Source-by-source wavelet optimization processing method based on near-field measurement, computer program product and device

Abstract

The present application relates to a source-by-source wavelet optimization processing method based on near-field measurement sub-waves, a computer program product and equipment. The method comprises: obtaining a plurality of near-field measurement sub-waves corresponding to each emission of a plurality of sources in a target work area, each source emitting a plurality of near-field measurement sub-waves in different directions; synthesizing the plurality of near-field measurement sub-waves emitted each time to obtain corresponding near-field sub-waves independent of direction; removing interference waves from each near-field sub-wave to obtain corresponding source sub-waves; establishing a standard sub-wave based on all source sub-waves; filtering and matching each corresponding source sub-wave emitted each time with the standard sub-wave to obtain a corresponding matched filter operator; and optimizing processing of seismic data of the target work area based on the matched filter operator of each source emitted each time. The method can eliminate the adverse effects of changes in different source sub-waves, improve the interpretability, amplitude preservation and resolution of the optimization results, and provide reliable basic data for fine reservoir prediction and interpretation.

Description

Technical Field

[0001] This invention relates to the field of marine seismic exploration, and in particular to a source-by-source wavelet optimization processing method, computer program product and equipment based on near-field measurement wavelet. Background Technology

[0002] As offshore seismic exploration and development deepens, the requirements for vertical resolution and amplitude preservation of reflection wave groups in seismic data used for reservoir prediction are becoming increasingly stringent. Wavelet optimization processing of offshore seismic exploration data plays a crucial role in improving data processing efficiency. To enhance operational efficiency, offshore seismic data acquisition typically employs a multi-source approach. Existing wavelet optimization methods are based on the assumption that the source wavelet remains constant during acquisition, applying a uniform source wavelet to all sources for optimization. For example, using the same far-field wavelet to optimize seismic data from all sources.

[0003] However, in actual data acquisition, the source wavelets of different seismic sources often exhibit variations due to objective factors such as the air gun mechanism and sea conditions. In recent seismic data processing in a certain sea area, significant differences in the characteristics of different source wavelets were observed, particularly in the bubble response characteristics. If the conventional approach of using a uniform source wavelet for wavelet optimization of the entire 3D data (or the entire survey line) is still employed, the adverse effects of variations in the source wavelets—namely, poor amplitude preservation and low resolution—cannot be eliminated in the final seismic data processing results. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a source-by-source wavelet optimization processing method, computer program product and device based on near-field measurement wavelet.

[0005] The technical solution adopted by this invention to solve its technical problem is as follows: A source-by-source wavelet optimization processing method based on near-field measurement wavelets is constructed, comprising the following steps: S1, acquiring near-field measurement wavelets corresponding to each emission from multiple seismic sources in the target work area, wherein each emission from each seismic source corresponds to multiple near-field measurement wavelets in different directions; S2, synthesizing the multiple near-field measurement wavelets emitted each time to obtain corresponding direction-independent near-field wavelets; S3, removing interference waves from each near-field wavelet to obtain corresponding source wavelets; S4, establishing standard wavelets based on all source wavelets; S5, performing filtering matching between the source wavelets corresponding to each emission and the standard wavelets to obtain corresponding matched filtering operators; S6, optimizing the seismic data of the target work area based on the matched filtering operators emitted by each seismic source each time.

[0006] Furthermore, the removal of interference waves in each of the near-field wavelets in step S3 includes: performing ghost wave removal processing on each of the near-field wavelets respectively.

[0007] Furthermore, when the seabed reflection time of the target work area is less than or equal to the length of the source wavelet, the process of performing ghost wave removal on each near-field wavelet further includes performing seabed reflection removal on each near-field wavelet.

[0008] Furthermore, the step of performing seabed reflection removal processing on each of the near-field wavelets includes: acquiring near-channel seismic data for each transmission to establish a corresponding noise model; and removing seabed reflections from each near-field wavelet using an adaptive subtraction method based on the corresponding noise model.

[0009] Further, step S4 includes: calculating the average wavelet of all source wavelets, and obtaining the standard wavelet after debubbling and zero-phase processing.

[0010] Further, step S6 includes: performing bubble removal processing on the seismic data based on each matched filter operator.

[0011] Furthermore, the process after bubble removal also includes: zero-phase processing of the seismic data based on each matched filter operator.

[0012] Furthermore, after step S3, the method further includes: synthesizing the source wavelet and source ghost wave from each transmission to obtain the far-field wavelet of each transmission.

