Depth domain data processing initial speed model establishment method and device and electronic equipment

Abstract

The invention discloses a depth domain data processing initial speed model establishment method and device and electronic equipment, and the method comprises the steps: obtaining depth data based on the pre-stack time migration data of seismic data; interpreting stratum top and bottom layer positions with similar speeds on depth data to obtain layer position interpretation data, and extracting gradient structure tensor attributes; filtering the logging curve to obtain a low-frequency logging curve of which the frequency is lower than a set frequency value; establishing a time-depth conversion speed model based on the low-frequency logging curve, the logging layering information and the horizon interpretation data, and constraining the time-depth conversion speed model by adopting a gradient structure tensor attribute; converting the root-mean-square velocity obtained by time domain processing into interval velocity, and fusing the interval velocity into a time-depth conversion velocity model to obtain a depth domain data processing initial velocity model; according to the method, the depth domain seismic data processing efficiency can be improved, the research period is shortened, and the cost is saved.

Description

Technical Field

[0001] The present invention belongs to the field of seismic data processing in the depth domain, and more specifically, relates to a method, device and electronic device for establishing an initial velocity model for depth domain data processing. Background Art

[0002] As most domestic oil fields enter the late exploration stage at present, the structures in the study area are becoming more and more complex, and the requirements for imaging technology are getting higher and higher. Seismic data processing is gradually changing from prestack time migration to prestack depth migration. Depth domain seismic data processing can effectively solve problems such as depth error, imaging of high-steep structures, and unclear fracture characteristics that cannot be solved by time domain processing.

[0003] In depth domain seismic data processing, the most important thing is to obtain a high-precision velocity model. In the conventional depth domain data processing process, the initial velocity model for depth domain data processing is obtained by converting the root mean square velocity in the time domain into a layer velocity as the initial velocity model for depth domain processing, and then through multiple rounds of iteration, the velocity is gradually made to approach the true formation velocity, thereby improving the imaging quality. However, the overall efficiency is low, time-consuming and laborious, and the cost is high. Summary of the Invention

[0004] The object of the present invention is to propose a method, device and electronic device for establishing an initial velocity model for depth domain data processing, so as to improve the efficiency of depth domain seismic data processing, shorten the research cycle and save costs.

[0005] To achieve the above object, in a first aspect, the present invention proposes a method for establishing an initial velocity model for depth domain data processing, including:

[0006] Obtaining depth data based on prestack time migration data of seismic data;

[0007] Interpret the top and bottom layer positions of the strata with similar velocities on the depth data to obtain layer position interpretation data, and extract the gradient structure tensor attribute;

[0008] Filter the logging curves to obtain low-frequency logging curves with a frequency lower than a set frequency value;

[0009] Based on the low-frequency logging curves, logging stratification information and the layer position interpretation data, establish a time-depth conversion velocity model, and use the gradient structure tensor attribute to constrain the time-depth conversion velocity model;

[0010] Convert the root mean square velocity obtained by time domain processing into a layer velocity, and incorporate the layer velocity into the time-depth conversion velocity model to obtain an initial velocity model for depth domain data processing.

[0011] Optionally, the obtaining depth data based on prestack time migration data of seismic data includes:

[0012] Perform prestack time migration on seismic data to obtain prestack time migration data;

[0013] Use the time migration velocity to perform time-depth conversion on the prestack time migration data to obtain the depth data.

[0014] Optionally, before performing prestack time migration on seismic data, it further includes:

[0015] Analyze the geological characteristics of the work area to obtain the velocities of different strata in the study area, and regard the strata with similar velocities as a velocity unit.

[0016] Optionally, the set frequency value is 60HZ.

[0017] Optionally, the establishing of the time-depth conversion velocity model based on the low-frequency logging curve, logging stratification information, and the horizon interpretation data includes:

[0018] Based on the low-frequency logging curve, logging stratification information, and the horizon interpretation data, establish a time-depth conversion velocity model through least squares interpolation.

