Layer adaptive matching interpretation method, equipment and medium based on multi-period seismic data volume

Abstract

The present invention belongs to the field of geophysical exploration and discloses a method for adaptive horizon matching interpretation based on multi-period seismic data. This method performs a first round of horizon matching interpretation and a second round of horizon matching interpretation on corresponding horizons in old and new seismic data, thereby completing adaptive horizon matching interpretation based on multi-period seismic data. This method fully utilizes the previous achievements in horizon interpretation using old seismic data, significantly improving the accuracy and efficiency of horizon interpretation. The present invention is suitable for horizon interpretation in seismic exploration.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration and relates to a stratigraphic interpretation method, specifically a stratigraphic adaptive matching interpretation method, equipment, and medium based on multi-period seismic data volumes. Background Technology

[0002] In seismic exploration, stratigraphic interpretation is the foundation and key to the interpretation of seismic data. Accurate and reasonable stratigraphic interpretation is related to the accuracy of structural maps and plays a vital role in understanding underground structures and the exploration and development of oil and gas.

[0003] In recent years, various oilfields in China have conducted secondary or tertiary 3D seismic data acquisitions, accumulating a large amount of 3D seismic data acquired at different times. Furthermore, seismic data acquired in the same period may also involve secondary or tertiary processing. Generally, earlier acquired or processed seismic data is referred to as "old seismic data," while newly acquired or processed seismic data is called "new seismic data." Old seismic data contains a wealth of previous stratigraphic interpretation methods and results, with significant reference and application value. However, due to differences in time, energy, bandwidth, and phase between old and new seismic data, stratigraphic matching interpretation is necessary to fully utilize the old seismic data.

[0004] Currently, there are three main methods for stratigraphic interpretation: manual stratigraphic interpretation, automatic stratigraphic interpretation, and intelligent stratigraphic interpretation. Manual stratigraphic interpretation involves interpreting stratigraphic data on a two-dimensional seismic profile, using peaks, troughs, or zero phase in the seismic data to determine the phase axis. This method relies heavily on the interpreter's subjective experience, resulting in a large workload, low efficiency, and high ambiguity. Automatic stratigraphic interpretation interprets seismic data based on the kinematic and dynamic characteristics of earthquakes, using methods such as waveform similarity and correlation. However, subsurface conditions are complex and variable, and seismic data is often affected by noise, multiples, and surface interference, which reduces the accuracy of automatic stratigraphic interpretation. Intelligent stratigraphic interpretation, limited by training levels and network structure algorithms, also struggles to guarantee accuracy.

[0005] The aforementioned problems of low accuracy and low efficiency in stratigraphic matching interpretation have led to low utilization of previous stratigraphic interpretation results, which seriously hinders seismic exploration work, making the research on related technologies extremely urgent. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention aims to provide a layer-adaptive matching interpretation method based on multi-period seismic data volumes, thereby improving the accuracy and efficiency of layer interpretation for new seismic data.

[0007] Another objective of this invention is to provide computer equipment and media for running or storing computer programs that execute the above-described adaptive matching interpretation method based on multi-period seismic data volumes.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] For the corresponding layers in the old and new earthquake data, a first round of layer matching interpretation and a second round of layer matching interpretation are performed, thus completing the adaptive layer matching interpretation based on multi-period earthquake data volumes.

[0010] New and old earthquake data are obtained through preliminary data processing used to match the data.

[0011] The layer matching interpretation involves calculating the correction within the correction window for multiple seismic traces in both new and old seismic data. For the middle seismic trace, the average correction is added to the middle seismic trace to obtain the corrected middle seismic trace. For the other seismic traces besides the middle seismic trace, the correction is added to each of the other seismic traces to obtain the corrected remaining seismic traces.

[0012] Multiple seismic channels refer to three or more odd-numbered seismic channels.

[0013] When there are three seismic channels, the middle seismic channel is the second seismic channel, and the remaining seismic channels refer to the first and third seismic channels.

[0014] When there are 5 seismic channels, the middle seismic channel is the third seismic channel, and the remaining seismic channels refer to the first, second, fourth, and fifth seismic channels.

