A prestack angle-gather modeling method based on well forward modeling simulation

Abstract

The application provides a prestack divided-angle gather modeling method based on well forward simulation and relates to the technical field of oil seismic exploration, and comprises the following steps: performing resolution enhancement processing on offset pure wave data volume A to obtain data volume B, and performing band-pass filtering processing on the offset pure wave data A to obtain data volume C; taking the data volume B as near-angle, the data volume A as middle-angle and the data volume C as far-angle to statistically obtain an amplitude proportion relationship one of well flanking gathers; statistically obtaining an amplitude proportion relationship two of near, middle and far-angle target layer sections by well forward simulation with simulation data to obtain an amplitude correction factor; performing amplitude correction on the data volume B, the data volume A and the data volume C by using the amplitude correction factor, outputting corrected near, middle and far-angle data volumes B1, A1 and C1, and merging the three data volumes B1, A1 and C1 to output divided-angle gathers. The method can generate high-quality near, middle and far-angle prestack gathers data, and meets the demand of prestack inversion.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil seismic exploration technology, in particular to a pre-stack split-angle gather modeling method. BACKGROUND

[0002] Seismic reservoir prediction technology tends to be mature and plays an important role in oil and gas exploration, evaluation and development production. Complex underground targets require high-deterministic pre-stack seismic reservoir prediction technology, but pre-stack split-angle gather optimization and massive data processing of land acquisition are bottlenecks restricting the application of technology production.

[0003] The land acquisition seismic data gather has problems of uneven coverage times of each angle deep and shallow layers and noise development, and pre-stack migration and stacking are key technologies for improving the signal-to-noise ratio of land seismic data. Seismic data acquisition is multiple coverage data, that is, each data point is multiple stacked data of sound signals collected by adjacent geophones arranged in cross with the source shot point. Landforms and obstacle avoidance will lead to inconsistent coverage times of each point. Nowadays, high-density seismic acquisition technology no longer implements noise reduction measures such as adjacent road car ban in field acquisition, resulting in a decrease in the signal-to-noise ratio of land actual acquisition pre-stack gathers, but the decrease in the signal-to-noise ratio of seismic acquisition data is compensated by increasing the coverage times and indoor processing technology. The relatively effective method for generating angle gathers in the past includes expanding near, medium and far offset data channels, that is, each half of the adjacent angle data coincides with the previous data, so that the signal-to-noise ratio of the generated angle stacked data is relatively high. This technology requires analysts to manually set the cut-off range of the fitting stacked data and strict quality control, and has a high requirement for the technical level of analysts, and needs high-performance computer clusters, massive storage and other software and hardware support.

[0004] Even if high-performance computers and massive calculations are used, the low signal-to-noise ratio of pre-stack split-angle gathers in important fields such as land desert areas and mountain front structural belts still cannot meet the requirements of data for pre-stack inversion. The fatal problem encountered in the method of obtaining split-angle gathers from pre-stack gathers is the reduction of coverage times in the generation process of split-angle gathers. In the absence of data coincidence, the coverage times of each angle stacking profile are reduced to at most one-third of the channel participating in the calculation relative to the pure wave stacking, which leads to a significant decrease in the signal-to-noise ratio of near and far angles relative to the signal-to-noise ratio of medium angles, and the signal-to-noise ratio of medium angles also decreases relative to the pure wave data. The data stacking method uses at most 50% of the channel data, so it still cannot avoid the reduction of signal-to-noise ratio caused by the reduction of coverage times.

[0005] In summary, the split-angle gather data of land acquisition seismic pre-stack migration output in the past cannot meet the production requirements of seismic reservoir prediction for fidelity, high resolution, high signal-to-noise ratio and high efficiency due to uneven coverage times and low signal-to-noise ratio. SUMMARY

[0006] The application aims to provide a pre-stack angle gather modeling method based on well forward modeling, which can generate high-quality near, medium and far angle pre-stack gather data to meet the needs of pre-stack inversion, and the calculation process does not require mass data storage and high-performance computers, and the interpretive processing quality control and production application are simple.

