A method and system for predicting earthquakes of clustered structures in fractured-cavern bodies

Abstract

The application provides a fracture-vug body inner structure cluster structure seismic prediction method and system, and belongs to the field of oil and gas exploration reservoir prediction. The method firstly determines the range of the fracture-vug body inner structure, then adopts a small bin waveform difference analysis method to predict the boundary characteristics of the cluster structure of the fracture-vug body inner structure, and finally adopts a transverse amplitude plane attribute feature as a constraint to determine the development position of the cluster structure. The application aims at the problem of fracture-vug body inner structure prediction, and develops a fracture-vug body inner structure cluster structure seismic prediction technology combined with longitudinal waveform difference and transverse amplitude plane change constraints, improves the prediction accuracy of the inner structure cluster structure, improves the reliability and stability of the prediction, and helps efficient oil and gas reservoir exploration and development work. Based on the idea of weakening channel equalization processing and improving the characterization ability of single-channel seismic data, a small bin attribute calculation method is adopted to improve the prediction accuracy of small-scale information.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of oil and gas exploration reservoir prediction, and particularly relates to a seismic prediction method and system for a cluster structure inside a fracture-cave body. BACKGROUND

[0002] With the continuous advancement of the national "Deep Earth No. 1" project - the exploration and development of the Shunbei oil and gas field, drilling has revealed the difference of the fracture-cave body internal structure, which presents the characteristics of cluster structure.

[0003] Chinese patent publication CN109959965A discloses a method and device for obtaining the internal structure of a carbonate rock fracture-cave body, which comprises: obtaining a string-bead-shaped seismic facies outside the carbonate rock fracture-cave body through a preset feature; obtaining the porosity threshold of the target layer inside the carbonate rock fracture-cave body; processing the seismic data through seismic inversion technology to obtain the porosity data body of the cave-type reservoir and the porosity data body of the pore-hole-type reservoir inside the carbonate rock fracture-cave body; obtaining the porosity data body of the fracture-type reservoir inside the carbonate rock fracture-cave body through seismic data; and determining the spatial structure of the fracture-cave body according to the string-bead-shaped seismic facies outside the carbonate rock fracture-cave body, the porosity threshold of the target layer inside the carbonate rock fracture-cave body, the porosity data body of the cave-type reservoir, the porosity data body of the pore-hole-type reservoir, and the porosity data body of the fracture-type reservoir. The method and device for obtaining the internal structure of the carbonate rock fracture-cave body provided in the present application obtain the spatial structure of the fracture-cave body.

[0004] Chinese patent publication CN107272064A discloses a method for depicting the internal structure of a carbonate rock fracture-cave body, which comprises: obtaining the seismic data corresponding to the fracture-cave body based on existing geological knowledge; and using frequency spectrum decomposition to apply high-frequency information to depict the internal structure of the fracture-cave.

[0005] Chinese journal "Xinjiang Petroleum Geology" published in June 2011 disclosed the quantitative description technology and application of carbonate rock fracture-cave bodies in the Yingshan Formation in the northern slope of Tazhong, aiming at various reservoir geological models, a large number of forward researches were carried out, and the response geophysical response characteristics were analyzed, and the wave impedance inversion prediction method was optimized, and was converted into a porosity body, so that the effective reservoir space of the reservoir can be obtained, and finally the quantitative description of the effective reservoir space of the karst fracture-cave body is realized. The application of actual data verifies that the quantitative description of the fracture-cave body is one of the effective ways for efficient exploration and development of carbonate rock oil and gas reservoirs.

[0006] In August 2022, the Chinese journal "Oil Geophysical Prospecting" published a method for depicting the boundaries of carbonate fracture-cave bodies using gradient structural tensors. For gradient structural tensor attributes, the geometric significance and the influence of each calculation step were discussed and analyzed in detail. It is believed that the smoothing of tensor elements is the key to depicting fracture-cave bodies, and the second and third eigenvalues have certain ability to depict carbonate fracture-cave bodies. In summary, compared with conventional attributes such as amplitude variation rate, gradient structural tensor attributes can more continuously depict carbonate fracture-cave bodies.

