A method, apparatus, device and medium for generating a seismic facies map

Abstract

The application belongs to the technical field of earthquake analysis, and particularly relates to a method, device, equipment and medium for generating an earthquake facies map. The application obtains seismic data of a target formation and a constant frequency width of the target formation; converts the seismic data from time domain data to frequency domain data according to the constant frequency width; obtains a preset classification number range; determines an inter-class interval change graph of the target formation according to the classification number range; determines a target classification number according to the inter-class interval change graph; and generates a planar earthquake facies map of the target formation according to the target classification number. The method considers that the equal-thickness time window cannot well reflect the corresponding relationship between the waveform and the reservoir and the calculation complexity of the conventional classification algorithm, and adopts the transformation of the time domain non-equal-length seismic signal to the frequency domain, the selection of a suitable constant frequency width for classification, and the satisfaction of the actual classification analysis needs of the seismic data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of seismic analysis, and particularly relates to a method, device, equipment and medium for generating a seismic facies map. BACKGROUND

[0002] Generally, seismic attributes can only reflect several physical parameters (such as amplitude, phase, frequency, etc.) of seismic signals, and cannot completely describe the overall abnormal situation of the seismic signals. The overall abnormal situation of the seismic signals is closely related to the shape of the seismic trace, and the change of any physical parameter always corresponds to the change of the shape of the seismic trace. Therefore, by analyzing the change of the seismic trace, the change rule of the seismic facies can be understood, and the change rule of the sedimentary facies and the lithofacies can be inferred.

[0003] At present, the popular seismic facies analysis methods at home and abroad mainly include waveform classification method, seismic attribute feature mapping method and facies division method based on seismic geomorphology. Among them, the waveform classification method is a relatively mature seismic facies analysis method, which has been widely applied in lithology prediction, sand body prediction, oil and gas reservoir prediction and other fields. However, the traditional seismic waveform classification method mainly divides the seismic facies based on the similarity of the waveforms of the seismic traces in the isochronous window, and is only suitable for the target layer with stable stratum thickness and small change of seismic time thickness. When the stratum thickness changes, the seismic time thickness will also change, which means that the equal-length seismic waveforms extracted along the layer do not represent the complete lithology information of the target layer, and even may cause the "time penetration" phenomenon, so that the corresponding relationship between the reservoir and the seismic waveform cannot be accurately revealed. In order to solve this problem, it is necessary to develop a seismic waveform analysis technology suitable for variable time window.

[0004] In the application of actual variable time window waveform classification technology, based on the conventional two-dimensional self-organizing feature mapping network (SOM) and dynamic time warping (DTW) method, when facing actual seismic data, multiple neurons often map the same class of seismic facies, so that the network scale is large, the calculation amount is large, and there is waveform distortion in the signal resampling process, which is not conducive to the comprehensive and determination of the final classification result, so directly using these methods is not suitable for waveform classification technology under large-scale seismic data conditions. SUMMARY

[0005] To solve the above technical problems, the application provides a seismic facies map generation method, device, equipment and medium. The application obtains seismic data of a target formation and a constant frequency width of the target formation; converts the seismic data from time domain data to frequency domain data according to the constant frequency width; obtains a preset classification number range; determines a class interval change graph of the target formation according to the classification number range; determines a target classification number according to the class interval change graph; and generates a planar seismic facies map of the target formation according to the target classification number. The method considers that the equal-thickness time window cannot well reflect the corresponding relationship between the waveform and the reservoir and the calculation complexity of the conventional classification algorithm, and adopts the conversion of the time domain non-equal-length seismic signal to the frequency domain, the selection of a suitable constant frequency width for classification, and the satisfaction of the actual classification analysis needs of the seismic data.

[0006] To solve the above technical problems, the application provides a seismic facies map generation method, device, equipment and medium. The application obtains seismic data of a target formation and a constant frequency width of the target formation; converts the seismic data from time domain data to frequency domain data according to the constant frequency width; obtains a preset classification number range; determines a class interval change graph of the target formation according to the classification number range; determines a target classification number according to the class interval change graph; and generates a planar seismic facies map of the target formation according to the target classification number. The method considers that the equal-thickness time window cannot well reflect the corresponding relationship between the waveform and the reservoir and the calculation complexity of the conventional classification algorithm, and adopts the conversion of the time domain non-equal-length seismic signal to the frequency domain, the selection of a suitable constant frequency width for classification, and the satisfaction of the actual classification analysis needs of the seismic data.

