Mountain-front-zone shallow conglomerate velocity modeling method and system based on time-frequency electromagnetism

Abstract

The invention relates to the technical field of oil and gas geophysical prospecting engineering, in particular to a piedmont zone shallow layer conglomerate velocity modeling method and system based on time-frequency electromagnetism. The method comprises the following steps: S1, acquiring logging data and time-frequency electromagnetic data of a conglomerate area in a mountain front zone; s2, processing the acquired time-frequency electromagnetic data to obtain a high-frequency-band resistivity data body; s3, according to the obtained logging data, statistical analysis is conducted on logging resistivity and sound wave speed in the logging data, and a resistivity and sound wave speed conversion model is obtained; s4, in combination with a resistivity and sound wave speed conversion model, confirming a sound wave speed-resistivity empirical relational expression of which the correlation coefficient is greater than or equal to a set threshold value; s5, the high-frequency-band resistivity data body is substituted into the sound wave speed-resistivity empirical relational expression, and a speed data body of the mountain-front shallow conglomerate is obtained; according to the method, the speed modeling precision of the shallow conglomerate region is improved, a foundation is laid for implementation of a deep fine structure, and the imaging precision of seismic data is improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas geophysical exploration engineering, and in particular to a method and system for modeling shallow conglomerate velocity in a piedmont zone based on time-frequency electromagnetics. Background Art

[0002] Seismic exploration in the piedmont zone often has the characteristics of "double complexity" of complex surface conditions and complex geological structures, which brings great challenges to seismic exploration, mainly manifested in low signal-to-noise ratio of seismic data, large lateral velocity changes, developed faults, and difficult seismic imaging. In the stage of seismic data migration imaging processing, the imaging accuracy of all migration methods depends on the accuracy of the velocity model. Accurate velocity modeling is the basis of high-precision imaging. The internal velocity variation characteristics of the conglomerate body developed near the surface in the piedmont zone are unclear and the conglomerate boundary is blurred in velocity imaging, making seismic shallow velocity modeling difficult.

[0003] Prior art CN105093279A discloses a method for tomographic inversion of three-dimensional seismic first-arrival waves and Fresnel bodies for piedmont belts, comprising: obtaining the first-arrival wave time of seismic data; establishing a discrete model; calculating the travel time of the first-arrival wavefront; determining the ray path; calculating the Fresnel body; establishing a tomographic inversion equation; solving the inversion equation; replacing the velocity model in step (2) with the velocity model in step (7), and repeating steps (2) to (7) to obtain the final velocity model. In order to solve the problem of depth domain velocity modeling in seismic data processing, the above method usually uses regional geological data. The initial velocity model is quickly established based on the constraints of structural knowledge and well data information. The depth domain velocity model is optimized through coherent inversion, tomography technology combined with velocity scanning to establish an accurate velocity model. However, the complexity of the piedmont zone often leads to low precision of prestack time migration imaging. The initial velocity model established by the structural model based on the interpretation of the prestack time migration results has low accuracy. Even if the velocity model is iteratively optimized through subsequent coherent inversion, tomography inversion and other methods, the velocity model established by this method is still difficult to meet the requirements of imaging accuracy of high and steep structures in the piedmont zone.

[0004] In order to solve the problems existing in the above methods, the prior art CN106597533A discloses a depth domain velocity modeling method for processing seismic data in the piedmont belt, which includes the following steps: 1) using velocity constraint inversion to perform local single-point control on the root mean square velocity of prestack time migration, and then converting it into time domain layer velocity through the DIX formula, and then performing time-depth conversion to obtain the depth domain layer velocity body and smoothing it to obtain the depth domain initial layer velocity model of prestack depth migration; 2) grid tomography inversion optimization velocity model; 3) using the optimized velocity model to perform prestack depth migration on all data in the work area to obtain the depth domain stacked data body, and then proportionally convert it to the time domain, perform structural interpretation in the time domain, and obtain the structural model; 4) using well constraints to perform tomographic inversion optimization on the structural model, that is, the method improves the accuracy of the depth domain velocity model of the deep migration imaging processing of the high-steep structure in the piedmont belt, makes the homing accuracy of the high-steep structure migration in the piedmont belt higher, and improves the degree of well-seismic coincidence.