[0007] In January 2023, the Chinese journal "Oil and Gas Geology and Recovery" published a method for predicting fracture-cave reservoirs based on well-controlled multi-attribute machine learning. Based on well-to-seismic calibration, different sensitive seismic attribute values at the leakage point position were extracted as data input arrays, and the reservoir type defined according to the characteristics of the leakage point was used as the output array to form the training set data. Then, the support vector machine method was used to train the training set data to obtain a prediction model consistent with the prior information and well-to-seismic. Finally, a method for predicting fracture-cave reservoirs based on well-controlled multi-attribute machine learning was proposed. In practical data applications, this method can well reflect the characteristics of the real reservoir type and has a high degree of agreement with the drilling characteristics.

[0008] Current fracture-cave body depiction techniques are based on large-scale depiction ideas, such as energy-based attributes, which can only depict the macroscopic fracture-cave body outline and cannot effectively describe the internal structure characteristics of the fracture-cave body. This brings greater difficulties to subsequent quantitative description of fracture-cave bodies and well trajectory design, and the effective prediction of the internal cluster structure becomes more important. SUMMARY

[0009] The purpose of the present application is to solve the problems existing in the prior art, and to provide a method and system for predicting the internal cluster structure of a fracture-cave body. The method uses original seismic data to qualitatively depict the overall outline of the fracture-cave body, and then uses a small bin attribute calculation approach to determine the development position of the cluster structure by combining longitudinal waveform difference analysis and horizontal amplitude plane attribute characteristics, thereby achieving fine prediction of the internal cluster structure of the fracture-cave body and a high well-to-seismic agreement rate, and achieving good application results in practical data applications.

[0010] The present application is achieved by the following technical solutions:

[0011] In a first aspect of the present application, a method for predicting the internal cluster structure of a fracture-cave body is provided. First, the internal range of the fracture-cave body is determined, then a small bin waveform difference analysis method is used to predict the boundary characteristics of the cluster structure in the internal range of the fracture-cave body, and finally, the development position of the cluster structure is determined by using horizontal amplitude plane attribute characteristics as a constraint.

[0012] Further improvements of the present application are as follows:

[0013] The method comprises:

[0014] Firstly, an original seismic data body is acquired;

[0015] Secondly, energy attribute calculation is carried out by using the original seismic data body, the outline of the fracture-cave body is predicted, the threshold of the energy attribute is analyzed according to well-seismic joint analysis, and thus the inner range of the fracture-cave body is determined;

[0016] Thirdly, within the range of the fracture-cave body, small bin longitudinal waveform difference analysis is carried out, and the boundary information of the inner cluster structure is determined;

[0017] Fourthly, the development position of the inner cluster structure is determined by using horizontal amplitude plane attribute feature constraint.

[0018] The further improvement of the present application is that:

[0019] Secondly, energy attribute calculation is carried out by using the original seismic data body, the outline of the fracture-cave body is predicted, the threshold of the energy attribute is analyzed according to well-seismic joint analysis, and thus the inner range of the fracture-cave body is determined, specifically:

[0020] Firstly, the sensitive attributes of the coherent energy gradient, the instantaneous energy and the tensor to the fracture-cave body outline are extracted from the original seismic data body, so as to realize the outline prediction of the fracture-cave body; then the energy attribute values of the emptying loss points, the logging first and second reservoirs and other positions of a plurality of actually drilled wells are counted, and the average value is taken as the threshold, so as to determine the inner range of the fracture-cave body.

[0021] The further improvement of the present application is that:

[0022] The tensor attribute is extracted by using formula (1):

[0023]

[0024] In the formula, λ i is the tensor attribute, is the gradient of the seismic data s, and eig(*) is the eigenvalue of the matrix *.

[0025] The further improvement of the present application is that:

[0026] The instantaneous energy attribute is extracted by using formula (2):

[0027] E ins (t)=s(t) ∧ 2+H[s(t)] ∧ 2 (2)

[0028] In the formula, E ins (t) is the instantaneous energy attribute of the seismic data s(t), and H[] is the Hilbert transform.

[0029] Further improvement of the present application is that:

[0030] The threshold value is extracted by using formula (3):

[0031]

[0032] In the formula, A i is the energy class attribute value at the i-th point position, V th is the determined threshold value.

