Waveform component clustering-based surrounding rock heterogeneity influence removal method

Abstract

The invention relates to the technical field of oil-gas exploration, in particular to a surrounding rock heterogeneity influence removal method based on waveform component clustering, which is characterized in that by combining waveform decomposition and reconstruction with waveform clustering, geological significance represented by seismic information is clear while strong reflection is removed, and result reasonability is verified by combining various factors, so that the influence of the surrounding rock heterogeneity is eliminated. And a prediction result most conforming to the geological viewpoint is obtained. According to the method, seismic facies analysis is carried out on the waveform component body, geophysical information is associated with geological information, and the problem that waveform decomposition data is indefinite in geological significance is solved. Waveform clustering is utilized to describe component volume seismic facies, seismic reflection characteristic planes are visualized, data dimension reduction is achieved, component volumes are sorted based on a seismic facies plane distribution diagram, and sorting efficiency is improved. A seismic interpretation problem is converted into a qualitative problem of a waveform component, the influence of volcanic surrounding rock on seismic reflection characteristics of a target stratum is removed, it is guaranteed that a prepared interpretation scheme is reasonable, and quality control is conducted through multiple factors in each stage.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and in particular to a method for removing the influence of surrounding rock heterogeneity based on waveform component clustering. Background Art

[0002] In the practice of oil and gas seismic exploration, volcanic rocks have a strong shielding and absorption effect on seismic waves, and often form strong reflections and multiple reflections. When the strong wave impedance difference reflection interface caused by volcanic rocks overlaps with the reflection interface of the target layer, the effective reservoir information is submerged in the strong reflection, or the target reservoir is close to the strong reflection layer, resulting in the shielding of the signal by the strong reflection, causing the quality of seismic data of the underlying igneous rock to deteriorate, which brings difficulties to seismic interpretation.

[0003] There are two main methods for separating and identifying weaker reflective targets in a strong reflective background: one is to directly identify weak reflective targets by improving resolution and enhancing weak signals; the other is to identify weak reflective targets by removing strong reflective shielding and reducing its impact on weak reflective targets.

[0004] At present, the method of removing strong reflection shielding is mainly based on the decomposition and reconstruction of seismic wavelets, and the most common methods include matching pursuit separation and wavelet transform. A large number of scholars have continuously updated the matching pursuit based on the large number of iterations and long calculation time of matching pursuit, and proposed many methods to optimize the matching pursuit algorithm; in terms of wavelet transform, some scholars use Morlet wavelets to decompose seismic signals into a series of seismic waves of different frequencies, which can achieve high-precision reservoir identification and prediction after reconstruction. Similarly, many scholars have used other methods to remove strong reflection shielding, such as: based on the singular value decomposition method and the EMD maximum energy decomposition method, the maximum component is removed and then reconstructed to achieve the purpose of removing strong reflection shielding; based on the long and short cycle theory, PCA and Wheeler transform are combined to remove strong reflection shielding, etc.

[0005] Seismic wave decomposition and reconstruction methods for removing strong reflection shielding all have defects. Many methods do not clearly define the geological significance of the removed seismic information, and cannot verify the correctness of the results after de-emphasis. Although some methods verify the rationality of the results from multiple angles such as seismic paleo-geomorphology, seismic attributes, and logging information based on geological models, if the geological significance of the removed strong reflection information is not clearly defined, it is impossible to qualitatively characterize the seismic response, and the results are non-unique. Such risky methods are difficult to be formally put into oil and gas exploration work. Therefore, how to identify the geological significance corresponding to the waveform component data has become an urgent problem to be solved. Summary of the invention

[0006] In view of the defects of the prior art, the present invention proposes a method for removing the influence of surrounding rock heterogeneity based on waveform component clustering. Through the technical combination of waveform decomposition and reconstruction and waveform clustering, it can remove strong reflections while clarifying the geological significance represented by its seismic information. In addition, it combines various factors to verify the rationality of the results and obtain the prediction results that best conform to the geological viewpoint.

