Time-varying deconvolution method, device, equipment and medium

Abstract

The invention discloses a time-varying deconvolution method and device. The method comprises the following steps: a, arranging all acquired seismic data according to a seismic channel sequence, and acquiring a first non-zero sampling point and a last non-zero sampling point of each channel of seismic data to determine an effective seismic data range in each channel of seismic data; b, for each channel of seismic data, setting a time window length, and starting to open a time window from a first non-zero sampling point based on a prediction step length; c, intercepting first seismic data in the time window in the effective seismic data range, and calculating an autocorrelation function of the first seismic data; d, determining a prediction operator sequence of each effective sampling point in the first seismic data based on the autocorrelation function and the prediction step length; and e, performing convolution calculation on the prediction operator sequence and the first seismic data, and determining a deconvolution seismic result of the first seismic data. Through the scheme of the invention, the vertical resolution of the seismic data is effectively improved.

Description

Technical Field

[0001] The invention relates to the field of petroleum exploration, and in particular to a time-varying deconvolution method, system, equipment and medium. Background Art

[0002] In recent years, the requirements for seismic data in oil and gas exploration in the eastern and western regions have become increasingly higher. For oil fields in the eastern region, seismic data are required to be able to characterize small fault blocks with a few meters of amplitude, small faults with a few meters of drop, and thin sand layers with a thickness of several meters; for oil fields in the western region, seismic data are required to accurately identify formation pinch-out points, correctly understand heterogeneous reservoirs, and finely characterize ancient karst landforms. To meet the above requirements, high-resolution processing of seismic data is required.

[0003] In the related technology, deconvolution technology is the main means to improve the resolution in seismic data processing. By calculating the inverse seismic wavelet, an inverse filter is established to compress the seismic wavelet, thereby improving the resolution of seismic data. The current deconvolution technology needs to be processed on a longer record of seismic wavelets. On the one hand, it takes into account the assumption of white noise of reflection coefficients, and on the other hand, it is also to reduce the impact of truncation effect. However, seismic wavelets are time-varying, so that seismic wavelets in longer records cannot meet the stability requirements of deconvolution technology. Means to improve resolution also include inverse Q filtering technology, that is, according to the Q absorption model, the high-frequency energy absorption caused by the propagation of seismic wavelets in underground media is compensated to achieve the purpose of improving the resolution of seismic data. However, the inverse Q filter has poor adaptability. When applied, it requires multiple experiments to determine the appropriate Q value, and manual intervention is required to achieve better results.

[0004] The above-mentioned related technologies all have certain defects, which easily cause greater uncertainty in seismic data and make it difficult to effectively improve the resolution of seismic data. Summary of the invention

[0005] In view of this, the present invention proposes a time-varying deconvolution method, system, device and medium, which solves the problems of missing files, multiple files, files that are not the latest, files placed in the wrong place, etc. in the manual packaging process, releases the productivity of developers and improves the work efficiency of developers.

[0006] Based on the above purpose, an embodiment of the present invention provides a time-varying deconvolution method, which specifically includes the following steps:

[0007] a. Arrange all acquired seismic data in the order of seismic traces, obtain the first non-zero sampling point and the last non-zero sampling point of each seismic data to determine the effective seismic data range in each seismic data;

[0008] b. For each seismic data, set the time window length, and start to open the time window from the first non-zero sampling point based on the prediction step length;

[0009] c. intercepting the first seismic data within the time window in the effective seismic data range, and calculating the autocorrelation function of the first seismic data;

[0010] d. determining a prediction operator sequence for each valid sampling point in the first seismic data based on the autocorrelation function and the prediction step size;

[0011] e. Performing convolution calculation on the prediction operator sequence and the first seismic data to determine the deconvolution seismic result of the first seismic data.

[0012] In some embodiments, step d comprises:

[0013] Based on a finite interval formula, the autocorrelation function and the prediction step, an error function of the first seismic data is determined, wherein the finite interval formula is:

[0014]

[0015] The error function is set equal to a preset target value to determine the prediction operator, wherein the formula for setting the error function equal to the preset target value is expressed as:

[0016]

[0017] In the formula, τ is the prediction step length, N is the sum of the time window length and the prediction step length, S is the first earthquake data, φ is the error function, and X τ and X τ+1 Represents a prediction operator.