[0033] Further improvement of the present application is that:

[0034] Thirdly, in the range of the fracture-vug body, the difference of the longitudinal waveforms of the small facets is analyzed to determine the boundary information of the internal cluster structure, specifically:

[0035] Firstly, the relevant time window size W and the facet size are set, and then the Pearson correlation coefficient of the central seismic trace and the adjacent trace is calculated by using formula (4) at each sampling point in the threshold range of the fracture-vug body:

[0036]

[0037] In the formula, X and Y are vectors, and the covariance is:

[0038] cov(X,Y)=E((X-E(X))(Y-E(Y)))=E(XY)-E(X)E(Y),

[0039] The standard deviation is:

[0040] The correlation of the adjacent waveforms is statistically analyzed, and then the multi-channel average result or the least relevant result by using formula (5) is used as the final correlation coefficient of the sampling point in the facet.

[0041]

[0042] In the formula, ρ j is the Pearson correlation coefficient of the central trace and the j adjacent trace, and L is the number of adjacent traces.

[0043] Meanwhile, the prior information such as the logging interpretation conclusion is used to determine the correlation coefficient threshold value reflecting the boundary information of the internal cluster structure.

[0044] Further improvement of the present application is that:

[0045] Fourthly, the development position of the internal cluster structure is comprehensively determined by using the horizontal amplitude plane attribute feature constraint, specifically:

[0046] For the transverse feature analysis of the cluster structure of the inner part of the fracture-cave body, the conclusion of the forward modeling of the fracture-cave body containing the inner part cluster reservoir is utilized, that is, the wave shape "pulling up" phenomenon appears in the inner part cluster reservoir structure, and formula (6) is used to obtain the gradient data of the isochronous slice or the isobath slice of the seismic data;

[0047]

[0048] In the formula, S amp (t, l) is the seismic amplitude of the lth trace at t time in the time window;

[0049] Then, the isochronous or isobath slice with the most obvious inner part feature is selected, the amplitude gradient at the boundary of the inner part cluster structure determined in the third step is extracted, and the gradient change characteristics of the inner part boundaries are analyzed in sequence two by two, wherein when the gradient at the first boundary is positive and the gradient at the second boundary is negative, the data between the two boundaries is set to 1 by using formula (7), which represents the development position of the inner part reservoir, and the rest is set to 0, which represents the non-reservoir (or surrounding rock) position, so that the development position of the inner part cluster structure can be determined;

[0050]

[0051] In the formula, IS is the prediction result of the inner part cluster structure at t time, and e1 and e2 are the first and second inner part boundaries after sequential combination, respectively.

[0052] In the second aspect of the present application, a seismic prediction system for the inner part cluster structure of the fracture-cave body is provided, and the system comprises:

[0053] A data acquisition unit is used to acquire the original seismic data body;

[0054] A fracture-cave body range determination unit is connected with the data acquisition unit, and is used to utilize the original seismic data body to perform energy attribute calculation, predict the outline of the fracture-cave body, analyze the threshold of the energy attribute according to well-seismic joint analysis, and thus determine the inner part range of the fracture-cave body;

[0055] A boundary information determination unit is connected with the fracture-cave body range determination unit, and is used to develop the facet longitudinal waveform difference analysis in the fracture-cave body range, and determine the boundary information of the inner part cluster structure;

[0056] A comprehensive determination unit is connected with the boundary information determination unit, and is used to utilize the transverse amplitude plane attribute feature constraint to comprehensively determine the development position of the inner part cluster structure.

[0057] In a third aspect, the present application provides a computer readable storage medium, which stores at least one program executable by a computer, and the at least one program, when executed by the computer, causes the computer to perform the steps of the method for predicting the cluster structure of the fracture-cave interior.

[0058] Compared with the prior art, the present application has the following beneficial effects:

[0059] The present application aims at the problem of predicting the structure of the fracture-cave interior, and develops a seismic prediction technology for the cluster structure of the fracture-cave interior, which is jointly constrained by the longitudinal waveform difference and the lateral amplitude plane change, so as to improve the prediction accuracy of the cluster structure of the fracture-cave interior, improve the reliability and stability of the prediction, and help the efficient exploration and development of oil and gas reservoirs.