[0015] As a limitation of this invention, the preliminary data processing includes matching processing of old and new seismic data networks, shaping processing of old and new seismic data, and interpolation processing of new seismic data.

[0016] As a further limitation of the present invention, the matching process of new and old seismic data networks involves transforming or interpolating the new and old seismic data networks in three dimensions to obtain new and old seismic data with matching line numbers and trace numbers in the network.

[0017] Seismic data network refers to a grid of lines consisting of line numbers and track numbers that indicates the location of seismic work areas.

[0018] The three-dimensional coordinate transformation method refers to the transformation between the geodetic coordinate system and the relative coordinate system, and the re-division of the seismic traces based on the transformed relative coordinate system, and the calculation of the line number and trace number of the seismic traces in the relative coordinate system.

[0019] Interpolation methods refer to interpolating large-interval survey networks to reduce the network intervals, and then extracting survey networks of a specified interval from the small-interval networks according to actual needs.

[0020] Line number refers to the seismic survey line recorded in the Y direction of the seismic data.

[0021] The track number refers to the seismic survey line recorded in the X direction of the seismic data.

[0022] As a further limitation of the present invention, the new and old seismic data shaping process involves passing the new and old seismic data through a filter to obtain new and old seismic data with matched quality. The quality matching includes matching of time, energy, bandwidth and phase.

[0023] As a further limitation of the present invention, the interpolation processing of the new seismic data is to perform linear interpolation processing on each channel of the new seismic data at a sampling interval of 0.1ms.

[0024] The sampling interval refers to the interval at which seismic data is recorded in a discrete manner in the time domain.

[0025] As a limitation of this invention, the first round of stratigraphic matching interpretation includes the following steps performed sequentially.

[0026] S1. Match the old seismic data layers to the new seismic data. Starting from the beginning of the line number or trace number direction, select 2n+1 seismic traces for each of the old and new seismic data. Use the large-scale correction window length W as the window length of the correction time window, so that the (n+1)th seismic trace in the 2n+1 seismic traces is located at the window length of W / 2, where n is a natural number greater than 0.

[0027] The calibration window refers to the time range within which seismic data is extracted.

[0028] S2. The large-scale correction values ​​for each seismic trace in the old and new seismic data are calculated using the cross-correlation algorithm and denoted as ΔT1, ΔT2, ΔT3, ..., ΔT 2n-1 ΔT 2n and ΔT 2n+1 Calculate the average value ΔT of the large-scale correction. a Adding this to the (n+1)th seismic trace data in the old earthquake data yields the (n+1)th seismic trace after large-scale correction.

[0029] Cross-correlation algorithm refers to an algorithm for calculating the correlation between two seismic signals.

[0030] S3. Move forward one path and repeat steps S1-S2 until all large-scale corrected intermediate seismic traces are obtained.

[0031] S4. Add the corresponding large-scale correction to the first to nth seismic traces and the nth to penultimate seismic traces in the old seismic data to obtain the remaining seismic traces after large-scale correction.

[0032] All the intermediate seismic traces after large-scale correction and the remaining seismic traces after large-scale correction constitute the first round of horizon matching data.

[0033] As a further limitation of the present invention, the large-scale correction window length W is 50-100ms.

[0034] As a limitation of this invention, the second round of stratigraphic matching interpretation includes the following steps performed sequentially.

[0035] P1. Match the first round of horizon matching data to the new seismic data. For the horizon matching between the old and new seismic data, start from the starting point of the line number or trace number direction respectively, and select 2n+1 seismic traces for each. Use the small-scale correction window length S as the window length of the correction time window, so that the (n+1)th seismic trace in the 2n+1 seismic traces is located at the window length of S / 2, where n is a natural number greater than 0.

[0036] P2. The small-scale corrections for the first-round horizon matching data and the new seismic data are calculated using the cross-correlation algorithm and denoted as Δt1, Δt2, Δt3, ..., Δt 2n-1 Δt 2n and Δt 2n+1 The average value of the small-scale correction, Δta, is calculated and added to the (n+1)th seismic trace data in the first round of layer matching data to obtain the (n+1)th seismic trace after small-scale correction.