[0007] The application is implemented by the following technical solutions:

[0008] A pre-stack angle gather modeling method based on well forward modeling, comprising the following steps:

[0009] Step S1: improving the resolution of the offset pure wave data body A to obtain data body B, and performing band-pass filtering on the offset pure wave data A to obtain data body C;

[0010] Step S2: taking data body B as the near angle, data body A as the medium angle, and data body C as the far angle to statistically obtain the amplitude proportion relationship one;

[0011] Step S3: statistically obtaining the amplitude proportion relationship two of the near, medium and far angle target layer section by well forward modeling using simulation data;

[0012] Step S4: obtaining the amplitude correction factor according to the amplitude proportion relationship one in step S2 and the amplitude proportion relationship two in step S3;

[0013] Step S5: using the amplitude correction factor in S4 to perform amplitude correction on data body B, data body A and data body C, and merging the three corrected data bodies to output the angle gather.

[0014] Further, in step S1, the resolution of the offset pure wave data body A is improved by wavelet deconvolution to obtain data body B, and the specific steps include:

[0015] Step S101: performing sampling rate reduction interpolation processing on data A;

[0016] Step S102: performing statistical zero-phase wavelet deconvolution on the data after the above interpolation processing;

[0017] Step S103: performing filtering and denoising processing on the data after zero-phase wavelet deconvolution processing;

[0018] Step S104: performing point, line, surface and volume quality control analysis on the data after denoising;

[0019] Step S105: outputting the final resolution improvement processing result data body B.

[0020] Further, the specific steps of step S3 for statistically obtaining the amplitude proportion relationship two of the near, medium and far angle target layer section by well forward modeling using simulation data include:

[0021] Step S301: wave equation forward modeling is carried out by using well data to generate wave equation simulation gather data under different incident angles X, Y and Z, wherein X is near angle, Y is middle angle, Z is far angle, and 1°X<Y<Z<45°;

[0022] Step S302: in the simulation obtained different incident angle gather data, the reflection peak or trough of the target layer section is selected;

[0023] Step S303: the reflection waveform amplitude of the target layer section under different incident angles is counted;

[0024] Step S304: the amplitude proportional relationship two of the target layer section under different incident angles is obtained according to the counting result of step S303.

[0025] Further, the wave equation simulation gather data under incident angles X, Y and Z in step S301 are respectively the amplitude change average values of the incident angle intervals 1°-X, X-Y and Y-Z.

[0026] Further, the X is 12°, the Y is 24°, and the Z is 36°.

[0027] Further, the amplitude proportional relationship one in step S2 includes:

[0028] The ratio M1 of the data body B to the data body A;

[0029] The ratio 1 of the data body A to the data body A;

[0030] The ratio N1 of the data body C to the data body A;

[0031] The amplitude proportional relationship two of the target layer section under different incident angles in step S304 includes:

[0032] The ratio M2 of the amplitude change average value under 1°-X incident angle to the amplitude change average value under X-Y incident angle;

[0033] The ratio 1 of the amplitude change average value under X-Y incident angle to the amplitude change average value under X-Y incident angle;

[0034] The ratio N2 of the amplitude change average value under Y-Z incident angle to the amplitude change average value under X-Y incident angle.

[0035] Further, the amplitude correction factor in step S4 is:

[0036]

[0037]

[0038] Further, in step S5, the data volume B is corrected to obtain data volume B1 using correction factor m, the data volume A is corrected to obtain data volume A1 using correction factor 1, and the data volume C is corrected to obtain data volume C1 using correction factor n.

[0039] Further, in step S5, before merging the three data volumes B1, A1 and C1, the data volume B1, A1 and C1 are respectively changed to X, Y and Z.

[0040] Compared with the prior art, the present application has the following beneficial effects:

[0041] Firstly, the present application provides a pre-stack angle gather modeling method based on well forward modeling, which uses full-offset stacked pure wave data as model data, instead of partial stacked data as model data, and the output pre-stack gather signal-to-noise ratio is obviously improved; the pre-stack gather is the basic data of pre-stack time migration, and the noise signal in the actual pre-stack gather is removed, so that the accuracy of the result data is higher than that of the basic data, and high-quality pre-stack angle gather data is obtained. Thus, the problem of uneven coverage and low signal-to-noise ratio of land seismic data is avoided, the well-seismic correlation coefficient is improved, the elastic parameter inversion error is reduced, the foundation for obtaining deterministic pre-stack inversion elastic parameters is laid, and the demand of pre-stack inversion is met.

