Effective river channel sand body reservoir identification method based on post-stack earthquake prediction

Abstract

The invention relates to an effective river channel sand body reservoir identification method based on post-stack earthquake prediction, and belongs to the technical field of oil exploration and development. Comprising the following steps: carrying out sedimentary evolution analysis according to three-dimensional seismic data of a target area and a relative isochronous interpretation horizon based on spatial relative resolution, obtaining a spatial position of a river channel sand body, and judging a vertical evolution law of a river channel; three-dimensional seismic data spectrum analysis is carried out for river channel sand body characteristics of a target area, an inner river channel is displayed in detail, and combination characteristics of river channel sand bodies in different periods are determined; and fault grading classification is carried out according to the hydrocarbon source rock and fracture characteristics of the target area, the river channel sand body for effective hydrocarbon supply is distinguished, and the effective reservoir in the river channel sand body is identified. Through fault grading and classification, the purposes of identifying the distribution characteristics and the longitudinal distribution positions of the riverway in the research area on the plane and the overlapping relation of the riverway in different periods and further identifying the effective reservoir in the riverway sand body are achieved.

Description

Technical Field

[0001] The invention relates to an effective river channel sand body reservoir identification method based on post-stack seismic prediction, belonging to the technical field of petroleum exploration and development. Background Art

[0002] In the exploration and development of oil fields, river channel sand reservoirs are one of the most important reservoirs for oil and gas. How to identify the distribution characteristics of river channel sand bodies and then identify the river channel sand bodies that can effectively supply hydrocarbons, that is, effective reservoirs, is the key to increasing oil and gas production.

[0003] In terms of channel sand body prediction, the related technologies proposed a prediction method of superposition prediction of azimuth gather angle (CN107656312A) and a method of fine identification of channel sand bodies (CN 105372703 A). These methods have achieved certain results in channel sand body identification, but they also have some defects. The distribution of channel sand bodies obtained by the first method of superposition prediction of azimuth gather angle is mainly for fine identification of channels perpendicular to the main source direction. The degree of identification of channels that are not perpendicular to the main source direction is low, resulting in differences between the final prediction results and the actual situation; the second method of fine identification of channel sand bodies can identify channel sand bodies with complex superposition relationships, but the steps are cumbersome, especially the workload of interpreting the envelope surface of channel sand bodies is huge, which is not suitable for large-scale channel identification, and the interpretation is subjective, which may lead to differences between the final prediction results and the actual situation; both methods lack in-depth research on channel sand bodies that effectively supply hydrocarbons. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention discloses a method for identifying effective channel sand body reservoirs based on post-stack seismic prediction, which extracts isochronous slices of instantaneous attribute bodies through three-dimensional seismic data and relatively isochronous interpretation horizons; utilizes spectrum decomposition and attribute extraction relationships to carry out three-primary color fusion display; through fault grading and classification, accurately identifies effective faults that can communicate source rocks to channel sand bodies and will not dredge upward, thereby determining effective channel sand body reservoirs; ultimately, the purpose of identifying the planar distribution characteristics of the channel in the study area, the longitudinal distribution position, and the superposition relationship of channels of different periods is achieved, thereby identifying the effective reservoirs in the channel sand bodies.

[0005] The technical solution adopted by the present invention is: an effective river channel sand reservoir identification method based on post-stack seismic prediction, comprising the following steps:

[0006] The first step is to conduct sedimentary evolution analysis based on the 3D seismic data of the target area and the relatively isochronous interpretation horizons based on the relative spatial resolution, obtain the spatial position of the river channel sand body, and determine the vertical evolution law of the river channel;

[0007] The second step is to conduct 3D seismic data spectrum analysis based on the characteristics of the river channel sand bodies in the target area, to show the inner river channel in detail and to clarify the combination characteristics of river channel sand bodies in different stages;

[0008] The third step is to carry out fault classification and classification based on the source rocks and fault characteristics of the target area, identify the river sand bodies that effectively supply hydrocarbons, and identify the effective reservoirs in the river sand bodies.