[0007] The first aspect provides a seismic facies map generation method, including: obtaining seismic data of a target formation and a constant frequency width of the target formation; converting the seismic data from time domain data to frequency domain data according to the constant frequency width; obtaining a preset classification number range; determining a class interval change graph of the target formation according to the classification number range; determining a target classification number according to the class interval change graph; and generating a planar seismic facies map of the target formation according to the target classification number.

[0008] In some embodiments, the obtaining of the constant frequency width of the target formation includes: obtaining an upper layer seismic reflection time length of an upper layer of the target formation; obtaining a lower layer seismic reflection time length of a lower layer of the target formation; and determining the constant frequency width according to the upper layer seismic reflection time length and the lower layer seismic reflection time length.

[0009] In some embodiments, the determination of the constant frequency width according to the upper layer seismic reflection time length and the lower layer seismic reflection time length includes: determining a maximum time difference value between the upper layer and the lower layer of the target formation according to the upper layer seismic reflection time length and the lower layer seismic reflection time length; and determining the constant frequency width according to the maximum time difference value between the upper layer and the lower layer.

[0010] In some embodiments, the determination of the class interval change graph of the target formation according to the classification number range includes: inputting each classification number in the classification number range into a one-dimensional self-organizing feature mapping network respectively to obtain a model trace corresponding to each classification number; determining a sum of intra-class distances of each classification number according to the model trace; and determining the class interval change graph according to the sum of the intra-class distances of each classification number.

[0011] In some embodiments, the determining the class interval change diagram with respect to the classification comprises: obtaining a preset threshold; comparing the sum of the intra-class distances corresponding to each classification number with the preset threshold; when the sum of the intra-class distances corresponding to any classification number is less than the preset threshold, retaining the sum of the intra-class distances corresponding to the classification number and the model trace; and determining the class interval change diagram with respect to the classification according to the retained sums of the intra-class distances.

[0012] In some embodiments, the determining the target classification number according to the class interval change diagram comprises: determining a target sum of intra-class distances according to the class interval change diagram; and determining the target classification number according to the target sum of intra-class distances.

[0013] In some embodiments, the generating the planar seismic facies diagram of the target formation according to the target classification number comprises: determining a target model trace according to the target classification number; and performing classification processing on the frequency domain data according to the target model trace and the target classification number by using the similarity principle to generate the planar seismic facies diagram of the target formation.

[0014] In a second aspect, the present application provides a device for generating a seismic facies diagram, comprising: a first obtaining module configured to obtain seismic data of a target formation and constant frequency bandwidth of the target formation; a first executing module configured to convert the seismic data from time domain data to frequency domain data according to the constant frequency bandwidth; a second obtaining module configured to obtain a preset classification number range; a first determining module configured to determine a class interval change diagram with respect to the classification of the target formation according to the classification number range; a second determining module configured to determine a target classification number according to the class interval change diagram; and a second executing module configured to generate a planar seismic facies diagram of the target formation according to the target classification number.

[0015] In a third aspect, the present application provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program which is executed by the processor to perform the method of the first aspect.

[0016] In a fourth aspect, the present application provides a storage medium which stores a computer program capable of being executed by one or more processors, and the computer program can be used to implement the method of the first aspect.

[0017] The beneficial effects of the present invention include: obtaining seismic data of a target formation and a constant bandwidth of the target formation; converting the seismic data from time domain data to frequency domain data based on the constant bandwidth; obtaining a preset classification number range; determining a graph of the inter-class spacing versus classification of the target formation based on the classification number range; determining a target number of classifications based on the graph of the inter-class spacing versus classification; and generating a planar seismic phase map of the target formation based on the target number of classifications. This method addresses the issues of equal-thickness time windows that cannot effectively reflect the correspondence between waveforms and reservoirs, as well as the computational complexity of conventional classification algorithms. Instead, it transforms seismic signals of unequal length in the time domain into the frequency domain and selects an appropriate constant bandwidth for classification, meeting the practical needs of seismic data classification and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings, which include:

[0019] Figure 1 An overall flow chart of a method for generating a seismic phase map provided in an embodiment of the present application;

[0020] Figure 2 This is a structural block diagram of a device for generating a seismic phase map provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] If similar descriptions of "first\second\third" appear in the application documents, the following explanation will be added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to be limiting of this application.