[0042] Secondly, the present application uses pre-stack migration result data with high imaging accuracy, and the seismic reflection positioning accuracy is high; after improving the resolution, the near-angle vertical resolution is high, the frequency band is widened, and it is more conducive to describing and predicting the thickness change trend of the target rock mass, so that the pre-stack inversion algorithm can be used to predict the reservoir in the area with low pre-stack signal-to-noise ratio such as desert area and piedmont structural belt, and the technical application field is expanded.

[0043] Thirdly, at the present stage, the number of seismic acquisition coverage is hundreds to thousands of times, and the pre-stack data is huge. The present application carries out modeling on the basis of post-stack seismic data, the generated three-angle pre-stack gather data is small, the storage resources and computing resources occupied by pre-stack inversion are small, and it is conducive to production application and popularization.

[0044] Fourthly, the present application does not need to manually set the cut-off range of the fitting stacked data and quality control by an analysis personnel, and subjective error is eliminated. DETAILED DESCRIPTION

[0045] Figure 1 The flowchart of the pre-stack angle gather modeling method based on well forward modeling in an embodiment;

[0046] Figure 2 The flowchart of improving the resolution of the offset pure wave data volume A to obtain data volume B in an embodiment;

[0047] Figure 3This is the migration pure wave cross-well profile when the data is not time sampled and interpolated;

[0048] Figure 4 This is the cross-well profile after zero-phase statistical deconvolution processing when the data is not time-sampled and interpolated;

[0049] Figure 5 This is the pure wave cross-well profile after time sampling and interpolation of the data;

[0050] Figure 6 The cross-well profile is obtained after performing zero-phase statistical deconvolution processing on the data after time sampling interpolation;

[0051] Figure 7 This is the pre-stack gather change diagram from 1° to 45° for forward modeling of actual well data;

[0052] Figure 8 For the Figure 7 The change of the reflection layer amplitude with the angle picked by the layer indicated by the middle arrow;

[0053] Figure 9 Schematic diagram of inversion of elastic parameters of wellside channels using modeling gather data to build a model based on well data;

[0054] Figure 10 This is a cross-plot of pre-stack inversion elastic parameters of the well. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0057] like Figure 1 -shown: A pre-stack angle gather modeling method based on well forward simulation, comprising the following steps:

[0058] Step S1: performing resolution enhancement on the offset pure wave data volume A to obtain a data volume B, and performing band-pass filtering on the offset pure wave data A to obtain a data volume C;

[0059] In some embodiments: wideband, resolution enhancement processing is performed on the post-stack seismic data using wavelet deconvolution, predictive filtering or Q compensation techniques;

[0060] In this embodiment, resolution enhancement is performed on the offset pure wave data volume A by wavelet deconvolution to obtain a data volume B, and the specific steps include:

[0061] Step S101: performing sampling rate reduction interpolation on the data A;

[0062] Step S102: performing statistical zero-phase wavelet deconvolution on the data after the interpolation, and the zero-phase wavelet is an ideal symmetric wavelet without components before zero time, and the time domain and amplitude spectrum of typical Ricker, ormsby and other different frequency wavelets are shown in the classical seismic data processing principle. According to the data processing specification, the post-stack pure wave data of seismic data processing is usually assumed to be zero-phase.

[0063] Step S103: performing filtering and denoising on the data after the zero-phase wavelet deconvolution processing;

[0064] The zero-phase wavelet is an ideal symmetric wavelet without components before zero time, and the time domain and amplitude spectrum of typical Ricker, ormsby and other different frequency wavelets are shown in the classical seismic data processing principle. According to the data processing specification, the post-stack pure wave data of seismic data processing is usually assumed to be zero-phase. The consensus of seismic wavelet research at home and abroad is that high-frequency zero-phase wavelets have high resolution and good identification of reflection layer groups.

[0065] The minimum phase wavelet is relatively important because the wavelet excited by the explosive source in the past seismic acquisition is closest to the minimum phase wavelet. The high-frequency minimum phase wavelet (e.g. above 80 Hz) has no components before zero time, high resolution, but poor identification of reflection layer groups.

[0066] For actual pre-stack gathers, seismic data processing usually uses minimum phase wavelets to perform predictive deconvolution; for post-stack seismic pure wave data which is usually zero-phase, zero-phase wavelet deconvolution is used for resolution enhancement. Research on the use of zero-phase wavelet deconvolution on post-stack pure wave data to improve the resolution of seismic data conforms to the seismic data processing specification.

