Velocity spectrum label manufacturing method, device, equipment and medium

Abstract

The invention belongs to the technical field of seismic exploration, and particularly discloses a velocity spectrum label manufacturing method, which comprises the following steps: S1, acquiring a seismic stack velocity spectrum; s2, picking up speed label data; s3, speed prediction is carried out through an intelligent speed spectrum interpretation system, label abnormal points are positioned and corrected, and corrected speed label data are obtained; s4, calculating an energy extreme value of the corrected speed label data, and optimizing the corrected speed label data according to an energy extreme value curve and a set speed fine tuning parameter; and S5, further optimizing the optimized correction speed label data according to the energy extreme value curve and the set minimum time parameter of the increase point to obtain final optimized correction speed label data. According to the invention, the quantity and quality of the speed label data can be improved, and quality guarantee is provided for intelligent speed spectrum pickup. The method is suitable for velocity spectrum interpretation.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic exploration, and particularly relates to a method, device, equipment and medium for making velocity spectrum tags. Background Art

[0002] The picking of seismic velocity spectra is a key step in the seismic data processing flow, and the velocity picking result directly affects the quality of seismic imaging. In the usual velocity spectrum interpretation work, technicians need to repeatedly compare and verify a number of reference information and complete a large number of human-computer interaction operations. This is not only time-consuming and laborious, but also requires rich regional processing experience. It is one of the most time-consuming manual links in the seismic data processing flow. With the explosion of the amount of collected data and the increasing requirements of the client for velocity density, the cost of manual high-density interpretation is huge. There is an urgent need for automation in the velocity picking process to reduce the time and labor costs while ensuring the picking quality, shorten the seismic data processing cycle, and thus improve production efficiency.

[0003] In recent years, with the progress of artificial intelligence technology, it has also attracted the wide attention of seismic exploration researchers and the fierce competition of oil and gas service companies. The intelligent picking quality of velocity spectra is affected subjectively by the "intelligent velocity spectrum interpretation system" on the one hand, and objectively by the quantity and quality of velocity tags on the other hand. In particular, the quality of velocity tags seriously affects the accurate judgment of the target layer velocity spectrum by the "intelligent velocity spectrum interpretation system". A certain scale of velocity tags is the basis for the "intelligent velocity spectrum interpretation system" to carry out model training. Therefore, it is imperative to quickly and accurately increase the number of tags.

[0004] In actual production, affected by the velocity spectrum data and limited by the display accuracy, some tags will have vertical missing and picking deviations, resulting in incorrect velocity spectrum picking. Using the incorrect velocity cannot correctly image the reflection wave of the target layer, causing the generation of geological illusions, and further leading to exploration failure. Usually, the shallow interference is severe, the deep seismic reflection signal has weak energy and low signal-to-noise ratio, which will result in the lack of energy in the shallow and deep velocity spectra. If there is a situation of lack of energy in the velocity spectra in this area, it will lead to incomplete velocity tag data picked by the processing personnel. Therefore, a velocity tag making technology that can correctly guide the "intelligent velocity spectrum interpretation system" has received attention. It can improve the quality of velocity tags while also improving the intelligent picking quality of velocity spectra. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, equipment and medium for making velocity spectrum tags to improve the quantity and quality of velocity tags.

[0006] The technical methods adopted by the present invention to achieve the above purpose are as follows:

[0007] A method for making velocity spectrum labels, comprising the following steps:

[0008] S1. Obtain the seismic stacking velocity spectrum;

[0009] S2. Based on the distribution of energy clusters in the seismic stacking velocity spectrum, the morphology of the common azimuth gather, and the characteristics of small stacking segments, pick up velocity label data;

[0010] S3. Perform velocity prediction through an intelligent velocity spectrum interpretation system, locate label anomaly points, and make corrections to obtain corrected velocity label data;

[0011] S4. Calculate the energy extreme value of the corrected velocity label data, and optimize the corrected velocity label data according to the energy extreme value curve and the set velocity fine-tuning parameters;

[0012] S5. Calculate the energy extreme value of the corrected velocity label data, and further optimize the optimized corrected velocity label data according to the energy extreme value curve and the set minimum time parameter for added points to obtain the finally optimized corrected velocity label data.