[0025] Embodiment 1:

[0026] At present, the popular seismic facies analysis methods at home and abroad mainly include waveform classification method, seismic attribute feature mapping method and facies division method based on seismic geomorphology. Among them, the waveform classification method is a relatively mature seismic facies analysis method, which has been widely used in lithology prediction, sand body prediction, oil and gas reservoir prediction and other fields. However, the traditional seismic waveform classification method mainly divides the seismic facies based on the similarity of the waveforms of seismic traces in the isochronous window, and is only suitable for the target layer with stable stratum thickness and small change of seismic time thickness. When the stratum thickness changes, the seismic time thickness will also change, which means that the equal-length seismic waveforms extracted along the layer do not represent the complete lithology information of the target layer, and even may cause the "time penetration" phenomenon, so that the corresponding relationship between the reservoir and the seismic waveform cannot be accurately revealed. In order to solve this problem, it is necessary to develop a seismic waveform analysis technology suitable for variable time window.

[0027] In view of the problems in the prior art, such as Figure 1 As shown in FIG. 1, the application provides a method for generating a seismic facies map, which is applied to an electronic device, such as a server, a mobile terminal, a computer, a cloud platform, etc. The function realized by the device data processing provided in the embodiments of the application can be realized by calling program code by the processor of the electronic device, wherein the program code can be saved in a computer storage medium. The method for generating a seismic facies map comprises the following steps:

[0028] Step S1: acquiring seismic data of a target stratum and constant frequency width of the target stratum.

[0029] Since in actual cases, the thickness of all strata is not constant, and the stratum thickness presents different change ranges at different positions, and due to the change of the stratum thickness, the seismic time thickness will also change, which means that the equal-length seismic waveforms extracted along the layer do not represent the complete lithology information of the target layer, and even may cause the "time penetration" phenomenon, so that the prior art cannot accurately reveal the corresponding relationship between the reservoir and the seismic waveform. In order to solve this problem, it is necessary to develop a seismic waveform analysis technology suitable for variable time window.

[0030] Therefore, in addition to acquiring necessary seismic data, the constant frequency width of the target stratum also needs to be acquired in the application. The constant frequency width can truly reflect the situation of the target stratum and ensure that the extracted seismic waveforms have isochronous significance.

[0031] Therefore, in some embodiments, the step S1 "obtaining a constant frequency bandwidth of the target formation" comprises:

[0032] Step S11: obtaining an upper formation seismic reflection duration of an upper formation of the target formation.

[0033] Step S12: obtaining a lower formation seismic reflection duration of a lower formation of the target formation.

[0034] Step S13: determining the constant frequency bandwidth according to the upper formation seismic reflection duration and the lower formation seismic reflection duration.

[0035] The thickness of the target formation is not consistent, so we need to determine the constant frequency bandwidth of the target formation by means of the upper and lower formations of the target formation. The upper formation seismic reflection duration represents the seismic reflection duration of the upper formation of the target formation, and the lower formation seismic reflection duration represents the seismic reflection duration of the lower formation of the target formation. We can determine the constant frequency bandwidth of the target formation by the seismic reflection durations of the adjacent two formations of the target formation.

[0036] In some embodiments, the step S13 "determining the constant frequency bandwidth according to the upper formation seismic reflection duration and the lower formation seismic reflection duration" comprises:

[0037] Step S131: determining an upper and lower formation maximum time difference value of the target formation according to the upper formation seismic reflection duration and the lower formation seismic reflection duration.

[0038] Step S132: determining the constant frequency bandwidth according to the upper and lower formation maximum time difference value.

[0039] The difference between the upper formation seismic reflection duration and the lower formation seismic reflection duration (i.e. the upper and lower formation time difference value) can represent the seismic time thickness of the target formation. The upper and lower formation maximum time difference value can represent the thickest seismic time of the target formation. After determining the upper and lower formation maximum time difference value, the constant frequency bandwidth can be determined by the following formula:

[0040]

[0041] In the formula,

[0042] Max h = max(HorTop n -HorBot n )

[0043] Maxh represents the upper and lower formation maximum time difference value, Len fHorTop represents the upper layer seismic reflection time length, HorBot represents the lower layer seismic reflection time length, and [x] represents rounding up the real number x to the nearest integer, that is, [x] is the smallest integer not less than x.