[0013] The present invention also constructs a computer program product, including a computer program that, when executed by a processor, implements the steps of the source-by-source wavelet optimization processing method based on near-field measurement wavelet as described above.

[0014] The present invention also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the source-by-source wavelet optimization processing method based on near-field measurement wavelets as described above.

[0015] Implementing this invention has the following beneficial effects: By collecting near-field measurement wavelets from each source's emission, synthesizing near-field wavelets, removing interference waves, and obtaining source wavelets from each emission, and then filtering and matching each emission's source wavelet with a standard wavelet, these wavelets are applied to seismic data for optimization. This eliminates the adverse effects of wavelet variations between different sources and multiple emissions from the same source, resulting in seismic data that accurately reflects the spatial variations of source wavelets. This improves the interpretability, amplitude preservation, and resolution of the optimization results, thereby providing reliable basic data for refined reservoir interpretation and data prediction. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0017] Figure 1 This is a superimposed display of near-field measurement wavelets from two seismic sources arranged in the construction sequence along a certain survey line;

[0018] Figure 2a This is a near-field measurement wavelet superposition display diagram of source 1 on a certain survey line;

[0019] Figure 2b This is a display diagram of the near-field measurement wavelet superposition of source 2 on a certain survey line;

[0020] Figure 3a This is a short-path profile of seismic data from source 1 of a certain survey line;

[0021] Figure 3b This is a short-path profile of seismic data from source 2 of a certain survey line;

[0022] Figure 4 This is a schematic flowchart of an embodiment of the source-by-source wavelet optimization processing method based on near-field measurement wavelet of the present invention;

[0023] Figure 5 This is a diagram showing multiple near-field measurement wavelets from the original measurements;

[0024] Figure 6 This is a diagram illustrating near-field wavelet synthesis using near-field measurement wavelets;

[0025] Figure 7 This is a diagram showing the near-field sub-wave after the removal of the ghost wave;

[0026] Figure 8a This is a diagram showing the near-field wavelet before reflection from the seabed;

[0027] Figure 8b This is a diagram showing the near-field wavelet after reflection from the seabed;

[0028] Figure 9 This is a standard wavelet illustration;

[0029] Figure 10 This is a diagram illustrating the matched filter operator for a certain earthquake source;

[0030] Figure 11a This is a display of the seismic data before optimization processing;

[0031] Figure 11b This is a display image of the optimized seismic data;

[0032] Figure 12a This is a schematic diagram of the autocorrelation of seismic data before bubble removal and zero-phase processing;

[0033] Figure 12b This is a schematic diagram of the autocorrelation of seismic data after bubble removal and zero-phase processing;

[0034] Figure 13a This is a superimposed profile of seismic data obtained using conventional far-field wavelet optimization processing;

[0035] Figure 13b It is a superimposed profile of seismic data obtained by optimizing the technical solution of this invention;

[0036] Figure 14 This is a comparison chart of the spectra of the technical solution of this invention and conventional far-field wavelet optimization processing;

[0037] Figure 15 This is a comparison diagram of the remaining phase after using the technical solution of this invention and after conventional far-field wavelet optimization processing;

[0038] Figure 16 This is a mosaic display of the offset profile and the synthesized record after optimization using the technical solution of this invention;

[0039] Figure 17a This is a diagram illustrating the far-field wavelet synthesis using near-field wavelet synthesis.

[0040] Figure 17b This is a diagram of the far-field wavelet obtained from actual measurements;

[0041] Figure 18 This is a comparison of the spectra of far-field wavelets obtained through two different methods. Detailed Implementation

[0042] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0043] The source-by-source wavelet optimization processing method based on near-field measurement wavelets of this invention is used to optimize seismic data obtained from multi-source detection schemes. Multi-source detection schemes typically employ dual or more sources to simultaneously or alternately emit source wavelets to excite the target seismic area, obtaining seismic data from the reflected waves. In this embodiment, an air gun array is used as the source; in other embodiments, an electric spark source can also be used. Simultaneously, sensors (e.g., hydrophones) are installed at multiple receiver points near the sources to measure the source wavelets of each source, helping to correct wave group characteristics in seismic data processing.