[0005] However, the existing methods do not mention a method for obtaining a resistivity data volume based on time-frequency electromagnetic data and establishing a velocity model between logging resistivity and acoustic wave velocity to obtain a velocity data volume with higher inversion accuracy.

[0006] Therefore, there is an urgent need to provide a shallow gravel velocity modeling method and system in the foreland belt based on time-frequency electromagnetics. Compared with the existing technology, it can realize the conversion between resistivity and velocity, obtain a high-precision gravel area velocity model, and improve the imaging accuracy of the foreland belt. Summary of the invention

[0007] The present invention solves the technical problems existing in the prior art and provides a method and system for modeling the velocity of shallow conglomerate in a piedmont zone based on time-frequency electromagnetics.

[0008] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0009] The velocity modeling method of shallow conglomerate in the piedmont zone based on time-frequency electromagnetic includes the following steps:

[0010] S1. Obtain logging data and time-frequency electromagnetic data in the conglomerate area of ​​the piedmont zone;

[0011] S2, processing the time-frequency electromagnetic data obtained in S1 to obtain a high-frequency resistivity data volume;

[0012] S3, based on the well logging data obtained in S1, statistically analyzing the well logging resistivity and acoustic wave velocity in the well logging data to obtain a resistivity and acoustic wave velocity conversion model;

[0013] S4, combining the resistivity and acoustic wave velocity conversion model obtained in step S3, confirming the acoustic wave velocity-resistivity empirical relationship whose correlation coefficient is greater than or equal to a set threshold;

[0014] S5. Substitute the high-frequency resistivity data volume obtained in step S2 into the acoustic velocity-resistivity empirical relationship confirmed in step S4 to obtain the velocity data volume of the shallow conglomerate in the piedmont zone.

[0015] Furthermore, S4 specifically includes the following steps:

[0016] S401, determining a calculation formula for the correlation coefficient between logging resistivity and acoustic wave velocity;

[0017] S402, obtaining the actual logging resistivity and acoustic wave velocity in the conglomerate area, and calculating the correlation coefficient between each logging resistivity and acoustic wave velocity according to the correlation coefficient calculation formula in S401;

[0018] S403, selecting the logging resistivity and the acoustic wave velocity whose correlation coefficient is greater than or equal to a set threshold value from the correlation coefficients of the logging resistivity and the acoustic wave velocity calculated in S402;

[0019] S404, based on the logging resistivity and acoustic wave velocity selected in S403, combined with the resistivity and acoustic wave velocity conversion model obtained in step S3, confirm the acoustic wave velocity-resistivity empirical relationship whose correlation coefficient is greater than or equal to the set threshold.

[0020] Furthermore, the calculation formula of the correlation coefficient between resistivity and acoustic wave velocity described in step S401 is:

[0021]

[0022] In the above formula, r represents the correlation coefficient between the acoustic velocity of the conglomerate layer and the logging resistivity of the conglomerate layer, n represents the discrete points of the logging resistivity data and the acoustic velocity data, and x i represents the resistivity of the i-th conglomerate layer, represents the mean resistivity of the conglomerate layer, y i represents the acoustic wave velocity of the i-th conglomerate layer, Represents the mean value of the acoustic wave velocity in the conglomerate layer.