[0060] Based on the idea of weakening the channel equalization processing and improving the characterization ability of single-channel seismic data, the present application proposes a small bin attribute calculation method to improve the prediction accuracy of small-scale information. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 is a flowchart of the method for predicting the cluster structure of the fracture-cave interior provided by the present application;

[0062] Figure 2 is a fracture-cave geological model diagram;

[0063] Figure 3 is a migration imaging profile near the fracture-cave;

[0064] Figure 4 is a fracture-cave instantaneous amplitude attribute diagram;

[0065] Figure 5 is a fracture-cave correlation coefficient diagram;

[0066] Figure 6 is a lateral amplitude gradient diagram at the boundary of the interior;

[0067] Figure 7 is a superimposed diagram of the equal seismic waveform and the prediction result of the interior structure;

[0068] Figure 8 is a superimposed diagram of the seismic profile and the prediction result of the interior structure. DETAILED DESCRIPTION

[0069] The present application will be further described in detail below with reference to the accompanying drawings:

[0070] The present application adopts the idea of weakening the channel equalization processing, reduces the bin for attribute calculation, and proposes a seismic prediction technology for the cluster structure of the fracture-cave interior, which is jointly constrained by the longitudinal waveform difference and the lateral amplitude plane change, so as to improve the characterization ability of the cluster structure of the fracture-cave interior.

[0071] The present application uses original seismic data, adopts energy attribute to depict the overall profile of fracture-cave body, sets energy threshold as the boundary of fracture-cave body, thereby determines the inner range of fracture-cave body; within the range, adopts facet wave form difference analysis method to predict the boundary features of cluster structure in the inner part of fracture-cave body, then adopts lateral amplitude variation features as the constraint, finally determines the development position of cluster structure, realizes the fine prediction of cluster structure in the inner part of fracture-cave body.

[0072] The present application provides a kind of fracture-cave body inner cluster structure seismic prediction method, as shown in Figure 1 The main content mainly includes energy attribute calculation and threshold determination, longitudinal waveform difference analysis and lateral amplitude plane variation feature analysis, cluster structure prediction three parts, the method implementation is as follows:

[0073]

Example 1

[0074] The method specifically includes the following steps:

[0075] First, obtain the original seismic data body;

[0076] Second, use the original seismic data body to calculate the energy attribute, predict the profile of fracture-cave body, analyze the threshold of energy attribute according to well-seismic joint analysis, thereby determine the inner range of fracture-cave body;

[0077] First, extract the coherent energy gradient, instantaneous energy, tensor and other sensitive attributes of the original seismic data body to realize the profile prediction of fracture-cave body; then, combine the lost circulation point position of the actual drilled well, the well logging one and two reservoir division information, etc., to calibrate the threshold of energy attribute representing the profile of fracture-cave body (through the statistical value of energy attribute at the lost circulation point, well logging one and two reservoirs and other positions of multiple actual drilled wells, the average value can be taken as the threshold, the process is calibration process), the threshold can be the average value of multiple well calibration thresholds according to the actual situation, thereby determining the inner range of fracture-cave body.

[0078] The tensor attribute formula is (formula 1):

[0079]

[0080] In the formula, λ i is the tensor attribute, is the gradient of seismic data s, eig(*) is the eigenvalue of matrix *;

[0081] The instantaneous energy attribute formula is (formula 2):

[0082] E ins (t)=s(t) ∧ 2+H[s(t)] ∧ 2 (2)

[0083] E ins (t) is the instantaneous energy attribute of the seismic data s(t), and H[] is the Hilbert transform;

[0084] The threshold determination formula is (formula 3):

[0085]

[0086] A i is the energy attribute value at the i-th point position, V th is the determined threshold value.

[0087] Thirdly, in the range of the fracture-cave body, a small bin longitudinal waveform difference analysis is carried out to determine the boundary information of the internal cluster structure.

[0088] In the range of the string bead profile, the Pearson correlation coefficient of the small bin at each trace and each sample point is calculated to highlight the longitudinal waveform difference characteristics, and the threshold value is determined to represent the boundary information of the internal cluster.