[0044] During the data collection process, the measured source wavelet often undergoes certain changes due to objective factors such as the air gun mechanism and sea conditions. Figure 1The image shows the superposition of near-field measurement wavelets from two sources arranged in the construction sequence along a certain survey line. The odd-numbered channels are the superposition of near-field measurement wavelets from source 1, and the even-numbered channels are the superposition of near-field measurement wavelets from source 2. A distinct sawtooth pattern can be observed, indicating that there are significant differences in the wavelet characteristics of the two sources. Figure 2a and Figure 2b The near-field measurement wavelets of source 1 and source 2 of the survey line are displayed separately, that is, the wavelets of the two sources are displayed separately. It can be seen that the wavelets of the same source have certain changes during the construction process, and the differences between wavelets of different sources are even greater. Figure 3a and Figure 3b The following diagrams show the short-path profiles of the seismic data corresponding to the two sources of the survey line. The wave group characteristics of the two seismic data in the profiles also show significant differences.

[0045] Existing technologies use a uniform source wavelet to optimize seismic data obtained from the same transmission from two sources. However, this fails to eliminate the adverse effects of variations in the source wavelet, leading to poor amplitude preservation and low resolution in the final seismic data processing results. Therefore, this invention provides a source-by-source wavelet optimization method based on near-field measurement wavelets, eliminating the adverse effects of source wavelet variations during acquisition on subsequent processing.

[0046] like Figure 4 As shown, in one embodiment of the source-by-source wavelet optimization processing method based on near-field measurement wavelet of the present invention, the following steps are included:

[0047] S1. Obtain the near-field measurement wavelets corresponding to each emission of multiple seismic sources in the target work area. Each emission of each seismic source corresponds to multiple near-field measurement wavelets in different directions.

[0048] In this embodiment, the target work area refers to a certain survey line, along which multiple seismic sources emit source wavelets to excite the corresponding strata. Near-field measurement wavelets refer to wavelets directly observed near the seismic source, measured by sensors placed near the source. Several hydrophones and marine seismic exploration instruments are deployed in the near-field region of the seismic source, such as... Figure 5 As shown, for each shot fired from a seismic source, several near-field measurement wavelets can be recorded simultaneously and independently, each with a different direction.

[0049] S2. Synthesize the multiple near-field measurement wavelets emitted each time to obtain the corresponding direction-independent near-field wavelets.

[0050] like Figure 6 As shown, in this embodiment, all near-field measurement wavelets corresponding to each shot are linearly superimposed to obtain a near-field wavelet that is independent of direction. Other synthesis methods can also be used in other embodiments.

[0051] S3. Remove the interference waves from each near-field wavelet to obtain the corresponding source wavelet.

[0052] Specifically, due to environmental factors such as sea conditions, in addition to the source wavelet emitted by the earthquake source, there will also be interference waves in the near-field measurement wavelet. Removing the interference waves can make the measurement more closely resemble the actual source wavelet emitted by the earthquake source.

[0053] In one embodiment, step S3, removing interference waves from each near-field wavelet, includes performing ghost wave removal processing on each near-field wavelet separately. The interference waves in this embodiment include, but are not limited to, source ghost waves and cable ghost waves. Technicians can select any suitable ghost wave suppression technique based on the specific conditions of the exploration area data. The processing result is as follows: Figure 7 As shown, the near-field wavelet changes after being affected by the ghost wave. By removing the ghost wave and suppressing the sidelobes generated by the ghost wave, the near-field wavelet can be made closer to the true source wavelet.

[0054] Understandably, if the seabed reflection time is greater than the length of a source wavelet, the near-field wavelet suppressed by the ghost wave can be used as the source wavelet. When the target area is in shallow water, and the seabed reflection time is less than or equal to the length of a source wavelet during the detection process, refer to... Figure 8a The near-field measurement wavelet collected and recorded contained seabed reflected waves, causing waveform distortion. Seabed reflected waves are waves reflected back from the seabed from the signal emitted by the seismic source; after suppressing ghost waves, seabed reflected waves still need to be processed.

[0055] In one embodiment, when the seabed reflection time of the target work area is less than or equal to the length of the source wavelet, after performing ghost wave removal processing on each near-field wavelet, the method further includes performing seabed reflection removal processing on each near-field wavelet.

[0056] Specifically, the process of removing seabed reflections for each near-field wavelet includes: acquiring near-channel seismic data for each transmission to establish a corresponding noise model; and removing seabed reflections from each near-field wavelet using an adaptive subtraction method based on the corresponding noise model.

