Seismic data optimization method and device, electronic equipment and storage medium

Abstract

The invention discloses a seismic data optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target logging curve corresponding to a target mine, and determining seismic waveform data corresponding to the target mine; determining at least one stratum to be selected corresponding to the target area, and determining a correlation coefficient sequence based on the stratum to be selected, the target logging curve and the seismic waveform data; wherein the correlation coefficient sequence comprises a logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence; and determining data correlation parameters based on the correlation coefficient sequence, and performing optimization based on the correlation parameters to determine target seismic data. Based on the technical scheme, the correlation coefficient sequences corresponding to different stratums are determined through the logging curve and the seismic waveform data, and then the target seismic data are determined based on the correlation coefficient sequences, so that accurate evaluation of the seismic data is realized, and the accuracy of the target seismic data is improved.

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] As oilfield exploration deepens, 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 stratigraphic 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 depth-domain seismic data to solve the problem that existing depth-domain seismic data processing methods are difficult to determine whether 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] Acquire a target well logging curve corresponding to a target mine, and determine 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, 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;

[0008] Data correlation parameters are determined based on the correlation coefficient sequence, and target seismic data are optimally 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, used 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, used to determine at least one to-be-selected stratum corresponding to the target area, and determine a correlation coefficient sequence based on the to-be-selected stratum, the target logging curve and the seismic waveform data; wherein the correlation coefficient sequence includes a logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence;

[0012] The target data determination module is used to determine data correlation 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, the electronic device 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 described in 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 described in any embodiment of the present invention when the computer instructions are executed.

[0018] The technical solution of the embodiment of the present invention obtains a target logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one to-be-selected stratum corresponding to a target area, determines a correlation coefficient sequence based on the to-be-selected stratum, the target logging curve and the seismic waveform data, and then determines data-related parameters based on the correlation coefficient sequence, and then determines target seismic data based on the correlation parameters. Based on the above technical solution, the correlation coefficient sequences corresponding to different strata are determined through logging curves and seismic waveform data, and then the target seismic data is determined based on the correlation coefficient sequence, thereby achieving accurate evaluation of seismic data and improving the accuracy of target seismic data.

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

[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 1It is a flowchart 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 It is a structural block diagram of a preferred device for seismic data provided by an embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the structure 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 scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 It 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, and the preferred device for seismic data can be implemented in the form of hardware and / or software. The preferred device for seismic data can be configured in an electronic device, and the electronic device can be a server, a terminal device, etc.

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

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

[0031] The target mine may be any mine in the exploration area. The target logging curve may be understood as a logging curve of a preset curve type, for example, a curve obtained by detecting a mine through a logging device, a gamma logging curve, a resistivity curve, an acoustic wave curve, etc. The seismic waveform data may be waveform data of elastic waves emitted from a source during an earthquake.

[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 may be determined from multiple mines in an exploration area, and a target logging curve corresponding to the target mine may be obtained, thereby determining seismic waveform data corresponding to each target mine. It should be noted that, when conducting exploration, the collected data corresponding to the exploration area may be stored in a preset database. When storing data, a relationship mapping table between data and mines may be established based on the association between different data and each mine. When extracting data, data associated with the target mine may be extracted from the database based on the target mine and the relationship mapping table.

[0033] On the basis of the above technical solution, the acquisition of the target logging curve corresponding to the target mine includes: determining the well logging curve to be processed corresponding to the target mine based on a preset well logging curve type; filtering the well 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 curve type of the logging curve that needs to be obtained in advance. The preset logging curve type includes an acoustic impedance curve. The logging curve to be processed can be an unprocessed logging curve, that is, an original logging curve detected by a logging instrument. The preset filtering range can be a preset frequency range for filtering the logging curve.

[0035] Specifically, the well logging curve to be processed corresponding to the target mine is determined based on the preset well logging curve type, and then the well logging curve to be processed is filtered based on the preset filter range to determine the target well logging curve. It should be noted that reservoir prediction personnel generally evaluate and compare the description ability of different seismic data on reservoir lithology by synthesizing seismic records and the degree of consistency between the final prediction results and the target mine, and then in order to ensure that the well logging curve can more accurately reflect the storage situation, the acoustic impedance curve corresponding to the target mine can be obtained, and then the well logging curve is filtered based on the preset filter range, which can be low-frequency filtering of the well logging curve to be processed, 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: acquiring 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 earthquake data can be understood as the collected earthquake data associated with the target area. The historical earthquake points can be the location information corresponding to each earthquake source. The target earthquake point can be understood as the earthquake 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, and the target seismic point corresponding to the target mine is determined based on the relative distance, 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 through historical seismic data, and the relative distance between the target mine and each seismic source is calculated, and then the seismic source closest to the target mine is used as the target seismic point, and the seismic waveform data of the target seismic point is obtained.