[0009] Furthermore, the first step of sedimentary evolution analysis specifically includes:

[0010] Step 1.1: According to the sedimentary characteristics and evolution laws of the river channel in the work area, clarify the seismic response characteristics corresponding to the river channel sand body;

[0011] Step 1.2: Use the relative isochronous method to interpret the seismic horizons at the top and bottom of the target layer;

[0012] Step 1.3: Use the mathematical transformation calculation method of principal component analysis to perform noise reduction on the original seismic data;

[0013] Step 1.4: Using the seismic data with the top and bottom of the target layer as the time window, extract the stratigraphic attribute bodies of instantaneous amplitude, instantaneous frequency and instantaneous phase;

[0014] Furthermore, the second step of conducting 3D seismic data spectrum analysis specifically includes:

[0015] Step 2.1: Using the three instantaneous stratigraphic attribute bodies in step 1.4, extract stratigraphic attribute slices with equal time intervals from bottom to top, preliminarily determine the distribution characteristics and boundaries of the channel sand bodies, and clarify the evolution of the channel sand bodies in different periods;

[0016] Step 2.2: Determine the main frequency and frequency band range of the earthquake and the river sand body respectively through spectrum analysis of the seismic data and the river sand body;

[0017] Step 2.3: Based on the obtained frequency band range of the channel sand body, perform spectrum decomposition on the seismic data calculated by principal component analysis; select the frequency division attribute bodies of each frequency band according to the displayed results, as the representative frequency division attribute bodies of low frequency, medium frequency and high frequency respectively;

[0018] Step 2.4: Select three attribute bodies that can express the characteristics of the river channel from the three instantaneous stratigraphic attribute bodies generated in step 1.4 and the three frequency-divided attribute bodies generated in step 2.3 according to the display results, perform RGB three-primary color fusion display, and extract stratigraphic attribute slices with equal time intervals from bottom to top to confirm the details of the river channel;

[0019] Furthermore, the third step of fault classification specifically includes:

[0020] Step 3.1: Classify and categorize the faults in the work area to find the faults that can connect the source rock to the reservoir.

[0021] Furthermore, the mathematical transformation calculation method in step 1.3 is specifically as follows:

[0022] Suppose there is a data set containing m samples and n features, represented as an m×n matrix X;

[0023] Data preprocessing: First, the original data is centered, that is, the mean of each feature is subtracted and the mean of the original data is adjusted to zero;

[0024] Calculate the covariance matrix C: For the centered data matrix X, the covariance matrix C is calculated using the following formula:

[0025] C=(1 / m)*X^T*X

[0026] Among them, X^T represents the transpose of X;

[0027] Eigenvalue decomposition: Perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue and the corresponding eigenvector; the eigenvalue represents the variance of the data in the direction of the eigenvector;

[0028] Eigenvalue sorting: Sort the eigenvalues ​​from large to small, and select the eigenvectors corresponding to the first k eigenvalues ​​as the principal components;

[0029] Dimensionality reduction: Project the data onto the selected k principal components to obtain the reduced-dimensional data matrix Y; the projection is calculated using the following formula:

[0030] Y=X*V

[0031] Where X is the centered data matrix and V is the matrix containing the selected k eigenvectors.

[0032] Furthermore, in the step 2.2, the main frequency of the earthquake is 30-40 Hz; the frequency band of the river sand body with a burial depth of 1000-2000m is 20-60 Hz.

[0033] Furthermore, in the step 2.3, the main frequency of the earthquake is used as the intermediate frequency part, the frequency lower than the main frequency of the earthquake is used as the low frequency part, and the frequency higher than the main frequency of the earthquake is used as the high frequency part, wherein the low frequency is sensitive to the thick value area of ​​the river channel, and the high frequency is sensitive to the thin value area of ​​the river channel.

[0034] Furthermore, the RGB three primary colors fusion in step 2.4 is specifically to fuse the low-frequency attribute body as red, the instantaneous amplitude attribute body as green, and the high-frequency attribute body as blue.

[0035] Furthermore, the basic principle of fault classification in step 3.1 is: to divide the fault into three different structural modes according to its growth position; one type of fault is a fault that can communicate with the source rock and the end position of the fault does not break through the top of the reservoir. This type of fault can guide oil and gas into the reservoir and store it therein, and is an effective fault; the second type of fault is a fault that breaks to the reservoir but cannot communicate with the source rock, and the reservoir cannot form a reservoir and cannot become an effective fault; the third type of fault is a fault that completely penetrates the source rock and the reservoir from bottom to top, and its extension length is relatively large, but the oil and gas will continue to be guided upward along the fault and cannot be stored in the reservoir, and cannot become an effective fault.

