Horizon adaptive matching interpretation method and device based on multi-stage seismic data volume, and medium

Abstract

The invention belongs to the field of geophysical exploration, and discloses a horizon self-adaptive matching interpretation method based on a multi-stage seismic data volume, which is used for performing a first round of horizon matching interpretation and a second round of horizon matching interpretation on corresponding horizon in new and old seismic data so as to finish the horizon self-adaptive matching interpretation based on the multi-stage seismic data volume. According to the method, the horizon interpretation result of previous people in the old seismic data is fully utilized, and the horizon interpretation precision and efficiency are remarkably improved. The method is suitable for horizon interpretation in seismic exploration.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical exploration and relates to a layer interpretation method, in particular to a layer adaptive matching interpretation method, equipment and medium based on multi-period seismic data bodies. Background Art

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

[0003] In recent years, domestic oil fields have carried out secondary or tertiary 3D seismic data acquisition, accumulating a large amount of 3D seismic data collected in different periods. In addition, seismic data collected in the same period will also involve secondary and tertiary processing. Seismic data collected or processed earlier are usually called old seismic data, and in contrast, newly collected or newly processed seismic data are called new seismic data. Old seismic data contains a wealth of previous stratigraphic interpretation methods and results, which have great reference and application value. However, due to the differences in time, energy, bandwidth and phase between old and new seismic data, stratigraphic matching interpretation is required to fully utilize old seismic data.

[0004] At present, there are three main methods for horizon matching interpretation: manual horizon interpretation, automatic horizon interpretation and intelligent horizon interpretation. Manual horizon interpretation refers to the interpretation of horizons on two-dimensional seismic profiles, and the interpretation of event axes is completed based on the peaks, troughs or zero phases of seismic data. This method relies on the subjective experience of the interpreters, has a large workload, low work efficiency and strong multi-solution. Automatic horizon interpretation interprets seismic data based on the kinematic and dynamic characteristics of earthquakes, from the waveform similarity and correlation of seismic waves. However, underground conditions are complex and changeable, and seismic data are often subject to many interference factors such as noise, multiple waves, and surface layers. These interference factors reduce the accuracy of automatic horizon interpretation. Due to the limitations of training level, network structure algorithm, etc., the interpretation accuracy of intelligent horizon interpretation is also difficult to guarantee.

[0005] The problems of low accuracy and low efficiency in the above-mentioned stratigraphic matching interpretation have led to low utilization of previous stratigraphic interpretation results, which has seriously hindered seismic exploration work. The need for research on related technologies is extremely urgent. Summary of the invention

[0006] In order to solve the above deficiencies in the prior art, the present invention aims to provide a layer adaptive matching interpretation method based on multi-period seismic data volumes, so as to improve the accuracy and efficiency of the layer interpretation of new seismic data.

[0007] Another object of the present invention is to provide a computer device and a medium for implementing or storing a computer program for executing the above-mentioned layer adaptive matching interpretation method based on multi-phase seismic data volume.

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

[0009] The first and second rounds of horizon matching interpretation are performed on the corresponding horizons in the new and old seismic data, that is, the horizon adaptive matching interpretation based on multi-period seismic data volumes is completed.

[0010] The new and old seismic data are obtained through preliminary data processing to match the data.

[0011] The horizon matching interpretation is to calculate the correction amounts in the correction time window of multiple seismic traces in the new and old seismic data respectively, and then add the average correction amount to the middle seismic trace of the multiple seismic traces to obtain the corrected middle seismic trace; for the remaining seismic traces except the middle seismic trace, add the correction amounts of the remaining seismic traces to each of the remaining seismic traces to obtain the corrected remaining seismic traces.

[0012] Multi-channel seismic traces refer to seismic traces with an odd number of 3 or more.

[0013] When the multi-channel seismic channels consist of three seismic channels, the middle seismic channel is the second seismic channel, and the remaining seismic channels are the first and third seismic channels.

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

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

[0016] As a further limitation of the present invention, the matching process of the new and old seismic data networks is to transform or interpolate the new and old seismic data networks through three-dimensional coordinates to obtain new and old seismic data with matching line numbers and track numbers in the networks.

