Seismic variable frequency energy fusion method, device and storage medium

Abstract

The application discloses a seismic variable-frequency energy fusion method and device and a storage medium, and belongs to the technical field of geological exploration. The method comprises the following steps in sequence: S1. performing frequency division processing on seismic data to obtain a low-frequency body, a main-frequency body and a relatively high-frequency body; S2. selecting the low-frequency body, the main-frequency body and the relatively high-frequency body with energy attributes having responses as an optimal low-frequency body, an optimal main-frequency body and an optimal relatively high-frequency body, and extracting energy attributes of the optimal low-frequency body, the optimal main-frequency body and the optimal relatively high-frequency body; and S3. adjusting the energy attribute weight proportion of the optimal low-frequency body and the optimal relatively high-frequency body to be higher than that of the optimal main-frequency body, and carrying out attribute fusion. The application adjusts the weight proportion of low-frequency information and relatively high-frequency information implied by seismic attributes, and combines the amplification and representation of energy attributes, so that various concealed geological abnormal bodies can be effectively identified, the operation is simple, the multiple solution problem is small, and the result is accurate and reliable.

Description

Technical Field

[0001] The present invention belongs to the field of geological exploration technology and relates to a seismic attribute fusion method, specifically a seismic frequency conversion energy fusion method, equipment and storage medium. Background Art

[0002] Seismic attributes can be defined as a measure of the geometric, kinematic, dynamic, or statistical characteristics of seismic data, with clear physical meaning. Currently, with the rapid development of computer technology, coupled with new knowledge from fields such as mathematics and information science, hundreds of thousands of seismic attributes have been derived. However, the complexity of subsurface geology and the uncertainty of seismic information make it impossible for any single seismic attribute to fully and accurately predict reservoir formations, oil and gas reserves, or describe reservoir characteristics. Therefore, the rational selection of attributes or the fusion of multiple attributes has become a key approach to improving reservoir prediction accuracy.

[0003] As one of the core technologies in oil and gas reservoir exploration, the ultimate goal of geophysical technicians is to better extract implicit geological information from seismic data, combine it with comprehensive analysis and comparison of geological and drilling data, and convert attribute information into parameters related to lithology, physical properties and reservoirs for comprehensive seismic geological analysis.

[0004] Based on current research both domestically and internationally, some image processing-based algorithms and statistical algorithms, such as texture attributes, energy half-time attributes, and structural tensor attributes, have achieved promising results in predicting specific geological bodies. Other literature suggests that using principal component analysis, cluster analysis, independent component analysis, and other methods to optimize attributes, or to experiment with multiple attribute methods and fuse attributes, has also yielded promising results.

[0005] Current technical solutions primarily rely on conventional attribute extraction or fusion, such as amplitude, energy, arc length, and energy half-time; or attribute fusion based on statistical attribute optimization followed by statistical attribute fusion, to identify geological anomalies. However, the identification of hidden geological anomalies is difficult, as these are often insensitive to these conventional attributes, resulting in fuzzy boundaries, high ambiguity, and low interpretation accuracy. Furthermore, conventional attribute extraction or attribute fusion analysis still presents the following challenges: First, while a certain attribute or category of attributes may be effective for a particular geological anomaly, it is not representative and still requires extensive analysis and method testing. Second, current attribute fusion methods rely excessively on statistical algorithms or random, repetitive analysis and testing, which is time-consuming and labor-intensive, requiring a significant amount of redundant, repetitive work and hindering technical judgment. Summary of the Invention

[0006] The purpose of the present invention is to provide a seismic frequency conversion energy fusion method, equipment and storage medium to solve the problem that conventional attribute extraction or fusion analysis has low interpretation accuracy, complex methods and is not representative of hidden geological anomalies.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A seismic frequency conversion energy fusion method comprises the following steps performed in sequence:

[0009] S1. Perform frequency division processing on the seismic data to obtain a frequency division body, wherein the frequency division body is divided into a low-frequency body, a main frequency body, and a relatively high-frequency body;

[0010] S2. Selecting low-frequency bodies, main-frequency bodies, and relatively high-frequency bodies with corresponding energy properties as preferred low-frequency bodies, preferred main-frequency bodies, and preferred relatively high-frequency bodies, and extracting the energy properties of the preferred low-frequency bodies, preferred main-frequency bodies, and preferred relatively high-frequency bodies;

[0011] S3. Adjust the energy attribute weight ratio of the preferred low-frequency body and the preferred relatively high-frequency body to be higher than the energy attribute of the preferred main-frequency body, and carry out attribute fusion;

[0012] The low-frequency body refers to a frequency body with a lower frequency than that of the main-frequency body, and the relatively high-frequency body refers to a frequency body with a higher frequency than that of the main-frequency body.

[0013] As a limitation, the frequencies of the frequency dividers form an arithmetic progression with a frequency difference of 2-6 Hz.