[0013] As a limitation: Step S1 is specifically:

[0014] Using the regional reference velocity and the pre-stack CMP seismic data gather collected in the field, scan according to the minimum percentage value of the regional reference velocity and the maximum percentage value of the regional reference velocity set in advance to obtain the seismic stacking velocity spectrum P(T i ,V j ), i = 1, 2,..., N, j = 1, 2,..., M, where P represents the stacking velocity spectrum, T represents the two-way travel time of the reflected wave at zero offset, V represents the stacking velocity of the reflected wave at zero offset, i is the serial number of the sampling point along the time direction, j is the serial number of the sampling point along the velocity direction, N is the total number of sampling points along the time direction, and M is the total number of sampling points along the velocity direction.

[0015] As a further limitation: The velocity label data in step S2 is P i (T i ,V i ), i = 1, 2,..., N.

[0016] As a further limitation: Step S3 is specifically:

[0017] S31. Input the velocity label data into the intelligent velocity spectrum interpretation system to start velocity prediction and obtain predicted velocity data;

[0018] S32. Locate label anomaly points through the maximum velocity error map; The maximum velocity error calculation formula is , where is the predicted velocity data in step S31, is the speed at the point corresponding to the predicted speed data in the speed tag data;

[0019] S33. Manually correct the abnormal points of the tag to obtain the corrected speed tag data.

[0020] As a further limitation: Step S4 is specifically as follows:

[0021] S41. Apply a sliding time window to each time sample point of the corrected speed tag data, calculate the energy value within each window, select the maximum energy value as the energy extreme value, and the energy extreme value point is (T k , V k );

[0022] S42. Set multiple groups of speed fine-tuning parameters. Each group of speed fine-tuning parameters includes a time parameter T k , a minimum value limit parameter L k and a maximum value limit parameter U k , that is, (T k , L k , U k ). The multiple groups of speed fine-tuning parameters are linearly interpolated into the corrected speed tag data from shallow to deep at intervals in ascending order of the numerical value of the time parameter T k ;

[0023] S43. Calculate the error σ between the pick-up point P(T, V) in the predicted speed data and the energy extreme value point (T k , V k ) at the same time position; The calculation formula of the error σ is: ;

[0024] S44. Determine whether the error σ satisfies the condition. If it is satisfied, move the pick-up point P(T, V) to the position where the energy extreme value point is (T k , V k ) to obtain the optimized corrected speed tag data. If it is not satisfied, do not move the pick-up point P(T, V).

[0025] As the final limitation: Step S5 is specifically as follows:

[0026] S51. Apply a sliding time window to each time sample point of the corrected speed tag data, calculate the energy value within each window, select the maximum energy value as the energy extreme value, and the energy extreme value point is (T k , V k );

[0027] S52. Set multiple groups of minimum time parameters for adding points. Each group of minimum time parameters for adding points includes a time parameter T k and a minimum time increment Inc k , that is, (Tk , Inc k ), the minimum time parameters of multiple groups of additional points are linearly interpolated from shallow to deep at intervals according to the time parameter T k in ascending order of numerical values into the optimized corrected velocity label data;

[0028] S53. Calculate the time difference T2 - T1 between the pickup points P1(T1, V1) and P2(T2, V2) in the predicted velocity data;

[0029] S54. Determine whether the time difference T2 - T1 satisfies the condition. If it is satisfied, discard a point far from the energy extreme point from P1(T1, V1) and P2(T2, V2); if not, both P1(T1, V1) and P2(T2, V2) are retained, and if point P2 is the first pickup point, based on Inc k add points in the shallow layer one by one while maintaining the trend of the original predicted velocity data curve; if point P1 is the last pickup point, based on Inc k add points in the deep layer one by one while maintaining the trend of the original predicted velocity data curve, to obtain the finally optimized corrected velocity label data.