[0007] To achieve the above object, the technical solution of the present invention is as follows:

[0008] A method for removing the influence of surrounding rock heterogeneity based on waveform component clustering includes the following steps:

[0009] S1. Collect data in the study area, establish a seismic interpretation area, and load relevant data; the data include: regional survey data, drilling and logging data, 3D seismic data, conventional logging curves, well deviation data, coring data, layering data, single well test capacity data, etc.

[0010] S2. Sequence division of single wells, establishment of sequence stratigraphic framework, and determination of sedimentary facies types and characteristics of target intervals in the study area;

[0011] S3. Based on the actual drilling data, field outcrop data and core thin section data, the target layer reservoir section is divided and reservoir well connection is established;

[0012] S4. Design the forward model of volcanic rocks in the study area based on the actual drilling formation lithology thickness and geophysical parameters, clarify the seismic reflection characteristics of volcanic rocks, and verify the rationality of well-seismic calibration;

[0013] Furthermore, in S4, based on the thickness of underground rock layers in the actual drilling and the rock physical parameters of each rock layer, a seismic forward model is established, and a Ricker wavelet excitation model is used to perform forward simulation.

[0014] S5. Determine the 3D seismic interpretation scheme based on the seismic forward modeling results and complete the detailed interpretation of the target layer;

[0015] Furthermore, in S5, with reference to seismic forward modeling, according to the seismic reflection characteristics of the volcanic rock development area, volcanic rocks are identified on the seismic profile, volcanic rocks and non-volcanic rocks are divided, and the interpretation phase of the volcanic rock development area is determined according to the volcanic rock development thickness.

[0016] S6. Combined with seismic profiles and seismic forward modeling, a time window is opened to extract the original seismic volume attributes, characterize the volcanic rock boundaries in the study area, and clarify the distribution law of volcanic rock thickness;

[0017] Furthermore, in S6, the time window range of the seismic slice is established with reference to the forward simulation, and the time window size is preferably such that it just includes the period when the seismic reflection characteristics are affected by the volcanic rock. The plane distribution of the volcanic rock is characterized by using the seismic attribute slices, and finally the plane attributes and the seismic profile are combined to further determine the distribution law of the volcanic rock thickness.

[0018] S7, opening a time window for the original earthquake volume to establish a training sample, performing waveform component decomposition based on PCA, decomposing the training sample into multiple waveform component volumes, and sorting the component volumes carrying main information;

[0019] Furthermore, in S7, the core idea of ​​principal component analysis (PCA) is to perform dimensionality reduction analysis on the data, which can effectively extract the "main" information in the data, reduce the dimension of complex data, thereby removing redundant components and mining the hidden information behind the data. The calculation process is as follows:

[0020] In m n-dimensional data, X n*m =[x1,x2,…x m ], where each x is an n-dimensional column vector,

[0021] (1) Decentralization,

[0022] (2) Calculate the covariance matrix,

[0023] (3) Perform eigenvalue decomposition on the covariance matrix to obtain the characteristic matrix (arranged in columns from large to small eigenvalues), and take the first k columns to form the matrix P n*k , P is equivalent to a coordinate system, and each column in P is a coordinate axis;

[0024] (4) Projecting the original data into the P coordinate system will obtain the reduced-dimensional data.

[0025] This set of data after dimensionality reduction is sorted according to the proportion of waveform component information. The higher the ranking, the greater the amount of information it carries and the higher the proportion. According to the amount of information carried by the waveform component, the component that carries the main information is selected.

[0026] S8, opening a time window, performing waveform clustering calculation on the selected component volume, extracting the component volume seismic phase distribution plane map, combining forward simulation and seismic reflection characteristics, and determining the volcanic rock seismic phase;

[0027] Furthermore, in S8, waveform clustering calculation is performed on the component volume based on the time window established in S6 to obtain a seismic phase plane distribution map of the component volume.

