A time-varying deconvolution method, device, equipment and medium

Abstract

The present invention discloses a time-varying deconvolution method and device. The method comprises: a. arranging all acquired seismic data in seismic trace sequence, obtaining the first and last non-zero sampling points of each seismic data trace to determine the effective seismic data range in each seismic data trace; b. setting a time window length for each seismic data trace, and opening a time window starting from the first non-zero sampling point based on a prediction step length; c. intercepting the first seismic data within the time window within the effective seismic data range, and calculating the autocorrelation function of the first seismic data; d. determining a prediction operator sequence for each effective sampling point in the first seismic data based on the autocorrelation function and the prediction step length; and e. performing a convolution calculation on the prediction operator sequence and the first seismic data to determine the deconvolution seismic result of the first seismic data. The solution of the present invention effectively improves the vertical resolution of seismic data.

Description

Technical Field

[0001] The present 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 eastern and western China have become increasingly stringent. For oilfields in the eastern region, seismic data must be able to characterize small fault blocks with amplitudes of several meters, small faults with drop heights of several meters, and thin sand layers with thicknesses of several meters. For oilfields in the western region, seismic data must accurately identify formation pinchouts, correctly understand heterogeneous reservoirs, and finely characterize paleokarst landforms. Meeting these requirements requires high-resolution processing of seismic data.

[0003] Among related technologies, deconvolution is the primary method for improving resolution in seismic data processing. By calculating inverse seismic wavelets and establishing an inverse filter to compress the seismic wavelets, the resolution of the seismic data is improved. Current deconvolution techniques require processing on a longer recording of the seismic wavelet. This is partly due to the assumption that the reflection coefficient is whitened, and partly to reduce the impact of truncation effects. However, the time-varying nature of seismic wavelets makes it impossible for seismic wavelets in longer recordings to meet the stability requirements of deconvolution techniques. Other methods for improving resolution include inverse Q filtering, which compensates for the high-frequency energy absorption caused by the propagation of seismic wavelets in the underground medium according to the Q absorption model, thereby improving the resolution of seismic data. However, inverse Q filtering has poor adaptability, requiring multiple experiments to determine the appropriate Q value, and manual intervention is required to achieve optimal results.

[0004] The above-mentioned related technologies all have certain defects, which easily lead to 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, and files placed in the wrong place during the manual packaging process, thereby releasing the production capacity of developers and improving their work efficiency.

[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 seismic trace order, 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;

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

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

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

[0012] In some embodiments, step d comprises:

[0013] An error function of the first seismic data is determined based on a finite interval formula, the autocorrelation function, and the prediction step size, 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, 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 a valid sampling point in the valid seismic data range, slide the time window downward along the sampling time corresponding to the valid sampling point, repeat steps c to e at the new valid sampling point until the time window slides to the last non-zero sampling point, and determine the deconvolution seismic result 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 prediction 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 the seismic data.

[0023] In some embodiments, 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 coefficient is adjusted according to a noise level in the entire seismic data, and a deconvolution seismic result of the first seismic data is optimized based on the white noise coefficient.

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

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

[0028] According to another aspect of the embodiments of the present invention, a computer device is provided, comprising: at least one processor; and a memory, wherein the memory stores a computer program that can be run 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 using the least squares prediction technology within a finite interval, slides the analysis window from top to bottom sampling point by sampling point, and calculates the autocorrelation function for the seismic data in each time window. The prediction operator sequence is calculated using the autocorrelation function, and deconvolution calculations are performed 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 the spatial and temporal directions, providing a new means of improving resolution for the actual processing of seismic data, and effectively improving the vertical resolution of seismic data.

[0032] These and other aspects of the present application will be more clearly understood in the following description of the embodiments. It should be understood that the above general description and the following detailed description are merely 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below 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 any 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 the cross-section conditions before and after the application of the time-varying deconvolution method provided by the present invention 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 a comparison of the final cross-section results before and after the application of the time-varying deconvolution method to a well line in a certain area;

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

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

[0040] Figure 7 This is a schematic structural diagram 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 new embodiments.

[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 conjunction 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. Subsequent embodiments will not explain this one by one.

[0044] Due to the absorption of the strata, the recorded reflected wavelets are no longer the shock waves input by the earthquake source into the earth. Due to the complex underground geology, the high-frequency attenuation of the seismic wavelets increases with the depth of the reflecting layer. This paper proposes a time-varying deconvolution method based on prediction theory, after predicting the absorption response. This method utilizes finite interval least squares prediction technology to address the time-varying nature of the seismic wavelets, improve the vertical resolution of the seismic data, and meet the requirements for high-resolution seismic data in seismic data interpretation and inversion.

[0045] Based on the above purpose, the first aspect of the embodiment 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 seismic trace order, 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 a prediction operator sequence for 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 a deconvolution seismic result of the first seismic data.

[0051] In some embodiments, 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 from seismic data processing is arranged in a trace sequence, and the length of each trace is fixed. However, the positions of the first and last non-zero sampling points in each trace are different. Therefore, it is necessary to first obtain the positions of the first and last non-zero sampling points in each trace, and then count the number of valid sampling points in the seismic data 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 of the seismic data in each time window can be calculated.