[0089] Specifically:

[0090] Firstly, the correlation time window size W (generally equivalent to the string bead size) and the bin size (generally set to 3x3) are set, and then the Pearson correlation coefficient of the center seismic trace and the adjacent trace is calculated (such as formula 4) at each sample point in the range of the fracture-cave body threshold value:

[0091]

[0092] In the formula, X and Y are vectors, and the covariance is:

[0093] cov(X,Y)=E((X-E(X))(Y-E(Y)))=E(XY)-E(X)E(Y),

[0094] The standard deviation is:

[0095] The statistical adjacent trace waveform correlation can be used to adopt the multi-trace average result or the least relevant result (such as formula 5) as the final correlation coefficient of the sampling point in the bin.

[0096]

[0097] In the formula, p j is the Pearson correlation coefficient of the center trace and the j adjacent trace, and L is the number of adjacent traces.

[0098] Meanwhile, the prior information such as well logging interpretation conclusion is used to determine the correlation coefficient threshold of the boundary information of the reaction inside cluster structure (the average value of the correlation coefficient threshold of the multiple actually drilled wells, the logging first and second reservoirs and other positions is taken as the threshold value through the statistics).

[0099] Fourth step, the development position of the inside cluster structure is comprehensively determined by using the lateral amplitude plane attribute feature constraint;

[0100] Specifically,

[0101] For the lateral feature analysis of the inside cluster structure of the fracture-cave body, the forward conclusion of the fracture-cave body model (similar to Figure 2 ) containing the inside cluster reservoir is used, that is, the wave shape "pulling up" phenomenon appears in the inside cluster reservoir structure, and the gradient data (such as formula 6) of the isochronous slice or the isobath slice of the seismic data is calculated.

[0102]

[0103] In the formula, S amp (t, l) is the seismic amplitude of the lth trace at t time in the time window;

[0104] Then, the isochronous or isobath slice with the most obvious inside feature is selected, the amplitude gradient at the boundary of the inside cluster structure determined in the foregoing is extracted, and the gradient change characteristics of the inside boundary sequence are analyzed, wherein when the gradient at the first boundary is positive and the gradient at the second boundary is negative, the data between the two boundaries is set to 1, representing the inside reservoir development position, and the rest is set to 0 (such as formula 7), representing the non-reservoir (or surrounding rock) position, that is, the development position of the inside cluster structure can be determined.

[0105]

[0106] In the formula, IS is the prediction result of the inside cluster structure at t time, and e1 and e2 are the first and second inside boundaries after the sequence combination respectively.

[0107] Fifth step, the development characteristics of the spatial three-dimensional cluster structure are obtained by using the results of the above longitudinal and lateral joint constraints, that is, "the inside cluster boundary is determined by the longitudinal wave shape difference, and the inside cluster position is determined by the lateral amplitude attribute", which provides a reliable basis for subsequent comprehensive evaluation.

[0108] The implementation of the method of the application is as follows:

[0109]

Example 2

[0110] The application is applied to the prediction of the inside structure of the carbonate fracture-cave body in the Tarim Basin.

[0111] Firstly, according to the drilled well information of the carbonate rock in Tarim Basin, a fracture-cave model with internal structure is established, as shown in the accompanying Figure 2 Fig. 1. Wherein, the surrounding rock velocity is 6000m / s; the thickness of the two clusters of fracture-cave is 50m, the width is 50m and 30m respectively, the velocity is 4000m / s, and the interval between the two clusters is 10m. Then, by using the wave equation forward technology, the shot record is obtained by forward calculation with 25Hz main frequency wavelet, and CMP gathers are extracted therefrom, and the accurate imaging is carried out by using the pre-stack depth migration algorithm; it can be known from the analysis that the seismic response of the internal cluster structure will appear the waveform "pulling up" phenomenon, and the imaging result of the local amplification near the fracture-cave is shown in the accompanying Figure 3 Fig. 2.