[0030] The present invention also discloses a velocity spectrum label making device, including:

[0031] An earthquake stacking velocity spectrum acquisition module for acquiring an earthquake stacking velocity spectrum;

[0032] A velocity label data pickup module for picking up velocity label data based on the distribution of energy clusters, the morphology of common shot gathers, and the characteristics of small stacking segments of the earthquake stacking velocity spectrum;

[0033] A velocity label data correction module for performing velocity prediction through an intelligent velocity spectrum interpretation system, locating label abnormal points, and making corrections to obtain corrected velocity label data;

[0034] A corrected velocity label data optimization module for calculating the energy extreme value of the corrected velocity label data and optimizing the corrected velocity label data according to the energy extreme value curve and the set velocity fine-tuning parameters;

[0035] A finally optimized corrected velocity label data module for calculating the energy extreme value of the corrected velocity label data and further optimizing the optimized corrected velocity label data according to the energy extreme value curve and the set minimum time parameter for adding points to obtain the finally optimized corrected velocity label data.

[0036] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the above method is implemented.

[0037] The present invention also discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented.

[0038] Due to the adoption of the above solution, compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0039] (1) A method for making velocity spectrum labels provided by the present invention performs a maximum velocity error check on velocity label data, quickly locates data points with large errors, corrects the velocity label data, and optimizes it. If there are data points picked up that deviate from the energy extreme point, velocity level fine-tuning is performed. For the shallow and deep layers with missing energy, vertical adjustment of the velocity label is performed according to the velocity trend and the inference of the effective energy cluster, quickly improving the quantity and quality of the velocity label data, providing quality assurance for intelligent velocity spectrum picking, and further improving the imaging quality.

[0040] (2) The present invention also provides a corresponding implementation device, electronic device, and readable storage medium, further making the method more practical, and the device, electronic device, and readable storage medium have corresponding advantages.

[0041] The present invention is applicable to velocity spectrum interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 Schematic diagram of the seismic stacking velocity spectrum P(T i , V j ) of Embodiment 1 of the present invention;

[0044] Figure 2 Schematic diagram of the velocity label data P i (T i , V i ) of Embodiment 1 of the present invention;

[0045] Figure 3 Schematic diagram of locating label abnormal points in Embodiment 1 of the present invention;

[0046] Figure 4 Schematic diagram of correcting velocity label data in Embodiment 1 of the present invention;

[0047] Figure 5 Schematic diagram of the position of the picked point P(T, V) in the velocity spectrum in the predicted velocity data in Embodiment 1 of the present invention;

[0048] Figure 6 Schematic diagram of the optimized corrected velocity label data in Embodiment 1 of the present invention;

[0049] Figure 7 Schematic diagram of the corrected speed label data finally optimized in Embodiment 1 of the present invention;

[0050] Figure 8 Schematic diagram of the structure of a speed spectrum label making device in Embodiment 2 of the present invention;

[0051] Figure 9 Schematic diagram of the structure of an electronic device in Embodiment 2 of the present invention. Detailed implementation manners

[0052] The present invention will be further described below in conjunction with embodiments. However, those skilled in the art should understand that the present invention is not limited to the following embodiments, and any improvements and equivalent changes made on the basis of the specific embodiments of the present invention are within the scope of protection of the claims of the present invention.

[0053] Embodiment 1 A method for making a speed spectrum label

[0054] A method for making a speed spectrum label includes the following steps:

[0055] S1. Using the regional reference speed and the prestack CMP seismic data gather collected in the field, and scanning according to the minimum percentage value and the maximum percentage value of the regional reference speed set in advance to obtain the seismic stacking velocity spectrum P(T i ,V j ), i = 1, 2,..., N, j = 1, 2,..., M, where P represents the stacking velocity spectrum, T represents the two-way travel time of the reflected wave at zero offset, V represents the stacking velocity of the reflected wave at zero offset, i is the serial number of the sampling point along the time direction, j is the serial number of the sampling point along the velocity direction, N is the total number of sampling points along the time direction, and M is the total number of sampling points along the velocity direction. The schematic diagram of the seismic stacking velocity spectrum P(T i ,V j ) is shown in Figure 1 ;