[0067] Step S104: carry out point, line, surface and volume quality control analysis on the de-noised data, if the processed data is not up to standard, return to step S103 to further optimize the processing parameters. The quality control analysis on the 2D and 3D data after the wavelet deconvolution to improve the resolution belongs to the content required by the processing specification, and this workflow can find problems such as data truncation and abnormal processing results in the high-steep reflection area of the original seismic data;

[0068] Step S105: output the final resolution-improved processed result data volume B.

[0069] The migration pure wave result archive data is usually 2ms sampling or 4ms sampling, and the highest frequency of the seismic data is 500Hz and 250Hz, but the seismic spectrum has a certain degree of symmetry, and the highest seismic main frequency corresponding to 4ms sampling is only 125Hz. Considering that the coal seam and steeply inclined seismic strong reflection is easy to produce false reflection when improving the resolution, therefore, reducing the sampling interval of the seismic data by interpolation can reduce the abnormal reflection.

[0070] Figure 3 and Figure 4 The comparative diagram of the migration pure wave crosswell profile ( Figure 3 ) without time sampling interpolation of the data and the zero-phase statistical deconvolution processed crosswell profile ( Figure 4 ) without time sampling interpolation of the data is shown, it can be seen that the post-stack zero-phase statistical wavelet deconvolution to improve the resolution will cause the false image of the abnormal increase of the coal seam seismic reflection; Figure 5 and Figure 6 The migration pure wave crosswell profile ( Figure 5 ) and the zero-phase statistical deconvolution processed crosswell profile ( Figure 6 ) after steps S101, S102 and S103 are shown, it can be obviously seen that the crosswell quality control line improves the resolution effect, in this example, the spectrum of the target layer is widened by 10Hz, so that the seismic data can reflect the thickness change of the target sand body.

[0071] Step S2: take data volume B as the near angle, data volume A as the middle angle, and data volume C as the far angle to statistically analyze the amplitude ratio relationship one;

[0072] In this embodiment, the amplitude ratio relationship one in step S2 includes:

[0073] The ratio M1 of data volume B to data volume A;

[0074] The ratio 1 of data volume A to data volume A;

[0075] The ratio N1 of data volume C to data volume A;

[0076] Step S3: The amplitude ratio relationship two of the target layer section at near, middle and far angles is calculated by well forward modeling and simulation data;

[0077] In some embodiments: the specific steps of calculating the amplitude ratio relationship two of the target layer section at near, middle and far angles by well forward modeling and simulation data in step S3 include:

[0078] Step S301: The wave equation Aki-Richards approximation equation forward modeling is carried out by using well data to generate wave equation simulation gather data at different incident angles X, Y and Z, wherein X is near angle, Y is middle angle, and Z is far angle, 1°X<Y<Z<45°;

[0079] Step S302: The reflection wave peak or trough of the target layer section is selected from the simulation obtained different incident angle gather data;

[0080] Step S303: The reflection wave amplitude of the target layer section at different incident angles is counted;

[0081] Step S304: The amplitude ratio relationship two of the target layer section at different incident angles is obtained according to the counting result of step S303.

[0082] The linear modeling method usually only simulates the angle gather within 39°, and the error of linear modeling is large when the angle is more than 39°. In some preferred embodiments, X, Y and Z are 10°, 20° and 30° respectively, or 12°, 24° and 36° respectively, or 13°, 26° and 39° respectively.

[0083] In the present embodiment: the incident angle X in step S301 is 12°, the incident angle Y is 24°, and the incident angle Z is 36°, and the wave equation simulation gather data at 12°, 24° and 36° respectively refers to the average amplitude change in the incident angle interval of 1°-12°, 12°-24° and 24°-36°;

[0084] The amplitude ratio relationship two of the target layer section at different incident angles in step S304 includes:

[0085] The ratio M2 of the average amplitude change at 1°-12° incident angle to the average amplitude change at 12°-24° incident angle;

[0086] The ratio 1 of the average amplitude change at 12°-24° incident angle to the average amplitude change at 12°-24° incident angle;

[0087] The ratio N2 of the average amplitude change at 24°-36° incident angle to the average amplitude change at 12°-24° incident angle.

[0088] The well data forward modeling is divided into angle gathers and the amplitude change of the middle layer with angle is counted as Figure 7 andFigure 8 As shown, the pre-stack gathers from 1° to 45° angles simulated by actual well data ( Figure 7 ) and the reflection layer amplitude curve picked up along the layer indicated by the arrow ( Figure 8 ), in this embodiment M2=1.07, N2=0.93.