[0037] P3. Move forward one path and repeat steps P1-P2 until all small-scale corrected intermediate seismic traces are obtained.

[0038] P4. Add the corresponding small-scale correction to the first to nth seismic traces and the nth to penultimate seismic traces in the first round of horizon matching data to obtain the remaining seismic traces after small-scale correction.

[0039] All the intermediate seismic traces after small-scale correction and the remaining seismic traces after small-scale correction constitute the second round of horizon matching data.

[0040] As a further limitation of the present invention, the small-scale correction window length S is 20-50ms.

[0041] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the layer adaptive matching interpretation method based on multi-period seismic data volume as described in any of the above technical solutions.

[0042] The present invention also provides a computer-readable storage medium having a computer program that executes the layer adaptive matching interpretation method based on multi-period seismic data volume as described in any of the above technical solutions.

[0043] By adopting the above-described technical solution, the beneficial effects achieved by this invention compared to the prior art are as follows:

[0044] (1) In the early data processing of this invention, the numbering of the old and new seismic data networks on the same line number and the same trace number is consistent, which is beneficial to coordinate positioning during the later layer interpretation.

[0045] (2) The shaping process in this invention effectively eliminates the differences in energy, phase and other aspects between the old and new seismic data, thereby making the two sets of data more consistent.

[0046] (3) In this invention, a sampling interval of 0.1ms is used when interpolating new seismic data, which can effectively reduce the error of correction and improve the accuracy of layer interpretation.

[0047] (4) In this invention, the first round of horizon matching interpretation has a higher degree of matching and higher horizon interpretation accuracy compared with the horizon interpretation of old earthquake data directly on new earthquake data, and the second round of horizon matching interpretation further improves the horizon interpretation accuracy.

[0048] (5) This invention innovatively develops a layer adaptive matching interpretation method, which makes full use of the results of previous layer interpretation. Based on the matching and shaping of line numbers and trace numbers of new and old seismic data, the maximum cross-correlation number of the same layer under a certain time window is calculated to obtain the correction amount of the layer migration matching. By analogy, the correction amount in all overlapping areas of new and old seismic data can be obtained, thereby realizing the migration matching interpretation of old seismic data layers on new seismic data layers, and greatly improving the layer interpretation efficiency. Attached Figure Description

[0049] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0050] Figure 1 This is a comparison of seismic profiles before and after the reshaping process in Embodiment 1 of the present invention. Figure 1 Figure (a) in the figure is a seismic profile based on old earthquake data. Figure 1 Figure (b) shows the seismic profile of the new seismic data before shaping. Figure 1 Figure (c) in the figure is a seismic profile of the new seismic data after reshaping.

[0051] Figure 2 This is a cross-sectional view of the new seismic data after linear interpolation processing in Embodiment 1 of the present invention;

[0052] Figure 3 This is a cross-sectional view showing the selection of large-scale correction window lengths for new and old seismic data in Embodiment 1 of the present invention. Figure 3 Figure (a) in the figure shows the stratigraphic sequence of the old seismic data and the selection of the large-scale correction window length for the old seismic data. Figure 3Figure (b) in the figure is a cross-sectional view of the selection of the large-scale correction window length between the old seismic data and the new seismic data;

[0053] Figure 4 This is a schematic diagram illustrating the calculation of the correction amount using the cross-correlation algorithm in Embodiment 1 of the present invention. Figure 4 Figure (a) in the figure is a large-scale correction window profile of old seismic data. Figure 4 Figure (b) in the figure is a large-scale correction window profile of the new seismic data. Figure 4 Figure (c) in the diagram is a schematic diagram of the cross-correlation algorithm for calculating the correction amount;

[0054] Figure 5 This is a first-round horizon matching profile of the new seismic data in Embodiment 1 of the present invention;

[0055] Figure 6 This is a profile view selected for the small-scale correction window of the first round of horizon matching data and new seismic data in Embodiment 1 of the present invention. Figure 6 Figure (a) in the figure shows the stratigraphic sequence of the old seismic data and the selection of the small-scale correction window length for the old seismic data. Figure 6 Figure (b) in the figure is a cross-sectional view of the small-scale correction window length selection between the first round of horizon matching data and the new seismic data;

[0056] Figure 7 This is a second-round horizon matching profile of the new seismic data in Embodiment 1 of the present invention;

[0057] Figure 8 This is a cross-sectional view illustrating the matching of multiple layers in Embodiment 1 of the present invention. Detailed Implementation

[0058] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the described embodiments are only used to explain the present invention and do not limit the present invention.