[0044] Step S2: converting the seismic data from time domain data to frequency domain data according to the constant frequency width.

[0045] Since the thickness of the target formation is inconsistent, the seismic signals of the target earthquake are not equal in length in the time domain, so the obtained seismic signals need to be filtered and expressed in the frequency domain.

[0046] First, initialize the real part and imaginary part dataRe and dataIm of the Fourier transform parameter data, and the data length is Len f . Save the seismic data of the target formation to dataRe, and handle the deficiency by zero padding, and dataIm is zero. Save the time domain data after conversion to the frequency domain by fast Fourier transform to obtain the new real part and imaginary part fftRe and fftIm, and save the results. Indicated as:

[0047] (fftRe, fftIm) = fft(dataRe, dataIm)

[0048]

[0049] Where fft() represents fast Fourier transform, Cube represents saved frequency domain data, l = 1, 2, …, nLines, nLines represents the total number of lines, c = 1, 2, …, nCDPs, nCDPs represents the number of traces per line, t = 1, 2, …, Len f / 2, indicating only taking the first half of the transformed data.

[0050] Step S3: obtain a preset classification number range.

[0051] The selection of the classification number determines the quality of the final seismic facies map, and improper selection of the classification number may result in a too rough final seismic facies map. Therefore, in the present application, according to the complexity of the seismic signal, the size of the target interval and the degree of understanding of the seismic data, a classification number range is preset, for example, the classification number range C can be set to 5-15.

[0052] Step S4: determine the target formation inter-class distance change graph according to the classification number range.

[0053] In the present application, since the range of the number of categories is preset, a suitable number of categories needs to be selected from the numerous numbers of categories in the range of the number of categories, and the class interval change with category diagram can well represent the performance quality of the seismic data under different numbers of categories.

[0054] Therefore, in some embodiments, the step S4 "determining the class interval change with category diagram of the target formation according to the range of the number of categories" comprises:

[0055] Step S41: inputting each number of categories in the range of the number of categories into a one-dimensional self-organizing feature mapping network respectively to obtain a model trace corresponding to each number of categories.

[0056] In the present application, each number of categories in the range of the number of categories is first input into a one-dimensional self-organizing feature mapping network, so that the self-organizing feature mapping network classifies the frequency domain data according to the input number of categories to obtain a model trace of the frequency domain data determined by the self-organizing feature mapping network according to the input number of categories.

[0057] In the application of actual variable time window waveform classification technology, the conventional two-dimensional self-organizing feature mapping network (SOM) and dynamic time warping (DTW) method often have multiple neurons mapping the same seismic facies when facing actual seismic data, resulting in a large network size, large amount of calculation and waveform distortion in the signal resampling process, which is not conducive to the comprehensive and determination of the final classification result. Therefore, directly using these methods is not suitable for waveform classification technology in large-scale seismic data. In the application of ordinary two-dimensional Kohonen (self-organizing feature mapping network) network structure, multiple neurons often map the same seismic facies, resulting in a large network size and slow convergence speed of the algorithm, which is not conducive to the comprehensive and determination of the final classification result. Therefore, in the present embodiment, the original two-dimensional Kohonen network is improved according to the resolution of the seismic data and the requirements of seismic facies classification, and a network structure with fewer neurons and one-dimensional output layer is used to meet the needs of practical application.

[0058] The improved one-dimensional Kohonen (self-organizing feature mapping network) network is different from the two-dimensional array in the structure of the output layer, adopts a linear topology structure composed of one-dimensional output nodes. The network still adjusts the field and value of weight correction according to the response of the output layer neurons to the input neurons, effectively reducing the size of the network and reducing the complexity.

[0059] Step S42: determining the sum of intra-class distances of each number of categories according to the model trace.

[0060] Step S43: determining the class interval change with category diagram according to the sum of intra-class distances of each number of categories.

[0061] When the frequency domain waveform classification is performed, we calculate the sum of intra-class distances ERR under each classification number, and finally generate the inter-class distance variation diagram with the change of classification, which is expressed as:

[0062]

[0063] where ERR is the sum of intra-class distances to be obtained, is a sample belonging to the i-th class, i = 1, …, K, K ∈ (5, 15) represents the current classification number, j = 1, …, n(i), () is the corresponding weight vector of the class.