[0017] The seismic data survey network refers to the grid lines composed of line numbers and channel numbers that indicate the location of the seismic work area.

[0018] The three-dimensional coordinate conversion method refers to the conversion between the geodetic coordinate system and the relative coordinate system, and the redivision of the seismic channels according to the converted relative coordinate system, and the calculation of the line number and channel number of the seismic channels in the relative coordinate system.

[0019] The interpolation method refers to interpolating the large-interval measurement network to make the measurement network interval smaller, and then extracting the small-interval measurement network into the specified-interval measurement network according to actual needs.

[0020] The line number refers to the seismic survey line where the seismic data is recorded in the Y direction.

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

[0022] As a further limitation of the present invention, the shaping process of new and old seismic data is to pass the new and old seismic data through a filter to obtain new and old seismic data with quality matching, and 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 the new seismic data track by track at a sampling interval of 0.1 ms.

[0024] Sampling interval refers to the interval between recordings of seismic data in a discrete manner in the time domain.

[0025] As a limitation of the present invention, the first round of horizon matching interpretation includes the following steps performed in sequence:

[0026] S1. Match the old seismic data layer to the new seismic data. Start from the starting point of the line number or track number direction in the matching layer of the new and old seismic data, select 2n+1 seismic tracks respectively, and use the large-scale correction window length W as the window length of the correction time window, so that the n+1th seismic track in the 2n+1 seismic tracks is located at the window length of W / 2, where n is a natural number greater than 0.

[0027] The correction time window refers to the time range for extracting seismic data.

[0028] S2. The cross-correlation algorithm is used to calculate the large-scale correction of each seismic channel of the new and old seismic data, which are recorded as ΔT1, ΔT2, ΔT3, ΔT 2n-1 , ΔT 2n and ΔT 2n+1 , calculate the average value of large-scale correction ΔT a , added to the n+1th seismic trace data in the old seismic data, to obtain the n+1th seismic trace after large-scale correction.

[0029] The cross-correlation algorithm refers to an algorithm that calculates the degree of correlation between two seismic signals.

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

[0031] S4. Add the corresponding large-scale correction values ​​to the first to nth seismic channel data and the nth to last seismic channel data to the first to last seismic channel data in the old seismic data, respectively, to obtain the remaining seismic channels 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 50-100 ms.

[0034] As a limitation of the present invention, the second round of horizon matching interpretation includes the following steps performed in sequence:

[0035] P1. Match the first round of layer matching data to the new seismic data. Start from the starting point of the line number or track number direction in the layer matching between the new and old seismic data, select 2n+1 seismic tracks respectively, and use the small-scale correction window length S as the window length of the correction time window, so that the n+1th seismic track in the 2n+1 seismic tracks is located at the S / 2 window length, where n is a natural number greater than 0.

[0036] P2. The small-scale corrections of the first round of horizon matching data and new seismic data are calculated using the cross-correlation algorithm and are denoted as Δt1, Δt2, Δt3, Δt4, Δt5, Δt6, Δt7, Δt8, Δt9, Δt10, Δt111, Δt22, Δt33, Δt12, Δt13, Δt14, Δt15, Δt16, Δt17, Δt18, Δt19, Δt210, Δt221 2n-1 , Δt 2n and Δt 2n+1 , calculate the average value of the small-scale correction value Δta, add it to the data of the n+1th seismic trace in the first round of layer matching data, and get the n+1th seismic trace after small-scale correction.

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

[0038] P4. Add the corresponding small-scale correction amounts to the first to nth seismic trace data and the nth to last seismic trace data to the first to last seismic trace data in the first round of layer matching data, and 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 20-50 ms.

[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, the method for adaptive layer matching interpretation based on multi-period seismic data bodies described in any one of the above technical solutions is implemented.

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

[0043] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0044] (1) The early data processing in the present invention makes the numbers of the survey network of new and old seismic data on the same line number and the same channel number consistent, which is beneficial to the coordinate positioning during the later layer interpretation.

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

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

[0047] (4) Compared with the stratigraphic interpretation of old seismic data directly on new seismic data, the first round of stratigraphic matching interpretation in the present invention has a higher matching degree and a higher stratigraphic interpretation accuracy, and the second round of stratigraphic matching interpretation further improves the stratigraphic interpretation accuracy.