[0112] Then, the instantaneous amplitude attribute is extracted from the migration imaging data to realize the outline representation of the fracture-cave, and the threshold of the instantaneous amplitude attribute is calibrated in combination with the prior information, so as to determine the outline range of the fracture-cave, as shown in the accompanying Figure 4 Fig. 3; then, in the string bead outline range, the Pearson correlation coefficient of the facet at each sample point is calculated to highlight the longitudinal waveform difference characteristics, and the threshold is determined to represent the boundary information of the internal cluster, and the result is shown in the accompanying Figure 5 Fig. 4; then, the amplitude gradient data is calculated at the internal cluster boundary of the multiple isobath slices of the fracture-cave seismic data, and then the horizontal amplitude plane variation characteristics are extracted, and the result is shown in the accompanying Figure 6 Fig. 5.

[0113] Finally, the isobath slice with the most obvious internal characteristics is selected, that is, the depth is 1460m, and the change characteristics of the horizontal amplitude gradient of the slice are combined and analyzed to realize the determination of the development position of the internal cluster structure, and the seismic waveform and internal structure prediction result of the slice are shown in the accompanying Figure 7 Fig. 6; the result is projected into the seismic data, that is, the dashed rectangular frame, and the longitudinal waveform difference analysis and the horizontal amplitude plane characteristic constraint are combined, so that the final internal structure prediction result is obtained, as shown in the accompanying Figure 8 Fig. 7; the prediction result is consistent with the design cluster development characteristics in the Figure 2 model, and the prediction result has high reliability.

[0114] The application also provides a fracture-cave internal cluster structure seismic prediction system, and the implementation of the system is as follows:

[0115]

Example 3

[0116] The system comprises:

[0117] a data acquisition unit for acquiring original seismic data;

[0118] The fracture-cave body range determining unit is connected with the data acquisition unit, and is used for calculating energy attribute, predicting the outline of the fracture-cave body, analyzing the threshold of the energy attribute according to well-seismic joint analysis, and thus determining the inside range of the fracture-cave body by using the original seismic data body;

[0119] The boundary information determining unit is connected with the fracture-cave body range determining unit, and is used for carrying out the small face longitudinal waveform difference analysis in the fracture-cave body range, and determining the boundary information of the inside cluster structure.

[0120] The comprehensive judging unit is connected with the boundary information determining unit, and is used for comprehensively judging the development position of the inside cluster structure by using the transverse amplitude plane attribute feature constraint.

[0121] The application further provides a computer readable storage medium, and implementation of the computer readable storage medium is as follows:

[0122]

Embodiment 4

[0123] The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the fracture-cave body inside cluster structure seismic prediction method.

[0124] The above technical solution is only one embodiment of the application, and for those skilled in the art, on the basis of the disclosed principle, various types of improvements or modifications can be easily made, and the application is not limited to the technical solution described in the above embodiment, therefore, the above description is only preferred, and is not limited.

Claims

1. A method for predicting earthquakes of clustered structures in fracture-cavity bodies, characterized in that: The method comprises: The first step is to obtain the original seismic data volume; The second step is to use the original seismic data to calculate energy attributes and predict the outline of the fracture-cavity body. Based on the joint analysis of well and seismic data, the threshold of the energy attribute is analyzed to determine the inner range of the fracture-cavity body. The third step is to conduct a small-surface longitudinal waveform difference analysis within the fracture-cavity body to determine the boundary information of the internal cluster structure; The fourth step is to use the horizontal amplitude plane attribute feature constraints to comprehensively determine the development location of the inner cluster structure.

2. The earthquake prediction method for clustered structure in fracture-cavity body according to claim 1, characterized in that: The second step is to use the original seismic data to calculate energy attributes and predict the outline of the fracture-cavity body. Based on the joint analysis of well and seismic data, the threshold of the energy attribute is analyzed to determine the inner range of the fracture-cavity body. Specifically: First, the coherent energy gradient, instantaneous energy, and tensor sensitive attributes that characterize the contour of the fracture-vuggy body are extracted from the original seismic data volume to achieve the contour prediction of the fracture-vuggy body. Then, the energy attribute values ​​at the emptying and leakage points of multiple wells and the locations of the first and second type reservoirs in the well logging are statistically analyzed, and the average value is taken as the threshold to determine the inner range of the fracture-vuggy body.