[0018] In some embodiments, the prediction operator sequence is {1, 0, ... 0, X τ , X τ+1}.

[0019] In some embodiments, the method further comprises:

[0020] f. Determine the valid sampling points in the valid seismic data range, slide the time window downward along the sampling time corresponding to the valid sampling points, repeat steps c to e at the new valid sampling points until the time window slides to the last non-zero sampling point, and determine the deconvolution seismic results of the valid seismic data corresponding to the valid seismic data range.

[0021] In some embodiments, the method further comprises:

[0022] g. Based on the predicted step size increment, repeat steps b to f until the number of repetitions meets the preset number of iterations, and determine the deconvolution seismic results of all the seismic data.

[0023] In some implementations, the first non-zero sampling point and the last non-zero sampling point in each channel of seismic data are different.

[0024] In some embodiments, the method further comprises:

[0025] A white noise factor is adjusted according to the noise level in the entire seismic data, and a deconvolution seismic result of the first seismic data is optimized based on the white noise factor.

[0026] In some implementations, all of the seismic data are original seismic data collected in the field.

[0027] Another aspect of the embodiments of the present invention provides a time-varying deconvolution device that executes the above method.

[0028] According to another aspect of an embodiment of the present invention, a computer device is provided, comprising: at least one processor; and a memory, wherein the memory stores a computer program executable on the processor, and the computer program implements the steps of the above method when executed by the processor.

[0029] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program that implements the above method steps when executed by a processor.

[0030] The present invention has at least the following beneficial technical effects:

[0031] The present invention proposes a time-varying deconvolution method, which estimates a filter from actually collected seismic data, designs a separate prediction operator for each sampling point of each seismic channel by using the least square prediction technology in a finite interval, slides the analysis time window from top to bottom sampling point by sampling point and calculates the autocorrelation function for the seismic data in each time window, calculates the prediction operator sequence by the autocorrelation function, and performs deconvolution calculation on the obtained prediction operator sequence and the seismic data used to calculate the autocorrelation function, solves the time-varying problem of the sub-wave by adaptively changing the prediction operator sequence in the spatial and temporal directions, provides a new means of improving the resolution for the actual processing of seismic data, and effectively improves the vertical resolution of seismic data.

[0032] These and other aspects of the present application will be more concise and understandable in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A block diagram of an embodiment of a time-varying deconvolution method provided by the present invention;

[0035] Figure 2 A comparison diagram of an embodiment of the cross-section conditions before and after the time-varying deconvolution method provided by the present invention is applied in a certain exploration area;

[0036] Figure 3 A schematic diagram of an embodiment of the amplitude spectrum comparison display using the time-varying deconvolution method provided by the present invention;

[0037] Figure 4 A schematic diagram of an embodiment of the present invention showing the comparison of the final result profiles before and after the time-varying deconvolution method is applied to the well line in a certain area;

[0038] Figure 5 A comparison diagram of an embodiment of the comparison of well section curves before and after applying the time-varying deconvolution method provided by the present invention;

[0039] Figure 6 A schematic diagram of the structure of an embodiment of a computer device provided by the present invention;

[0040] Figure 7 A schematic diagram of the structure of an embodiment of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0041] Below, the present application is further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0042] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0043] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0044] Due to the absorption of the strata, the recorded reflected sub-waves are no longer shock waves input from the earthquake source to the earth. Due to the complex underground geological conditions, the high-frequency attenuation of the seismic sub-waves becomes more serious as the reflection layer goes deeper. The present invention proposes a time-varying deconvolution method based on prediction theory after predicting the absorption response. The finite interval least squares prediction technology is used to solve the time-varying nature of the seismic sub-waves, improve the vertical resolution of the seismic data, and meet the needs of seismic data interpretation and inversion for high-resolution seismic data.