[0057] In this embodiment, the near-field seismic data refers to the seismic data between the source excitation point and the near-field measurement wavelet observation point (i.e., the sensor location) during the exploration process. Each source emission generates corresponding near-field seismic data. A noise model is established for each emission based on the near-field seismic data. An adaptive filter is designed, the error between the filter output and the noise model is calculated, and the filter parameters are dynamically adjusted based on the error until a satisfactory error level is reached. The adjusted filter is then applied to the corresponding near-field wavelet to remove noise components, resulting in the following... Figure 8b The processing results shown are used as the source wavelets for each emission. Source-by-source de-submarine reflection processing eliminates the influence of submarine reflection on the near-field wavelet morphology, resulting in more accurate source wavelets.

[0058] S4. Establish a standard wavelet based on all source wavelets.

[0059] Specifically, the average wavelet of all source wavelets is calculated, and the standard wavelet is obtained after bubble removal and zero-phase processing.

[0060] After summing all source wavelets and averaging them, an average wavelet is obtained. This average wavelet is then debubbled and zero-phased to obtain a standard wavelet, which is the desired wavelet in matched filtering. Figure 9 As shown, the phase spectrum of the standard wavelet is zero, and the amplitude spectrum curve is smooth within the range of 4-8 Hz without significant jitter. In other embodiments, the average wavelet can be further processed.

[0061] S5. Perform filtering and matching on the source wavelet corresponding to each emission and the standard wavelet to obtain the corresponding matched filter operator.

[0062] The source wavelet contains a bubble effect and is a non-zero phased wavelet. By sequentially filtering and matching the source wavelet of each source using a standard wavelet, a matched filter operator for each source is obtained (see reference). Figure 10 This process achieves the suppression of bubble response and zero-phase conversion. For specific filtering and matching steps, please refer to existing technologies.

[0063] S6. Optimize the seismic data of the target work area based on the matched filter operator of each source for each emission.

[0064] Specifically, the seismic data is debubbled based on various matched filter operators.

[0065] Seismic data, also known as seismic information, is typically acquired by geophones. This embodiment uses matched filtering operators for each shot to perform deconvolution operations on the corresponding seismic data, thereby effectively suppressing the bubble effect and improving the resolution and signal-to-noise ratio of the seismic data.

[0066] Following the debubbling process, the seismic data is further subjected to zero-phase processing based on various matched filter operators. In this embodiment, the matched filter operators for each shot are used to perform convolution operations on the corresponding seismic data, transforming the wavelets in the seismic data into zero-phase wavelets. A comparison of the profiles of the superimposed seismic data from each source before and after the debubbling and zero-phase optimization processes is provided by reference to... Figure 11a and Figure 11b It can be seen that after removing bubbles and zeroing phase, the relative relationship of amplitude energy changes significantly, and the seismic reflection structure is clearer under strong phase.

[0067] This invention does not require well logging data or far-field wavelets. It only needs to collect near-field measurement wavelets recorded during the acquisition process for each shot. By superimposing multiple near-field measurement wavelets measured source by source, the independent near-field wavelet corresponding to each shot record is obtained. Combined with ghost wave suppression, removal of seafloor reflection interference, and matched shaping filtering, the seismic data is optimized for each source using the corresponding matched filtering operator. This solves the differences in wavelet characteristics between different sources and between multiple emissions from the same source, especially the differences between different sources. It can obtain seismic data that accurately reflects the spatial variation of source wavelets, improves wavelet consistency, makes bubble suppression more reasonable, has higher zero-phase accuracy, and produces better wave group characteristics and higher resolution in the profile. This improves the interpretability, amplitude preservation, and resolution of seismic data, providing reliable basic data for subsequent data processing steps.

[0068] contrast Figure 12a and Figure 12b The autocorrelation comparison shows that the consistency of wavegroup characteristics in the seismic data has significantly improved. (Comparison) Figure 13a and Figure 13b The source-by-source wavelet optimization method based on near-field measurement wavelet of this invention produces better wave group characteristics and slightly higher resolution than the conventional method that uses uniform far-field wavelet optimization processing to obtain the superimposed profile. According to... Figure 14 It can be seen that the frequency component energy at the low-frequency end of the superimposed profile obtained by the technology of this invention is slightly lower, indicating that its bubble suppression effect is slightly better. According to Figure 15 It can be seen that the residual phase curve of the superimposed profile obtained by the technology of this invention is closer to zero, indicating that its zero-phase effect is slightly better. According to Figure 16 It can be seen that the post-stack time-shift profile of the present invention has a high degree of matching with the synthetic record, good consistency of wave group characteristics, good zero-phase effect of the profile, and high resolution, laying a good foundation for data interpretation.