3. The earthquake prediction method for clustered structure in fracture-cavity body according to claim 2, characterized in that: Use formula (1) to extract tensor attributes: Where λ i is a tensor attribute, is the gradient of the seismic data s, and eig(*) is the eigenvalue of the matrix *.

4. The earthquake prediction method for clustered structures in fracture-cavity bodies according to claim 3 is characterized in that: Formula (2) is used to extract the instantaneous energy properties: E ins (t)=s(t)∧2+H[s(t)]∧2 (2) Where, E ins (t) is the instantaneous energy attribute of the seismic data s(t), and H[] is the Hilbert transform.

5. The earthquake prediction method for clustered structure in fracture-cavity body according to claim 4, characterized in that: Formula (3) is used to extract the threshold: Where A i is the energy attribute value at the i-th point, V th is the determined threshold.

6. The earthquake prediction method for the clustered structure of fracture-cavity body according to claim 5, characterized in that: In the third step, small-surface longitudinal waveform difference analysis is carried out within the fracture-cavity body to determine the boundary information of the inner cluster structure. Specifically: First, the correlation time window size W and the bin size are set. Then, at each sampling point within the fracture-cavity threshold range, the Pearson correlation coefficient between the central seismic trace and the adjacent traces is calculated in turn using formula (4): Where X and Y are vectors and their covariance is: cov(X,Y)=E((XE(X))(YE(Y)))=E(XY)-E(X)E(Y), The standard deviation is: Count the waveform correlations of adjacent channels, and then use formula (5) to calculate the multi-channel average result or the least correlated result as the final correlation coefficient of the sampling point in the bin; Where, ρ j is the Pearson correlation coefficient between the central track and j adjacent tracks, and L is the number of adjacent tracks; At the same time, prior information such as well logging interpretation conclusions is used to determine the correlation coefficient threshold that reflects the boundary information of the internal cluster structure.

7. The earthquake prediction method for clustered structure in fracture-cavity body according to claim 6, characterized in that: In the fourth step, the development location of the inner cluster structure is comprehensively determined by using the horizontal amplitude plane attribute feature constraint, specifically: In order to analyze the lateral characteristics of the internal cluster structure of the fracture-cavity body, the forward modeling conclusion of the fracture-cavity body model containing the internal cluster reservoir is used, that is, the internal cluster reservoir structure will have a waveform "pull-up" phenomenon, and the gradient data is obtained from the isochronous slices or isodepth slices of the seismic data using formula (6); Where S amp (t, l) is the earthquake amplitude at the lth trace and time t in the time window; Afterwards, the isochronous or isobath slices with the most obvious internal features are selected, and the amplitude gradient is extracted at the boundary of the internal cluster structure determined in the third step. Then, the gradient variation characteristics of the internal boundaries are analyzed in pairs. When the gradient at the first boundary is positive and the gradient at the second boundary is negative, formula (7) is used. The data within the two boundaries are set to 1, representing the development location of the internal reservoir, and the rest are set to 0, representing the non-reservoir or surrounding rock location. This can realize the determination of the development location of the internal cluster structure. Where IS is the prediction result of the insider cluster structure at time t, e1 and e2 are the first and second insider boundaries after sequential combination, respectively.

8. A system for earthquake prediction of clustered structures in fractured caverns, characterized in that: The system comprises: Data acquisition unit: used to obtain original seismic data volume; Fracture-cavity range determination unit: connected to the data acquisition unit, used to calculate energy attributes using the original seismic data volume, predict the outline of the fracture-cavity body, analyze the threshold of the energy attribute based on the joint analysis of well and seismic data, and thus determine the inner range of the fracture-cavity body; Boundary information determination unit: connected to the fracture-cavity range determination unit, used to carry out small-surface longitudinal waveform difference analysis within the fracture-cavity range to determine the boundary information of the inner cluster structure; Comprehensive judgment unit: connected to the boundary information determination unit, used to comprehensively determine the development position of the inner cluster structure by using the horizontal amplitude plane attribute feature constraint.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps of the earthquake prediction method for clustered structures in fracture-cavity bodies according to any one of claims 1 to 7.

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