[0045] Based on the above purpose, the first aspect of the embodiments of the present invention provides an embodiment of a time-varying deconvolution method. Figure 1 As shown, it includes the following steps:

[0046] a. Arrange all acquired seismic data in the order of seismic traces, obtain the first non-zero sampling point and the last non-zero sampling point of each seismic data to determine the effective seismic data range in each seismic data;

[0047] b. For each seismic data, set the time window length and start to open the time window from the first non-zero sampling point based on the prediction step length;

[0048] c. intercepting the first seismic data within the time window in the effective seismic data range, and calculating the autocorrelation function of the first seismic data;

[0049] d. Determine the prediction operator sequence of each valid sampling point in the first seismic data based on the autocorrelation function and the prediction step size;

[0050] e. Performing convolution calculation on the prediction operator sequence and the first seismic data to determine the deconvolution seismic result of the first seismic data.

[0051] In some implementations, the first non-zero sampling point and the last non-zero sampling point in each channel of seismic data are different.

[0052] In some embodiments, the seismic data of seismic data processing are arranged in the order of seismic traces, and the seismic trace length of each seismic trace is fixed, but the positions of the first non-zero sampling point and the last non-zero sampling point of each seismic trace length are different. Therefore, it is necessary to first obtain the positions of the first non-zero sampling point and the last non-zero sampling point in each seismic data, and count the number of valid sampling points in the seismic data, so as to determine the valid seismic data range.

[0053] In some embodiments, the autocorrelation function provides information about the amplitudes of different frequency components.

[0054] In one example, if the autocorrelation time window length is set to LW sampling points, the number of valid sampling points in the valid seismic data range is counted as LTR, the valid seismic data range is divided according to the time window length LW, and the time window is slid from the first non-zero sampling point to the last non-zero sampling point, then LTR-LW+1 time windows can be determined, and the autocorrelation function is calculated for the seismic data in each time window.

[0055] In some embodiments, in conventional deconvolution calculations, a time window with a larger time window length is usually used to calculate the deconvolution seismic results of seismic data, and the overlap rate between time windows is as high as 10% to 50%. The present invention can use a small window length to calculate the prediction error operator to further calculate the deconvolution seismic results, thereby reducing the overlap rate between time windows. Taking 4 milliseconds of data sampling as an example, the time window length can range from 40 to 400 milliseconds.

[0056] In some implementations, the start time of the first time window and the end time of the last time window of the application can be specified by the user.

[0057] The present invention proposes a time-varying deconvolution method, which estimates a filter based on actually collected seismic data, slides the analysis time window from top to bottom sampling point by sampling point, calculates the autocorrelation function for the seismic data in each time window, calculates a prediction operator sequence based on the autocorrelation function, and performs deconvolution calculation on the obtained prediction operator sequence and the seismic data used to calculate the autocorrelation function. The time-varying problem of the wavelet is solved by adaptively changing the prediction operator sequence in space and time directions, which provides a new means of improving the resolution for the actual processing of seismic data and effectively improves the vertical resolution of seismic data.

[0058] In some embodiments, step d comprises:

[0059] Based on the finite interval formula, the autocorrelation function and the prediction step, the error function of the first seismic data is determined, wherein the finite interval formula is:

[0060]

[0061] The error function is set equal to the preset target value to determine the prediction operator, wherein the formula for setting the error function equal to the preset target value is expressed as:

[0062]

[0063] In some embodiments, the prediction operator sequence is {1, 0, ... 0, X τ , X τ+1}.

[0064] In the above formula, τ is the prediction step length, N is the sum of the time window length and the prediction step length, S is the first earthquake data, φ is the error function, and X τ and X τ+1 Represents a prediction operator.

[0065] In some embodiments, the finite interval formula is a finite interval least squares formula. Since the time window for calculating the prediction operator is small, in order to overcome the influence of the truncation effect on the calculation of the prediction operator, the effective interval formula is used to calculate the prediction operator.

[0066] In some embodiments, the prediction operator is calculated for the seismic data in the first time window using the reverse prediction technique corresponding to the above formula, that is, a separate prediction operator is calculated for each sampling point in each seismic trace. Furthermore, a recursive method is used to derive the prediction operator of the sampling point corresponding to the next time point using the prediction operators at the sampling points at the known time points, so as to automatically track the changes of the input prediction operator sequence during the recursive process.