[0059] Example 1: A Stratum Adaptive Matching Interpretation Method Based on Two Seismic Data Volumes (Old and New)

[0060] This embodiment presents a layer adaptive matching interpretation method based on two seismic data volumes, old and new. It uses five seismic traces as a unit for layer adaptive matching interpretation, and the specific method includes the following steps performed sequentially:

[0061] 1) The old and new seismic data networks are transformed or interpolated in three dimensions to obtain the old and new seismic data with matching line numbers and trace numbers in the network.

[0062] 2) The old and new earthquake data are processed by filters to obtain old and new earthquake data with consistent energy domain ranges. (See [link]) Figure 1 .Depend on Figure 1 It can be seen that, Figure 1In Figure (a), the energy range of the old earthquake data is -300 to 300. Figure 1 In Figure (b), the energy range of the new earthquake data is -180 to 180, showing significant energy differences. Figure 1 In Figure (c), the energy range of the new seismic data after shaping is -300 to 300. Therefore, the shaping process makes the new and old seismic data more consistent in energy, which is more conducive to the calculation of subsequent layer correction.

[0063] 3) New seismic data are linearly interpolated channel by channel at a sampling interval of 0.1 ms. (See below) Figure 2 .

[0064] 4) Match the layers of the old seismic data to the new seismic data. The matching layers should start from the beginning of the line number or trace number direction, selecting five seismic traces (see below). Figure 3 The large-scale correction window length is 50 ms, and the third seismic trace in the five seismic traces is located 25 ms above and below it. Figure 3 It can be seen that, Figure 3 In Figure (a), the period from 1100ms to 1160ms represents a large-scale correction window of 50ms for old seismic data. Figure 3 In Figure (b), the period from 1100ms to 1160ms represents the large-scale correction window length of the new seismic data, which is 50ms. Therefore, the large-scale correction window length of the new and old seismic data remains consistent in their trace number directions.

[0065] 5) The large-scale correction values ​​for each seismic trace in the old and new seismic data were calculated using the cross-correlation algorithm. (See attached figure.) Figure 4 These are denoted as ΔT1, ΔT2, ΔT3, ΔT4, and ΔT5, respectively. The average large-scale correction ΔTa is calculated and added to the third seismic trace data in the old earthquake data to obtain the large-scale corrected third seismic trace. Figure 4 It can be seen that, Figure 4 Figure (a) in the middle Figure 4 The time positions of the layers in Figure (b) are the same. Figure 4 In Figure (b), the older earthquake data is significantly higher than the newer earthquake data. Figure 4 In Figure (c), the correction value after cross-correlation calculation is positive. Therefore, the old seismic traces need to have the correction value increased in order to match the new seismic data, especially the fifth seismic trace.

[0066] 6) Move forward one path and repeat steps 4-5. Select five seismic traces. Given a large-scale correction window length, the third seismic trace in the five seismic traces is located at 1 / 2 window length. Use the cross-correlation algorithm to calculate the large-scale correction amount of each seismic trace in the new and old seismic data, and calculate the average large-scale correction amount. Add it to the corresponding intermediate seismic trace data in the old seismic data to obtain the corrected seismic trace. Repeat this process until all large-scale corrected intermediate seismic traces are obtained.

[0067] 7) Add the corresponding large-scale correction to the first, second, penultimate, and penultimate seismic traces in the old seismic data to obtain the remaining seismic traces after large-scale correction.