[0019] Optionally, the converting the root-mean-square velocity obtained by time domain processing into interval velocity includes:

[0020] Convert the root-mean-square velocity obtained by time domain processing into interval velocity through the Dix formula.

[0021] Optionally, after obtaining the initial velocity model for depth domain data processing, it further includes:

[0022] Based on the initial velocity model for depth domain data processing, carry out migration imaging work.

[0023] In a second aspect, the present invention proposes an electronic device, which includes:

[0024] At least one processor; and,

[0025] A memory communicatively connected to the at least one processor; wherein,

[0026] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for establishing the initial velocity model for depth domain data processing according to any one of the first aspect.

[0027] In a third aspect, the present invention proposes a non-transitory computer-readable storage medium, which stores computer instructions for causing a computer to execute the method for establishing the initial velocity model for depth domain data processing according to any one of the first aspect.

[0028] In a fourth aspect, the present invention provides an apparatus for establishing an initial velocity model for deep domain data processing, comprising:

[0029] a deep data acquisition module configured to obtain deep data based on the prestack time migration data of seismic data;

[0030] a horizon interpretation module configured to interpret the top and bottom horizons of strata with similar velocities on the deep data to obtain horizon interpretation data, and extract gradient structure tensor attributes;

[0031] a filtering module configured to filter well logging curves to obtain low-frequency well logging curves with frequencies lower than a set frequency value;

[0032] a velocity model establishment module configured to establish a time-depth conversion velocity model based on the low-frequency well logging curves, well logging stratification information, and the horizon interpretation data, and use the gradient structure tensor attributes to constrain the time-depth conversion velocity model; and convert the root-mean-square velocity obtained from time domain processing into interval velocity and incorporate it into the time-depth conversion velocity model to obtain an initial velocity model for deep domain data processing.

[0033] The beneficial effects of the present invention are as follows:

[0034] The present invention combines geological understanding, drilling and logging data, interpreted horizons, and gradient structure tensor attributes to establish an initial velocity model for deep domain data processing. Compared with using only the time domain processing velocity as the initial velocity, both the longitudinal and transverse accuracies are improved, and the number of iterations of the velocity model is reduced. Conventionally, when using the time domain processing velocity as the initial velocity, about 6-7 rounds of iteration are required for the imaging effect of deep domain seismic data to be optimal. However, with the initial velocity model for deep domain data processing established by combining geological understanding, drilling and logging data, and time migration data in this method, the number of iterations can be reduced to 3-4 rounds. For each project, 3 rounds of velocity iteration updates and migration work can be reduced. On the one hand, it reduces the cluster usage time, saving a large amount of costs and computing time. On the other hand, it can shorten the working area of the entire project research and improve efficiency.

[0035] The system of the present invention has other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent detailed implementation manners, or will be described in detail in the accompanying drawings incorporated herein and the subsequent detailed implementation manners. These accompanying drawings and detailed implementation manners are used together to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. In the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0037] Figure 1 The step diagram of a method for establishing an initial velocity model for deep domain data processing according to the present invention is shown.

[0038] Figure 2 The flowchart of a method for establishing an initial velocity model for deep domain data processing according to an embodiment of the present invention is shown.

[0039] Figure 3 The comparison diagram of the initial velocity models established by the conventional method and the method of the present invention in an embodiment of the present invention is shown.

[0040] Figure 4 The comparison diagram of the migrated gather obtained by migration imaging using different initial velocity models in an embodiment of the present invention is shown.

[0041] Figure 5 The comparison diagram of the migration imaging results (migration profiles) obtained by migration imaging using different initial velocity models in an embodiment of the present invention is shown. Detailed implementation manners

[0042] The present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0043] Embodiment 1

[0044] As Figure 1 shown, this embodiment provides a method for establishing an initial velocity model for deep domain data processing, including:

[0045] S1: Obtaining depth data based on the prestack time migration data of seismic data;

[0046] The method for obtaining depth data in this step specifically includes:

[0047] Performing prestack time migration on the seismic data to obtain prestack time migration data;

[0048] Performing time-depth conversion on the prestack time migration data using the time migration velocity to obtain depth data.