[0056] S2. Based on the distribution of the energy clusters of the seismic stacking velocity spectrum, the morphology of the large gather, and the characteristics of the small stacking segments, pick up the speed label data P i (T i ,V i ), i = 1, 2,..., N, as shown in Figure 2 ;

[0057] S3. Perform speed prediction through the intelligent velocity spectrum interpretation system, locate the label abnormal points, and perform correction to obtain the corrected speed label data; specifically:

[0058] S31. Input the speed label data into the intelligent speed spectrum interpretation system to start speed prediction and obtain predicted speed data;

[0059] S32. Locate the label anomaly points through the maximum speed error graph. The label anomaly points are data points with relatively large maximum speed error values and relatively small quantities. See Figure 3 . In this embodiment, the minimum value of the maximum speed error is 32, and the maximum value is 420; the calculation formula for the maximum speed error is . Among them, is the predicted speed data in step S31, and is the speed at the point corresponding to the predicted speed data in the speed label data.

[0060] S33. Manually correct the label anomaly points to obtain corrected speed label data. See Figure 4 .

[0061] S4. Calculate the energy extreme value of the corrected speed label data, and optimize the corrected speed label data based on the energy extreme value curve and the set speed fine-tuning parameters; specifically:

[0062] S41. Apply a sliding time window at each time sample point of the corrected speed label data, calculate the energy value within each window, and select the maximum energy value as the energy extreme value. The energy extreme value point is (T k , V k ), k = 1, 2,..., N;

[0063] S42. Set four groups of speed fine-tuning parameters. Each group of speed fine-tuning parameters includes a time parameter T k , a minimum limit parameter L k and a maximum limit parameter U k , that is, (T k , L k , U k ). The four groups of speed fine-tuning parameters are linearly interpolated into the corrected speed label data at intervals from shallow to deep in ascending order of the numerical value of the time parameter T k . In this embodiment, the four groups of speed fine-tuning parameters set are (200, -2, 2), (800, -2, 2), (2000, -1.5, 1.5), and (5000, -1, 1);

[0064] S43. Calculate the error σ between the pickup point P(T, V) in the predicted speed data and the energy extreme value point (T k , V k ) at the same position in terms of time; the position of the pickup point P(T, V) in the predicted speed data in the speed spectrum is as shown in Figure 5 . The calculation formula for the error σ is: ;

[0065] S44. Determine whether the error σ satisfies the condition. If it is satisfied, move the pickup point P(T, V) to the position where the energy extreme point is (T k , V k ), and obtain the optimized corrected speed label data, as shown in Figure 6 . If it is not satisfied, do not move the pickup point P(T, V).

[0066] S5. Calculate the energy extreme value of the corrected speed label data. According to the energy extreme value curve and the set minimum time parameter of the added points, further optimize the optimized corrected speed label data to obtain the finally optimized corrected speed label data; specifically:

[0067] S51. Apply a sliding time window to each time sample point of the corrected speed label data, calculate the energy value within each window, and select the maximum energy value as the energy extreme value. The energy extreme point is (T k , V k );

[0068] S52. Set four groups of minimum time parameters for the added points. Each group of minimum time parameters for the added points includes the time parameter T k and the minimum time increment Inc k , that is, (T k , Inc k ). The four groups of minimum time parameters for the added points are linearly interpolated into the optimized corrected speed label data in ascending order of the numerical value of the time parameter T k ; in this embodiment, the four groups of minimum time parameters for the added points set are (200, 70), (800, 100), (2000, 200), and (5000, 500);

[0069] S53. Calculate the time difference T2 - T1 between the pickup points P1(T1, V1) and P2(T2, V2) in the predicted speed data;

[0070] S54. Determine whether the time difference T2 - T1 satisfies the condition. If it is satisfied, discard a point far from the energy extreme point from P1(T1, V1) and P2(T2, V2); if it is not satisfied, both P1(T1, V1) and P2(T2, V2) are retained, and if point P2 is the first pickup point, then on the basis of retaining the original trend of the predicted speed data curve, according to Inc k add shallow points one by one. Inc k determines the density of the added data points. The smaller Inc k , the more data points are added. Inc kThe larger it is, the fewer data points are added; if point P1 is the last picked point, then based on the trend of the original predicted speed data curve, according to Inc k add points in the deep layer one by one to obtain the finally optimized corrected speed label data; in this embodiment, there is energy loss in the shallow layer and no energy loss in the deep layer. The finally optimized corrected speed label data is shown in Figure 7 .