[0023] Furthermore, S3 also includes the following steps:

[0024] S301, selecting logging resistivity data and acoustic wave velocity data of the conglomerate area in the logging data of the conglomerate area in the piedmont zone, and performing logarithmic processing on the acquired logging resistivity data;

[0025] S302, processing the well logging resistivity data and the acoustic wave velocity data after logarithmic processing to obtain discrete points of the well logging resistivity data and the acoustic wave velocity data;

[0026] S303, statistically analyzing the well logging resistivity data and the discrete points of the acoustic wave velocity after logarithmic processing to obtain a well logging resistivity and acoustic wave velocity conversion model.

[0027] Furthermore, the discrete points of the well logging resistivity data and the acoustic wave velocity after logarithmic processing obtained in step S302 are set as n in the calculation formula of the correlation coefficient between resistivity and acoustic wave velocity in step S401.

[0028] Furthermore, the acoustic wave velocity-resistivity empirical relationship with a correlation coefficient greater than or equal to a set threshold value confirmed in step S404 is:

[0029] y=1777.9e 0.3238x

[0030] In the above formula, y represents the acoustic wave velocity of the conglomerate layer, and x represents the resistivity of the conglomerate layer. Both x and y are measured data.

[0031] Furthermore, step S4 also includes verifying the accuracy of the acoustic wave velocity-resistivity empirical relationship. Step S5 is performed only after the verification is passed. Otherwise, the acoustic wave velocity-resistivity empirical relationship is adjusted until the accuracy of the acoustic wave velocity-resistivity empirical relationship is verified.

[0032] Furthermore, the specific method for verifying the accuracy of the acoustic wave velocity-resistivity empirical relationship is: the logging resistivity measured in the conglomerate area is substituted into the acoustic wave velocity-resistivity empirical relationship obtained in step S404 to obtain the acoustic wave velocity, and the obtained acoustic wave velocity is compared with the acoustic wave velocity measured in the well. If the change trend is consistent, it is passed, otherwise it is failed.

[0033] Furthermore, the set threshold in S403 is 0.7.

[0034] Furthermore, S2 specifically includes the following steps:

[0035] S201, selecting high frequency band data from the time-frequency electromagnetic data obtained in S1;

[0036] S202, performing three-dimensional inversion processing on the high-frequency band data obtained in step S201 to obtain a high-frequency band resistivity data volume.

[0037] Furthermore, the time-frequency electromagnetic data in S201 is the time-frequency electromagnetic data obtained by processing the time-frequency electromagnetic signals collected in the field, and then selecting high-frequency band data based on the distribution range of the emission frequency of the measured time-frequency electromagnetic signals.

[0038] Furthermore, the emission frequency of the time-frequency electromagnetic signal is 0.01 Hz-334 Hz.

[0039] Furthermore, the selected high-frequency band data is data in the frequency band of 0.5 Hz-334 Hz.

[0040] A shallow conglomerate velocity modeling system for a piedmont belt based on time-frequency electromagnetics comprises a data acquisition module, a time-frequency electromagnetic data processing module, a well logging data analysis module, an empirical relationship module and a velocity data volume generation module, wherein the data acquisition module is respectively connected to the time-frequency electromagnetic data processing module and the well logging data analysis module, the well logging data analysis module is connected to the empirical relationship module, and the empirical relationship module and the electromagnetic data processing module are respectively connected to the velocity data volume generation module.

[0041] Furthermore, the empirical relationship module further comprises a determination module and a verification module, wherein the input end of the determination module is connected to the well logging data analysis module, the output end of the determination module is connected to the input end of the verification module, and the output end of the verification module is connected to the velocity data volume generation module.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) The present invention uses the resistivity data of the high-frequency electromagnetic band to obtain the fine spatial distribution characteristics of the shallow conglomerate area, which has a good characterization of the conglomerate boundary, and then obtains a high-precision velocity model of the shallow conglomerate area through the relationship between resistivity and acoustic wave velocity. This method overcomes the difficulty of unclear velocity variation characteristics inside the conglomerate body and unclear conglomerate boundary in velocity imaging when seismic velocity modeling is performed in the conglomerate area, improves the velocity modeling accuracy of the shallow conglomerate area, ensures the shallow velocity accuracy of the shallow, medium and deep velocity modeling in the area, lays the foundation for the implementation of deep fine structure, and improves the imaging accuracy of seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention.