[0049] Before step S1, it further includes:

[0050] Analyzing the geological characteristics of the work area to obtain the velocities of different strata in the study area, and taking the strata with similar velocities as a velocity unit.

[0051] S2: Interpret the top and bottom horizons of formations with similar velocities on the depth data to obtain horizon interpretation data, and extract the gradient structure tensor attributes;

[0052] S3: Filter the logging curves to obtain low-frequency logging curves with frequencies lower than the set frequency value;

[0053] In this step, it is preferable to set the frequency value to 60 HZ.

[0054] Specifically, during the processing of seismic data in the depth domain, low-frequency velocities are required. Therefore, the logging curves are filtered to remove the curve features greater than or equal to 60 HZ, and only the low-frequency information is retained.

[0055] S4: Establish a time-depth conversion velocity model based on the low-frequency logging curves, logging stratification information, and horizon interpretation data, and use the gradient structure tensor attributes to constrain the time-depth conversion velocity model;

[0056] In this step, based on the low-frequency logging curves, logging stratification information, and horizon interpretation data, a time-depth conversion velocity model is established through least squares interpolation.

[0057] Specifically, the logging curves and logging stratification in step S3 and the interpreted horizons in step S2 are used to establish a time-depth conversion velocity model, which is constrained by the gradient structure tensor attributes. The advantage of this velocity model is relatively high longitudinal accuracy and good conformity with the structure, but the lateral accuracy is insufficient.

[0058] S5: Convert the root-mean-square velocity obtained from the time-domain processing into interval velocity, and incorporate the interval velocity into the time-depth conversion velocity model to obtain an initial velocity model for depth-domain data processing.

[0059] In this step, the root-mean-square velocity obtained from the time-domain processing is converted into interval velocity through the Dix formula.

[0060] In this embodiment, after obtaining the initial velocity model for depth-domain data processing, it further includes:

[0061] S6: Conduct migration imaging work based on the initial velocity model for depth-domain data processing.

[0062] Specifically, the root-mean-square velocity obtained from the time-domain processing is converted into interval velocity and incorporated into the time-depth conversion velocity model obtained in step S4 to establish a new velocity model, which makes up for the deficiency of lateral accuracy. Then, this new velocity model is used as the initial velocity model for depth-domain data processing to conduct migration imaging work.

[0063] Embodiment 2

[0064] To make the objectives, technical solutions and advantages of the present invention clearer, the following takes a certain area in Sichuan as an example to introduce the work of velocity modeling in the process of deep-domain data processing, so as to explain the present invention in more detail.

[0065] The flow of a method for establishing an initial velocity model for deep-domain data processing in this embodiment is as Figure 2 shown and includes the following steps:

[0066] (1) Conduct geological feature analysis of the work area. By analyzing, it is found that the interval velocities of the Jurassic Suining Formation in this area are similar, with an average formation velocity of 1985 m / s; the interval velocities of the Shaximiao Formation are similar, with an average velocity of 43908 m / s; the interval velocities of the Zhenzhuchong-Lianggaoshan Formation are 4600 m / s; the interval velocities of the Xujiahe Formation are similar, with an average velocity of 4950 m / s; and the interval velocities of the Leikoupo Formation are similar, with an average velocity of 5163 m / s.

[0067] (2) Conduct pre-stack time migration of seismic data. Use the time migration velocity to perform time-depth conversion on the pre-stack time migration data, and interpret the bottom boundary horizons of the Suining Formation, the Shaximiao Formation, the Zhenzhuchong Formation, the Xujiahe Formation, and the Leikoupo Formation on the converted depth data.

[0068] (3) Since low-frequency velocity is required in the process of deep-domain seismic data processing and high-frequency information will cause poor imaging, it is necessary to filter the high-frequency logging curves. In the study, the curve characteristics of the logging curves of wells ST1, YT1, QT1, and ZT1 greater than 60 HZ are filtered out, and only the low-frequency information below 60 Hz is retained.