[0036] The present invention discloses an effective channel sand reservoir identification method based on post-stack seismic prediction, and its beneficial effect is that the method used by the present invention utilizes the three-dimensional seismic data of the target area and the relatively isochronous interpretation horizon to carry out sedimentary evolution analysis based on spatial relative resolution to judge the vertical evolution law of the channel, thereby achieving the effect of obtaining the spatial position of the channel sand; utilizing the stratigraphic attribute slice, the spectrum decomposition of the wavelet transform and the RGB three-primary color fusion display method, the inner channel is carefully displayed, and the effect of clarifying the combination characteristics of the channel sand bodies of different periods can be achieved; according to the hydrocarbon source rock and fault characteristics of the target area, the fault classification is carried out to identify the channel sand bodies that effectively supply hydrocarbons, and the effect of identifying the effective reservoir in the channel sand bodies can be achieved. Compared with the prior art, the present invention only needs to interpret the top and bottom surfaces of the target layer to complete the channel prediction within the study area. At the same time, since the channel is contained in the target layer, the real situation of the channel can be objectively and completely reflected through attribute prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 : Schematic diagram of the process of an embodiment of the present invention;

[0039] Figure 2 This is a diagram showing the results of spectrum decomposition using the wavelet transform method in an embodiment of the present invention;

[0040] Figure 3 This is a graph showing the attribute slice results of the RGB three-primary color fusion method in an embodiment of the present invention;

[0041] Figure 4 is a cross-sectional schematic diagram of fault classification in an embodiment of the present invention;

[0042] Figure 5 It is a result display diagram of using the fault classification result combined with the river sand reservoir to divide the favorable closure in the embodiment of the present invention.

[0043] Specific implementation methods

[0044] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In order to further understand the content of the present invention, the present invention is further described below in conjunction with specific implementation methods.

[0046] Embodiment 1:

[0047] like Figures 1 to 5 As shown in the figure, an effective channel sand reservoir identification method based on post-stack seismic prediction first conducts sedimentary evolution analysis on the three-dimensional seismic data of the target area and the relative isochronous interpretation horizon based on the relative spatial resolution, obtains the spatial position of the channel sand body, and determines the vertical evolution law of the channel. Specifically, the following steps are included: Step 1.1: According to the sedimentary characteristics and evolution law of the channel in the work area, the seismic response characteristics corresponding to the channel sand body are clarified; Step 1.2: The seismic horizons at the top and bottom of the target layer are interpreted using the relative isochronous method; Step 1.3: The original seismic data is subjected to noise reduction processing using the mathematical transformation calculation method of principal component analysis (Principal Component Analysis); wherein the mathematical transformation calculation method is specifically as follows:

[0048] Suppose there is a data set containing m samples and n features, represented as an m×n matrix X;

[0049] Data preprocessing: First, the original data is centered, that is, the mean of each feature is subtracted and the mean of the original data is adjusted to zero;

[0050] Calculate the covariance matrix C: For the centered data matrix X, the covariance matrix C is calculated using the following formula:

[0051] C=(1 / m)*X^T*X

[0052] Among them, X^T represents the transpose of X;

[0053] Eigenvalue decomposition: Perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue and the corresponding eigenvector; the eigenvalue represents the variance of the data in the direction of the eigenvector;

[0054] Eigenvalue sorting: Sort the eigenvalues ​​from large to small, and select the eigenvectors corresponding to the first k eigenvalues ​​as the principal components;

[0055] Dimensionality reduction: Project the data onto the selected k principal components to obtain the reduced-dimensional data matrix Y; the projection is calculated using the following formula:

[0056] Y=X*V

[0057] Where X is the centered data matrix and V is the matrix containing the selected k eigenvectors.

[0058] Step 1.4: Using the seismic data with the top and bottom of the target layer as the time window, extract the stratigraphic attribute bodies of instantaneous amplitude, instantaneous frequency and instantaneous phase;

