Method for identifying volcanic institutions based on seismic data

Abstract

The invention provides a volcanic institution identification method based on seismic data. The volcanic institution identification method comprises the following steps: step 1, obtaining result seismic data including post-stack seismic data; step 2, carrying out construction smoothing processing on vibration signals in the post-stack seismic data; step 3, carrying out low-pass filtering on the vibration signal after construction smoothing processing; and step 4, obtaining the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, and identifying the vibration signal as a spurting phase mechanism. The low-pass filtering can eliminate the influence of the high-frequency component in the vibration signal on the dominant frequency. Furthermore, by calculating the amplitude and the instantaneous frequency, the strong amplitude and the low frequency are taken into the main mechanism identification basis, interference can be eliminated, and the underground structure or geologic features can be identified more effectively.

Description

Technical Field

[0001] The present invention belongs to the technical field of petroleum exploration seismic data interpretation, and in particular relates to a volcanic structure identification method based on seismic data. Background Art

[0002] In recent years, significant progress has been made in the field of offshore oil and gas exploration, especially in the exploration of volcanic rock-related oil and gas reservoirs. As a type of unconventional oil and gas reservoir, it has enormous potential and is expected to open up new areas of offshore oil and gas exploration. However, in offshore exploration practice, volcanic types such as ancient volcanoes under the seabed are often encountered. These geological structures are often difficult to obtain sufficient data support, with few or even no drilling wells, and are blocked by volcanic rocks, resulting in generally low-quality seismic data. Taking a block in the South China Sea as an example, the existing seismic data in this area is affected by large areas of volcanic rocks, showing characteristics such as low signal-to-noise ratio, low seismic signal frequency, low resolution, and poor overall imaging quality. These problems directly lead to the extremely difficult identification of volcanic structures.

[0003] In the exploration and study of volcanic-related oil and gas reservoirs, the three-dimensional characterization of volcanic structures is crucial. Understanding these structures involves the storage, migration, and eruption processes of magma, and in turn, the existence and possible distribution of volcanic oil and gas reservoirs.

[0004] Comprehensive research at home and abroad indicates that existing volcanic structure classification and characterization methods based on 3D seismic data primarily rely on factors such as reflection, amplitude, continuity differences, and geometric characteristics (Zou Caineng et al., 2008; Zuo Guoping et al., 2011). However, most research remains limited to the 2D domain, focusing primarily on slice and profile analysis, with relatively little research on 3D characterization. For example, Zhang et al. (2011) used seismic reflection intensity of volcanic rocks combined with amplitude attributes to assist in volcanic structural mapping; Zhu et al. (2014) exploited the chaotic discontinuities of volcanic conduits and employed variance attributes to characterize volcanic conduits in 3D, while also using pure amplitude classification to characterize volcanic effusive facies in 3D; Zhao (2018) combined seismic facies morphological characteristics with gravity and magnetic anomaly data to characterize volcanic effusive and conduit facies in both plane and cross-section, respectively; Liang (2019) employed a method that combined multiple amplitude attributes with ant-body morphology to characterize the extent of volcanic effusive facies in plane; and Yue (2021) used pure geometric morphological characteristics of seismic facies to perform 2D characterization of volcanic structures in local cross-sections. In general, the main identification method of volcanic eruption phase focuses on seismic reflection intensity, while the identification of volcanic channel phase relies on fault-like properties. Its identification method is similar to that of faults. Although the effect is certain, it is obviously affected by fault interference and may produce false images in volcanic shielding areas. It is also affected by low signal-to-noise ratio and high noise areas. Summary of the Invention

[0005] The purpose of the present invention is to provide a volcanic structure identification method based on seismic data that can reduce the influence of noise interference on volcanic structure identification.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] A volcanic structure identification method based on seismic data is characterized by comprising the following steps:

[0008] Step 1: Acquire the final seismic data including post-stack seismic data; Step 2: Perform structural smoothing on the vibration signal in the post-stack seismic data; Step 3: Perform low-pass filtering on the vibration signal after structural smoothing; Step 4: Acquire the amplitude and instantaneous frequency of the vibration signal after low-pass filtering and identify it as an ejection phase mechanism.

[0009] Preferably, it also includes, step 6, performing dip edge enhancement processing on the vibration signal after the smoothing processing constructed in step 2; step 7, calculating the inconsistency of the vibration signal after the dip edge enhancement processing, and identifying the part where the inconsistency exceeds a specific value as a channel phase volcanic structure; step 8, selecting the part where the inconsistency exceeds a specific value to form a plurality of voxels to characterize the channel phase volcanic structure.

