surface velocity and the second surface thickness.

[0096] It should be noted that the method for determining the second surface velocity and the second surface thickness using micrologging inversion can be referred to the previous description, and will not be repeated here.

[0097] It should be understood that there should also be multiple second surface velocities and second surface thicknesses, which can be stored in a list.

[0098] Step S206: Generate a second velocity-thickness curve based on the second surface velocity and the second surface thickness.

[0099] For example, such as Figure 8 As shown, the second velocity thickness curve (or micrologging interpretation result curve) is also plotted using MATLAB software.

[0100] Step S207: Determine whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve.

[0101] In one possible embodiment, prior to step S207, the micrologging interpretation data anomaly identification method further includes: superimposing the first velocity thickness curve with the second velocity thickness curve.

[0102] For example, the first velocity-thickness curves for all micrologging locations were plotted using MATLAB software (e.g., Figure 9 (as shown) and the second velocity thickness curve (as shown) Figure 10 ), and superimpose the curves together (e.g. Figure 11 As shown), the stability and effectiveness of the second velocity thickness curve in the micrologging interpretation results are comprehensively evaluated using the first velocity thickness curve. Simultaneously, based on... Figure 11 The anomaly curve can be used to quickly evaluate and identify anomalies in the micro-logging interpretation, such as... Figure 11 As shown in the first velocity thickness curve (abnormal curve 1) and the second velocity thickness curve (abnormal curve 2), both curves are abnormal, indicating that there is a problem with the micrologging data and it needs to be remeasured or reinterpreted.

[0103] It is understandable that the micrologging interpretation data anomaly identification method provided in this embodiment first rapidly picks up the initial arrival of historical seismic observation data acquired during seismic exploration, and then uses a tomographic static correction method to invert the surface velocity and thickness. Next, it extracts the velocity and thickness information of the micrologging location, and then uses MATLAB software to plot the inverted first velocity-thickness curve and the micrologging interpretation second velocity-thickness curve, respectively. By cross-validating the first and second velocity-thickness curves, anomalies in the micrologging interpretation results can be quickly identified. From the application effect of the invention, the anomaly curve reflects the problems in the micrologging and identifies the location of the anomaly points. This is a highly practical quality control and evaluation technique for seismic exploration micrologging data acquisition, which can effectively improve the accuracy of micrologging data, thereby improving the quality of single-shot acquisition in seismic exploration and the accuracy of static correction calculations in data processing.

[0104] It should be noted that, Figure 6 -11 is only used to illustrate the first velocity thickness curve and the second velocity thickness curve, and the specific values ​​present in the figure are not intended to limit the technical solution to be protected in this application.

[0105] Third embodiment:

[0106] Based on the same inventive concept, this embodiment provides a device for identifying anomalies in micro-logging interpretation data, such as... Figure 12 As shown, the micro-logging interpretation data anomaly identification device includes: a first acquisition unit 510, a second acquisition unit 520, a first inversion unit 530, a first curve generation unit 540, a second inversion unit 550, a second curve generation unit 560, and an anomaly identification unit 570. The specific functions of each unit are as follows:

[0107] The first acquisition unit 510 is used to acquire the first arrival wave from historical earthquake observation data;

[0108] The second acquisition unit 520 is used to acquire the coordinate data of the micro-logging well to be identified;

[0109] The first inversion unit 530 is used to perform tomographic inversion on the surface layer based on the coordinate data and the first arrival wave to obtain the surface velocity and surface thickness of different surface layers.

[0110] The first curve generation unit 540 is used to generate a first velocity-thickness curve based on the surface velocity and the surface thickness.

[0111] The second inversion unit 550 is used to perform tomographic inversion on the micro-logging well to obtain the second surface velocity and the second surface thickness.

[0112] The second curve generation unit 560 is used to generate a second velocity-thickness curve based on the second surface velocity and the second surface thickness.

[0113] The anomaly identification unit 570 is used to determine whether the second velocity thickness curve is abnormal based on the first velocity thickness curve.

[0114] In one possible embodiment, the apparatus further includes an overlay unit, the overlay unit being configured to overlay the first velocity-thickness curve with the second velocity-thickness curve.

[0115] In one possible embodiment, the first inversion unit 530 is specifically configured to: acquire the travel time of the first arrival wave; obtain the surface velocity of different surface layers based on the travel time and the coordinate data; and obtain the surface thickness based on the surface velocity.

[0116] In one possible embodiment, obtaining the surface velocities of different surfaces based on the travel time and the coordinate data includes: spatially decomposing the travel time to model the travel time picking from the excitation point to the receiver point in the coordinate data; and solving the modeled data to obtain the surface velocities of different surfaces.

[0117] Optionally, travel time can be picked up to satisfy:

[0118]

[0119] Where: X represents the distance from the excitation point to the receiver point; X sd =|R s ,R d |; Let τ be the surface velocity, and τ be the modeled time delay function. R represents the coordinate data.