[0067] The present invention proposes a time-varying deconvolution method, which utilizes the least squares prediction technology within a finite interval to design a separate prediction operator for each sampling point of each seismic channel, improves the prediction operator sequence for calculating the autocorrelation function, and performs deconvolution calculation on the obtained prediction operator sequence and the seismic data used to calculate the autocorrelation function. The time-varying problem of the sub-wave is solved by adaptively changing the prediction operator sequence in the spatial and temporal directions, thereby effectively improving the vertical resolution of the seismic data.

[0068] In some embodiments, the method further comprises:

[0069] f. Determine the valid sampling points in the valid seismic data range, slide the time window downward along the sampling time corresponding to the valid sampling points, repeat steps c to e at the new valid sampling points until the time window slides to the last non-zero sampling point, and determine the deconvolution seismic results of the valid seismic data corresponding to the valid seismic data range.

[0070] In some embodiments, the method further comprises:

[0071] g. Based on the predicted step increment, repeat steps b to f until the number of repetitions meets the preset number of iterations, and determine the deconvolution seismic results of all seismic data.

[0072] In some embodiments, in actual applications, the deconvolution seismic results can be obtained by using the same prediction step length to calculate the prediction operator to perform prediction deconvolution iteration, that is, the seismic data with improved resolution can be obtained. It is also possible to use different prediction step lengths to calculate the prediction operator to perform prediction deconvolution iteration to obtain the seismic data with improved resolution, and the number of iterations and the prediction step length increment can be set, and steps b to f in the above method are repeated until the expected result that meets the number of iterations is obtained.

[0073] In some embodiments, the method further comprises:

[0074] According to the noise level in the entire seismic data, a white noise factor is adjusted, and the deconvolution seismic result of the first seismic data is optimized based on the white noise factor.

[0075] In some embodiments, the purpose of adding the white noise coefficient is mainly divided into two aspects. On the one hand, it is to ensure the stability of the prediction operator during the calculation process, and on the other hand, it is to prevent the random noise in the seismic data from being amplified and drowning the result of the deconvolution. In one example, the white noise coefficient can be artificially adjusted according to the noise level in the recorded seismic data. For example, the white noise coefficient can be preset to 0.01%.

[0076] In some embodiments, all seismic data are raw seismic data acquired in the field.

[0077] In some embodiments, Figure 2 A comparison diagram of an embodiment of the cross-section conditions before and after the time-varying deconvolution method is applied in a certain exploration area provided by the present invention, wherein: Figure 2 In the figure, a represents the profile of a certain exploration area before the time-varying deconvolution method is applied, b represents the profile of a certain exploration area after the purchased related application software is applied, and c represents the profile of a certain exploration area after the time-varying deconvolution method of the present invention is applied. Figure 3 This is a schematic diagram of an embodiment of the amplitude spectrum comparison display using the time-varying deconvolution method provided by the present invention. Figure 2 and Figure 3 As shown, compared with the effect of external related applications, the resolution of seismic data obtained by the time-varying deconvolution method of the present invention is significantly improved, and the small faults in the seismic data are more clearly portrayed.

[0078] In some embodiments, Figure 4 A schematic diagram of an embodiment of the present invention showing the comparison of the final result profiles before and after the time-varying deconvolution method is applied to a well line in a certain area, Figure 4 In the figure, (1) shows the final result profile before the time-varying deconvolution method is applied to the well line in a certain area, and (2) shows the final result profile after the time-varying deconvolution method is applied to the well line in a certain area. Figure 5A comparison diagram of an embodiment of the present invention for comparing the well section curves before and after the time-varying deconvolution method is applied, wherein curve a represents the well section curve before the time-varying deconvolution method is applied, curve b represents the well section curve after the time-varying deconvolution method is applied, and curve c represents the well synthetic record well section curve. Figure 4 and Figure 5 As shown, the vertical resolution of seismic data is improved to a certain extent after using the time-varying deconvolution method of the present invention, and the consistency of well synthetic records is improved.