[0068] 8) All the intermediate seismic traces after large-scale correction and the remaining seismic traces after large-scale correction constitute the first round of horizon matching data, see [link to data]. Figure 5 .Depend on Figure 5 It can be seen that the old earthquake data layers are slightly higher and slightly lower than the new earthquake data layers, resulting in low matching accuracy. However, the first round of layer matching data after correction matches the new earthquake data layers better.

[0069] 9) Match the first round of horizon matching data onto the new seismic data. For the horizon matching between the old and new seismic data, start from the beginning of the line number or trace number direction, selecting five seismic traces from each direction. (See attached image) Figure 6 The small-scale correction window length is 20 ms, and the third seismic trace in the five seismic traces is located at a window length of 10 ms. Figure 6 It can be seen that, Figure 6 In Figure (a), the data from 1110ms to 1130ms represents the old seismic data horizon and the small-scale correction window length of 20ms for the old seismic data. Figure 6 Figure (b) shows the small-scale correction window length of 20ms between the first round of horizon matching data and the new seismic data, from 1110ms to 1130ms. Therefore, the small-scale correction window lengths of the first round of horizon matching data and the new seismic data are consistent in their trace number directions.

[0070] 10) The small-scale corrections of the first-round horizon matching data and the new seismic data are calculated using the cross-correlation algorithm and denoted as Δt1, Δt2, Δt3, Δt4 and Δt5 respectively. The average value of the small-scale corrections, Δta, is calculated and added to the third seismic trace data in the first-round horizon matching data to obtain the third seismic trace after small-scale correction.

[0071] 11) Move forward one path and repeat steps 9-10. Select five seismic traces, give a small-scale correction window length, and place the third seismic trace in the five seismic traces at 1 / 2 window length. Use the cross-correlation algorithm to calculate the small-scale correction amount of each seismic trace in the new and old seismic data, and calculate the average small-scale correction amount. Add it to the corresponding intermediate trace seismic trace data in the first round of layer matching data to obtain the corrected seismic trace. Repeat this process until all small-scale corrected intermediate seismic traces are obtained.

[0072] 12) Add the corresponding small-scale correction to the first seismic trace data, second seismic trace data, penultimate seismic trace data and penultimate seismic trace data in the first round of horizon matching data to obtain the remaining seismic traces after small-scale correction.

[0073] 13) All the intermediate seismic traces after small-scale correction and the remaining seismic traces after small-scale correction constitute the second round of horizon matching data, see [link to data]. Figure 7 .Depend on Figure 7 It can be seen that the accuracy of the second round of stratum matching data is further improved compared to the first round of stratum matching data.

[0074] 14) Following steps 4)-13), complete the matching for other layers. See explanation below. Figure 8 .Depend on Figure 8 It can be seen that the layer matching interpretation of layers T1, T2, and T3 has high accuracy.

[0075] The method described in this embodiment enables rapid matching of old seismic data horizons with new seismic data horizons. It can fully utilize the horizon interpretation results of previous researchers in old seismic data. Based on the matching and shaping processing of line numbers and trace numbers of old and new seismic data, the maximum cross-correlation number of the same horizon under a certain time window is calculated to obtain the correction amount for the horizon migration matching. By analogy, the correction amount in all overlapping areas of old and new seismic data can be obtained, thereby realizing the migration matching interpretation of old seismic data horizons with new seismic data horizons, which significantly improves the accuracy and efficiency of horizon interpretation.

[0076] Example 2: A Stratum Adaptive Matching Interpretation Method Based on Two Seismic Data Volumes (Old and New)

[0077] This embodiment presents a layer-adaptive matching interpretation method based on two seismic data volumes, old and new. It uses seven seismic traces as a unit for layer-adaptive matching interpretation, and the specific method includes the following steps performed sequentially:

[0078] Steps 1)-3) are the same as in Example 1.

[0079] 4) Match the old seismic data layers to the new seismic data. The matching layers for the old and new seismic data start from the starting point of the line number or trace number direction, select seven seismic traces, and set the large-scale correction window length to 80ms. The fourth seismic trace in the seven seismic traces is located at the 40ms window length.