[0028] S9. Based on the seismic phase plane distribution map of each component, analyze the coupling relationship between the seismic phase distribution of volcanic rocks and the actual distribution range of volcanic rocks, and determine the component carrying volcanic rock information and the thickness of volcanic rocks in each component in combination with the distribution law of volcanic rock thickness;

[0029] Furthermore, in S9, the waveform component decomposition decomposes the original seismic volume into multiple waveform component volumes according to the seismic waveform. Since the seismic waveform is directly affected by the changes in the lithology sequence of the underground rock formations, each component volume can be regarded as carrying certain lithology information.

[0030] The traditional method of identifying the component volume carrying volcanic rock information is to integrate the volcanic rock forward simulation in S4, compare the component volume profile with the original seismic volume profile, and determine the component volume carrying volcanic rock information. This method has no process constraints and is only guided by the reservoir prediction goal, ignoring the uncertainty of the component volume removed in the process.

[0031] According to waveform clustering, the seismic phase plane distribution of each component is identified, and multiple component bodies related to volcanic rocks are identified quickly and efficiently, and their geological significance is given; the seismic profile of the component body is identified, and the volcanic rock boundary on the component body seismic profile is tested for the correlation between the plane matching and the seismic phase. The profile review is to test whether the waveform of the component body is consistent with the seismic phase (waveform characteristics) on the plane. If the reflection characteristics of the profile are inconsistent with the seismic phase, consider the horizon problem and the time window problem, and adjust and test again in S8. For the robustness of the method (or poor data conditions), the correlation threshold can be appropriately lowered to include more associated component bodies (later, multiple associated component bodies are arranged and combined to form multiple removal schemes, and the accuracy of volcanic rock component removal is further improved based on the reconstruction geological quality control, which is equivalent to screening the associated component bodies from multiple steps and multiple angles, matching the volcanic rock distribution on the plane and matching the inheritance relationship of landform, reservoir, and sedimentary phase in the result).

[0032] S10, removing the component volume carrying volcanic rock information, and reconstructing the remaining waveform component volumes to obtain a reconstructed seismic volume;

[0033] Furthermore, the waveform reconstruction in S10 is to re-combine and calculate the waveform component bodies after the waveform is decomposed. In order to enhance the feasibility of the method, a combination method is specified: the component body is divided into two parts, one part is a component body with certain association (a component body that clearly contains volcanic rock information), and the other part is a component body with weak certainty. The associated component body with weak certainty is arranged and combined with the component body with certain association to form multiple schemes. For each scheme, the waveform component body that needs to be removed is reconstructed.

[0034] S11. Based on well-seismic calibration, the actual well reservoir is projected onto the seismic profile, and the reservoir plane distribution is predicted by selecting the attributes in combination with the seismic reflection characteristics of the reservoir segment of the reconstructed seismic volume;

[0035] Furthermore, the S11, based on well seismic calibration, statistically reconstructs seismic reflection characteristics of the actual well reservoir development area on the seismic body, and extracts and reconstructs seismic body attributes using root mean square amplitude slices;

[0036] S12, based on the actual well reservoir distribution, paleo-geomorphology, and sedimentary facies inheritance, reservoir prediction geological quality control is performed from multiple angles; if the attribute prediction accuracy is greater than the requirement and conforms to the geological law, the prediction is completed; if the consistency is poor, return to step S10;

[0037] Through seismic forward modeling, the influence range of volcanic rock thickness changes on the seismic reflection characteristics of the original strata is determined, and the difference in seismic reflection characteristics between the two without volcanic rock development is compared to provide a basis for the horizon interpretation plan. At the same time, the well seismic calibration is carried out by using the reflection coefficient and Ricker wavelet convolution to calculate the synthetic record, check the degree of consistency between the forward modeling and the actual drilling seismic profile, and analyze the development trend of volcanic rocks in the study area.

