Preferred method, device, electronic equipment and storage medium of seismic data

Abstract

The application discloses a kind of preferred method, device, electronic equipment and storage medium of seismic data, wherein the method comprises: obtaining target well curve corresponding to target mine, and determining seismic waveform data corresponding to the target mine;Determine at least one to be selected stratum corresponding to target area, determine correlation coefficient sequence based on the to-be-selected stratum, target well curve and seismic waveform data;Wherein, the correlation coefficient sequence includes well curve correlation coefficient sequence and seismic waveform correlation coefficient sequence;Determine data correlation parameter based on the correlation coefficient sequence, and determine target seismic data based on the correlation parameter.The above technical solution is based on well curve and seismic waveform data to determine the correlation coefficient sequence corresponding to different strata, and then determine the target seismic data based on the correlation coefficient sequence, which realizes the accurate evaluation of seismic data and improves the accuracy of target seismic data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, electronic equipment and storage medium for optimizing seismic data. Background Art

[0002] With the deepening of oilfield exploration, the use of seismic data to solve more detailed reservoir description problems has received increasing attention. In order to make the processed deep-domain seismic data reflect the formation information more accurately, it is also necessary to evaluate and optimize the deep-domain seismic data.

[0003] However, it is difficult to directly determine the impact and extent of the existing deep-domain seismic data in the interpretation process through seismic synthetic records, and it is impossible to achieve accurate evaluation of seismic data. Summary of the Invention

[0004] The present invention provides a method, device, electronic device and storage medium for evaluating deep-domain seismic data to solve the problem that existing deep-domain seismic data processing methods are difficult to determine whether the seismic data meets preset requirements, which results in the processed target seismic data being unable to reflect the actual state of the formation.

[0005] According to one aspect of the present invention, there is provided a method for optimizing seismic data, the method comprising:

[0006] Acquiring a target well logging curve corresponding to a target mine, and determining seismic waveform data corresponding to the target mine;

[0007] Determine at least one to-be-selected formation corresponding to the target area, and determine a correlation coefficient sequence based on the to-be-selected formation, target well logging curves, and seismic waveform data; wherein the correlation coefficient sequence includes a well logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence;

[0008] Data correlation parameters are determined based on the correlation coefficient sequence, and target seismic data are determined based on the correlation parameters.

[0009] According to another aspect of the present invention, there is provided a device for optimizing seismic data, the device comprising:

[0010] a data acquisition module, configured to acquire a target well logging curve corresponding to a target mine and determine seismic waveform data corresponding to the target mine;

[0011] a correlation coefficient determination module, configured to determine at least one to-be-selected formation corresponding to a target area, and determine a correlation coefficient sequence based on the to-be-selected formation, target well logging curves, and seismic waveform data; wherein the correlation coefficient sequence includes a well logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence;

[0012] The target data determination module is used to determine data-related parameters based on the correlation coefficient sequence, and to optimally determine target seismic data based on the correlation parameters.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] 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 to enable the at least one processor to perform the preferred method for seismic data according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions for enabling a processor to implement the preferred method for seismic data according to any embodiment of the present invention when the computer instructions are executed.

[0018] The technical solution of an embodiment of the present invention obtains a target well logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one selected stratum corresponding to a target area. A correlation coefficient sequence is determined based on the selected stratum, the target well logging curve, and the seismic waveform data. Data-related parameters are then determined based on the correlation coefficient sequence. Target seismic data is then optimally determined based on the correlation parameters. Based on the above technical solution, correlation coefficient sequences corresponding to different strata are determined using well logging curves and seismic waveform data, and target seismic data is then determined based on the correlation coefficient sequence. This achieves accurate evaluation of seismic data and improves the accuracy of the target seismic data.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1This is a flow chart of a method for optimizing seismic data provided by an embodiment of the present invention;

[0022] Figure 2 is a flow chart of a method for optimizing seismic data provided by an embodiment of the present invention;

[0023] Figure 3 This is a structural block diagram of an optimal device for seismic data provided by an embodiment of the present invention;

[0024] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description 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 numbers 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] Figure 1 This is a flow chart of a method for optimizing seismic data provided by an embodiment of the present invention. This embodiment can be applied to determining a correlation coefficient sequence corresponding to each stratum based on logging curves and seismic data, and determining target seismic data according to the correlation coefficient sequence. The method can be executed by a preferred device for seismic data, which can be implemented in the form of hardware and / or software. The preferred device for seismic data can be configured in an electronic device, which can be a server, a terminal device, etc.