[0069] (4) Establish a time-depth conversion velocity model by least-square interpolation for the logging curves after high-frequency removal, logging stratification, the horizons of the Suining Formation, the Shaximiao Formation, the Zhenzhuchong Formation, the Xujiahe Formation, and the Leikoupo Formation, and use the gradient structure tensor attribute for constraint. The advantage of this velocity model is higher longitudinal accuracy, but the lateral accuracy is insufficient.

[0070] (5) Convert the root-mean-square velocity of the work area in the time domain processing into interval velocity through the Dix formula and incorporate it into the velocity model in (4) to establish a new velocity model, which makes up for the shortcoming of insufficient lateral accuracy.

[0071] (6) Use the velocity model in (5) as the initial velocity model for deep-domain processing and conduct migration imaging work.

[0072] In the specific implementation process of the project, such as Figure 3As shown in the figure, two different velocity models were established by two methods respectively. From the comparison of the velocity models, compared with the initial velocity model on the left that directly converts the time migration velocity to the interval velocity by the conventional method, the initial model constructed by the method of the present invention under well control constraint on the right has higher accuracy and better matches the structure of the work area.

[0073] As Figure 4 shown in the figure, migration imaging was carried out on the work area through two different initial models. First, from the comparison of the migration gathers, compared with the migration gathers on the left that directly convert the time migration velocity to the interval velocity by the conventional method, the flatness of the migration gathers constructed by the method of the present invention under well control constraint on the right is higher. It should be noted that whether the gathers are flattened is an important basis for the rationality of the velocity model. The flatter the gathers, the more reasonable the velocity model, and the better the final imaging effect, which can reduce the number of subsequent velocity model iterations.

[0074] As Figure 5 shown in the figure, from the migration imaging results of the two different initial velocity models, compared with the migration imaging of the velocity model on the left that directly converts the time migration velocity to the interval velocity by the conventional method, the migration imaging effect of the initial velocity model under the well control structure constraint of the method of the present invention on the right is significantly improved, with stronger in-phase axis focusing and more obvious channel features.

[0075] Using the two velocity models to carry out depth domain processing, to obtain the optimal imaging with the same small effect, the velocity model of the conventional time migration velocity converted to interval velocity was iterated 7 times. While the initial velocity model with well control structure constraint established by the method of the present invention achieved the same effect after 3 iterations. The average time for one round of the velocity model is 8 days, and saving 4 rounds of time saves 32 days, greatly improving the efficiency of depth domain seismic data processing. In each round of velocity update, a large amount of cluster computing resources are required, and a large amount of electricity and depreciation costs are incurred. Saving 3 - 4 velocity update processes can also save a large amount of costs.

[0076] Embodiment 3

[0077] This embodiment provides an apparatus for establishing an initial velocity model for depth domain data processing, including:

[0078] A depth data acquisition module, configured to obtain depth data based on the pre-stack time migration data of seismic data;

[0079] A horizon interpretation module, configured to interpret the top and bottom horizons of strata with similar velocities on the depth data to obtain horizon interpretation data, and extract gradient structure tensor attributes;

[0080] A filtering module, configured to filter the logging curves to obtain low-frequency logging curves with frequencies lower than a set frequency value;

[0081] A velocity model building module, configured to build a time-depth conversion velocity model based on the low-frequency logging curves, logging stratification information, and the horizon interpretation data, and use the gradient structure tensor attribute to constrain the time-depth conversion velocity model; and convert the root-mean-square velocity obtained by time-domain processing into interval velocity and incorporate it into the time-depth conversion velocity model to obtain an initial velocity model for depth-domain data processing.

[0082] In this embodiment, it further includes a formation velocity analysis module, configured to analyze the geological characteristics of the work area to obtain the velocities of different formations in the study area, and regard the formations with similar velocities as a velocity unit.

[0083] In this embodiment, it further includes a migration imaging module, configured to carry out migration imaging work based on the initial velocity model for depth-domain data processing.

[0084] Embodiment 4

[0085] This embodiment provides an electronic device, which includes:

[0086] At least one processor; and,

[0087] A memory communicatively connected to the at least one processor; wherein,

[0088] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for building the initial velocity model for depth-domain data processing described in any of the above embodiments.