[0064] In some embodiments, the step S43 of "determining the inter-class distance variation diagram with the change of classification according to the sum of intra-class distances of each classification number" comprises:

[0065] Step S431: obtaining a preset threshold.

[0066] Step S432: comparing the sum of intra-class distances corresponding to each classification number with the preset threshold, when the sum of intra-class distances corresponding to any classification number is less than the preset threshold, retaining the sum of intra-class distances corresponding to the classification number and the model channel.

[0067] Step S433: determining the inter-class distance variation diagram according to the retained sum of intra-class distances.

[0068] However, in actual operation, not every classification number corresponding to the sum of intra-class distances can be used to generate the inter-class distance variation diagram with the change of classification, after all, some classification numbers correspond to the sum of intra-class distances which is too large, even if it is used to generate the inter-class distance variation diagram with the change of classification, it will not be selected, but it will also increase the calculation amount, so there is a preset threshold in the application, which filters the sum of intra-class distances corresponding to each classification number, and retains the model channel and the sum of intra-class distances whose sum of intra-class distances is less than the preset threshold, and generates the inter-class distance variation diagram with the change of classification according to the retained sum of intra-class distances.

[0069] Step S5: determining the target classification number according to the inter-class distance variation diagram with the change of classification.

[0070] In some embodiments, the step S5 of "determining the target classification number according to the inter-class distance variation diagram with the change of classification" comprises:

[0071] Step S51: determining the target sum of intra-class distances according to the inter-class distance variation diagram with the change of classification.

[0072] Step S52: determining the target classification number according to the target sum of intra-class distances.

[0073] Generally, as the number of categories increases, the inter-class distance gradually decreases. In theory, the minimum ERR is the optimal number of categories, but it is difficult to find this point in practical applications. In this embodiment, we usually find the point where the curve of inter-class distance changes with the number of categories is monotonous and tends to be flat, which is the appropriate number of categories. Therefore, we can determine the sum of the target intra-class distance by the graph of inter-class distance changing with the number of categories. Since the sum of intra-class distance is corresponding to the number of categories, the target classification number can be determined by the sum of the target intra-class distance.

[0074] Step S6: generating a planar seismic facies map of the target formation according to the target classification number.

[0075] In some embodiments, step S6 "generating a planar seismic facies map of the target formation according to the target classification number" comprises:

[0076] Step S61: determining a target model trace according to the target classification number.

[0077] Step S62: performing classification processing on the frequency domain data according to the target model trace and the target classification number by using the similarity principle to generate a planar seismic facies map of the target formation.

[0078] Since the model trace corresponding to the classification number in the graph of inter-class distance changing with the number of categories is retained in step S432, the target model trace can be determined by the target classification number. After determining the target model trace, the frequency domain data can be classified and processed by using the similarity principle according to the target model trace and the target classification number, and then the appropriate planar seismic facies map of the target formation is obtained.

[0079] Therefore, the present application considers that the equal-thickness time window cannot well reflect the corresponding relationship between the waveform and the reservoir, and the calculation complexity of the conventional classification algorithm, etc. The time domain non-equal-length seismic signal is transformed to the frequency domain, a suitable constant frequency width is selected for classification, and the existing two-dimensional Kohonen network is changed to a one-dimensional Kohonen network structure. Thus, the actual classification and analysis needs of seismic data can be met, the network size can be effectively controlled, the complexity can be reduced, and the classification effect can be improved.

[0080] Embodiment 2:

[0081] Based on the foregoing embodiments, an embodiment of the present application provides a device for generating a seismic phase map. The modules included in the device, and the units included in each module, can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0082] like Figure 2 As shown, a device for generating a seismic phase map includes: a first acquisition module 1, a first execution module 2, a second acquisition module 3, a first determination module 4, a second determination module 5 and a second execution module 6.

[0083] The first acquisition module 1 is used to acquire seismic data of a target formation and a constant bandwidth of the target formation. The first execution module 2 is used to convert the seismic data from time domain data to frequency domain data based on the constant bandwidth. The second acquisition module 3 is used to acquire a preset classification number range. The first determination module 4 is used to determine a class interval variation diagram of the target formation based on the classification number range. The second determination module 5 is used to determine a target classification number based on the class interval variation diagram. The second execution module 6 is used to generate a planar seismic phase map of the target formation based on the target classification number.

[0084] In some embodiments, the first acquisition module 1 includes: a third acquisition module, a fourth acquisition module and a third determination module.