[0089] Step S4: deriving an amplitude correction factor based on the amplitude proportional relationship 1 in step S2 and the amplitude proportional relationship 2 in step S3;

[0090] In this embodiment, the amplitude correction factor in step S4 is:

[0091]

[0092]

[0093] Step S5: Use the amplitude correction factor in S4 to perform amplitude correction on data volume B, data volume A, and data volume C, and merge the three corrected data volumes to output the angle-based gather.

[0094] In this embodiment: using correction factor m to correct data volume B to obtain data volume B1, using correction factor 1 to correct data volume A to obtain data volume A1, and using correction factor n to correct data volume C to obtain data volume C1;

[0095] Before merging the three data bodies B1, A1, and C1, the process also includes: changing the headers of the data bodies B1, A1, and C1 to the headers of the original data bodies B, A, and C, respectively, that is, changing the 37th to 41st bytes to X, Y, and Z, respectively. The header part of the seismic data standard format SEGY stipulates that the 37th to 41st bytes record the offset distance / offset angle (offset) value.

[0096] The essence of seismic data processing is imaging. Step S104 removes various noises to highlight the effective wave signals of the subsurface strata. The stacked data from prestack migration has a higher signal-to-noise ratio than the prestack gathers. This is the result of stacking the sub-offset traces within the effective offset, encompassing effective signals from the subsurface strata at all angles. Therefore, starting from the stacked data from prestack migration, reverse modeling and analysis of the prestack sub-angle gathers is performed, circumventing the problem of uneven coverage across offsets caused by complex factors such as the surface on land.

[0097] like Figure 9 As shown in the figure: the pre-stack angle gather modeling method in the above embodiment is used to generate basic data that can meet the requirements of shallow, medium and deep pre-stack seismic reservoir prediction in the target area. The pre-stack inversion is carried out using this modeling method, which improves the well-seismic correlation coefficient (the well-seismic correlation coefficient reaches above 0.9) and reduces the inversion error of the elastic parameters, laying the foundation for obtaining pre-stack inversion elastic parameters with high certainty.

[0098] As Figure 10 shown: pre-stack inversion elastic parameter crossplot identifies thin gas layer and potential target layer, pre-stack inversion increases five elastic parameters of shear wave impedance, density, P-wave velocity, S-wave velocity, P / S velocity ratio compared with post-stack seismic inversion, which can be used for reservoir description and provide basic data for rock mechanics modeling, expanding the application of seismic inversion technology in exploration and development, evaluation and geological engineering.

[0099] Some of the terms mentioned in this specification are explained as follows:

[0100] Migrated pure wave data: formal result data of seismic data processing, used for seismic interpretation, reservoir prediction and archiving.

[0101] Post-stack resolution enhancement processing data: the result data after widening the data spectrum by methods such as zero-phase wavelet deconvolution, spectral whitening and Q compensation on the formal result data of seismic data processing.

[0102] Band-pass filter processing data: seismic data with improved signal-to-noise ratio by band-pass filtering on the formal result data of seismic data processing.

[0103] Pre-stack time migration output gather data: after pre-stack time migration and positioning processing of seismic data, the best stacking imaging result is obtained by cutting part of the gather, which is the basic data for pre-stack reservoir prediction research. Pre-stack gather optimization and angle processing are usually carried out on the basis of this data.

[0104] Angle-dependent pre-stack gather: a gather data arranged by near, medium and far angles that can be directly used for pre-stack seismic inversion and reservoir prediction while maintaining AVO / AVA amplitude relationship.

[0105] Wave equation forward modeling pre-stack gather: under the conditions of selected wavelet, maximum observation offset (including trace interval) or maximum incidence angle, the numerical simulation is carried out by applying Kirchhoff wave equation to generate pre-stack seismic response gather of the formation based on the P-wave and S-wave velocities and density obtained from well logging. This data is mainly used for fidelity control analysis of well-side gather and AVO analysis of pre-stack seismic reservoir prediction.

[0106] Aki-Richards approximation equation: a commonly used approximate formula for characterizing seismic reflection amplitude at different incidence angles

[0107] The above embodiments are only for illustrating the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and implement it, and cannot limit the protection scope of the present application. Any equivalent transformation or modification made in accordance with the spirit and essence of the present application shall be covered within the protection scope of the present application.