[0045] Figure 2 It is a schematic diagram of the high frequency resistivity data volume of the present invention.

[0046] Figure 3 It is a schematic diagram of the conversion model of logging resistivity and acoustic wave velocity of the present invention.

[0047] Figure 4 It is a comparative schematic diagram for verifying the accuracy of the acoustic wave velocity-resistivity empirical relationship of the present invention.

[0048] Figure 5 It is a schematic diagram of the velocity data body of shallow conglomerate in the piedmont zone of the present invention.

[0049] Figure 6 It is an overall schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the protection scope of the present invention.

[0051] like Figure 1 As shown, the present invention provides a method for modeling shallow conglomerate velocity in a piedmont zone based on time-frequency electromagnetics, comprising the following steps:

[0052] S1, obtaining well logging data and time-frequency electromagnetic data in the conglomerate area of ​​the piedmont zone, selecting the high-frequency band of the obtained electromagnetic data for processing, and obtaining a high-frequency band resistivity data volume; specifically comprising the following steps:

[0053] S101, selecting high-frequency band data from the time-frequency electromagnetic data. In this embodiment, the high-frequency band data is data in the frequency range of 0.5 Hz-334 Hz;

[0054] S102, perform three-dimensional inversion processing on the high-frequency band data obtained in S101 to obtain a high-frequency band resistivity data body. Based on the high-frequency band resistivity data body, the distribution characteristics of the shallow conglomerate in the piedmont zone can be obtained (such as Figure 2 shown).

[0055] Furthermore, time-frequency electromagnetic data is obtained by processing time-frequency electromagnetic signals collected in the field to obtain accurate time-frequency electromagnetic data. According to the distribution range of the measured emission frequency of the time-frequency electromagnetic signal, the data participating in the inversion processing is selected. The distribution range of the emission frequency of the time-frequency electromagnetic signal is 0.01Hz-334Hz, and the high-frequency band data of 0.5Hz-334Hz is selected to participate in the inversion processing.

[0056] S2, based on the logging data of the conglomerate area in the piedmont zone obtained in S1, statistically analyzing the resistivity and acoustic wave velocity of the logging in the conglomerate area, and establishing a conversion model between resistivity and acoustic wave velocity; specifically comprising the following steps:

[0057] S201, in order to obtain the relationship between resistivity and acoustic wave velocity, the resistivity data and acoustic wave velocity data of the shallow conglomerate area are selected from the logging data of the conglomerate area in the piedmont zone, and the resistivity data are logarithmically processed to obtain discrete points of the resistivity data and the acoustic wave velocity data;

[0058] S202, logarithmically process the resistivity data obtained in step S201, and statistically analyze the resistivity logarithmic data and discrete points of the acoustic wave velocity to obtain a resistivity and acoustic wave velocity conversion model, such as Figure 3 shown.

[0059] S3, according to the resistivity and acoustic wave velocity conversion model described in S2, obtain the acoustic wave velocity-resistivity empirical relationship, determine the acoustic wave velocity-resistivity empirical relationship with a correlation coefficient greater than or equal to a set threshold, because the correlation coefficient is used to characterize the strength and direction of the correlation between two variables, when the correlation coefficient is greater than 0.7, it means that the two variables are highly correlated, so it is only necessary to determine the acoustic wave velocity-resistivity empirical relationship when the correlation coefficient is greater than or equal to 0.7, so the threshold is preferably set to 0.7. Specifically comprising the following steps:

[0060] S301, determine the calculation formula of the correlation coefficient between resistivity and acoustic wave velocity, which is specifically expressed by the following formula:

[0061]

[0062] In the above formula, r represents the correlation coefficient between the velocity of the conglomerate layer and the resistivity of the conglomerate layer, n represents the discrete points of the resistivity data and the acoustic velocity data obtained in step S201, and x i represents the resistivity of the i-th conglomerate layer, represents the mean resistivity of the conglomerate layer, y i represents the acoustic wave velocity of the i-th conglomerate layer, Represents the mean value of the acoustic wave velocity in the conglomerate layer.