[0059] Secondly, we will conduct spectrum analysis of three-dimensional seismic data based on the characteristics of the channel sand bodies in the target area, display the inner channel in detail, and clarify the combination characteristics of the channel sand bodies in different periods; specifically, step 2.1: using the three instantaneous stratigraphic attribute bodies in step 1.4, extract stratigraphic attribute slices with equal time intervals from bottom to top, preliminarily determine the distribution characteristics and boundaries of the channel sand bodies, and clarify the evolution of the channel sand bodies in different periods; step 2.2: through spectrum analysis of seismic data and channel sand bodies, determine the main frequency and frequency band range of seismic and channel sand bodies respectively; the main frequency of seismic is 30-40Hz; the frequency band range of channel sand bodies with a burial depth of 1000-2000m is 20-60Hz. Step 2.3: If Figure 2 As shown in the figure, according to the obtained frequency band range of the river sand body, the spectrum decomposition is carried out for the seismic data calculated by principal component analysis; the main frequency of the earthquake is taken as the intermediate frequency part, the part below the main frequency of the earthquake is the low frequency part, and the part above the main frequency of the earthquake is the high frequency part, among which the low frequency is sensitive to the thick value area of ​​the river channel, and the high frequency is sensitive to the thin value area of ​​the river channel. According to the displayed results, the frequency division attribute bodies of each frequency band are selected as the representative frequency division attribute bodies of low frequency, intermediate frequency and high frequency respectively; Step 2.4: As shown in the figure Figure 3 As shown, three attribute bodies that can express the characteristics of the river channel are selected from the three instantaneous stratigraphic attribute bodies generated in step 1.4 and the three frequency-division attribute bodies generated in step 2.3 according to the display results, and RGB three-primary color fusion display is performed, in which the low-frequency attribute body is used as red, the instantaneous amplitude attribute body is used as green, and the high-frequency attribute body is used as blue for fusion. At the same time, stratigraphic attribute slices with equal time intervals are extracted from bottom to top to confirm the details of the river channel.

[0060] Finally, according to the source rock and fault characteristics of the target area, fault classification is carried out to identify the river sand bodies that effectively supply hydrocarbons and identify the effective reservoirs in the river sand bodies. Specifically, step 3.1: classify the faults in the work area and find faults that can connect the source rock to the reservoir. The basic principle of fault classification is: divide the faults into three different structural modes according to their growth positions; one type of fault is a fault that can connect to the source rock and the end position of the fault does not break through the top of the reservoir. This type of fault can guide oil and gas into the reservoir and store it there, and is an effective fault; the second type of fault is a fault that breaks to the reservoir but cannot connect to the source rock. The reservoir cannot form a reservoir and cannot become an effective fault; the third type of fault is a fault that completely penetrates the source rock and reservoir from bottom to top. It has a large extension length, but the oil and gas will continue to be guided upward along the fault and cannot be stored in the reservoir, so it cannot become an effective fault. Figure 4 and 5 As shown in the figure, the river channel near the red fault is an effective river channel sand body reservoir. Through the classification of faults, the effective faults that can connect the source rock to the river channel sand body and will not be diverted upward can be accurately identified, thereby confirming the effective river channel sand body reservoir.

[0061] in:

[0062] 1. The principal component analysis calculation is a noise reduction process for earthquakes. The signal-to-noise ratio of the processed profile is significantly improved, which can enhance the continuity of the identified river channel.

[0063] 2. The instantaneous amplitude attribute slice can clearly reflect the characteristics of the river channel with a thickness greater than 20m on the plane, the instantaneous frequency attribute slice can better highlight the river channel with a small scale of about 5 to 8m, and the instantaneous phase attribute slice can well characterize the boundary of the river channel.

[0064] 3. The sedimentary evolution characteristics of the river channel can be shown by isochronous stratigraphic attribute slicing.

[0065] 4. RGB primary color fusion can solve the problem of identifying rivers of different scales because it can fuse different attributes for display.

[0066] 5. The stratigraphic isochronous attribute slicing under the fusion of RGB primary colors can solve the problem of fine depiction of river channels under complex superposition conditions.

[0067] 6. Accurately identifying effective fractures that connect source rocks and lead to reservoirs can confirm effective channel sand reservoirs, further quantitatively identify favorable traps, and provide a basis for well deployment.

[0068] The following are explanations of the professional terms used in the present invention:

[0069] (1) Earthquake attribute body

[0070] Seismic attribute bodies refer to the physical and mathematical statistical methods that classify seismic attributes into nine categories, such as amplitude, frequency, phase, and energy, based on the kinematic and dynamic characteristics of seismic waves. Each category contains more than a dozen subcategories of attributes. The physical quantity of the statistical characteristics has a clear physical meaning and characterizes the attribute transformation in three-dimensional space in a classified manner.

[0071] (2) Spatial relative resolution

[0072] The geological interpretation based on the spatial relative changes of seismic attributes caused by geological bodies on isochronous geological slices and the vertical continuous changes of seismic attributes caused by geological bodies between isochronous geological slices, and the ultimate ability to understand the spatial distribution of geological bodies is called spatial relative resolution.