[0079] The present invention proposes a time-varying deconvolution method, which estimates a filter from actually collected seismic data, designs a separate prediction operator for each sampling point of each seismic channel by using the least square prediction technology in a finite interval, slides the analysis time window from top to bottom sampling point by sampling point and calculates the autocorrelation function for the seismic data in each time window, improves the prediction operator sequence for calculating the autocorrelation function, and performs deconvolution calculation on the obtained prediction operator sequence and the seismic data used to calculate the autocorrelation function. The time-varying problem of the sub-wave is solved by adaptively changing the prediction operator sequence in the spatial and temporal directions, which provides a new means of improving the resolution for the actual processing of seismic data and effectively improves the vertical resolution of seismic data.

[0080] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0081] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is clear explanation in this article, the execution of these steps does not have strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0082] Based on the same inventive concept, a second aspect of the embodiment of the present invention provides a time-varying deconvolution device, which executes the above-mentioned time-varying deconvolution method.

[0083] Based on the same inventive concept, according to another aspect of the present invention, Figure 6As shown, an embodiment of the present invention further provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, the steps of the above method are performed.

[0084] Based on the same inventive concept, according to another aspect of the present invention, Figure 7 As shown, an embodiment of the present invention further provides a computer-readable storage medium 40, which stores a computer program 410 for executing the above method when executed by a processor.

[0085] Finally, it should be noted that a person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program can be a disk, an optical disk, a read-only storage memory (ROM) or a random access memory (RAM), etc. The above-mentioned computer program embodiments can achieve the same or similar effects as the corresponding above-mentioned arbitrary method embodiments.

[0086] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0087] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0088] It should be understood that, as used herein, the singular forms "a", "an" are intended to include the plural forms as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations including one or more of the associated listed items.

[0089] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A time-varying deconvolution method, characterized in that: include: a. Arrange all acquired seismic data in the order of seismic traces, obtain the first non-zero sampling point and the last non-zero sampling point of each seismic data to determine the effective seismic data range in each seismic data; b. For each seismic data, set the time window length, and start to open the time window from the first non-zero sampling point based on the prediction step length; c. intercepting the first seismic data within the time window in the effective seismic data range, and calculating the autocorrelation function of the first seismic data; d. Based on the and the prediction step length, determining a prediction operator sequence for each valid sampling point in the first seismic data; e. Performing convolution calculation on the prediction operator sequence and the first seismic data to determine the deconvolution seismic result of the first seismic data.

2. The method according to claim 1, characterized in that Step d includes: Based on a finite interval formula, the autocorrelation function and the prediction step, an error function of the first seismic data is determined, wherein the finite interval formula is: The error function is set equal to a preset target value to determine the prediction operator, wherein the formula for setting the error function equal to the preset target value is expressed as: In the formula, τ is the prediction step length, N is the sum of the time window length and the prediction step length, S is the first earthquake data, φ is the error function, and X τ and X τ+1 Represents a prediction operator.

3. The method according to claim 2, characterized in that The prediction operator sequence is {1,0,...0,X τ , X τ+1 }.

4. The method according to claim 1, characterized in that: The method further comprises: f. Determine the valid sampling points in the valid seismic data range, slide the time window downward along the sampling time corresponding to the valid sampling points, repeat steps c to e at the new valid sampling points until the time window slides to the last non-zero sampling point, and determine the deconvolution seismic results of the valid seismic data corresponding to the valid seismic data range.

5. The method according to claim 4, characterized in that The method further comprises: g. Based on the predicted step size increment, repeat steps b to f until the number of repetitions meets the preset number of iterations, and determine the deconvolution seismic results of all the seismic data.

6. The method according to claim 1, characterized in that The first non-zero sampling point and the last non-zero sampling point in each seismic data are different.

7. The method according to claim 1, characterized in that The method further comprises: A white noise factor is adjusted according to the noise level in the entire seismic data, and a deconvolution seismic result of the first seismic data is optimized based on the white noise factor.

8. The method according to claim 1, characterized in that All the seismic data are original seismic data collected in the field.

9. A time-varying deconvolution device, characterized in that: The device executes the method according to any one of claims 1 to 7.

10. A computer device comprising: at least one processor; as well as A memory storing a computer program executable on the processor, wherein the processor executes the steps of the method according to any one of claims 1 to 8 when executing the program.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are performed.

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