[0048] (5) The present invention innovatively develops a method for adaptive stratigraphic matching interpretation, making full use of the previous stratigraphic interpretation achievements. Based on the matching and shaping processing of the line numbers and channel numbers of the new and old seismic data, the maximum mutual correlation coefficient of the same stratigraphic layer under a certain time window is calculated to obtain the correction amount of the stratigraphic migration matching. By analogy, the correction amount in all repeated areas of the new and old seismic data can be obtained, thereby realizing the migration matching interpretation of the stratigraphic layer of the old seismic data on the stratigraphic layer of the new seismic data, and greatly improving the stratigraphic interpretation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0050] Figure 1 This is a comparison diagram of the seismic profiles before and after the shaping process in Example 1 of the present invention. Figure 1 (a) is the seismic profile of old earthquake data. Figure 1 (b) is the seismic profile of the new seismic data before shaping. Figure 1 (c) is the seismic profile of the new seismic data after shaping;

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

[0052] Figure 3 A cross-sectional diagram is selected for the large-scale correction window length of new and old seismic data in Example 1 of the present invention. Figure 3 (a) is the profile diagram of the old seismic data layer and the large-scale correction window length of the old seismic data. Figure 3(b) Selecting profiles for the old seismic data horizons and the large-scale correction window length for the new seismic data;

[0053] Figure 4 Schematic diagram of the cross-correlation algorithm for calculating the correction amount in Embodiment 1 of the present invention, Figure 4 (a) is the long section of the large-scale correction window of old earthquake data. Figure 4 (b) is the long section of the large-scale correction window of the new seismic data. Figure 4 (c) is a schematic diagram of the correction amount calculated by the cross-correlation algorithm;

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

[0055] Figure 6 The cross-section diagram is selected for the first round of horizon matching data and the small-scale correction time window of new seismic data in Example 1 of the present invention, Figure 6 (a) is the profile diagram of the old seismic data layer and the small-scale correction window length of the old seismic data. Figure 6 (b) The profile diagram for selecting the small-scale correction window length of the first round of horizon matching data and the new seismic data;

[0056] Figure 7 The second round of horizon matching profile of the new seismic data in Example 1 of the present invention;

[0057] Figure 8 This is a cross-sectional diagram of the interpretation of multiple layer matching in Example 1 of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below through specific embodiments and drawings. It should be understood that the described embodiments are only used to explain the present invention, but not to limit the present invention.

[0059] Example 1 A layer adaptive matching interpretation method based on new and old seismic data volumes

[0060] This embodiment is a layer adaptive matching interpretation method based on new and old seismic data volumes, taking five seismic traces as a unit to perform layer adaptive matching interpretation. The specific method includes the following steps performed in sequence:

[0061] 1) The new and old seismic data network is transformed or interpolated by three-dimensional coordinates to obtain the new and old seismic data with matching line numbers and channel numbers in the network.

[0062] 2) The new and old earthquake data are processed by filter shaping to obtain the new and old earthquake data with the same energy domain range. Figure 1 .Depend on Figure 1 It can be seen that Figure 1 (a) The energy range of the old earthquake data is -300 to 300. Figure 1 (b) The energy range of the new seismic data is -180 to 180, and the energy difference is large. Figure 1 The energy value range of the new seismic data after shaping in (c) 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 subsequent calculation of the stratigraphic correction amount.

[0063] 3) The new seismic data is processed by linear interpolation at a sampling interval of 0.1 ms. Figure 2 .

[0064] 4) Match the old seismic data layer to the new seismic data. Start from the starting point of the line number or trace number direction in the layer where the old and new seismic data are matched, and select five seismic traces. Figure 3 , the large-scale correction window length is 50ms, and the third seismic trace among the five seismic traces is located at the upper and lower 25ms window lengths. Figure 3 It can be seen that Figure 3 In (a), the window length from 1100ms to 1160ms is 50ms for the large-scale correction of old seismic data. Figure 3 In (b), the large-scale correction window length of the new seismic data is 50 ms from 1100 ms to 1160 ms. Therefore, the large-scale correction window lengths of the new and old seismic data remain consistent in the channel number direction.