[0055] In some embodiments, conventional deconvolution calculations typically use a relatively large time window length to calculate deconvolution results for seismic data, with the overlap between time windows often reaching 10% to 50%. The present invention can further calculate deconvolution results using a prediction error operator with a smaller window length, thereby reducing the overlap between time windows. Taking 4 millisecond data sampling as an example, the time window length can range from 40 to 400 milliseconds.

[0056] In some embodiments, the start time of the first time window of the application 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 window from top to bottom sampling point by sampling point, and calculates the autocorrelation function for the seismic data in each time window. The prediction operator sequence is calculated based on the autocorrelation function, and the obtained prediction operator sequence is deconvolved with 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, providing a new means of improving resolution for the actual processing of seismic data, and effectively improving 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 size, 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, 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, a prediction operator is calculated for the seismic data in the first time window using a reverse prediction technique corresponding to the above formula. This means that a separate prediction operator is calculated for each sampling point in each seismic trace. Furthermore, a recursive method is employed to derive the prediction operators for the sampling points at a given time point from the prediction operators at the next time point, automatically tracking changes in the input prediction operator sequence during the recursive process.

[0067] The present invention proposes a time-varying deconvolution method, which uses the least square prediction technology in 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, and the vertical resolution of the seismic data is effectively improved.

[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 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 seismic data.

[0072] In some embodiments, in actual applications, deconvolution seismic results, i.e., seismic data with enhanced resolution, can be obtained by using the same prediction step size to calculate the prediction operator and perform predictive deconvolution iterations. Seismic data with enhanced resolution can also be obtained by using different prediction step sizes to calculate the prediction operator and perform predictive deconvolution iterations. The number of iterations and the prediction step size increment can be set, and steps b through f of the above method can be repeated until a desired result is obtained that satisfies the number of iterations.

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

[0074] 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.

[0075] In some implementations, the white noise coefficient is added for two main purposes: first, to ensure the stability of the prediction operator during the calculation process; second, to prevent random noise in the seismic data from being amplified and thus overwhelmed by the deconvolution results. In one example, the white noise coefficient can be manually adjusted based on 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 collected in the field.

[0077] In some embodiments, Figure 2 This is a comparison diagram of the cross-section before and after the application of the time-varying deconvolution method in a certain exploration area provided by the present invention, wherein: Figure 2 In the figure, a represents the cross-section of a certain exploration area before the application of the time-varying deconvolution method, b represents the cross-section of a certain exploration area after the application of the purchased related application software, and c represents the cross-section of a certain exploration area after the application of the time-varying deconvolution method of the present invention. 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 results 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 This is a schematic diagram of an embodiment of the present invention providing a comparison display of the final cross-section results before and after the application of the time-varying deconvolution method 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 5This is a comparison diagram of an embodiment of the present invention for comparing the well section curves before and after the application of the time-varying deconvolution method. In the figure, curve a represents the well section curve before the application of the time-varying deconvolution method, curve b represents the well section curve after the application of the time-varying deconvolution method, and curve c represents the well section curve of the well synthetic record. Figure 4 and Figure 5 As shown in FIG. 1 , 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 degree of coincidence 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 using the least squares prediction technology within a finite interval, slides the analysis 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 calculations 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 the spatial and temporal directions, providing a new means of improving resolution for the actual processing of seismic data, and effectively improving the vertical resolution of seismic data.

[0080] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0081] It should be understood that, although the above is described in a certain order, these steps are not necessarily performed in sequence according to 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 multiple steps or multiple 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 carried out in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps.

[0082] Based on the same inventive concept, a second aspect of an embodiment of the present invention provides a time-varying deconvolution device that implements 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 those skilled in the art will 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. 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. The storage medium of the program can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). The above-mentioned computer program embodiments can achieve the same or similar effects as any of the corresponding aforementioned method embodiments.

[0086] It will also be appreciated by those skilled in the art that the various exemplary logic blocks, modules, circuits and algorithmic 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 of 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 can be made without departing from the scope of the disclosure of the embodiments of 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 can be described or required in individual form, they can also be understood as multiple unless expressly limited to the singular.

[0088] It should be understood that, as used herein, the singular forms "a" and "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" is intended to include any and all possible combinations of one or more of the associated listed items.

[0089] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative 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. Within the spirit of the embodiments of the present invention, the technical features of the above embodiments or different embodiments may be combined, and there are many other variations of different aspects of the above embodiments of the present invention, 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 scope of protection 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 seismic trace order, obtain the first non-zero sampling point and the last non-zero sampling point of each seismic data trace to determine the effective seismic data range of each seismic data trace; b. For each seismic data trace, set a time window length, and start opening the time window from the first non-zero sampling point based on the prediction step length; c. intercepting first seismic data within the time window in the valid seismic data range, and calculating an autocorrelation function of the first seismic data; 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; e. performing a convolution calculation on the prediction operator sequence and the first seismic data to determine a deconvolution seismic result of the first seismic data; Step d includes: An error function of the first seismic data is determined based on a finite interval formula, the autocorrelation function, and the prediction step size, 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: 。 2. The method according to claim 1, characterized in that The prediction operator sequence is {1,0,…0, , }.

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

4. The method according to claim 3, characterized in that The method further comprises: g. Repeating steps b to f based on the predicted step size increment until the number of repetitions meets the preset number of iterations, thereby determining the deconvolution seismic results of all the seismic data.

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

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

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

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

9. 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 7 when executing the computer program.

10. 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 7 are performed.

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