[0080] 5) The large-scale corrections for each seismic trace in the new and old earthquake data are calculated using the cross-correlation algorithm and denoted as ΔT1, ΔT2, ΔT3, ΔT4, ΔT5, ΔT6 and ΔT7 respectively. The average large-scale correction ΔTa is calculated and added to the fourth seismic trace data in the old earthquake data to obtain the fourth seismic trace after large-scale correction.

[0081] 6) Move forward one track and repeat steps 4-5. Select seven seismic traces. Given a large-scale correction window length, the fourth seismic trace in the seven seismic traces is located at 1 / 2 window length. Use the cross-correlation algorithm to calculate the large-scale correction amount of each seismic trace in the new and old seismic data, and calculate the average large-scale correction amount. Add it to the corresponding intermediate seismic trace data in the old seismic data to obtain the corrected seismic trace. Repeat this process until all large-scale corrected intermediate seismic traces are obtained.

[0082] 7) Add the corresponding large-scale correction to the first, second, third, third-to-last, second-to-last, and last seismic traces in the old seismic data to obtain the remaining seismic traces after large-scale correction.

[0083] 8) All the intermediate seismic traces after large-scale correction and the remaining seismic traces after large-scale correction constitute the first round of horizon matching data.

[0084] 9) Match the first round of horizon matching data to the new seismic data. The horizons for matching the old and new seismic data start from the starting point of the line number or trace number, and seven seismic traces are selected for each. The small-scale correction window length is 30ms, and the fourth seismic trace in the seven seismic traces is located at the 15ms window length.

[0085] 10) The small-scale correction values ​​of the first-round horizon matching data and the new seismic data are calculated using the cross-correlation algorithm and denoted as Δt1, Δt2, Δt3, Δt4, Δt5, Δt6 and Δt7 respectively. The average value of the small-scale correction value Δta is calculated and added to the fourth seismic trace data in the first-round horizon matching data to obtain the fourth seismic trace after small-scale correction.

[0086] 11) Move forward one path and repeat steps 9-10. Select seven seismic traces, give a small-scale correction window length, and place the fourth seismic trace in the seven seismic traces at 1 / 2 window length. Use the cross-correlation algorithm to calculate the small-scale correction amount of each seismic trace in the new and old seismic data, and calculate the average small-scale correction amount. Add it to the corresponding intermediate trace seismic trace data in the first round of layer matching data to obtain the corrected seismic trace. Repeat this process until all small-scale corrected intermediate seismic traces are obtained.

[0087] 12) Add the corresponding small-scale correction to the first, second, third, third-to-last, second-to-last, and last seismic traces in the first round of horizon matching data to obtain the remaining seismic traces after small-scale correction.

[0088] 13) All the intermediate seismic traces after small-scale correction and the remaining seismic traces after small-scale correction constitute the second round of horizon matching data.

[0089] 14) Following steps 4)-13), complete the matching and interpretation of other layers.

[0090] Example 3: A computer device

[0091] This embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, to implement the layer adaptive matching interpretation method based on multi-period seismic data volumes of Embodiment 1 or 2.

[0092] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0093] The processor may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The processor is used to execute computer-readable instructions stored in the memory.

[0094] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0095] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0096] Example 4: A computer-readable storage medium

[0097] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the layer adaptive matching interpretation method based on multi-period seismic data volumes as described in Embodiment 1 or 2.

[0098] The computer-readable storage medium stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods of the foregoing embodiments are performed.