[0029] like Figure 1 As shown, the method includes:

[0030] S110 , obtaining a target well logging curve corresponding to a target mine, and determining seismic waveform data corresponding to the target mine.

[0031] The target mines may be any mine within the exploration area. The target well logging curve may be a well logging curve of a predetermined curve type, such as a curve obtained by detecting the mine using a well logging device, such as a gamma log curve, a resistivity curve, or an acoustic wave curve. Seismic waveform data may be waveform data of elastic waves emitted from the earthquake source.

[0032] Specifically, a target logging curve corresponding to a target mine is obtained, and seismic waveform data corresponding to the target mine is determined. For example, a target mine can be determined from multiple mines in an exploration area, and a target logging curve corresponding to the target mine is obtained, and then seismic waveform data corresponding to each target mine is determined. It should be noted that the collected data corresponding to the exploration area can be stored in a preset database during exploration. When storing data, a relationship mapping table between data and mines can be established based on the association relationship between different data and each mine. Then, when extracting data, data associated with the target mine can be extracted from the database based on the target mine and the relationship mapping table.

[0033] Based on the above technical solution, the acquisition of the target logging curve corresponding to the target mine includes: determining the logging curve to be processed corresponding to the target mine based on a preset logging curve type; filtering the logging curve to be processed based on a preset filtering range to determine the target logging curve.

[0034] The preset curve type can be understood as the type of well logging curve that needs to be acquired in advance. Preset well logging curve types include acoustic impedance curves. The well logging curve to be processed can be an unprocessed well logging curve, that is, a raw well logging curve obtained by logging instruments. The preset filter range can be a pre-set frequency range for filtering the well logging curve.

[0035] Specifically, a pre-processed well logging curve corresponding to the target mine is determined based on a preset well logging curve type, and then the pre-processed well logging curve is filtered based on a preset filter range to determine the target well logging curve. It should be noted that reservoir forecasters generally evaluate and compare the ability of different seismic data to describe reservoir lithology by synthesizing seismic records and the degree of consistency between the final prediction results and the target mine. In order to ensure that the well logging curve can more accurately reflect the storage conditions, an acoustic impedance curve corresponding to the target mine can be obtained, and then the well logging curve can be filtered based on a preset filter range, which can be a low-frequency filtering process on the pre-processed well logging curve, and the preset filter range can be 0-200Hz.

[0036] On the basis of the above technical solution, the determination of the seismic waveform data corresponding to the target mine includes: obtaining historical seismic data corresponding to the target area, and determining at least one historical seismic point based on the historical seismic data; determining the relative distance between the historical seismic point and the target mine, determining the target seismic point corresponding to the target mine based on the relative distance, and determining the seismic waveform data based on the target seismic point.

[0037] The historical seismic data can be understood as the collected seismic data associated with the target area. The historical seismic points can be the location information corresponding to each seismic source. The target seismic point can be understood as the seismic source corresponding to the target mine.

[0038] Specifically, historical seismic data corresponding to the target area is obtained, and at least one historical seismic point is determined based on the historical seismic data, and then the relative distance between the historical seismic point and the target mine is determined. Based on the relative distance, a target seismic point corresponding to the target mine is determined, and the seismic waveform data is determined based on the target seismic point. For example, the location of each seismic source in the target area can be determined using historical seismic data, and the relative distance between the target mine and each seismic source is calculated. The seismic source closest to the target mine is then used as the target seismic point, and seismic waveform data for the target seismic point is obtained.

[0039] S120: Determine at least one to-be-selected formation corresponding to the target area, and determine a correlation coefficient sequence based on the to-be-selected formation, the target well logging curve, and the seismic waveform data.

[0040] The correlation coefficient sequence includes a well logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence. The selected strata can be stratigraphic information corresponding to the target area, such as lithologic strata primarily divided by lithology, biostratigraphic strata based on fossils, and temporal or chronological strata based on formation time. The correlation coefficient sequence can be understood as a sequence of correlation coefficients, used to reflect the degree of linear correlation between the target mine data.

[0041] Specifically, at least one stratum to be selected corresponding to the target area is determined, and a correlation coefficient sequence is determined based on the stratum to be selected, the target logging curve and the seismic waveform data. For example, the correlation coefficient corresponding to each stratum to be selected is determined, and a correlation coefficient sequence corresponding to the target mine is constructed based on the correlation coefficient.