[0010] Preferably, step 4 is: in step 4, the amplitude and instantaneous frequency of the vibration signal after low-pass filtering are obtained, identified as an overflow phase mechanism, and a plurality of voxels are formed; and in step 5, the overflow phase mechanism is depicted according to the plurality of voxels.

[0011] Preferably, step 4 is: obtaining the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, selecting multiple groups of amplitudes higher than different amplitudes and frequencies lower than a certain value, identifying them as the overflow phase mechanism, and forming several levels of voxels;

[0012] Step 5 is, in step 5, respectively characterizing and obtaining an early-stage overflow phase mechanism and a late-stage overflow phase mechanism according to the voxels of the plurality of levels.

[0013] Preferably, step 4 is to obtain the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, take the square root of the amplitude and instantaneous frequency, select multiple groups of amplitude roots higher than different amplitudes and frequency roots lower than a certain value, identify them as the overflow phase mechanism, and form several levels of voxels.

[0014] In summary, low-pass filtering can eliminate the influence of high-frequency components in vibration signals on the main frequency. Furthermore, by calculating amplitude and instantaneous frequency, strong amplitude and low frequency can be incorporated into the main structure identification basis, eliminating interference and more effectively identifying underground structures or geological features. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 , which is a flow chart of a volcanic structure identification method based on seismic data according to an embodiment of the present invention.

[0016] Figure 2 , which is a flow chart of a volcanic structure identification method based on seismic data according to another embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be described more clearly and completely below by way of embodiments in conjunction with the accompanying drawings.

[0018] Figure 1 , is a flow chart of a volcanic structure identification method based on seismic data according to an embodiment of the present invention. Figure 1 As shown, the volcanic structure identification method based on seismic data involved in this embodiment includes the following steps:

[0019] Step S1, obtaining the final seismic data including post-stack seismic data;

[0020] Step S2, performing structural smoothing processing on the vibration signal in the post-stack seismic data;

[0021] Step S3, performing low-pass filtering on the vibration signal after the structural smoothing process;

[0022] Step S4, obtaining the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, and identifying it as an overflow phase mechanism.

[0023] Post-stack seismic data refers to the result of a series of data processing steps on seismic records. Seismic records are the vibration signals recorded by seismic instruments during earthquake events. This raw seismic data undergoes a series of processing steps, including noise removal, filtering, correction, and stacking, to obtain clearer and more useful earthquake information. This processed seismic data is called post-stack seismic data and is commonly used in fields such as seismological research, geological exploration, and earthquake monitoring.

[0024] Applying "structural smoothing" to post-stack seismic data can reduce or eliminate clutter caused by volcanic shielding. This volcanic shielding causes seismic waves to reflect and refract as they pass through volcanic layers, generating clutter in the seismic data. Structural smoothing attempts to remove or reduce this clutter, making the seismic data clearer and easier to interpret and analyze.

[0025] Structural smoothing is achieved by filtering or signal processing the seismic data. The following are the steps to perform structural smoothing:

[0026] Preprocessing data: First, some preprocessing is required for the post-stack seismic data, including removing possible noise, correcting data offset or time delay, etc.

[0027] Select the appropriate filtering method: Determine the filtering method that suits the characteristics of the current data. Filtering can selectively pass or suppress signals within a specific frequency range.

[0028] Adjust filter parameters: According to the geological model and processing requirements, adjust the filter parameters to achieve the best filtering effect.

[0029] Apply Filter Processing: Use the selected filter and apply it to the post-stack seismic data.

[0030] Evaluate processing effectiveness: The processed data needs to be evaluated to ensure that the filtering process has achieved the desired effect and that no important geological information has been lost.

[0031] Iterative optimization: Based on the evaluation results, you may need to repeatedly adjust the filtering parameters or try different processing methods until you obtain satisfactory results.

[0032] Through these steps, the post-stack seismic data can be structurally smoothed to reduce or eliminate the clutter reflections caused by the shielding effect of volcanic rocks, thereby obtaining clearer and more interpretable seismic data.

[0033] A seismic waveform is a seismic signal (vibration signal) observed by a seismic instrument when recording an earthquake event. It reflects the reflection, refraction, and scattering characteristics of the underground structure. Smoothing can be performed on seismic waveforms to reduce noise, enhance signal characteristics, and eliminate or weaken clutter reflections caused by volcanic rock shielding. In seismic data, seismic waveforms can be obtained from seismic profiles, seismic records, or other seismic observation data. Smoothing generally involves filtering, denoising, and signal enhancement of these seismic waveforms to make the seismic data clearer and easier to interpret and analyze. In this case, smoothing helps eliminate clutter reflections caused by volcanic rock shielding, thereby enhancing the continuity and interpretability of the seismic data.