[0063] S302, obtaining the actual logging resistivity and acoustic wave velocity in the conglomerate area, and calculating the correlation coefficient between each logging resistivity and acoustic wave velocity according to the correlation coefficient calculation formula;

[0064] S303, selecting the well logging resistivity and the acoustic wave velocity with a correlation coefficient greater than or equal to 0.7 from the correlation coefficients of the well logging resistivity and the acoustic wave velocity calculated in step S302;

[0065] S304, according to the well logging resistivity and acoustic wave velocity conversion model obtained in step S2 and the well logging resistivity and acoustic wave velocity with a correlation coefficient greater than or equal to 0.7 obtained in step S303, determine the acoustic wave velocity-resistivity empirical relationship when the correlation coefficient is greater than or equal to 0.7, which is specifically expressed as:

[0066] y=1777.9e 0.3238x

[0067] In the above formula, y represents the acoustic wave velocity of the conglomerate layer, and x represents the resistivity of the conglomerate layer. Both x and y are measured data.

[0068] S4. According to the acoustic wave velocity-resistivity empirical relationship obtained in step S304, the acoustic wave velocity is calculated using the logging resistivity measured in the conglomerate area to verify the accuracy of the acoustic wave velocity-resistivity empirical relationship, such as Figure 4 shown.

[0069] Furthermore, the measured logging resistivity in the conglomerate area is substituted into the acoustic velocity-resistivity empirical relationship obtained in step S304 to obtain the acoustic velocity, and the obtained acoustic velocity is compared with the acoustic velocity measured in the well. If the calculated acoustic velocity is consistent with the change trend of the acoustic velocity measured in the well, it indicates that the empirical relationship is accurate, otherwise it indicates that the empirical relationship is inaccurate. The empirical relationship is fine-tuned according to the change trend of the resistivity and acoustic velocity conversion model until the empirical relationship is accurate.

[0070] S5, the high frequency resistivity data obtained in step S1 is introduced into the verified acoustic velocity-resistivity empirical relationship to obtain the shallow conglomerate velocity data of the piedmont zone, such as Figure 5 shown.

[0071] The present invention uses the resistivity data of the high frequency band of electromagnetic in the time-frequency region to obtain the fine spatial distribution characteristics of the shallow conglomerate area, and has a good characterization of the boundary of the conglomerate body. Then, through the relationship between resistivity and acoustic wave velocity, a high-precision velocity model of the shallow conglomerate area is obtained. This method overcomes the difficulty of unclear velocity variation characteristics inside the conglomerate body and unclear conglomerate boundary in velocity imaging when seismic velocity is modeled in the conglomerate area, improves the velocity modeling accuracy of the shallow conglomerate area, ensures the shallow velocity accuracy of the shallow, medium and deep velocity modeling in the area, lays the foundation for the implementation of deep fine structures, improves the imaging accuracy of seismic data, and is conducive to its wide application in seismic data velocity modeling.

[0072] like Figure 6 As shown, the present invention also provides a shallow gravel velocity modeling system in the piedmont zone based on time-frequency electromagnetics, including a data acquisition module, a time-frequency electromagnetic data processing module, a well logging data analysis module, an empirical relationship module and a velocity data volume generation module. The data acquisition module is connected to the time-frequency electromagnetic data processing module and the well logging data analysis module respectively, the well logging data analysis module is connected to the empirical relationship module, and the empirical relationship module and the electromagnetic data processing module are connected to the velocity data volume generation module respectively.