[0038] Furthermore, in S12, first, the consistency between the actual drilling reservoir and the well seismic data is checked. The reservoir distribution predicted by the amplitude attribute must correspond to the information on the well. On this basis, the paleo-geomorphology and sedimentary facies inheritance are further checked. On the basis of clarifying that the reservoir is controlled by the facies, the sedimentary phase can be used to evaluate the reservoir. Based on the continuity of sedimentary facies evolution, the distribution characteristics of the upper and lower small layers of the beach can be used to infer the distribution trend of the target layer sedimentary facies belt to evaluate the rationality of the reservoir plane extension direction; in terms of geomorphology, priority is given to reservoirs located in favorable locations such as paleo-geomorphological highlands and slope breaks. If the attribute prediction accuracy is greater than the requirement and matches the above three situations, the prediction is completed. If the match is poor, return to step S9.

[0039] Compared with the prior art, the advantages of the present invention are:

[0040] (1) Compared with previous methods for removing strong reflection shielding, this method performs seismic phase analysis on waveform component volumes for the first time, links geophysical information with geological information, and solves the problem of unclear geological significance of waveform decomposition data.

[0041] (2) The process of sorting component volumes does not completely rely on the profile comparison method. Waveform clustering is used to characterize the seismic phases of the component volumes, and the seismic reflection feature plane is visualized to achieve data dimensionality reduction. The component volumes are sorted based on the seismic phase plane distribution map, which effectively improves the sorting efficiency.

[0042] (3) This method transforms the complex and multi-solution seismic interpretation problem into a qualitative problem of waveform components, removes the influence of volcanic surrounding rocks on the seismic reflection characteristics of the target layer, ensures the rationality of the proposed interpretation scheme, and performs quality control with multiple factors at each stage, which can be iteratively tested to ensure that the final result is consistent with geological understanding. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a method for removing the influence of surrounding rock heterogeneity based on waveform component clustering of the present invention;

[0044] Figure 2 It is a grid diagram of formation well connection in an embodiment of the present invention;

[0045] Figure 3 A reservoir well connection grid diagram in an embodiment of the present invention;

[0046] Figure 4 It is the synthetic record calibration in the embodiment of the present invention;

[0047] Figure 5 It is a forward modeling of volcanic rock earthquake in an embodiment of the present invention;

[0048] Figure 6 is an original seismic volume amplitude attribute map in an embodiment of the present invention;

[0049] Figure 7 It is a plane distribution diagram of component volume seismic phase in an embodiment of the present invention;

[0050] Figure 8 A seismic volume amplitude attribute map reconstructed in an embodiment of the present invention;

[0051] Fig. 9 This is the sedimentary phase diagram of Maoerdi in the embodiment of the present invention;

[0052] Fig.10 The Mao-Di sedimentary phase diagram in the embodiment of the present invention

[0053] Fig.11 This is the paleo-geomorphological restoration map of the target layer in the embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0055] like Figure 1 As shown, a method for removing the influence of surrounding rock heterogeneity based on waveform component clustering includes the following steps:

[0056] S1. Collect and organize basic data, establish seismic interpretation work area, and load relevant data;

[0057] S2. Divide the single well sequence, and determine the sedimentary facies type and characteristics of the target layer in the study area based on the single well sedimentary facies analysis and combined with the sedimentary facies comparison of connected wells;

[0058] like Figure 2 As shown, the sequences are divided based on the GR logging curves and an isochronous stratigraphic framework is established.

[0059] S3. Divide the actual drilling reservoir section according to the GR curve and the three porosity logging curves (AC curve, CNL curve, DEN curve), and analyze the reservoir development advantage area through the well connection profile;

[0060] like Figure 3 As shown in the figure, two sets of reservoirs are developed at the top of the Mao 2nd Member in the study area. The reservoir thickness is thin. From the rock properties, it can be seen that the reservoir properties of the formation at Well Shangfeng 8 in this section are the best.

[0061] S4. Statistical analysis of the thickness of various lithologies, acoustic wave velocity, and rock density near the target layer of the actual drilling, establish a theoretical formation model, and conduct forward simulation through Ricker wavelet excitation;

[0062] Use acoustic waves and density logging curves to calculate synthetic records, perform well-seismic calibration, confirm that the original seismic profile contains seismic reflection characteristics of volcanic rock formations, and adjust the forward model according to the seismic profile until the forward simulation results match the profile;

[0063] like Figure 4 As shown in the figure, the well-seismic calibration determines the position of the Longtan Formation in the original seismic body in the study area, and analyzes the seismic reflection characteristics of the Longtan Formation and its surrounding rocks.