[0073] (3) Principal component analysis

[0074] Principal component analysis is a commonly used dimensionality reduction technique used to extract the main features from high-dimensional data. The original data is projected into a new coordinate system through linear transformation so that the projected data has the largest variance. The following is the formula and principle of the PCA algorithm.

[0075] Suppose there is a data set containing m samples and n features, which can be represented as an m×n matrix X. The goal of PCA is to find a set of orthogonal bases so that the variance of the projection is maximized after the data is projected onto this set of bases. This set of orthogonal bases is called principal components, which are linear combinations of the original features.

[0076] Data preprocessing: First, the original data is centered, that is, the mean of each feature is subtracted and the mean of the data is adjusted to zero.

[0077] Calculate the covariance matrix: The covariance matrix describes the correlation between data features. For the centered data matrix X, the covariance matrix C can be calculated by the following formula:

[0078] C=(1 / m)*X^T*X

[0079] Where X^T represents the transpose of X.

[0080] Eigenvalue decomposition: Perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue and the corresponding eigenvector. The eigenvalue represents the variance of the data in the direction of the eigenvector.

[0081] Eigenvalue sorting: Sort the eigenvalues ​​from large to small, and select the eigenvectors corresponding to the first k eigenvalues ​​as the principal components.

[0082] Dimensionality reduction: Project the data onto the selected k principal components to obtain the reduced-dimensional data matrix Y. The projection can be calculated using the following formula:

[0083] Y=X*V

[0084] Where X is the centered data matrix and V is the matrix containing the selected k eigenvectors.

[0085] Through the above steps, PCA reduces the original high-dimensional data set to a lower-dimensional space. Each dimension in the new coordinate system represents a main feature of the original data. This can reduce the dimension of the data while retaining the most important information, which helps to visualize and analyze the data and improve the performance of subsequent machine learning algorithms.

[0086] (4) Spectral decomposition

[0087] Spectral decomposition is a seismic interpretation method based on frequency information. The basic principle of spectral decomposition is based on the tuning characteristics and notch characteristics of a single-layer medium. The key lies in the selection of time-frequency analysis methods. This time, the wavelet transform method is used for spectral decomposition. The seismic data is decomposed from the original time domain into a frequency domain within a specified range. The algorithm is as follows:

[0088] Suppose there exists a function ψ(t) such that

[0089]

[0090] Among them, ψ(t) is the base wavelet or mother wavelet. From the above formula, we can see that the base wavelet has positive and negative on the time axis, that is, it has a certain oscillation. At the same time, the wavelet must also have tight support. When it exceeds a certain range, its amplitude decays rapidly to zero, which is exactly the concept of "small". ψ(t) is transformed by scaling and translation to form a function family

[0091]

[0092] Among them, a>0, is the scale factor, and b is the translation factor.

[0093] With the concept of wavelet, the wavelet transform (WT) of a signal s(t) is defined as follows:

[0094]

[0095] (5) Fusion of RGB primary colors

[0096] The RGB three-primary color fusion principle is a color model based on visual perception and optical principles. In color representation, the RGB model uses three basic colors of red (R), green (G) and blue (B) to mix to produce the desired color. In the RGB model, the three basic colors of red, green and blue are used as the main color components. This is because the cones in the human visual system have the highest sensitivity to these three colors. This means that humans are more sensitive to red, green and blue light, so using these colors of light can create richer and more accurate color images.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying effective river channel sand reservoirs based on post-stack seismic prediction, characterized in that: The following steps are involved: The first step is to conduct sedimentary evolution analysis based on the 3D seismic data of the target area and the relatively isochronous interpretation horizons based on the relative spatial resolution, obtain the spatial position of the river channel sand body, and determine the vertical evolution law of the river channel; The second step is to conduct 3D seismic data spectrum analysis based on the characteristics of the river channel sand bodies in the target area, to show the inner river channel in detail and to clarify the combination characteristics of river channel sand bodies in different stages; The third step is to carry out fault classification and classification based on the source rocks and fault characteristics of the target area, identify the river sand bodies that effectively supply hydrocarbons, and identify the effective reservoirs in the river sand bodies.

2. The effective river channel sand body reservoir identification method according to claim 1 is characterized in that: The first step of sedimentary evolution analysis includes: Step 1.1: According to the sedimentary characteristics and evolution laws of the river channel in the work area, clarify the seismic response characteristics corresponding to the river channel sand body; Step 1.2: Use the relative isochronous method to interpret the seismic horizons at the top and bottom of the target layer; Step 1.3: Use the mathematical transformation calculation method of principal component analysis to perform noise reduction on the original seismic data; Step 1.4: Using the seismic data with the top and bottom of the target layer as the time window, extract the stratigraphic attributes of instantaneous amplitude, instantaneous frequency, and instantaneous phase.