[0085] The third acquisition module is configured to acquire the duration of the seismic reflection of an upper layer of the stratum above the target stratum. The fourth acquisition module is configured to acquire the duration of the seismic reflection of a lower layer of the stratum below the target stratum. The third determination module is configured to determine the constant bandwidth based on the seismic reflection durations of the upper layer and the lower layer.

[0086] In some embodiments, the third determination module includes: a fourth determination module and a fifth determination module.

[0087] The fourth determining module is used to determine the maximum time difference between the upper and lower layers of the target stratum according to the upper layer seismic reflection duration and the lower layer seismic reflection duration. The fifth determining module is used to determine the constant bandwidth according to the maximum time difference between the upper and lower layers.

[0088] In some embodiments, the first determination module 4 includes: a third execution module, a sixth determination module, and a seventh determination module.

[0089] The third execution module is configured to input each of the classification numbers in the classification number range into a one-dimensional self-organizing feature mapping network respectively to obtain a model trace corresponding to each of the classification numbers. The sixth determination module is configured to determine a sum of intra-class distances of each of the classification numbers according to the model trace. The seventh determination module is configured to determine the inter-class distance variation diagram according to the sum of intra-class distances of each of the classification numbers.

[0090] In some embodiments, the seventh determination module comprises a fifth acquisition module, a fourth execution module and an eighth determination module.

[0091] The fifth acquisition module is configured to acquire a preset threshold. The fourth execution module is configured to compare the sum of intra-class distances corresponding to each of the classification numbers with the preset threshold, and when the sum of intra-class distances corresponding to any of the classification numbers is less than the preset threshold, retain the sum of intra-class distances corresponding to the classification number and the model trace. The eighth determination module is configured to determine the inter-class distance variation diagram according to the retained sums of intra-class distances.

[0092] In some embodiments, the second determination module 5 comprises a ninth determination module and a tenth determination module.

[0093] The ninth determination module is configured to determine a target sum of intra-class distances according to the inter-class distance variation diagram. The tenth determination module is configured to determine a target classification number according to the target sum of intra-class distances.

[0094] In some embodiments, the second execution module 6 comprises an eleventh determination module and a fifth execution module.

[0095] The eleventh determination module is configured to determine a target model trace according to the target classification number. The fifth execution module is configured to perform classification processing on the frequency domain data according to the target model trace and the target classification number by using the similarity principle to generate a planar seismic facies map of the target formation.

[0096] The above-described modules in the seismic facies map generation device can be realized by software, hardware or a combination thereof. The above-described modules can be embedded in or independent of a processor in a device in a hardware form, or can be stored in a memory in a processing device in a software form, so as to be called and executed by a processor to perform operations corresponding to the above-described modules. It should be noted that the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. Another division manner can be used in actual implementation.

[0097] Embodiment 3:

[0098] The third aspect provides an electronic device comprising a storage and a processor. The storage stores a computer program. When the processor executes the computer program, the steps of the method for generating a seismic facies map are implemented.

[0099] Embodiment 4:

[0100] The fourth aspect provides a storage medium storing a computer program, which can be executed by one or more processors, and the computer program can be used to implement the steps of the method for generating a seismic facies map according to any one of the first aspect.

[0101] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the computer program can include the processes of the above-mentioned embodiments. Any reference to a memory, storage, database or other medium used in the embodiments provided in the present application can include at least one of a non-volatile memory and a volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory or an optical memory. The volatile memory can include a random access memory (RAM) or an external cache memory. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM).

[0102] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that the size of the sequence number of the above-mentioned processes in various embodiments of the present application does not mean the execution order. The execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence number of the embodiments of the present application is only for description, not representing the advantages or disadvantages of the embodiments.

[0103] It should be noted that, in the present document, the terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a list of elements are not limited to those elements, but can also include other elements not expressly listed, or also include elements inherent in such processes, methods, articles, or apparatuses. Without further limitation, an element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0104] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed components can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0105] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; and part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0106] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0107] Those of ordinary skill in the art can understand that all or part of the steps of the above method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the above method embodiments when executed; and the foregoing storage medium includes: mobile storage equipment, read only memory (ROM, Read Only Memory), magnetic disc or optical disc, and various storage program codes.

[0108] Alternatively, the above-mentioned integrated units of the present application, if realized in the form of software function modules and sold or used as independent products, can also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes mobile storage devices, ROM, magnetic disks or optical disks, and various media capable of storing program codes.