[0065] 5) The large-scale correction of each seismic trace of the new and old seismic data is calculated using the cross-correlation algorithm. Figure 4 , respectively denoted as ΔT1, ΔT2, ΔT3, ΔT4 and ΔT5, and the average large-scale correction ΔTa is calculated and added to the third seismic channel data in the old seismic data to obtain the third seismic channel after large-scale correction. Figure 4 It can be seen that Figure 4 (a) Figure 4 (b) The temporal position of the middle layer is the same, Figure 4 (b) The old earthquake data are obviously higher than the new earthquake data. Figure 4 In (c), the correction amount after cross-correlation calculation is positive. Therefore, the old earthquake data seismic trace needs to increase the correction amount to match the new earthquake data, especially the fifth seismic trace.

[0066] 6) Move forward one channel and repeat steps 4-5 to select five seismic traces. Given the large-scale correction window length, the third seismic trace among the five seismic traces is located at 1 / 2 of the window length. The large-scale correction amount of each seismic trace of the new and old seismic data is calculated using the cross-correlation algorithm, and the average large-scale correction amount is calculated and added to the corresponding middle seismic trace data in the old seismic data to obtain the corrected seismic trace until all the large-scale corrected middle seismic traces are obtained.

[0067] 7) Add the corresponding large-scale correction amount to the first seismic channel data, the second seismic channel data, the second-to-last seismic channel data and the first-to-last seismic channel data in the old seismic data to obtain the remaining seismic channels 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. Figure 5 .Depend on Figure 5 It can be seen that the old seismic data layers are somewhat above and somewhat below the new seismic data layers, and the matching accuracy is not high, but the first round of layer matching data after correction is more matched with the new seismic data layers.

[0069] 9) Match the first round of layer matching data to the new seismic data. Start from the starting point of the line number or trace number direction in the layer matching between the new and old seismic data, and select five seismic traces respectively. Figure 6 , the small-scale correction window length is 20ms, and the third seismic trace among the five seismic traces is located at the 10ms window length. Figure 6 It can be seen that Figure 6 In (a), the window length from 1110ms to 1130ms is the old seismic data layer and the small-scale correction window length of the old seismic data is 20ms. Figure 6 (b) From 1110ms to 1130ms, the small-scale correction window length of the first-round layer matching data and the new seismic data is 20ms. Therefore, the small-scale correction window length of the first-round layer matching data and the new seismic data is consistent in the channel number direction.

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

[0071] 11) Move forward one channel, repeat steps 9-10, select five seismic traces, give a small-scale correction window length, the third seismic trace among the five seismic traces is located at 1 / 2 of the window length, use the cross-correlation algorithm to calculate the small-scale correction amount of each seismic trace of the new and old seismic data, and calculate the average small-scale correction amount, add it to the corresponding middle seismic trace data in the first round of stratigraphic matching data, and get the corrected seismic trace, until all the small-scale corrected middle seismic traces are obtained.

[0072] 12) Add the corresponding small-scale correction amount to the first seismic trace data, the second seismic trace data, the second-to-last seismic trace data and the first-to-last seismic trace data in the first round of layer 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. Figure 7 .Depend on Figure 7 It can be seen that compared with the first round of layer matching data, the accuracy of the second round of layer matching data is further improved.

[0074] 14) Follow steps 4)-13) to complete the matching of other layers. Figure 8 .Depend on Figure 8 It can be seen that the horizon matching interpretation work of the three layers T1, T2 and T3 has a high accuracy.

[0075] The method of this embodiment realizes the rapid matching of old seismic data layers on new seismic data layers, and can make full use of the layer interpretation results of predecessors in old seismic data. On the basis of the matching and shaping processing of the line numbers and channel numbers of the new and old seismic data, the maximum mutual correlation coefficient of the same layer in a certain time window is calculated to obtain the correction amount of the layer migration matching. By analogy, the correction amount in all repeated areas of the new and old seismic data can be obtained, thereby realizing the migration matching interpretation of the old seismic data layers on the new seismic data layers, which significantly improves the accuracy and efficiency of the layer interpretation.