[0064] like Figure 5 As shown in the figure, the forward simulation results confirm that in the volcanic rock distribution area at the bottom of the Longtan Formation in the study area, the wave crest is pulled up, which is about half a phase different from the area without volcanic rocks, and is a parallel strong reflection. As the thickness of the volcanic rock decreases, the wave crest amplitude gradually weakens, the phase gradually moves down, and it is a complex wave shape, while the area without volcanic rocks is a stable parallel strong reflection.

[0065] S5. In this study, volcanic rocks are developed in the southwest of the study area, showing complex wave or biaxial weak-medium reflection characteristics on the seismic profile, and the normal shale at the bottom of the Longtan Formation on the east side shows parallel strong reflection. Through seismic forward simulation and analysis of the original seismic body, the interpretation phase of the target layer is determined, and fine tracking is performed to achieve the interpretation of the target layer;

[0066] S6. Drift up 15 ms and down 10 ms along the target layer to establish a time window and perform root mean square amplitude (RMS) attribute slicing on the original seismic volume.

[0067] like Figure 6 As shown, from the properties, it can be seen that volcanic rocks are distributed in the western part of the study area, and the volcanic rock overflow channel is obvious. There is no volcanic rock distribution in the east, realizing the delineation of the volcanic rock boundary.

[0068] S7. Set up a time window along the target layer and select appropriate training samples. At the same time, select the appropriate number of components based on the problem requirements. In this case, the inventor selected the original seismic body within the time window of 100ms upward drift and 200ms downward drift along the layer as the training sample, and the number of components is 30; the waveform component bodies are sorted according to the amount of information carried by the original seismic body, and the first ranked one is called sub-component body No. 1, and so on. In this study, the component bodies with information accounting for more than 1% are selected, that is, the first eight waveform component bodies;

[0069] S8, set up a time window along the target layer with an upward drift of 15ms and a downward drift of 10ms, and perform waveform clustering calculation on component bodies 1-8;

[0070] like Figure 7 As shown, the plane distribution of the seismic phases of the 1-8 components is obviously different, and some seismic phases are highly consistent with the distribution of volcanic rocks.

[0071] S9. Based on the planar seismic phase distribution of components 1-8 in S8, combined with seismic forward modeling, volcanic rock boundaries and other factors, the geological significance of each type of seismic phase is assigned, and the associated components and the components with weak correlation are divided according to the consistency between the seismic phase and the volcanic rock distribution. When reviewing the seismic profile, check the consistency between the profile reflection characteristics and the seismic phase waveform characteristics. If the consistency is poor, return to S8 and modify the time window range. In this study, components 1 and 3 are confirmed to be associated components, components 4 and 5 are correlated, but weaker than the former, and the rest are components with weak correlation.

[0072] S10, 1, and 3 components carry thick volcanic rock information, with obvious seismic phase characteristics and strong correlation with volcanic rocks, while 4 and 5 components carry thin volcanic rock information, with seismic phase characteristics that are difficult to identify and weakly correlated. Therefore, this study adopts three reconstruction schemes, eliminating 1 and 3 components, eliminating 1, 3, and 4 components, and eliminating 1, 3, 4, and 5 components, and reconstructing the remaining waveform components.

[0073] S11, the reservoir reflection characteristics in the reconstructed seismic volume are strong amplitude, high frequency "bright spot" reflections. The RMS amplitude attribute slices of the reconstructed seismic volume are performed under the background of the S6 time window to amplify the difference between the reservoir amplitude characteristics and non-reservoir;

[0074] like Figure 8 As shown, after eliminating the No. 1, 3, 4, and 5 components, the de-emphasis is obvious, and the reservoir development area and the undeveloped area are obviously different. The Maokou top reservoir is mainly developed in the southwest of the study area, and reservoirs are developed in some areas in the northeast.