[0014] As another limitation, the weight ratio of the energy attributes of the preferred low-frequency body, the preferred main-frequency body and the preferred high-frequency body is 1.1-1.3:1:1.1-1.3.

[0015] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned earthquake frequency conversion energy fusion method when executing the computer program.

[0016] The present invention also provides a computer-readable storage medium, which stores a computer program for executing the above-mentioned earthquake frequency conversion energy fusion method.

[0017] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared with the prior art:

[0018] ① The seismic frequency conversion energy fusion method, equipment, and storage medium provided by the present invention adjust the weight ratio of the low-frequency information and relatively high-frequency information implicit in the seismic attributes, and combine it with the amplification and characterization of energy attributes to effectively identify hidden geological anomalies and their sweet spots. This solves the problem of conventional attribute fusion in which the boundaries of hidden geological anomalies are vague and have multiple solutions, providing seismic support for well placement, exploration evaluation, and reservoir prediction.

[0019] ② The seismic frequency conversion energy fusion method, equipment and storage medium provided by the present invention can effectively identify and characterize a variety of hidden geological anomalies and are universal;

[0020] ③ The seismic frequency conversion energy fusion method, equipment and storage medium provided by the present invention do not rely on statistical algorithms or a large number of randomly repeated method experiments or property tests, which greatly simplifies the operation process, reduces human uncontrollable factors, reduces multi-solution, and provides accurate and reliable results.

[0021] The present invention adjusts the weight ratio of low-frequency information and relatively high-frequency information implicit in seismic attributes, and combines the amplification and characterization effects of energy attributes to effectively identify a variety of hidden geological anomalies. It has simple operation, low multi-solution potential, and accurate and reliable results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flowchart of the earthquake frequency conversion energy fusion method in Example 1;

[0023] Figure 2 This is the frequency characteristic diagram of the seismic data in Example 1;

[0024] Figure 3 This is the energy change diagram of the 12Hz earthquake frequency division volume in Example 1;

[0025] Figure 4 This is a comparison chart of the plane results of seismic data processing in Example 1, where Figure 4 a is the normal energy attribute plane, Figure 4 b is the amplitude + energy + frequency attribute fusion plane, Figure 4 c is the frequency conversion energy attribute fusion plane. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below by way of specific examples. It should be understood that the described examples are only used to illustrate the present invention and are not intended to limit the present invention.

[0027] Example 1 Earthquake frequency conversion energy fusion method

[0028] This embodiment discloses a method for fusion of earthquake frequency conversion energy, and its flow chart is as follows: Figure 1 As shown, this embodiment characterizes and identifies a river channel sand body (hidden geological anomaly body), specifically including the following steps performed in sequence:

[0029] S1. Based on seismic data, time-frequency analysis and seismic frequency division technology are used to perform frequency division processing on seismic data. Frequency division algorithms include wavelet transform, S transform, and matching pursuit, which can be flexibly selected according to the time limit required by the project. This embodiment uses wavelet transform; the frequency division body can be obtained using most current seismic commercial software. Since it is necessary to obtain the information reflection characteristics of the hidden geological anomaly body, the bandwidth and main frequency characteristics of the seismic data are first determined, such as Figure 2 As shown, the parameters that need to be divided are then determined.

[0030] Depend on Figure 2 As we can see, the bandwidth of seismic data is 8-50Hz, with a dominant frequency of 30Hz. When dividing the frequencies, the low-frequency body is generally selected to be slightly higher than the lowest frequency of 8Hz, typically between 12-16Hz. The frequency division bodies form an arithmetic progression, with a frequency difference of 2-6Hz.

[0031] The frequency division bodies obtained in this embodiment are: low-frequency body: 12 Hz, 18 Hz and 24 Hz; main frequency body: 30 Hz; relatively high-frequency body: 36 Hz, 42 Hz and 48 Hz (frequency difference is 6 Hz).

[0032] The frequency difference can also be selected as any number between 2-6Hz. For example, when the frequency difference is selected as 2Hz, the obtained crossover bodies are: low frequency body: 12Hz, 14Hz, 16Hz, 18Hz, 20Hz, 22Hz, 24Hz, 26Hz and 28Hz; main frequency body: 30Hz; relatively high frequency body: 32Hz, 34Hz, 36Hz, 38Hz, 40Hz, 42Hz, 44Hz, 46Hz and 48Hz.

[0033] S2. Perform quality control analysis on the multiple frequency-fraction bodies obtained, that is, select the low-frequency body, main-frequency body and relatively high-frequency body that respond to the energy attributes of the target layer as the preferred low-frequency body, preferred main-frequency body and preferred relatively high-frequency body, and extract the energy attributes of the preferred low-frequency body, preferred main-frequency body and preferred relatively high-frequency body.