[0076] Example 2 A layer adaptive matching interpretation method based on new and old seismic data volumes

[0077] This embodiment is a layer adaptive matching interpretation method based on new and old seismic data volumes. Seven seismic traces are used as a unit to perform layer adaptive matching interpretation. The specific method includes the following steps performed in sequence:

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

[0079] 4) Match the old seismic data layer to the new seismic data. Start from the starting point of the line number or track number direction in the matching layer of the new and old seismic data. Select seven seismic traces. The large-scale correction window length is 80ms. The fourth seismic trace among the seven seismic traces is located at the 40ms window length.

[0080] 5) The cross-correlation algorithm is used to calculate the large-scale correction value of each seismic channel of the new and old seismic data, which are recorded as ΔT1, ΔT2, ΔT3, ΔT4, ΔT5, ΔT6 and ΔT7 respectively, and the average large-scale correction value ΔTa is calculated and added to the data of the fourth seismic channel in the old seismic data to obtain the fourth seismic channel after large-scale correction.

[0081] 6) Move forward one channel and repeat steps 4-5 to select seven seismic traces. Given a large-scale correction window length, the fourth seismic trace among the seven seismic traces is located at 1 / 2 of the window length. The large-scale correction amount of each seismic trace of the new and old seismic data is calculated using a cross-correlation algorithm, and the average large-scale correction amount is calculated and added to the corresponding middle seismic trace data in the old seismic data to obtain the corrected seismic trace until all the large-scale corrected middle seismic traces are obtained.

[0082] 7) Add the corresponding large-scale correction amount to the first seismic channel data, the second seismic channel data, the third seismic channel data, the third-to-last seismic channel data, the second-to-last seismic channel data and the first-to-last seismic channel data in the old seismic data to obtain the remaining seismic channels 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 layer matching data to the new seismic data. Start from the starting point of the line number or track number direction in the layer matching between the new and old seismic data, select seven seismic traces respectively, and the small-scale correction window length is 30ms. The fourth seismic trace among the seven seismic traces is located at the 15ms window length.

[0085] 10) The cross-correlation algorithm is used to calculate the small-scale correction amounts of the first round of layer matching data and the new seismic data, which are recorded as Δt1, Δt2, Δt3, Δt4, Δt5, Δt6 and Δt7 respectively. The average value of the small-scale correction amount Δta is calculated and added to the fourth seismic trace data in the first round of layer matching data to obtain the fourth seismic trace after small-scale correction.

[0086] 11) Move forward one channel, repeat steps 9-10, select seven seismic traces, give a small-scale correction window length, the fourth seismic trace among the seven seismic traces is located at 1 / 2 of the window length, use the cross-correlation algorithm to calculate the small-scale correction amount of each seismic trace of the new and old seismic data, and calculate the average small-scale correction amount, add it to the corresponding middle seismic trace data in the first round of stratigraphic matching data, and get the corrected seismic trace, until all the small-scale corrected middle seismic traces are obtained.

[0087] 12) Add the corresponding small-scale correction amounts to the first seismic trace data, the second seismic trace data, the third seismic trace data, the third-to-last seismic trace data, the second-to-last seismic trace data and the first-to-last seismic trace data in the first round of stratigraphic 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) Follow steps 4)-13) to complete the matching interpretation of other layers.

[0090] Embodiment 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, so as to implement the layer adaptive matching interpretation method based on multi-period seismic data volume of embodiment 1 or 2.

[0092] The memory is used to store non-temporary 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 (cache), etc. 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 forms of processing units with data processing capability and / or instruction execution capability, and may control other components in the electronic device to perform desired functions. The processor is used to run the computer-readable instructions stored in the memory.

[0094] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0095] For detailed description of this embodiment, reference may be made to the corresponding description in the aforementioned embodiment, which will not be repeated here.

[0096] Embodiment 4 A computer readable storage medium

[0097] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the layer adaptive matching interpretation method based on multi-period seismic data volume of embodiment 1 or 2 is implemented.