[0042] On the basis of the above technical solution, the method for determining a correlation coefficient sequence based on the to-be-selected formation, the target well logging curve and the seismic waveform data includes: intercepting a well logging curve segment corresponding to the to-be-selected formation from the target well logging curve, and intercepting a seismic waveform segment corresponding to the to-be-selected formation from the seismic waveform data; and determining a correlation coefficient sequence corresponding to the to-be-selected formation based on the well logging curve segment and the seismic waveform segment.

[0043] The well logging curve segment can be understood as the well logging curve segment corresponding to the current formation to be selected, and the two have the same depth information. Correspondingly, the seismic waveform segment can be a seismic waveform intercepted based on the depth information of the formation to be selected.

[0044] Specifically, a well logging curve segment corresponding to the formation to be selected is intercepted from the target well logging curve, and a seismic waveform segment corresponding to the formation to be selected is intercepted from the seismic waveform data, and a correlation coefficient sequence corresponding to the formation to be selected is determined based on the well logging curve segment and the seismic waveform segment.

[0045] On the basis of the above technical solution, the correlation coefficient sequence corresponding to the to-be-selected formation is determined based on the well logging curve segment and the seismic waveform segment, including: determining the well logging curve correlation coefficient between each target mine based on the well logging curve segment, and determining the well logging curve correlation coefficient sequence based on the well logging curve correlation coefficient; determining the seismic waveform correlation coefficient between each target mine based on the seismic waveform segment, and determining the seismic waveform correlation coefficient sequence based on the seismic waveform correlation coefficient.

[0046] The well logging curve correlation coefficient may be the correlation coefficient between the well logging curve of the current mine and the well logging curves of other mines. Correspondingly, the well logging curve correlation coefficient sequence may be a correlation coefficient sequence determined based on the well logging curve correlation coefficients between the mines. The seismic waveform correlation coefficient sequence may be a correlation coefficient sequence determined based on the seismic waveform correlation coefficients between the mines.

[0047] Specifically, the well logging curve correlation coefficients between the target mines are determined based on the well logging curve segments, and a well logging curve correlation coefficient sequence is determined based on the well logging curve correlation coefficients. Furthermore, the seismic waveform correlation coefficients between the target mines are determined based on the seismic waveform segments, and a seismic waveform correlation coefficient sequence is determined based on the seismic waveform correlation coefficients. For example, correlation calculation formulas in Excel or other data processing software may be used to calculate and statistically calculate the correlation coefficients of low-frequency wave impedance logging curves between the wells to form a well logging curve correlation coefficient sequence; correlation calculation formulas in Excel or other data processing software may be used to calculate and statistically calculate the correlation coefficients of depth-domain seismic waveforms between the wells to form a seismic waveform correlation coefficient sequence.

[0048] S130. Determine data-related parameters based on the correlation coefficient sequence, and optimally determine target seismic data based on the correlation parameters.

[0049] The data correlation parameter may be an evaluation parameter for determining the degree of correlation between data, such as correlation and difference average. The target seismic data may be understood as the seismic data finally obtained by screening.

[0050] Specifically, data-related parameters are determined based on the correlation coefficient sequence, and target seismic data are preferentially determined based on the related parameters. For example, data corresponding to each formation data can be determined based on the related parameters, and then after the target formation is determined, the seismic data of the target formation is used as the target seismic data.

[0051] Based on the above technical solution, the data correlation parameters are determined based on the correlation coefficient sequence, including: determining the correlation and difference average corresponding to the current selected formation based on the logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence.

[0052] The correlation can be understood as the degree of correlation between the well logging curve and the seismic waveform in the currently selected formation.

[0053] Specifically, the correlation and difference average values ​​corresponding to the currently selected formation are determined based on the well logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence. For example, the well logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence can be compared, and the correlation and difference average values ​​of the well logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence can be calculated using the correlation calculation formula in Excel or other data processing software.

[0054] On the basis of the above technical solution, the target seismic data is preferentially determined based on the relevant parameters, including: evaluating the seismic waveform data based on the relevant parameters to obtain evaluation results corresponding to each seismic waveform; and determining the target seismic data based on the evaluation results.

[0055] The evaluation result can be understood as whether the current seismic waveform data is consistent with the corresponding logging curve.