Claims

1. A prestack angle-gather modeling method based on well forward modeling, characterized in that: The method comprises the following steps: Step S1: performing resolution enhancement processing on the offset pure wave data volume A to obtain a data volume B, and performing band-pass filtering processing on the offset pure wave data A to obtain a data volume C; Step S2: taking the data volume B as near-angle data, the data volume A as middle-angle data, and the data volume C as far-angle data to statistically obtain an amplitude ratio relationship one; Step S3: using well data in a work area where prestack seismic inversion is planned to perform well forward modeling, and using the simulated data to statistically obtain an amplitude ratio relationship two of near, middle, and far angles of a target layer; Step S4: obtaining an amplitude correction factor according to the amplitude ratio relationship one in Step S2 and the amplitude ratio relationship two in Step S3; Step S5: using the amplitude correction factor in Step S4 to perform amplitude correction on the data volume B, the data volume A, and the data volume C, obtaining and merging the three corrected data volumes B1, A1, and C1, and outputting the angle-dependent gather.

2. The prestack angle-gather modeling method based on well forward modeling according to claim 1, characterized in that: In Step S1, the resolution enhancement processing on the offset pure wave data volume A is performed by wavelet deconvolution, and the specific steps include: Step S101: performing sampling rate reduction interpolation processing on the data A; Step S102: performing statistical zero-phase wavelet deconvolution on the data after the interpolation processing; Step S103: performing filtering and denoising processing on the data after the zero-phase wavelet deconvolution processing; Step S104: performing point, line, surface, and volume quality control analysis on the data after the denoising; Step S105: outputting the final resolution enhancement processing result data volume B.

3. The prestack angle-gather modeling method based on well forward modeling according to claim 1, characterized in that: In Step S3, the specific steps of using well data to perform forward modeling and using the simulated data to statistically obtain the amplitude ratio relationship two of near, middle, and far angles of a target layer include: Step S301: using well data to perform wave equation forward modeling to generate wave equation simulation gather data under different incident angles X, Y, and Z, wherein X is a near angle, Y is a middle angle, and Z is a far angle, and 1°<X<Y<Z<45°; Step S302: selecting a reflection wave peak or trough of a target layer in the simulated different incident angle gather data; Step S303: statistically obtaining the amplitude values of the forward modeling seismic gather target reflection (wave peak or trough) of the target layer under different incident angles; Step S304: obtaining the amplitude ratio relationship two of the target layer under different incident angles according to the statistical results in Step S303.

4. The prestack angle-gather modeling method based on well forward modeling according to claim 3, characterized in that: The wave equation simulation gather data under the incident angles X, Y, and Z in Step S301 are respectively the average amplitude changes in the incident angle intervals 1°-X, X-Y, and Y-Z.

5. The prestack angle-gather modeling method based on well forward modeling according to claim 3, characterized in that: The X is 12°, the Y is 24°, and the Z is 36°.

6. The prestack angle-dependent gather modeling method based on well forward modeling according to claim 4, characterized in that: The amplitude ratio relationship one of the well trace in Step S2 includes: a ratio M1 of the data volume B to the data volume A; a ratio 1 of the data volume A to the data volume A; a ratio N1 of the data volume C to the data volume A; The amplitude ratio relationship two of the target layer under different incident angles in Step S304 includes: a ratio M2 of the average amplitude change under the incident angle 1°-X to the average amplitude change under the incident angle X-Y. The ratio 1 of the average value of amplitude variation under the X-Y incident angle to the average value of amplitude variation under the X-Y incident angle; The ratio N2 of the average value of amplitude variation under the Y-Z incident angle to the average value of amplitude variation under the X-Y incident angle.

7. The prestack angle-gather modeling method based on well forward modeling according to claim 6, characterized in that: The amplitude correction factor in step S4 is:

8. The prestack angle-gather modeling method based on well forward modeling according to claim 7, characterized in that: In step S5, the data body B1 is obtained by correcting the data body B using the correction factor m, the data body A1 is obtained by correcting the data body A using the correction factor 1, and the data body C1 is obtained by correcting the data body C using the correction factor n.

9. The prestack angle-gather modeling method based on well forward modeling of claim 1, wherein: In step S5, before merging the three data bodies B1, A1 and C1, the traces of the data bodies B1, A1 and C1 are respectively changed to X, Y and Z.

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