[0071] From Figure 3 and Figure 4 it can be seen that this embodiment can correct label data quickly and with high quality. From Figures 5 - 7 it can be seen that the speed label data generated by this embodiment can not only finely adjust the unreasonable velocity spectrum during picking, but also thin out the data where the picking is too dense and encrypt the data where the picking is missing, effectively improving the quantity and quality of the speed label data and providing guarantee for subsequent intelligent prediction.

[0072] Embodiment 2 A velocity spectrum label making device, equipment and medium

[0073] A velocity spectrum label making device, the structural schematic diagram of which is as shown in Figure 8 and includes:

[0074] An earthquake stacking velocity spectrum acquisition module for acquiring an earthquake stacking velocity spectrum;

[0075] A velocity label data picking module for picking velocity label data based on the distribution of energy clusters, the morphology of the common shot gather and the characteristics of small stacking segments of the earthquake stacking velocity spectrum;

[0076] A velocity label data correction module for performing velocity prediction through an intelligent velocity spectrum interpretation system, locating label abnormal points and performing correction to obtain corrected velocity label data;

[0077] A corrected velocity label data optimization module for calculating the energy extreme value of the corrected velocity label data and optimizing the corrected velocity label data according to the energy extreme value curve and the set velocity fine-tuning parameters;

[0078] A finally optimized corrected velocity label data module for calculating the energy extreme value of the corrected velocity label data and further optimizing the optimized corrected velocity label data according to the energy extreme value curve and the set minimum time parameter for adding points to obtain the finally optimized corrected velocity label data.

[0079] This embodiment also provides an electronic device, the structure of which is as shown in Figure 9 and includes a memory, a processor and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the method described in Embodiment 1 is implemented.

[0080] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in Embodiment 1 is implemented.

Claims

1. A method for making velocity spectrum labels, characterized in that, Including the following steps: S1. Obtain the seismic stacking velocity spectrum; S2. Pick up the velocity label data based on the distribution of energy clusters in the seismic stacking velocity spectrum, the morphology of the common shot gather, and the characteristics of small stacking segments; S3. Conduct velocity prediction through an intelligent velocity spectrum interpretation system, locate label anomaly points, and make corrections to obtain corrected velocity label data; S4. Calculate the energy extreme value of the corrected velocity label data, and optimize the corrected velocity label data according to the energy extreme value curve and the set velocity fine-tuning parameters; S5. Calculate the energy extreme value of the corrected velocity label data, and further optimize the optimized corrected velocity label data according to the energy extreme value curve and the set minimum time parameter for added points to obtain the finally optimized corrected velocity label data.

2. The method for manufacturing a velocity spectrum label according to claim 1, characterized in that, Step S1 is specifically as follows: Using the regional reference velocity and the pre-stack CMP seismic data gather collected in the field, scan to obtain the seismic stacking velocity spectrum P(T i ,V j ) according to the minimum percentage of the pre-set regional reference velocity and the maximum percentage of the regional reference velocity, where i = 1, 2,..., N and j = 1, 2,..., M. Here, P represents the stacking velocity spectrum, T represents the two-way travel time of the reflected wave at zero offset, V represents the stacking velocity of the reflected wave at zero offset, i is the serial number of the sampling point along the time direction, j is the serial number of the sampling point along the velocity direction, N is the total number of sampling points along the time direction, and M is the total number of sampling points along the velocity direction.

3. A method for manufacturing a velocity spectrum label according to claim 2, characterized in that, In step S2, the speed tag data is P i (T i , V i ), i = 1, 2, …, N.