[0056] Specifically, the seismic waveform data is evaluated based on the relevant parameters to obtain evaluation results corresponding to each of the seismic waveforms, and the target seismic data is determined based on the evaluation results. For example, the consistency between the current seismic waveform and the corresponding well logging curve can be determined based on the correlation and difference average value corresponding to the current seismic waveform, and then the corresponding evaluation result can be determined to determine the corresponding target seismic data based on the evaluation result. It should be noted that the technical solution provided in the embodiment of the present invention determines the target seismic data by comparing the consistency of the well logging curve with the corresponding seismic data, and then performing optimization based on the consistency, and then performing stratigraphic interpretation and reservoir prediction based on the optimized target seismic data in the subsequent processing process.

[0057] The technical solution of an embodiment of the present invention obtains a target well logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one selected stratum corresponding to a target area. A correlation coefficient sequence is determined based on the selected stratum, the target well logging curve, and the seismic waveform data. Data-related parameters are then determined based on the correlation coefficient sequence. Target seismic data is then optimally determined based on the correlation parameters. Based on the above technical solution, correlation coefficient sequences corresponding to different strata are determined using well logging curves and seismic waveform data, and target seismic data is then determined based on the correlation coefficient sequence. This achieves accurate evaluation of seismic data and improves the accuracy of the target seismic data.

[0058] Example 2

[0059] Figure 2 This is a flowchart of a method for optimizing seismic data provided by an embodiment of the present invention. This embodiment further optimizes the aforementioned method based on the aforementioned embodiment. For detailed implementation details, please refer to the technical solution of this embodiment. Technical terms that are identical or corresponding to those in the aforementioned embodiments are not further described here.

[0060] It's important to note that interpreters typically evaluate seismic data by comparing the accuracy of different seismic data sets in depicting subsurface structures. This comparison method ignores information such as stratum lithology, which is contained in the seismic data as a response to the overall subsurface situation. In particular, during the processing process, the choice of different algorithms and the setting of processing parameters, in order to address the conflict between massive computational effort and economic cost, can lead to information loss. Reservoir forecasters typically evaluate the ability of different seismic data sets to describe reservoir lithology by comparing synthetic seismic records and the degree of agreement between the final prediction results and the wells. Deep-domain synthetic seismic records are difficult to implement due to the influence of deep-domain seismic wavelets. Therefore, a fast and accurate seismic data optimization method is needed.

[0061] like Figure 2 The method of the embodiment of the present invention includes:

[0062] Obtain data to be processed: Specifically, collect the deep domain seismic data and all well data in the study area, select the wave impedance logging curve corresponding to the studied formation segment as the research object, and perform 0-200Hz filtering on all well wave impedance curves, that is, low-frequency logging curves, extract the deep domain seismic waveform at each well point location, and intercept the seismic waveform corresponding to the studied formation segment as the seismic response corresponding to the well logging curve.

[0063] Determine the correlation coefficient sequence: Specifically, use the correlation calculation formula in Excel or other data processing software to calculate and count the correlation coefficients of the low-frequency wave impedance logging curves between each well to form a logging curve correlation coefficient sequence R1 (Wi, Wj); use the correlation calculation formula in Excel or other data processing software to calculate and count the correlation coefficients of the depth domain seismic waveforms between each well to form a seismic waveform correlation coefficient sequence R2 (Si, Sj); and repeat the above steps for different depth domain seismic data to obtain the correlation coefficients of the seismic waveforms corresponding to different wells of the data to form a correlation coefficient sequence R3 (Si, Sj).

[0064] Data processing and evaluation of seismic data: Specifically, compare the correlation coefficient sequence R1 (Wi, Wj) and R2 (Si, Sj), and use the correlation calculation formula in Excel or other data processing software to calculate the correlation Corr1 (R1, R2) and the difference average value Average1 (R2-R1) of the two columns of data; further, compare the correlation coefficient sequence R1 (Wi, Wj) and R3 (Si, Sj), and use the correlation calculation formula in Excel or other data processing software to calculate the correlation Corr2 (R1, R3) and the difference average value Average2 (R3-R1) of the two columns of data.

[0065] Furthermore, comparing Corr1 and Corr2, a larger value indicates that the interwell seismic waveform changes are consistent with the low-frequency changes in the well logging curve, indicating higher data quality. Comparing Average1 and Average2, a smaller value indicates that the interwell seismic waveform changes are less different from the low-frequency changes in the well logging curve, indicating higher data quality. The seismic data with the best comprehensive evaluation is output as the target seismic data.