[0069] In one embodiment, step S3 is followed by: synthesizing the source wavelet and the source ghost wave of each transmission to obtain the far-field wavelet of each transmission.

[0070] By adding the corresponding source ghost wave to the near-field wavelet obtained in step S3, the far-field wavelet of this emission can be obtained. The far-field wavelet obtained using the near-field wavelet (reference) Figure 17a ) and far-field wavelet obtained by actual measurement in deep water (reference) Figure 17b By comparison, it can be seen that the calculated and measured far-field wavelet characteristics are very close, according to Figure 18 The low-frequency band of the spectrum is also quite close, so in the absence of actual measured far-field wavelets, this invention can also be used to obtain far-field wavelets.

[0071] In one embodiment of the computer program product of the present invention, the computer program product includes a computer program that, when executed by a processor, implements the source-by-source wavelet optimization processing method based on near-field measurement wavelet of any of the above embodiments.

[0072] In one embodiment, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, NAND flash memory, etc. In one embodiment, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0073] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0074] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic fields, and infrared radiation. Computer devices can convert the signals carrying computer programs into digital signals, thereby executing the computer programs. When a computer program runs on a computer device, its code causes the computer device to execute (more specifically, the processor of the computer device to execute) the method steps of various exemplary embodiments of this disclosure.

[0075] In one embodiment of the computer device of the present invention, it includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the source-by-source wavelet optimization processing method based on near-field measurement wavelets disclosed in any of the above embodiments. The computer device may be, but is not limited to, a laptop computer, an edge computer, a server, a workstation, or an industrial control computer.

[0076] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A source-by-source wavelet optimization processing method based on near-field measurement wavelets, characterized in that, Includes the following steps: S1. Obtain the near-field measurement wavelets corresponding to each emission of multiple seismic sources in the target work area. Each emission of each seismic source corresponds to multiple near-field measurement wavelets in different directions. S2. Synthesize the multiple near-field measurement wavelets emitted each time to obtain the corresponding direction-independent near-field wavelets; S3. Remove the interference waves from each of the near-field wavelets to obtain the corresponding source wavelets; S4. Establish a standard wavelet based on all source wavelets; S5. Perform filtering and matching on the source wavelet corresponding to each emission and the standard wavelet respectively to obtain the corresponding matching filter operator; S6. Optimize the seismic data of the target work area based on the matched filter operator emitted by each seismic source each time; Step S4 includes: The average wavelet of all source wavelets is calculated, and the standard wavelet is obtained after bubble removal and zero-phase processing.

2. The source-by-source wavelet optimization processing method based on near-field measurement wavelets according to claim 1, characterized in that, Step S3, which involves removing interference waves from each of the near-field wavelets, includes: Each of the near-field wavelets is subjected to ghosting processing.

3. The source-by-source wavelet optimization processing method based on near-field measurement wavelets according to claim 2, characterized in that, When the seabed reflection time of the target work area is less than or equal to the length of the source wavelet, the process of performing ghosting on each of the near-field wavelets further includes: Each of the near-field wavelets is subjected to seabed reflection removal processing.

4. The source-by-source wavelet optimization processing method based on near-field measurement wavelet according to claim 3, characterized in that, The step of performing seabed reflection removal processing on each of the near-field wavelets includes: Acquire near-track seismic data for each launch to establish a corresponding noise model; Based on the corresponding noise model, the seabed reflection is removed from each near-field wavelet using an adaptive subtraction method.

5. The source-by-source wavelet optimization processing method based on near-field measurement wavelets according to claim 1, characterized in that, Step S6 includes: The seismic data is bubble-removing processed based on various matched filter operators.

6. The source-by-source wavelet optimization processing method based on near-field measurement wavelet according to claim 5, characterized in that, The degassing process also includes: The seismic data is zero-phased based on each matched filter operator.

7. The source-by-source wavelet optimization processing method based on near-field measurement wavelet according to claim 1, characterized in that, Step S3 is followed by: The far-field wavelet of each emission is obtained by synthesizing the source wavelet and the source ghost wavelet from each emission.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the source-by-source wavelet optimization processing method based on near-field measurement wavelets as described in any one of claims 1 to 7.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the source-by-source wavelet optimization processing method based on near-field measurement wavelets as described in any one of claims 1-7.

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