4. A method for making a velocity spectrum label according to claim 3, characterized in that, Step S3 specifically includes: S31. Input the velocity label data into the intelligent velocity spectrum interpretation system to start velocity prediction and obtain predicted velocity data; S32. Locate the label anomaly points through the maximum speed error graph; the calculation formula for the maximum speed error is , where is the predicted speed data in step S31, is the speed at the point corresponding to the predicted speed data in the speed label data; S33. Manually correct the label anomaly points to obtain corrected velocity label data.

5. A method for manufacturing a velocity spectrum label according to claim 4, characterized in that, Step S4 specifically includes: S41. Apply a sliding time window to each time sample of the corrected speed tag data, calculate the energy value within each window, select the maximum energy value as the energy extreme value, and the energy extreme value point is (T k , V k ); S42. Set multiple groups of speed fine-tuning parameters, and each group of speed fine-tuning parameters includes a time parameter T k , a minimum limit parameter L k and a maximum limit parameter U k , that is, (T k , L k , U k ). The multiple groups of speed fine-tuning parameters are linearly interpolated into the corrected speed label data at intervals from shallow to deep in ascending order of the numerical value of the time parameter T k ; S43. Calculate the energy extreme point (T k , V k ) error σ at the same position in time as the pick-up point P(T, V) in the predicted speed data; the calculation formula for the error σ is: ; S44. Determine whether the error σ satisfies the condition. If it is satisfied, move the pick-up point P(T, V) to the position where the energy extreme point is (T k , V k ), and obtain the optimized corrected speed label data. If it is not satisfied, do not move the pick-up point P(T, V).

6. A method for manufacturing a velocity spectrum label according to claim 5, characterized in that, Step S5 specifically includes: S51. Apply a sliding time window to each time sample of the corrected speed tag data, calculate the energy value within each window, select the maximum energy value as the energy extreme value, and the energy extreme value point is (T k , V k ); S52. Set multiple groups of minimum time parameters for increment points. Each group of minimum time parameters for increment points includes a time parameter T k and a minimum time increment Inc k , that is, (T k , Inc k ). The multiple groups of minimum time parameters for increment points are linearly interpolated into the optimized corrected speed label data at intervals from shallow to deep in ascending order of the numerical value of the time parameter T k ; S53. Calculate the time difference T2 - T1 between the picked points P1(T1, V1) and P2(T2, V2) in the predicted velocity data; S54. Determine whether the time difference T2 - T1 satisfies the condition. If it is satisfied, discard one point that is far from the energy extreme point from P1(T1, V1) and P2(T2, V2); if it is not satisfied, both P1(T1, V1) and P2(T2, V2) are retained, and if point P2 is the first picked point, then on the basis of retaining the original predicted velocity data curve trend, according to Inc k add points in the shallow layer one by one; if point P1 is the last picked point, then on the basis of retaining the original predicted velocity data curve trend, according to Inc k add points in the deep layer one by one to obtain the finally optimized corrected velocity label data.

7. A velocity spectrum label making device, characterized in that, Including: A seismic stacking velocity spectrum acquisition module for obtaining the seismic stacking velocity spectrum; A velocity label data picking module for picking up the velocity label data based on the distribution of energy clusters in the seismic stacking velocity spectrum, the morphology of the common shot gather, and the characteristics of small stacking segments; A velocity label data correction module for conducting velocity prediction through an intelligent velocity spectrum interpretation system, locating label anomaly points, and making corrections to obtain corrected velocity label data; An optimized corrected velocity label data module for calculating the energy extreme value of the corrected velocity label data and optimizing the corrected velocity label data according to the energy extreme value curve and the set velocity fine-tuning parameters; A finally optimized corrected velocity label data module for calculating the energy extreme value of the corrected velocity label data and further optimizing the optimized corrected velocity label data according to the energy extreme value curve and the set minimum time parameter for added points to obtain the finally optimized corrected velocity label data.

8. An electronic device, characterized in that, Including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 - 6 is implemented.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the method according to any one of claims 1 - 6 is implemented.

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