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

[0040] The correlation coefficient sequence includes a logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence. The selected stratum may be stratigraphic information corresponding to the target area, such as a lithological stratum based on lithology, a biostratum based on fossils, and a temporal stratum or chronological stratum based on formation time. The correlation coefficient sequence may be understood as a sequence composed of correlation coefficients, which is used to reflect the 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 coefficients corresponding to each stratum to be selected are determined, and a correlation coefficient sequence corresponding to the target mine is constructed based on the correlation coefficients.

[0042] On the basis of the above technical solution, the method for determining a correlation coefficient sequence based on the formation to be selected, the target well logging curve and the seismic waveform data comprises: extracting a well logging curve segment corresponding to the formation to be selected from the target well logging curve, and extracting a seismic waveform segment corresponding to the formation to be selected from the seismic waveform data; and determining a correlation coefficient sequence corresponding to the formation to be selected 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 logging curve correlation coefficient may be the correlation coefficient between the current mine logging curve and the other mine logging curves, and correspondingly, the logging curve correlation coefficient sequence may be a correlation coefficient sequence determined based on the 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 target mines are determined based on the well logging curve segments, and the well logging curve correlation coefficient sequence is determined based on the well logging curve correlation coefficients, and then the seismic waveform correlation coefficients between target mines are determined based on the seismic waveform segments, and the seismic waveform correlation coefficient sequence is determined based on the seismic waveform correlation coefficients. For example, the correlation coefficients of low-frequency wave impedance well logging curves between wells can be calculated and counted using the correlation calculation formula in Excel or other data processing software to form a well logging curve correlation coefficient sequence; the correlation coefficients of depth domain seismic waveforms between wells can be calculated and counted using the correlation calculation formula in Excel or other data processing software to form a seismic waveform correlation coefficient sequence.

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

[0049] The data-related 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 screened.

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

[0051] On the basis of the above technical solution, the data correlation parameters are determined based on the correlation coefficient sequence, including: determining 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.

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

[0053] Specifically, the correlation and the average difference value 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 the average difference value 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 may be determined based on the correlation and difference average value corresponding to the current seismic waveform, and then the corresponding evaluation result is determined, so as 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 between the well logging curve and the corresponding seismic data, and then performing optimization based on the consistency, and then performing formation interpretation and reservoir prediction based on the optimized target seismic data in the subsequent processing process.

[0057] The technical solution of the embodiment of the present invention obtains a target logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one to-be-selected stratum corresponding to a target area, determines a correlation coefficient sequence based on the to-be-selected stratum, the target logging curve and the seismic waveform data, and then determines data-related parameters based on the correlation coefficient sequence, and then determines target seismic data based on the correlation parameters. Based on the above technical solution, the correlation coefficient sequences corresponding to different strata are determined through logging curves and seismic waveform data, and then the target seismic data is determined based on the correlation coefficient sequence, thereby achieving accurate evaluation of seismic data and improving the accuracy of target seismic data.

[0058] Embodiment 2

[0059] Figure 2 A flowchart of a preferred method for seismic data provided by an embodiment of the present invention. Based on the above embodiment, this embodiment further optimizes the preferred method for seismic data. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here.

[0060] It should be noted that interpreters generally evaluate seismic data by comparing the accuracy of underground structure depiction of different seismic data bodies. This comparison method ignores the information such as stratum lithology contained in the seismic data as a response to the comprehensive underground situation. Especially in the processing process, in order to deal with the contradiction between massive calculations and economic costs, the selection of different algorithms and the setting of processing parameters cause information loss. Reservoir forecasters generally evaluate and compare the description ability of different seismic data on reservoir lithology by synthetic seismic records and the degree of consistency 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, it is necessary to determine a fast and accurate seismic data optimization method.

[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 information 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, and intercept the seismic waveform corresponding to the studied formation segment as the seismic response corresponding to the logging curve of the well.

[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 the wells to form the 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 the wells to form the 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 the correlation coefficient sequence R3 (Si, Sj).

[0064] Data processing and evaluation of seismic data: Specifically, compare the correlation coefficient sequences 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 Average1 (R2-R1) of the two columns of data; further, compare the correlation coefficient sequences 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 Average2 (R3-R1) of the two columns of data.