[0089] The electronic device according to an embodiment of the present disclosure includes a memory and a processor, and the memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0090] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0091] Those skilled in the art should be able to understand that, in order to solve the technical problem of how to obtain good user experience effects, the present embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present disclosure.

[0092] For the detailed description of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0093] Embodiment 5

[0094] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the method for establishing an initial speed model of depth domain data processing described in any one of the foregoing embodiments.

[0095] According to an embodiment of the present disclosure, a non-transitory computer-readable instruction is stored on a computer-readable storage medium. When the non-transitory computer-readable instruction is run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present disclosure are executed.

[0096] The above 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 removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0097] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for establishing an initial velocity model for depth-domain data processing, characterized in that, it includes: Obtaining depth data based on the prestack time migration data of seismic data; Interpret the top and bottom horizons of strata with similar velocities on the depth data to obtain horizon interpretation data, and extract the gradient structure tensor attributes; Filter the logging curves to obtain low-frequency logging curves with frequencies lower than the set frequency value; Establish a time-depth conversion velocity model based on the low-frequency logging curves, logging stratification information, and the horizon interpretation data, and use the gradient structure tensor attributes to constrain the time-depth conversion velocity model; Convert the root-mean-square velocity obtained from time-domain processing into interval velocity, and incorporate the interval velocity into the time-depth conversion velocity model to obtain an initial velocity model for depth-domain data processing.

2. The method for establishing an initial velocity model for depth-domain data processing according to claim 1, characterized in that, The obtaining depth data based on the prestack time migration data of seismic data includes: Performing prestack time migration on the seismic data to obtain prestack time migration data; Performing time-depth conversion on the prestack time migration data using the time migration velocity to obtain the depth data.

3. The method for establishing an initial velocity model for depth-domain data processing according to claim 2, characterized in that, Before performing prestack time migration on the seismic data, it further includes: Analyze the geological characteristics of the work area to obtain the velocities of different strata in the study area, and regard the strata with similar velocities as a velocity unit.

4. The method for establishing an initial velocity model for depth-domain data processing according to claim 1, characterized in that, The set frequency value is 60HZ.

5. The method for establishing an initial velocity model for depth-domain data processing according to claim 1, characterized in that, The establishing a time-depth conversion velocity model based on the low-frequency logging curves, logging stratification information, and the horizon interpretation data includes: Based on the low-frequency logging curves, logging stratification information, and the horizon interpretation data, establish a time-depth conversion velocity model by least squares interpolation.

6. The method for establishing an initial velocity model for depth-domain data processing according to claim 1, characterized in that, The converting the root-mean-square velocity obtained from time-domain processing into interval velocity includes: Converting the root-mean-square velocity obtained from time-domain processing into interval velocity through the Dix formula.

7. The method for establishing an initial velocity model for depth-domain data processing according to claim 1, characterized in that, After obtaining the initial velocity model for depth-domain data processing, it further includes: Based on the initial velocity model for depth-domain data processing, carry out migration imaging work.

8. An electronic device, characterized in that, the electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for establishing an initial velocity model for depth-domain data processing according to any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the method for establishing an initial velocity model for deep domain data processing according to any one of claims 1-7.

10. An apparatus for establishing an initial velocity model for deep domain data processing, characterized in that, it comprises: a deep data acquisition module for obtaining deep data based on the pre-stack time migration data of seismic data; a horizon interpretation module for interpreting the top and bottom horizons of strata with similar velocities on the deep data to obtain horizon interpretation data and extract gradient structure tensor attributes; a filtering module for filtering well logs to obtain low-frequency well logs with frequencies lower than a set frequency value; a velocity model establishment module for establishing a time-depth conversion velocity model based on the low-frequency well logs, well log stratification information and the horizon interpretation data, and constraining the time-depth conversion velocity model with the gradient structure tensor attributes; and converting the root mean square velocity obtained by time domain processing into interval velocity and integrating it into the time-depth conversion velocity model to obtain an initial velocity model for deep domain data processing.

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