[0073] The data acquisition module is used to obtain the logging data and time-frequency electromagnetic data of the conglomerate area in the front mountain zone. The data acquisition module outputs the logging data to the logging data analysis module, and the data acquisition module outputs the time-frequency electromagnetic data to the time-frequency electromagnetic data processing module; the time-frequency electromagnetic data processing module is used to process the time-frequency electromagnetic data to obtain the high-frequency resistivity data body; the logging data analysis module is used to perform statistical analysis on the logging resistivity and acoustic wave velocity in the logging data to obtain the logging resistivity and acoustic wave velocity conversion model; the logging data analysis module outputs the obtained logging resistivity and acoustic wave velocity conversion model to the empirical relationship module.

[0074] The empirical relationship module also includes a determination module and a verification module. The determination module is used to process the well logging resistivity and acoustic wave velocity conversion model, and determine the acoustic wave velocity-resistivity empirical relationship when the correlation coefficient is greater than or equal to 0.7; the verification module is used to verify that the acoustic wave velocity-resistivity empirical relationship determined by the determination module is accurate, and is also used to fine-tune the acoustic wave velocity-resistivity empirical relationship; the verification module outputs the verified acoustic wave velocity-resistivity empirical relationship to the velocity data volume generation module, and the time-frequency electromagnetic data processing module outputs the high-frequency resistivity data volume obtained to the velocity data volume generation module; the velocity data volume generation module is used to bring the high-frequency resistivity data volume into the acoustic wave velocity-resistivity empirical relationship to obtain the shallow conglomerate velocity data volume in the piedmont zone.

[0075] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. Velocity modeling method of shallow conglomerate in the piedmont zone based on time-frequency electromagnetics, It is characterized in that The following steps are involved: S1. Obtain logging data and time-frequency electromagnetic data in the conglomerate area of ​​the piedmont zone; S2, processing the time-frequency electromagnetic data obtained in S1 to obtain a high-frequency resistivity data volume; S3, based on the well logging data obtained in S1, statistically analyzing the well logging resistivity and acoustic wave velocity in the well logging data to obtain a resistivity and acoustic wave velocity conversion model; S4, combining the resistivity and acoustic wave velocity conversion model obtained in step S3, confirming the acoustic wave velocity-resistivity empirical relationship whose correlation coefficient is greater than or equal to a set threshold; S5. Substitute the high-frequency resistivity data volume obtained in step S2 into the acoustic velocity-resistivity empirical relationship confirmed in step S4 to obtain the velocity data volume of the shallow conglomerate in the piedmont zone.

2. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 1, It is characterized in that S4 specifically includes the following steps: S401, determining a calculation formula for the correlation coefficient between logging resistivity and acoustic wave velocity; S402, obtaining the actual logging resistivity and acoustic wave velocity in the conglomerate area, and calculating the correlation coefficient between each logging resistivity and acoustic wave velocity according to the correlation coefficient calculation formula in S401; S403, selecting the logging resistivity and the acoustic wave velocity whose correlation coefficient is greater than or equal to a set threshold value from the correlation coefficients of the logging resistivity and the acoustic wave velocity calculated in S402; S404, based on the logging resistivity and acoustic wave velocity selected in S403, combined with the resistivity and acoustic wave velocity conversion model obtained in step S3, confirm the acoustic wave velocity-resistivity empirical relationship whose correlation coefficient is greater than or equal to the set threshold.

3. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 2, It is characterized in that The calculation formula of the correlation coefficient between resistivity and acoustic wave velocity described in step S401 is: In the above formula, r represents the correlation coefficient between the acoustic velocity of the conglomerate layer and the logging resistivity of the conglomerate layer, n represents the discrete points of the logging resistivity data and the acoustic velocity data, and x i represents the resistivity of the i-th conglomerate layer, represents the mean resistivity of the conglomerate layer, y i represents the acoustic wave velocity of the i-th conglomerate layer, Represents the mean value of the acoustic wave velocity in the conglomerate layer.

4. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 3, It is characterized in that S3 also includes the following steps: S301, selecting logging resistivity data and acoustic wave velocity data of the conglomerate area in the logging data of the conglomerate area in the piedmont zone, and performing logarithmic processing on the acquired logging resistivity data; S302, processing the well logging resistivity data and the acoustic wave velocity data after logarithmic processing to obtain discrete points of the well logging resistivity data and the acoustic wave velocity data; S303, statistically analyzing the well logging resistivity data and the discrete points of the acoustic wave velocity after logarithmic processing to obtain a well logging resistivity and acoustic wave velocity conversion model.

5. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 4, It is characterized in that The discrete points of the well logging resistivity data and the acoustic wave velocity after logarithmic processing obtained in step S302 are set as n in the calculation formula of the correlation coefficient between resistivity and acoustic wave velocity in step S401.

6. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 2, It is characterized in that The empirical relationship between acoustic velocity and resistivity with a correlation coefficient greater than or equal to the set threshold value confirmed in step S404 is: y=1777.9e 0.3238x In the above formula, y represents the acoustic wave velocity of the conglomerate layer, and x represents the resistivity of the conglomerate layer. Both x and y are measured data.

7. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 6, It is characterized in that Step S4 also includes verifying the accuracy of the acoustic wave velocity-resistivity empirical relationship. Step S5 is performed only after the verification is passed. Otherwise, the acoustic wave velocity-resistivity empirical relationship is adjusted until the accuracy of the acoustic wave velocity-resistivity empirical relationship is verified.

8. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 7, It is characterized in that The specific method for verifying the accuracy of the acoustic velocity-resistivity empirical relationship is as follows: the logging resistivity measured in the conglomerate area is substituted into the acoustic velocity-resistivity empirical relationship obtained in step S404 to obtain the acoustic velocity, and the obtained acoustic velocity is compared with the acoustic velocity measured in the well. If the change trends are consistent, it is passed, otherwise it is failed.

9. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 2, It is characterized in that The set threshold described in S403 is 0.

7.

10. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 1, It is characterized in that S2 specifically includes the following steps: S201, selecting high frequency band data from the time-frequency electromagnetic data obtained in S1; S202, performing three-dimensional inversion processing on the high-frequency band data obtained in step S201 to obtain a high-frequency band resistivity data volume.

11. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 10, It is characterized in that The time-frequency electromagnetic data described in S201 is the time-frequency electromagnetic data obtained by processing the time-frequency electromagnetic signals collected in the field, and then selecting high-frequency band data based on the distribution range of the emission frequency of the measured time-frequency electromagnetic signals.

12. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 11, It is characterized in that The emission frequency of the time-frequency electromagnetic signal is 0.01Hz-334Hz.

13. The method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to claim 11, It is characterized in that The selected high frequency band data is data in the frequency band of 0.5 Hz-334 Hz.

14. A system using the method for modeling shallow conglomerate velocity in the piedmont zone based on time-frequency electromagnetics according to any one of claims 1 to 13, It is characterized in that It includes a data acquisition module, a time-frequency electromagnetic data processing module, a logging data analysis module, an empirical relationship module and a velocity data volume generation module. The data acquisition module is connected to the time-frequency electromagnetic data processing module and the logging data analysis module respectively, the logging data analysis module is connected to the empirical relationship module, and the empirical relationship module and the electromagnetic data processing module are connected to the velocity data volume generation module respectively.

15. The time-frequency electromagnetic based shallow conglomerate velocity modeling system for piedmont zone according to claim 14, It is characterized in that The empirical relationship module also includes a determination module and a verification module. The input end of the determination module is connected to the logging data analysis module, the output end of the determination module is connected to the input end of the verification module, and the output end of the verification module is connected to the velocity data volume generation module.

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