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

[0099] The above-mentioned 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 mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0100] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is 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 replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in 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 volume, characterized in that: It performs the first and second rounds of horizon matching interpretation on the corresponding horizons in the new and old seismic data, that is, completes the horizon adaptive matching interpretation based on multi-period seismic data volumes; The new and old seismic data are obtained through preliminary data processing for data matching; The horizon matching interpretation is to calculate the correction amount in the correction time window of the multi-channel seismic traces in the new and old seismic data respectively, and then, for the middle seismic trace of the multi-channel seismic traces, add the average correction amount to the middle seismic trace to obtain the corrected middle seismic trace; for the remaining seismic traces except the middle seismic trace, add the correction amount of the remaining seismic traces to each of the remaining seismic traces to obtain the corrected remaining seismic traces respectively; The multi-channel seismic channels refer to more than 3 odd-numbered seismic channels.

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

3. The method for adaptive layer matching interpretation based on multi-period seismic data volume according to claim 2, characterized in that: The matching process of the new and old seismic data measurement networks is to transform or interpolate the new and old seismic data measurement networks by three-dimensional coordinates to obtain new and old seismic data with matching line numbers and track numbers in the measurement networks; The shaping process of the new and old seismic data is to pass the new and old seismic data through a filter to obtain new and old seismic data with quality matching, wherein the quality matching includes matching of time, energy, bandwidth and phase; The interpolation processing of the new seismic data is to perform linear interpolation processing on the new seismic data one by one at a sampling interval of 0.1 ms.

4. The method for adaptive layer matching interpretation based on multi-period seismic data volume according to claim 3 is characterized in that: The first round of horizon matching interpretation includes the following steps performed in sequence: S1. Match the old seismic data layer to the new seismic data. Starting from the starting point of the line number or trace number direction, select 2n+1 seismic traces respectively at the layer where the new and old seismic data are matched, and use the large-scale correction window length W as the window length of the correction time window, so that the n+1th seismic trace in the 2n+1 seismic traces is located at the W / 2 window length, where n is a natural number greater than 0; S2. The cross-correlation algorithm is used to calculate the large-scale correction of each seismic channel of the new and old seismic data, which are recorded as ΔT1, ΔT2, ΔT3, ΔT 2n-1 , ΔT 2n and ΔT 2n+1 , calculate the average value of large-scale correction ΔT a , added to the n+1th seismic trace data in the old seismic data, to obtain the n+1th seismic trace after large-scale correction; S3. Move forward one channel and repeat steps S1-S2 until all the large-scale corrected intermediate seismic traces are obtained; S4. Add the corresponding large-scale correction amount to the first to nth seismic channel data and the nth to last seismic channel data to the penultimate seismic channel data in the old seismic data, respectively, to obtain the remaining seismic channels 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.

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

6. The layer adaptive matching interpretation method based on multi-period seismic data volume according to claim 5 is characterized in that: The second round of horizon matching interpretation includes the following steps performed in sequence: P1. Match the first round of layer matching data to the new seismic data, start from the starting point of the line number or trace number direction at the layer where the new and old seismic data are matched, select 2n+1 seismic traces respectively, use the small-scale correction window length S as the window length of the correction time window, and make the n+1th seismic trace in the 2n+1 seismic traces located at the S / 2 window length, where n is a natural number greater than 0; P2. The small-scale corrections of the first round of horizon matching data and new seismic data are calculated using the cross-correlation algorithm and are denoted as Δt1, Δt2, Δt3, Δt4, Δt5, Δt6, Δt7, Δt8, Δt9, Δt10, Δt111, Δt22, Δt33, Δt12, Δt13, Δt14, 2n-1 , Δt 2n and Δt 2n+1 , calculate the average value of small-scale correction Δt a , added to the n+1th seismic trace data in the first round of layer matching data, to obtain the n+1th seismic trace after small-scale correction; P3. Move forward one channel and repeat steps P1-P2 until all small-scale corrected intermediate seismic traces are obtained; P4. Add the corresponding small-scale correction amount to the first seismic trace data to the nth seismic trace data and the penultimate nth seismic trace data to the first seismic trace data in the first round of layer matching data, and obtain the remaining seismic traces after small-scale correction; 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.

7. The layer adaptive matching interpretation method based on multi-period seismic data volume according to claim 6 is characterized in that: The small-scale correction window length S is 20-50 ms.

8. 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, the layer adaptive matching interpretation method based on multi-phase seismic data volume described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the layer adaptive matching interpretation method based on multi-phase seismic data volume as claimed in any one of claims 1 to 7.

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