3. The effective river channel sand body reservoir identification method according to claim 2 is characterized in that: The second step is to carry out 3D seismic data spectrum analysis, which specifically includes: Step 2.1: Using the three instantaneous stratigraphic attribute bodies in step 1.4, extract stratigraphic attribute slices with equal time intervals from bottom to top, preliminarily determine the distribution characteristics and boundaries of the channel sand bodies, and clarify the evolution of the channel sand bodies in different periods; Step 2.2: Determine the main frequency and frequency band range of the earthquake and the river sand body respectively through spectrum analysis of the seismic data and the river sand body; Step 2.3: Based on the obtained frequency band range of the channel sand body, perform spectrum decomposition on the seismic data calculated by principal component analysis; select the frequency division attribute bodies of each frequency band according to the displayed results, as the representative frequency division attribute bodies of low frequency, medium frequency and high frequency respectively; Step 2.4: Select three attribute bodies that can express the characteristics of the river channel from the three instantaneous stratigraphic attribute bodies generated in step 1.4 and the three frequency-division attribute bodies generated in step 2.3 according to the display results, perform RGB three-primary color fusion display, and extract stratigraphic attribute slices with equal time intervals from bottom to top to confirm the details of the river channel.

4. The effective river channel sand body reservoir identification method according to claim 3 is characterized in that: The third step is to carry out fault classification, which includes: Step 3.1: Classify and categorize the faults in the work area to find the faults that can connect the source rock to the reservoir.

5. The effective river channel sand body reservoir identification method according to claim 2 is characterized in that: The specific mathematical transformation calculation method in step 1.3 is: Suppose there is a data set containing m samples and n features, represented as an m×n matrix X; Data preprocessing: First, the original data is centered, that is, the mean of each feature is subtracted and the mean of the original data is adjusted to zero; Calculate the covariance matrix C: For the centered data matrix X, the covariance matrix C is calculated using the following formula: C=(1 / m)*X^T*X Among them, X^T represents the transpose of X; Eigenvalue decomposition: Perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue and the corresponding eigenvector; the eigenvalue represents the variance of the data in the direction of the eigenvector; Eigenvalue sorting: Sort the eigenvalues ​​from large to small, and select the eigenvectors corresponding to the first k eigenvalues ​​as the principal components; Dimensionality reduction: Project the data onto the selected k principal components to obtain the reduced-dimensional data matrix Y; the projection is calculated using the following formula: Y=X*V Where X is the centered data matrix and V is the matrix containing the selected k eigenvectors.

6. The effective river channel sand body reservoir identification method according to claim 3 is characterized in that: In step 2.2, the main frequency of the earthquake is 30-40 Hz; the frequency band of the river sand body with a burial depth of 1000-2000m is in the range of 20-60 Hz.

7. The effective river channel sand body reservoir identification method according to claim 3 is characterized in that: In step 2.3, the main frequency of the earthquake is taken as the intermediate frequency part, the frequency lower than the main frequency of the earthquake is taken as the low frequency part, and the frequency higher than the main frequency of the earthquake is taken as the high frequency part. The low frequency is sensitive to the thick value area of ​​the river channel, and the high frequency is sensitive to the thin value area of ​​the river channel.

8. The effective river channel sand body reservoir identification method according to claim 1 is characterized in that: In step 2.4, the RGB three primary colors are fused by taking the low-frequency attribute body as red, the instantaneous amplitude attribute body as green, and the high-frequency attribute body as blue.

9. The effective river channel sand body reservoir identification method according to claim 4, characterized in that: Step 3.1 The basic principle of fault classification is: divide the fault into three different structural modes according to its growth position; one type of fault is a fault that can communicate with the source rock and the end position of the fault does not break through the top of the reservoir. This type of fault can guide oil and gas into the reservoir and store it there, and is an effective fault; the second type of fault is a fault that breaks into the reservoir but cannot communicate with the source rock. The reservoir cannot form a reservoir and cannot become an effective fault; the third type of fault is a fault that completely penetrates the source rock and reservoir from bottom to top. It has a large extension length, but oil and gas will continue to be guided upward along the fault and cannot be stored in the reservoir, so it cannot become an effective fault.

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