[0075] S12. In this study, based on the attribute slices after three different reconstruction schemes, the actual drilling reservoir development, the inheritance relationship of high-quality phase belts and the paleo-geomorphology before the deposition of Maoerxia were used as references for analysis, and finally the reconstructed seismic volume without the 1st, 3rd, 4th and 5th component volumes was selected;

[0076] like Fig. 9 and Fig.10 As shown, the shoal from Maoyidi to Maoerdi in the study area gradually migrated toward the northeast, and the reservoir conditions at the top of Maoerdi met the migration pattern of the Maokou Formation shoal, and the facies belt favorable for reservoir development has obvious inheritance.

[0077] like Fig.11 As shown, the study area presents a geomorphic trend of high in the west and low in the east, with the topography extending in the northwest-south direction. The reservoirs characterized by the amplitude attributes are developed in the high-topography, shallow-water, high-energy slope-break zone, which conforms to the geological law.

[0078] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for removing the influence of surrounding rock heterogeneity based on waveform component clustering, characterized in that: The following steps are involved: S1. Collect data in the study area, establish a seismic interpretation area, and load relevant data; The data include: regional survey data, drilling and logging data, 3D seismic data, conventional logging curves, well deviation data, coring data, layer data, and single well test capacity; S2. Sequence division of single wells, establishment of sequence stratigraphic framework, determination of sedimentary facies types and characteristics of target intervals in the study area; S3. Based on the actual drilling data, field outcrop data and core thin section data, the target layer reservoir section is divided and reservoir well connection is established; S4. Design the forward model of volcanic rocks in the study area based on the actual drilling stratum lithology thickness and geophysical parameters, clarify the seismic reflection characteristics of volcanic rocks, and verify the rationality of well-seismic calibration; S5. Determine the 3D seismic interpretation scheme based on the seismic forward modeling results and complete the detailed interpretation of the target layer; S6. Combined with seismic profiles and seismic forward modeling, a time window is opened to extract the original seismic volume attributes, characterize the volcanic rock boundaries in the study area, and clarify the distribution law of volcanic rock thickness; S7, opening a time window for the original earthquake volume to establish a training sample, performing waveform component decomposition based on PCA, decomposing the training sample into multiple waveform component volumes, and sorting the component volumes carrying main information; S8, opening a time window, performing waveform clustering calculation on the selected component volume, extracting the component volume seismic phase distribution plane map, combining forward simulation and seismic reflection characteristics, and determining the volcanic rock seismic phase; S9. Based on the seismic phase plane distribution map of each component, analyze the coupling relationship between the seismic phase distribution of volcanic rocks and the actual distribution range of volcanic rocks, and determine the component carrying volcanic rock information and the thickness of volcanic rocks in each component in combination with the distribution law of volcanic rock thickness; S10, removing the component volume carrying volcanic rock information, and reconstructing the remaining waveform component volumes to obtain a reconstructed seismic volume; S11. Based on well-seismic calibration, the actual well reservoir is projected onto the seismic profile, and the reservoir plane distribution is predicted by selecting the attributes in combination with the seismic reflection characteristics of the reservoir segment of the reconstructed seismic volume; S12. Based on the actual well reservoir distribution, paleo-geomorphology, and sedimentary phase inheritance, reservoir prediction geological quality control is performed from multiple angles. If the attribute prediction accuracy is greater than the requirement and conforms to the geological laws, the prediction is completed. If the match does not meet the requirements, return to step S10.

2. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: In S4, based on the thickness of underground rock layers in actual drilling and the rock physical parameters of each rock layer, a seismic forward model is established, and a Ricker wavelet excitation model is used to perform forward simulation; Through seismic forward modeling, the impact range of volcanic rock thickness changes on the seismic reflection characteristics of the original strata is determined, and the difference in seismic reflection characteristics between the two when there is no volcanic rock development is compared to provide a basis for the stratigraphic interpretation plan; at the same time, the reflection coefficient and Ricker wavelet convolution are used to calculate the synthetic record for well seismic calibration, to test the degree of consistency between the forward simulation and the actual drilling seismic profile, and to analyze the development trend of volcanic rocks in the study area.

3. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: In S5, with reference to seismic forward modeling, volcanic rocks are identified on the seismic profile according to the seismic reflection characteristics of the volcanic rock development area, and volcanic rocks and non-volcanic rocks are divided into zones. The interpretation phase of the volcanic rock development area is determined according to the volcanic rock development thickness.

4. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: In S6, the time window range of the seismic slice is established with reference to the forward simulation. The time window size can include the period when the seismic reflection characteristics are affected by the volcanic rock. The plane distribution of the volcanic rock is characterized by using the seismic attribute slices. Finally, the plane attributes and the seismic profile are combined to further determine the distribution law of the volcanic rock thickness.

5. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: In S7, the core idea of ​​principal component analysis (PCA) is to perform dimensionality reduction analysis on the data and extract the "main" information from the data; the calculation process is as follows: In m n-dimensional data, X n*m =[x1,x2,…x m ], where each x is an n-dimensional column vector; (1) Decentralization, (2) Calculate the covariance matrix, (3) Perform eigenvalue decomposition on the covariance matrix to obtain the characteristic matrix (arranged in columns from large to small eigenvalues), and take the first k columns to form the matrix P n*k , P is equivalent to a coordinate system, and each column in P is a coordinate axis; (4) Projecting the original data into the P coordinate system will obtain the reduced-dimensional data. This set of reduced-dimensional data is sorted according to the proportion of waveform component information. The higher the ranking, the greater the amount of information it carries and the higher the proportion. According to the amount of information carried by the waveform component, the component that carries the main information is selected.

6. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: In S8, waveform clustering calculation is performed on the component volume based on the time window established in S6 to obtain a seismic phase plane distribution map of the component volume.

7. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: In S9, the waveform component decomposition decomposes the original seismic volume into multiple waveform component volumes according to the seismic waveform; since the seismic waveform is directly affected by the change of the lithology sequence of the underground rock formation, each component volume is regarded as carrying the lithology information; According to waveform clustering, the seismic phase plane distribution of each component is identified, multiple components related to volcanic rocks are identified, and their geological significance is given; Identify the component volume seismic profile, and check the matching correlation between the volcanic rock boundary on the component volume seismic profile and the seismic phase plane. The profile review is to check whether the component volume waveform is consistent with the earthquake on the plane. If the reflection characteristics of the profile are inconsistent with the seismic phase, consider the stratigraphic problem and time window problem, and adjust to S8 and then check again.

8. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: The waveform reconstruction in S10 is to recombine and calculate the waveform component bodies after the waveform is decomposed, and a combination method is specified: the component body is divided into two parts, one part is definitely associated, and the other part is not very certain. The associated component body with not very certainty is arranged and combined with the definitely associated component body to form multiple schemes. For each scheme, the waveform component body that needs to be removed is reconstructed.

9. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1 is characterized in that: The S11 is based on well seismic calibration, statistically reconstructing the seismic reflection characteristics of the actual well reservoir development area on the seismic body, and extracting and reconstructing the seismic body attributes using root mean square amplitude slices.

10. The method for removing the influence of surrounding rock heterogeneity based on waveform component clustering according to claim 1, characterized in that: Said S12, firstly, checks the consistency of the actual drilling reservoir with the wellbore and seismic data. The reservoir distribution predicted by the amplitude attribute should correspond to the information on the well. On this basis, further checks the paleo-geomorphology and sedimentary facies inheritance. On the basis of clarifying that the reservoir is controlled by the facies, the sedimentary facies are evaluated by using the sedimentary facies. Based on the continuity of the sedimentary facies evolution, the distribution trend of the sedimentary facies belt of the target layer is inferred by using the distribution characteristics of the beach bodies of the upper and lower layers to evaluate the rationality of the plane extension direction of the reservoir. In terms of geomorphology, priority is given to the reservoir being located in the paleo-geomorphological highland and the favorable position of the slope break. If the attribute prediction accuracy is greater than the requirement and matches the above three situations, the prediction is completed. If the match is poor, return to step S9.

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