[0034] During the quality control analysis process, it is necessary to check whether there is a certain change or response in the energy properties of the frequency division body selected in the target layer segment. If there is no change or response, it is necessary to adjust the selection of the frequency division body in the first step, such as encrypting the frequency division body by reducing the frequency difference, so as to obtain the preferred frequency division body with a change or response in the energy properties.

[0035] In this embodiment, the preferred low-frequency body is 12 Hz, the preferred main-frequency body is 30 Hz, and the preferred relatively high-frequency body is 42 Hz. The energy change of the preferred low-frequency body is as follows: Figure 3shown.

[0036] Depend on Figure 3 It can be seen that under the 12Hz frequency division energy body, there are obvious changes in the strength of its energy characteristics. This change may be caused by the development of geological anomalies.

[0037] S3. Adjust the weight ratio of the energy attributes of the preferred frequency-dividing body, increase the weight ratio of the energy attributes of the preferred low-frequency body and the preferred high-frequency body, so that the weight ratio of the energy attributes of the preferred low-frequency body, the preferred main-frequency body and the preferred high-frequency body are all between 1.1-1.3:1:1.1-1.3, and then perform RGB attribute fusion.

[0038] In this embodiment, the weight ratio of the energy attributes of the preferred low-frequency body, the preferred main-frequency body, and the preferred high-frequency body is adjusted to 1.2:1:1.2, and RGB attribute fusion is performed to obtain a plane result. The result is as follows: Figure 4 As shown in c, geological anomalies are identified and depicted, highlighting hidden geological anomalies.

[0039] For the same seismic data, only the energy attributes are extracted, and the results are as follows Figure 4 As shown in a.

[0040] Extract amplitude attributes, energy attributes and frequency attributes from the same seismic data and perform attribute fusion. The results are as follows: Figure 4 As shown in b.

[0041] Depend on Figure 4 By comparison, Figure 4 a) A large area of ​​strong energy is visible, but the river channel features are unclear; Figure 4 The river channel features can be seen in part b, but the superposition relationship of the river channels and the period distribution of the river channels are not clear; compared with other methods, the earthquake frequency conversion energy attribute fusion plane map obtained in this embodiment ( Figure 4 c) The distribution characteristics of river channels in different periods can be seen, and the boundaries are clear, which is conducive to the subsequent geophysical exploration technicians to further refine the period, scale, and distribution characteristics of the river channel.

[0042] If there is actual drilling in the study area, the plane results obtained by the present invention need to be analyzed in combination with the drilling data and geological laws. If the plane results obtained by the present invention do not match the drilling data well, it is necessary to return to adjust the selection of the frequency division body.

[0043] In this embodiment, no wells are drilled in the channel sand body study area, and no frequency division body adjustment is required.

[0044] Example 2 A computer device

[0045] 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 above-mentioned earthquake frequency conversion energy fusion method.

[0046] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0047] 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. The processor is configured to execute the computer-readable instructions stored in the memory.

[0048] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this 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 scope of protection of this disclosure.

[0049] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0050] Example 3 A computer-readable storage medium

[0051] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the earthquake frequency conversion energy fusion method is implemented.

[0052] The computer-readable storage medium stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods.

[0053] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

Claims

1. A seismic frequency conversion energy fusion method, characterized in that: The process includes the following steps: S1. Perform frequency division processing on the seismic data to obtain frequency division bodies, wherein the frequency division bodies are divided into a low-frequency body, a main-frequency body, and a relatively high-frequency body; wherein the low-frequency body refers to a frequency body with a lower frequency than the main-frequency body, and the relatively high-frequency body refers to a frequency body with a higher frequency than the main-frequency body; The frequencies of the frequency dividers form an arithmetic progression with a frequency difference of 2-6 Hz; S2. Perform quality control analysis on the multiple obtained frequency fractions, select low-frequency bodies, main-frequency bodies, and relatively high-frequency bodies that respond to the energy attributes of the target layer as preferred low-frequency bodies, preferred main-frequency bodies, and preferred relatively high-frequency bodies, and extract the energy attributes of the preferred low-frequency bodies, preferred main-frequency bodies, and preferred relatively high-frequency bodies; If, during the quality control analysis, the selected energy attribute of the target layer does not change or respond, the selection of the frequency division body in step S1 is adjusted to obtain a preferred frequency division body with a change or response in the energy attribute; S3. Adjust the energy attribute weight ratio of the preferred low-frequency body and the preferred relatively high-frequency body to be higher than the energy attribute of the preferred main-frequency body, and perform RGB attribute fusion to obtain a planar result; Among them, when the ratio of the energy attribute of the preferred main frequency body is 1, the weight ratio of the energy attributes of the preferred low frequency body and the preferred relatively high frequency body is between 1.1-1.

3.

2. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the earthquake frequency conversion energy fusion method according to claim 1 is implemented.

3. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program for executing the earthquake frequency conversion energy fusion method according to claim 1.

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