[0109] The above is only an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for generating a seismic phase map, characterized in that: include: Acquiring seismic data of a target formation and a constant bandwidth of the target formation; The obtaining of the constant bandwidth of the target formation includes: Obtaining the seismic reflection duration of an upper layer of a stratum above the target stratum; Obtaining the seismic reflection duration of a lower layer of a layer below the target layer; Determining the constant bandwidth according to the upper layer seismic reflection duration and the lower layer seismic reflection duration; The determining the constant bandwidth according to the upper layer seismic reflection duration and the lower layer seismic reflection duration includes: Determining a maximum time difference between upper and lower layers of the target stratum according to the upper layer seismic reflection duration and the lower layer seismic reflection duration; Determining the constant bandwidth according to the maximum time difference between the upper and lower layers; converting the seismic data from time domain data to frequency domain data according to the constant bandwidth; Get the preset range of classification numbers; Determine a graph of the distance between classes and the change of classifications of the target stratum according to the range of the classification number; The determining of the inter-class distance variation diagram of the target stratum according to the classification number range includes: Input each classification number within the classification number range into a one-dimensional self-organizing feature map network to obtain a model path corresponding to each classification number; Determine the sum of the intra-class distances for each classification number according to the model; Determine the distance between classes as a function of classification according to the sum of the intra-class distances of each classification number; Determine the target number of classifications according to the class distance variation diagram; A plane seismic phase map of the target stratum is generated according to the target classification number.

2. The method according to claim 1, characterized in that The step of determining the distance between classes as a function of classification based on the sum of the intra-class distances of each classification number includes: Get the preset threshold; Comparing the sum of the intra-class distances corresponding to each classification number with the preset threshold, when the sum of the intra-class distances corresponding to any classification number is less than the preset threshold, retaining the sum of the intra-class distances corresponding to the classification number and the model path; The graph of the distance between classes changing with classification is determined according to the sum of the multiple retained intra-class distances.

3. The method according to claim 2, characterized in that Determining the target number of classifications according to the class distance variation graph includes: Determine the sum of the distances within the target class according to the class distance variation diagram; The number of target classifications is determined according to the sum of the distances within the target class.

4. The method according to claim 1, wherein Generating a plane seismic phase map of the target stratum according to the target classification number includes: Determining a target model path according to the target classification number; The frequency domain data is classified and processed according to the target model trace and the target classification number using the similarity principle to generate a plane seismic phase map of the target stratum.

5. A device for generating a seismic phase map, characterized in that: include: A first acquisition module is used to acquire seismic data of a target formation and a constant bandwidth of the target formation; The first acquisition module includes: a third acquisition module, a fourth acquisition module and a third determination module; The third acquisition module is used to obtain the seismic reflection duration of the upper layer of the stratum above the target stratum; The fourth acquisition module is used to obtain the seismic reflection duration of the lower layer of the next layer of the target layer; The third determining module is used to determine the constant bandwidth according to the upper layer seismic reflection duration and the lower layer seismic reflection duration; The third determining module includes: a fourth determining module and a fifth determining module; The fourth determining module is used to determine the maximum time difference between the upper and lower layers of the target stratum according to the seismic reflection duration of the upper layer and the seismic reflection duration of the lower layer; The fifth determining module is configured to determine the constant bandwidth according to the maximum time difference between the upper and lower layers; A first execution module, configured to convert the seismic data from time domain data to frequency domain data according to the constant bandwidth; The second acquisition module is used to obtain a preset classification number range; A first determining module is used to determine a graph showing a change in the distance between classes of a target stratum according to the range of the number of classes; The first determining module includes: a third executing module, a sixth determining module and a seventh determining module; The third execution module is used to input each classification number within the classification number range into the one-dimensional self-organizing feature map network to obtain a model path corresponding to each classification number; The sixth determining module is used to determine the sum of the intra-class distances of each classification number according to the model; The seventh determination module is used to determine the distance between classes as a function of classification according to the sum of the intra-class distances of each classification number; A second determination module is used to determine the target number of classifications according to the class distance variation diagram; The second execution module is configured to generate a plane seismic phase map of the target stratum according to the target classification number.

6. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 4 is performed.

7. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors, and the computer program can be used to implement the method according to any one of claims 1 to 4.

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