[0065] Furthermore, by comparing Corr1 and Corr2, a large value indicates that the change of seismic waveforms between different wells is consistent with the low-frequency change trend of the logging curve, and the data quality is high; by comparing Average1 and Average2, a small value indicates that the difference between the change of seismic waveforms between different wells and the low-frequency change of the logging curve is small, and the data quality is high. The seismic data with the best comprehensive evaluation is output as the target seismic data.

[0066] The technical solution of the embodiment of the present invention obtains a target logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one to-be-selected stratum corresponding to a target area, determines a correlation coefficient sequence based on the to-be-selected stratum, the target logging curve and the seismic waveform data, and then determines data-related parameters based on the correlation coefficient sequence, and then determines target seismic data based on the correlation parameters. Based on the above technical solution, the correlation coefficient sequences corresponding to different strata are determined through logging curves and seismic waveform data, and then the target seismic data is determined based on the correlation coefficient sequence, thereby achieving accurate evaluation of seismic data and improving the accuracy of target seismic data.

[0067] Embodiment 3

[0068] Figure 3 The structure block diagram of a preferred 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 used to determine at least one to-be-selected stratum corresponding to the target area, and determine a correlation coefficient sequence based on the to-be-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 used to determine data correlation 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 well logging curve segment corresponding to the formation to be selected from the target well logging curve, and intercept the seismic waveform segment corresponding to the formation to be selected from the seismic waveform data; and determine the correlation coefficient sequence corresponding to the formation to be selected based on the well logging curve segment and the seismic waveform segment.

[0073] On the basis of 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] On the basis of the above technical solution, the data acquisition module is used to determine the well logging curve to be processed 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 well logging curve to be processed is filtered based on a preset filtering range to determine the target well logging curve.

[0077] On the basis of 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 the embodiment of the present invention obtains a target logging curve corresponding to a target mine, determines seismic waveform data corresponding to the target mine, and determines at least one to-be-selected stratum corresponding to a target area, determines a correlation coefficient sequence based on the to-be-selected stratum, the target logging curve and the seismic waveform data, and then determines data-related parameters based on the correlation coefficient sequence, and then determines target seismic data based on the correlation parameters. Based on the above technical solution, the correlation coefficient sequences corresponding to different strata are determined through logging curves and seismic waveform data, and then the target seismic data is determined based on the correlation coefficient sequence, thereby achieving accurate evaluation of seismic data and improving the accuracy of 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] Embodiment 4

[0081] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0082] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0083] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0084] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the preferred method for seismic data.

[0085] In some embodiments, the preferred method for seismic data may be implemented as a computer program, which is tangibly contained 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 the 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., by means of firmware).

[0086] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

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

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

[0089] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0090] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0091] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0092] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0093] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing seismic data, characterized in that: include: Acquire a target well logging curve corresponding to a target mine, and determine 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, 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; Data correlation parameters are determined based on the correlation coefficient sequence, and target seismic data are optimally determined based on the correlation parameters.

2. The method according to claim 1, characterized in that The method of determining the correlation coefficient sequence based on the selected formation, the target well logging curve and the seismic waveform data comprises: 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; A correlation coefficient sequence corresponding to the to-be-selected formation is determined based on the well logging curve segment and the seismic waveform segment.

3. The method according to claim 2, characterized in that The step of determining a correlation coefficient sequence corresponding to the to-be-selected formation based on the well logging curve segment and the seismic waveform segment comprises: Determine the well logging curve correlation coefficients between target mines based on the well logging curve segments, and determine the well logging curve correlation coefficient sequence based on the well logging curve correlation coefficients; The seismic waveform correlation coefficients between the target mines are determined based on the seismic waveform segments, and the seismic waveform correlation coefficient sequence is determined based on the seismic waveform correlation coefficients.

4. The method according to claim 1, characterized in that The determining of data-related parameters based on the correlation coefficient sequence comprises: The correlation and difference average values ​​corresponding to the current formation to be selected are determined based on the well logging curve correlation coefficient sequence and the seismic waveform correlation coefficient sequence.

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

6. The method according to claim 1, characterized in that The step of obtaining a target well logging curve corresponding to the target mine includes: Determining a well logging curve to be processed 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 well logging curve to be processed is filtered based on a preset filtering range to determine the target well logging curve.

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

8. An optimal device for seismic data, characterized in that: include: A data acquisition module, used 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, used to 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 logging curve and the seismic waveform data; wherein the correlation coefficient sequence includes a logging curve correlation coefficient sequence and a seismic waveform correlation coefficient sequence; The target data determination module is used to determine data correlation parameters based on the correlation coefficient sequence, and to optimally determine target seismic data based on the correlation parameters.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the preferred method for seismic data of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the preferred method for seismic data according to any one of claims 1-7 when executed by a processor.

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