[0034] The primary difference between low-pass filtering and other types of filtering, such as high-pass filtering, band-pass filtering, and band-stop filtering, lies in how they respond to signal frequency. A low-pass filter allows signals below a certain cutoff frequency to pass through while rejecting signals above that frequency. This filter can remove high-frequency noise or fine details while retaining the signal's low-frequency components. In seismic data processing, low-pass filtering is often used to smooth data, remove high-frequency noise or sudden changes, and enhance low-frequency signal characteristics.

[0035] In this embodiment, low-pass filtering can eliminate the influence of high-frequency components in the vibration signal on the main frequency.

[0036] When processing seismic data, amplitude and instantaneous frequency are calculated. Amplitude describes the strength of the seismic waves, while instantaneous frequency provides information about the temporal variation of the seismic signal. Incorporating strong amplitude and low frequency into primary structural identification criteria means these characteristics are used to identify subsurface structures or geological features, such as possible rock formations or strata.

[0037] Different volcanic structures exhibit distinct characteristics in conventional seismic data. Eruptive volcanoes exhibit continuous or intermittent reflections with strong amplitude and layered structures. Effusion-type volcanoes, on the other hand, exhibit short, massive eruptions in terms of distribution and overall volume. Volcanic channel volcanoes, on the other hand, exhibit longitudinal tubular or fissure-like structures of varying thickness, connected to the volcanic vent at their upper portion. Seismic activity exhibits low- to medium-frequency, weak-amplitude, chaotic reflections, and seismic axis-displacement faults at the interface with layered sedimentary strata.

[0038] Figure 2 This is a flow chart of a volcanic structure identification method based on seismic data according to another embodiment of the present invention.

[0039] Further, refer to Figure 2 , the same step S3 as the previous embodiment can be set in parallel:

[0040] Step S6, performing a dip edge enhancement process on the vibration signal after the smoothing process constructed in step S2;

[0041] Step S7, calculating the inconsistency of the vibration signal after the dip edge enhancement process, and identifying the part where the inconsistency exceeds a specific value as a channel phase volcanic structure;

[0042] Step S8: Select the portion where the inconsistency exceeds a specific value to form a number of voxels to characterize the channel phase volcanic structure.

[0043] In this embodiment, step S4 is further refined to obtain the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, identify it as an overflow phase mechanism, and form a plurality of voxels;

[0044] In order to depict the ejection phase mechanism, this embodiment adds step S5 to depict the ejection phase mechanism based on a plurality of voxels.

[0045] Step S4 of this embodiment can be further refined to obtain the amplitude and instantaneous frequency of the low-pass filtered vibration signal, select multiple groups of amplitudes above different amplitudes and frequencies below certain values, identify them as the overflow phase mechanism, and form several levels of voxels;

[0046] like Figure 2 As shown, step S5 can be further refined to respectively characterize and obtain the early-stage overflow phase mechanism and the late-stage overflow phase mechanism according to several levels of voxels.

[0047] Of course, if Figure 2 As shown, for further optimization, step S4 of this embodiment is further refined as follows: obtaining the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, taking the square root of the amplitude and instantaneous frequency, selecting multiple groups of amplitude roots higher than different amplitudes and frequency roots lower than a certain value, identifying them as ejection phase mechanisms, and forming several levels of voxels.

[0048] It should be noted that step S6 does not necessarily have to adopt the same structural smoothing processing method as that in step S3. Different structural smoothing processing methods can be adopted to adapt to the identification of the overflow phase mechanism and the channel phase mechanism respectively.

[0049] Based on this, through data adaptive processing, special identification, and voxel picking, the volcano's late effusive phase structure, early effusive phase structure, and channel phase structure were identified in the three-dimensional domain, and the three-dimensional spatial distribution of the three structures was obtained.

[0050] The effusive and channel phases of a volcano refer to different types of lava flows formed during an eruption. They differ in the flow patterns and characteristics of the lava flows.

[0051] The extrusive phase refers to the period during which magma erupts and flows from a volcanic vent. During this phase, magma rapidly flows out of the vent, forming surface eruption products such as lava flows and pyroclastic flows. The extrusive phase typically accompanies the explosive phase of a volcanic eruption and is characterized by the direct exposure of erupted material to the surface, forming volcanic eruptive structures such as volcanic cones and lava terraces.