[0066] The technical solution of an embodiment of the present invention obtains a target well logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one selected stratum corresponding to a target area. A correlation coefficient sequence is determined based on the selected stratum, the target well logging curve, and the seismic waveform data. Data-related parameters are then determined based on the correlation coefficient sequence. Target seismic data is then optimally determined based on the correlation parameters. Based on the above technical solution, correlation coefficient sequences corresponding to different strata are determined using well logging curves and seismic waveform data, and target seismic data is then determined based on the correlation coefficient sequence. This achieves accurate evaluation of seismic data and improves the accuracy of the target seismic data.

[0067] Example 3

[0068] Figure 3 This is a structural block diagram of an optimal device for seismic data provided by an embodiment of the present invention. Figure 3 As shown, the device includes: a data acquisition module 310 , a correlation coefficient determination module 320 and a target data determination module 330 .

[0069] The data acquisition module 310 is used to acquire a target well logging curve corresponding to a target mine and determine seismic waveform data corresponding to the target mine;

[0070] The correlation coefficient determination module 320 is configured to determine at least one selected stratum corresponding to the target area, and determine a correlation coefficient sequence based on the selected stratum, the target well logging curve, and the seismic waveform data; wherein the correlation coefficient sequence includes a well logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence;

[0071] The target data determination module 330 is configured to determine data-related parameters based on the correlation coefficient sequence, and to optimally determine target seismic data based on the correlation parameters.

[0072] On the basis of the above technical solution, the correlation coefficient determination module is used to intercept the logging curve segment corresponding to the to-be-selected formation from the target logging curve, and intercept the seismic waveform segment corresponding to the to-be-selected formation from the seismic waveform data; and determine the correlation coefficient sequence corresponding to the to-be-selected formation based on the logging curve segment and the seismic waveform segment.

[0073] Based on the above technical solution, the correlation coefficient determination module is used to determine the well logging curve correlation coefficient between each target mine based on the well logging curve segment, and determine the well logging curve correlation coefficient sequence based on the well logging curve correlation coefficient; determine the seismic waveform correlation coefficient between each target mine based on the seismic waveform segment, and determine the seismic waveform correlation coefficient sequence based on the seismic waveform correlation coefficient.

[0074] On the basis of the above technical solution, the target data determination module is used to determine the correlation and difference average value corresponding to the current formation to be selected based on the logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence.

[0075] On the basis of the above technical solution, the target data determination module is used to evaluate the seismic waveform data based on the relevant parameters to obtain evaluation results corresponding to each seismic waveform; and determine the target seismic data based on the evaluation results.

[0076] Based on the above technical solution, the data acquisition module is used to determine the logging curve to be processed corresponding to the target mine based on the preset logging curve type; wherein the preset logging curve type includes the acoustic impedance curve; the logging curve to be processed is filtered based on a preset filtering range to determine the target logging curve.

[0077] Based on the above technical solution, the data acquisition module is used to acquire historical seismic data corresponding to the target area, and determine at least one historical seismic point based on the historical seismic data; determine the relative distance between the historical seismic point and the target mine, determine the target seismic point corresponding to the target mine based on the relative distance, and determine the seismic waveform data based on the target seismic point.

[0078] The technical solution of an embodiment of the present invention obtains a target well logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one selected stratum corresponding to a target area. A correlation coefficient sequence is determined based on the selected stratum, the target well logging curve, and the seismic waveform data. Data-related parameters are then determined based on the correlation coefficient sequence. Target seismic data is then optimally determined based on the correlation parameters. Based on the above technical solution, correlation coefficient sequences corresponding to different strata are determined using well logging curves and seismic waveform data, and target seismic data is then determined based on the correlation coefficient sequence. This achieves accurate evaluation of seismic data and improves the accuracy of the target seismic data.

[0079] The preferred device for seismic data provided in the embodiment of the present invention can execute the preferred method for seismic data provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0080] Example 4

[0081] Figure 4A structural diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0082] As shown, Figure 4 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected to the at least one processor 11 in communication, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. 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.

[0083] Various 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 magnetic 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 telecommunications networks.

[0084] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose 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 performs various methods and processes described above, such as the preferred method of seismic data.

[0085] In some embodiments, the preferred method for seismic data may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the preferred method for seismic data described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the preferred method for seismic data in any other suitable manner (e.g., via firmware).

[0086] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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.

[0087] 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, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0089] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0090] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can 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.