[0099] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0100] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A layer-adaptive matching interpretation method based on multi-period seismic data volumes, characterized in that, By performing the first and second rounds of layer matching interpretation on the corresponding layers in the old and new earthquake data, adaptive layer matching interpretation based on multi-period earthquake data volumes is achieved. The old and new earthquake data are obtained through preliminary data processing used to match the data; The layer matching interpretation involves calculating the correction amount within the correction window for multiple seismic traces in both new and old seismic data. For the middle seismic trace, the average correction amount is added to the middle seismic trace to obtain the corrected middle seismic trace. For the other seismic traces besides the middle seismic trace, the correction amount for the other seismic traces is added to each of the other seismic traces to obtain the corrected other seismic traces. The term "multiple seismic channels" refers to three or more odd-numbered seismic channels. The first round of stratigraphic matching interpretation includes the following steps performed sequentially. S1. Match the old seismic data layers to the new seismic data. Starting from the beginning of the line number or trace number direction, select 2n+1 seismic traces for each of the old and new seismic data. Use the large-scale correction window length W as the window length of the correction time window, so that the (n+1)th seismic trace in the 2n+1 seismic traces is located at the window length of W / 2, where n is a natural number greater than 0. S2. The large-scale correction values ​​for each seismic trace in the old and new seismic data are calculated using the cross-correlation algorithm and denoted as ΔT1, ΔT2, ΔT3, ..., ΔT 2n-1 ΔT 2n and ΔT 2n+1 Calculate the average value ΔT of the large-scale correction. a Add the (n+1)th seismic trace data from the old earthquake data to obtain the (n+1)th seismic trace after large-scale correction. S3. Move forward one step and repeat steps S1-S2 until all large-scale corrected intermediate seismic traces are obtained; S4. Add the corresponding large-scale correction to the first to nth seismic traces and the nth to the last seismic traces in the old seismic data to obtain the remaining seismic traces after large-scale correction. All the intermediate seismic traces after large-scale correction and the remaining seismic traces after large-scale correction constitute the first round of horizon matching data. The second round of stratigraphic matching interpretation includes the following steps performed sequentially: P1. Match the first round of horizon matching data to the new seismic data. For the horizon matching between the old and new seismic data, start from the starting point of the line number or trace number direction respectively, and select 2n+1 seismic traces for each. Use the small-scale correction window length S as the window length of the correction time window, so that the (n+1)th seismic trace in the 2n+1 seismic traces is located at the window length of S / 2, where n is a natural number greater than 0. P2. The small-scale corrections for the first-round horizon matching data and the new seismic data are calculated using the cross-correlation algorithm and denoted as Δt1, Δt2, Δt3, ..., Δt 2n-1 , Δt 2n and Δt 2n+1 Calculate the average value Δt of the small-scale correction. a Add the (n+1)th seismic trace data from the first round of horizon matching data to obtain the (n+1)th seismic trace after small-scale correction; P3. Move forward one step and repeat steps P1-P2 until all small-scale corrected intermediate seismic traces are obtained; P4. Add the corresponding small-scale correction to the first seismic trace data to the nth seismic trace data and the nth-to-last seismic trace data to the first seismic trace data in the first round of horizon matching data to obtain the remaining seismic traces after small-scale correction. The intermediate seismic traces after small-scale correction and the remaining seismic traces after small-scale correction constitute the second round of horizon matching data.

2. The layer adaptive matching interpretation method based on multi-period seismic data volume according to claim 1, characterized in that, The preliminary data processing includes matching processing of old and new seismic data networks, shaping processing of old and new seismic data, and interpolation processing of new seismic data.

3. The layer adaptive matching interpretation method based on multi-period seismic data volume according to claim 2, characterized in that, The matching process of the new and old seismic data networks involves transforming or interpolating the new and old seismic data networks in three dimensions to obtain new and old seismic data with matching line numbers and trace numbers in the network. The new and old earthquake data shaping process involves passing the new and old earthquake data through a filter to obtain new and old earthquake data with matched quality. The quality matching includes matching in terms of time, energy, bandwidth, and phase. The interpolation processing of the new seismic data involves linearly interpolating each channel of the new seismic data at a sampling interval of 0.1ms.

4. The layer adaptive matching interpretation method based on multi-period seismic data volumes according to claim 3, characterized in that, The large-scale correction window length W is 50-100ms.

5. The layer adaptive matching interpretation method based on multi-period seismic data volume according to claim 4, characterized in that, The small-scale correction window length S is 20-50ms.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the layer adaptive matching interpretation method based on multi-period seismic data volumes as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that executes the layer adaptive matching interpretation method based on multi-period seismic data volumes as described in any one of claims 1 to 5.

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