[0052] The channelized phase refers to the period during a volcanic eruption when magma is transported to the surface through underground channels or fissures. During this phase, magma slowly moves along these channels or fissures, eventually reaching the surface. The channelized phase is characterized by relatively slow magma flow, forming underground channels or pipes. Magma may cool and solidify within these channels, forming underground structures such as basalt columns and magma tubes.

[0053] These two phases represent different stages and patterns of magma flow during an eruption. The effusive phase is usually the initial stage of an eruption, while the channeling phase may occur later in the eruption, but the duration and relative order of these phases can vary depending on the specific circumstances of the eruption.

[0054] A voxel is a data element in seismic data (a voxel is a unit of volume in three-dimensional space) with specific attributes or characteristics. Selecting multiple groups of amplitudes above different amplitudes represents selecting voxels with strong signals in the seismic data. These voxels correspond to specific parts or features of the subsurface structure. Distinguishing between late effusive and early effusive phases during the picking process requires the ability to distinguish between voxels of different phases generated by volcanic activity at different times in the seismic data, thereby better understanding the evolution of volcanic activity.

[0055] When processing seismic data, the amplitude and instantaneous frequency must first be calculated, with strong amplitudes and low frequencies serving as the primary basis for structural identification. This step provides a preliminary understanding and identification of subsurface structures or geological features. Once this identification basis is established, voxel picking can be performed, with high values ​​being prioritized, enabling the distinction of different volcanic phases.

[0056] The effusive phase structure characterization process first adaptively processes the resulting seismic data. Structural smoothing eliminates some of the clutter caused by volcanic rock shielding, enhancing data continuity. Low-pass filtering eliminates the influence of high-frequency components on the dominant frequency. The effusive phase structure is determined by using amplitude / instantaneous frequency, focusing on both strong amplitude and low frequency as the primary identification criteria. Finally, high-value voxels are selected to identify the effusive phase structure. This process exploits the energy shielding effect of late volcanic effusive structures on early effusive structures. By classifying the amplitude / instantaneous frequency, early and late effusive phase structures are identified. Clastic rock formations outside the early effusive phase are also screened out in this classification process due to their higher dominant frequencies.

[0057] In the characterization of volcanic channel phases, through pre-adaptive processing of seismic data, structural smoothing processing and dip edge enhancement processing are performed successively, which not only eliminates the broken reflection phenomenon caused by high-frequency noise due to the shielding effect of volcanic rocks, but also enhances the detectability of layered discontinuities in the data. The seismic data is processed to a state suitable for channel identification. Based on this set of data, the use of inconsistency to identify and characterize volcanic channels can achieve better results.

Claims

1. A volcanic structure identification method based on seismic data, characterized in that: The following steps are involved: Step 1, obtaining the final seismic data including post-stack seismic data; Step 2, performing structural smoothing processing on the vibration signal in the post-stack seismic data; Step 3, performing low-pass filtering on the vibration signal after the structural smoothing process; Step 4: Obtain the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, and identify it as an overflow phase mechanism.

2. The volcanic structure identification method based on seismic data according to claim 1, characterized in that: It also includes, Step 6, performing a dip edge enhancement process on the vibration signal after the smoothing process constructed in step 2; Step 7, calculating the inconsistency of the vibration signal after dip edge enhancement processing, and identifying the portion where the inconsistency exceeds a specific value as a channel phase volcanic structure; Step 8: Select the portion where the inconsistency exceeds a specific value to form a number of voxels to depict the channel phase volcanic structure.

3. The volcanic structure identification method based on seismic data according to claim 1, characterized in that: Step 4 is, Step 4, obtaining the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, identifying it as an overflow phase mechanism, and forming a plurality of voxels; The volcanic structure identification method based on seismic data also includes: Step 5: depict the ejection phase mechanism according to the plurality of voxels.

4. The volcanic structure identification method based on seismic data according to claim 3, characterized in that: Step 4 is, Step 4: obtaining the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, selecting multiple groups of amplitudes higher than different amplitudes and frequencies lower than certain values, identifying them as ejection phase mechanisms, and forming voxels of several levels; The step 5 is: Step 5: respectively characterize and obtain an early-stage overflow phase mechanism and a late-stage overflow phase mechanism according to the voxels of the plurality of levels.

5. The volcanic structure identification method based on seismic data according to claim 4, characterized in that: Step 4 is to obtain the amplitude and instantaneous frequency of the vibration signal after low-pass filtering, and take the square root of the amplitude and instantaneous frequency. A plurality of groups of amplitude roots higher than different amplitude values ​​and frequency roots lower than a certain value are selected and identified as the ejection phase mechanism, and several levels of voxels are formed.

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