[0091] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0092] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0093] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for optimizing seismic data, characterized in that: include: Acquiring a target well logging curve corresponding to a target mine, and determining seismic waveform data corresponding to the target mine; Determine at least one to-be-selected formation corresponding to the target area, and determine a correlation coefficient sequence based on the to-be-selected formation, target well logging curves, and seismic waveform data; wherein the correlation coefficient sequence includes a well logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence; Determining data correlation parameters based on the correlation coefficient sequence, and determining target seismic data based on the correlation parameters through optimization; The method of determining the correlation coefficient sequence based on the selected formation, the target well logging curve and the seismic waveform data includes: intercepting a well logging curve segment corresponding to the to-be-selected stratum from the target well logging curve, and intercepting a seismic waveform segment corresponding to the to-be-selected stratum from the seismic waveform data; Determining a correlation coefficient sequence corresponding to the to-be-selected formation based on the well logging curve segment and the seismic waveform segment; The determining of the correlation coefficient sequence corresponding to the to-be-selected formation based on the well logging curve segment and the seismic waveform segment comprises: Determining the well logging curve correlation coefficients between target mines based on the well logging curve segments, and determining the well logging curve correlation coefficient sequence R1(Wi, Wj) based on the well logging curve correlation coefficients; Determining seismic waveform correlation coefficients between target mines based on the seismic waveform segments, and determining a seismic waveform correlation coefficient sequence R2(Si, Sj) based on the seismic waveform correlation coefficients; The determining of data-related parameters based on the correlation coefficient sequence includes: The correlation Corr1 (R1, R2) and the difference average Average1 (R2-R1) corresponding to the current selected formation are determined based on the logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence, wherein Corr1 is a correlation function and Average1 is an average value function.

2. The method according to claim 1, characterized in that The step of preferentially determining target seismic data based on the relevant parameters includes: evaluating the seismic waveform data based on the relevant parameters to obtain evaluation results corresponding to each seismic waveform data; The target seismic data is determined based on the evaluation result.

3. The method according to claim 1, characterized in that The step of obtaining a target well logging curve corresponding to a target mine includes: Determining a to-be-processed well logging curve corresponding to the target mine based on a preset well logging curve type; wherein the preset well logging curve type includes an acoustic impedance curve; The to-be-processed well logging curve is filtered based on a preset filtering range to determine the target well logging curve.

4. The method according to claim 1, wherein The determining of seismic waveform data corresponding to the target mine includes: Acquiring historical earthquake data corresponding to the target area, and determining at least one historical earthquake point based on the historical earthquake data; The relative distance between the historical earthquake point and the target mine is determined, a target earthquake point corresponding to the target mine is determined based on the relative distance, and the earthquake waveform data is determined based on the target earthquake point.

5. An optimal device for seismic data, characterized in that: include: a data acquisition module, configured to acquire a target well logging curve corresponding to a target mine and determine seismic waveform data corresponding to the target mine; a correlation coefficient determination module, configured to determine at least one to-be-selected formation corresponding to a target area, and determine a correlation coefficient sequence based on the to-be-selected formation, target well logging curves, and seismic waveform data; wherein the correlation coefficient sequence includes a well logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence; a target data determination module, configured to determine data-related parameters based on the correlation coefficient sequence, and to determine target seismic data based on the correlation parameters; The correlation coefficient determination module is configured to extract a well logging curve segment corresponding to the to-be-selected stratum from the target well logging curve, and extract a seismic waveform segment corresponding to the to-be-selected stratum from the seismic waveform data; and determine a correlation coefficient sequence corresponding to the to-be-selected stratum based on the well logging curve segment and the seismic waveform segment; The correlation coefficient determination module is configured to determine the well logging curve correlation coefficients between target mines based on the well logging curve segments, and determine a well logging curve correlation coefficient sequence R1(Wi, Wj) based on the well logging curve correlation coefficients; determine the seismic waveform correlation coefficients between target mines based on the seismic waveform segments, and determine a seismic waveform correlation coefficient sequence R2(Si, Sj) based on the seismic waveform correlation coefficients; The target data determination module is used to determine the correlation Corr1 (R1, R2) and the difference average Average1 (R2-R1) corresponding to the current selected formation based on the logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence, wherein Corr1 is the correlation function and Average1 is the average value function.

6. 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, wherein the computer program is executed by the at least one processor to enable the at least one processor to perform the preferred method for seismic data according to any one of claims 1 to 4.

7. 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 seismic data optimization method according to any one of claims 1 to 4 when the computer instructions are executed.

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