[0120] In one possible embodiment, the surface layer thickness satisfies:

[0121]

[0122] Where: i represents the path corresponding to different coordinates in the coordinate data.

[0123] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program, which, when run by a processing device, executes the steps of any of the micro-logging interpretation data anomaly identification methods provided in Embodiment 2 above.

[0124] The computer program product of the micro-logging interpretation data anomaly identification method and device provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0125] In summary, the micrologging interpretation data anomaly identification method and device provided in this embodiment involves initial arrival picking of historical seismic observation data acquired during seismic exploration, followed by inversion of surface velocity and thickness using a tomographic static correction method. Then, velocity and thickness information at the micrologging locations are extracted. MATLAB software is used to plot the inverted velocity-thickness curves and the velocity-thickness curves interpreted by the micrologging data. By cross-validating these two curves, anomalies in the micrologging interpretation results can be quickly identified, and anomaly-prone microloggings can be rapidly located based on the curves. This effectively improves the accuracy of micrologging data, thereby enhancing the quality of single-shot acquisition in seismic exploration and the accuracy of static correction calculations in data processing. It is a highly practical technique for quality control and evaluation of micrologging data, with promising applications in efficient seismic exploration data acquisition.

[0126] It should be noted that the above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0127] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0128] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0129] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein 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.

[0131] 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.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, 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.

[0133] 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.

[0134] 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.

[0135] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

Claims

1. A method for identifying abnormality of microlog interpretation data, characterized in that, The method includes: Obtain the first arrival wave from historical earthquake observation data; Obtain the coordinate data of the micro-logging well to be identified; Based on the coordinate data and the first arrival wave, a tomographic inversion is performed on the surface layer to obtain the surface velocity and surface thickness of different surface layers. A first velocity-thickness curve is generated based on the surface velocity and the surface thickness; The micrologging was subjected to tomographic inversion to obtain the second surface velocity and the second surface thickness. A second velocity-thickness curve is generated based on the second surface velocity and the second surface thickness; Determine whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve.

2. The method of claim 1, wherein, Before determining whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve, the method further includes: The first velocity-thickness curve is superimposed with the second velocity-thickness curve.

3. The method of claim 1, wherein, Based on the coordinate data and the first arrival wave, a tomographic inversion is performed on the surface layer to obtain the surface velocity and surface thickness of different surface layers, including: Obtain the travel time of the first arrival wave; The surface velocity of different surface layers is obtained based on the travel time and the coordinate data. The surface thickness is obtained based on the surface velocity.

4. The method of claim 3, wherein, The step of obtaining the surface velocity of different surface layers based on the travel time and the coordinate data includes: The travel time is spatially decomposed to model the travel time picking from the excitation point to the receiver point in the coordinate data; The model is solved to obtain the surface velocities of different surface layers.

5. The method of claim 4, wherein, Travel time collection, satisfying: where: X represents the distance from the excitation point to the receiving point; X sd | R s R d| is the surface velocity, and τ is a modeled delay time function. R represents the coordinate data.​​ 6. The method of claim 5, wherein, The surface layer thickness satisfies: Where: i represents the path corresponding to different coordinates in the coordinate data.

7. A device for identifying abnormality in microlog interpretation data, characterized by, The device includes: The first acquisition unit is used to acquire the first arrival wave from historical earthquake observation data; The second acquisition unit is used to acquire the coordinate data of the micro-logging well to be identified; The first inversion unit is used to perform tomographic inversion on the surface layer based on the coordinate data and the first arrival wave to obtain the surface velocity and surface thickness of different surface layers. The first curve generation unit is used to generate a first velocity-thickness curve based on the surface velocity and the surface thickness. The second inversion unit is used to perform tomographic inversion on the micro-logging to obtain the second surface velocity and the second surface thickness. The second curve generation unit is used to generate a second velocity-thickness curve based on the second surface velocity and the second surface thickness. An anomaly identification unit is used to determine whether the second velocity-thickness curve is abnormal based on the first velocity-thickness curve.

8. The apparatus of claim 7, wherein, The device further includes: a stacking unit, the stacking unit being used for: The first velocity-thickness curve is superimposed with the second velocity-thickness curve.

9. The apparatus of claim 7, wherein, The first inversion unit is specifically used for, including: Obtain the travel time of the first arrival wave; The surface velocity of different surface layers is obtained based on the travel time and the coordinate data. The surface thickness is obtained based on the surface velocity.

10. The apparatus of claim 9, wherein, The step of obtaining the surface velocity of different surface layers based on the travel time and the coordinate data includes: The travel time is spatially decomposed to model the travel time picking from the excitation point to the receiver point in the coordinate data; The model is solved to obtain the surface velocities of different surface layers.

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