Pre-stack sub-angle gather modeling method based on well forward modeling

Abstract

The invention provides a well forward modeling-based pre-stack sub-angle gather modeling method, and relates to the technical field of petroleum seismic exploration, and the method comprises the following steps: carrying out the resolution improvement processing of an offset pure wave data volume A to obtain a data volume B, and carrying out the band-pass filtering processing of the offset pure wave data A to obtain a data volume C; taking the data volume B as a near angle, the data volume A as a middle angle and the data volume C as a far angle to count a well bypass amplitude proportional relation I; through well forward modeling, the second amplitude proportional relation of the near-angle target layer section, the medium-angle target layer section and the far-angle target layer section is counted through simulation data, and an amplitude correction factor is obtained; and performing amplitude correction on the data volume B, the data volume A and the data volume C by using the amplitude correction factor, outputting the corrected near-angle data volume B1, the corrected medium-angle data volume A1 and the corrected far-angle data volume C1, merging the three data volumes B1, A1 and C1, and outputting a sub-angle gather. High-quality pre-stack gather data of near, middle and far angles can be generated, and the requirement of pre-stack inversion is met.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum seismic exploration, and in particular to a pre-stack angle-divided gather modeling method. Background Art

[0002] Seismic reservoir prediction technology is becoming more mature and plays an important role in oil and gas exploration, evaluation and development. Complex underground targets require highly deterministic prestack seismic reservoir prediction technology, but the optimization of prestack angle gathers and massive data processing for land acquisition are bottlenecks that restrict the production and application of technology.

[0003] The onshore seismic data gathers have problems such as uneven coverage times of deep and shallow layers at different angles and noise development. Prestack migration and stacking are key technologies to improve the signal-to-noise ratio of onshore seismic data. The collected seismic data are all data covered multiple times, that is, each data point is multiple stacking data of the sound signals collected from adjacent detection points arranged crosswise with the source shot point. The land terrain and obstacles to avoid will lead to inconsistent coverage times for each point. Nowadays, high-density seismic acquisition technology no longer implements noise reduction measures such as prohibiting cars from driving on adjacent roads during field acquisition, resulting in a decrease in the signal-to-noise ratio of the actual land acquisition prestack traces, but the decrease in the signal-to-noise ratio of seismic acquisition data is compensated by increasing the number of coverage times and indoor processing technology. In the past, the more effective methods for generating angle gather data included expanding the near, medium, and far offset distance data channels, that is, half of the data at each adjacent angle overlaps with the previous data, so that the generated angle stacking data has a relatively high signal-to-noise ratio. This technology requires the processing analyst to manually set the resection range for the fitted stacking data and strictly control the quality, which has high requirements for the technical level of the analyst and requires software and hardware support such as high-performance computer clusters and massive storage.

[0004] Even with high-performance computers and massive calculations, the low signal-to-noise ratio of pre-stack angle gathers still cannot meet the data requirements of pre-stack inversion in important areas such as land deserts and piedmont tectonic belts. The fatal problem encountered by the method of obtaining angle gathers from pre-stack gathers is the reduction of the number of coverages in the generation process of angle gathers. In the absence of data overlap, the number of coverages of each angle stack profile is reduced to at most one-third of the gathers involved in the calculation relative to the stacked pure wave, which leads to a significant decrease in the near-angle and far-angle signal-to-noise ratio relative to the medium-angle signal-to-noise ratio, and the medium-angle signal-to-noise ratio relative to the stacked pure wave data is also reduced; the data superposition method only uses nearly 50% of the gather data at most, so it is still impossible to avoid the reduction in signal-to-noise ratio caused by the reduction in coverage.

[0005] In short, the angle gather data output by pre-stack migration of seismic acquisition on land in the past cannot meet the production requirements of seismic reservoir prediction, which requires both fidelity, high resolution, high signal-to-noise ratio and high efficiency, due to uneven coverage and low signal-to-noise ratio. Summary of the invention

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

[0007] The present invention is achieved through the following technical solutions:

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

[0009] Step S1: performing resolution enhancement processing on the offset pure wave data volume A to obtain data volume B, and performing bandpass filtering processing on the offset pure wave data A to obtain data volume C;

[0010] Step S2: taking data volume B as the near angle, data volume A as the middle angle, and data volume C as the far angle to calculate the well side channel amplitude ratio relationship 1;

[0011] Step S3: through well forward simulation, the amplitude ratio relationship of the near, middle and far angle target intervals is calculated using simulation data;

[0012] Step S4: deriving an amplitude correction factor according to the amplitude ratio relationship 1 in step S2 and the amplitude ratio relationship 2 in step S3;

[0013] 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 angle gathers.

[0014] Further: in step S1, the resolution of the migrated pure wave data volume A is improved by wavelet deconvolution method to obtain data volume 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 interpolated data;

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

[0018] Step S104: Perform point, line, surface and volume quality control analysis on the denoised data;

[0019] Step S105: Output the final resolution-enhanced processing result data volume B.

[0020] Further, in step S3, the specific steps of using simulation data to calculate the amplitude ratio relationship of the near, middle and far angle target layer segments through well forward simulation include:

[0021] Step S301: using well data to carry out wave equation forward simulation, generating wave equation simulation gather data under different incident angles X, Y, and Z, where X is the near angle, Y is the middle angle, and Z is the far angle, and 1°<X<Y<Z<45°;

[0022] Step S302: selecting the reflection peak or trough of the target layer segment from the simulated gather data of different incident angles;

[0023] Step S303: Counting the reflection waveform amplitude of the target layer at different incident angles;

[0024] Step S304: according to the statistical result of step S303, the second amplitude ratio relationship of the target layer segment under different incident angles is obtained.

[0025] Furthermore: the wave equation simulation gather data at the incident angles X, Y, and Z in step S301 are the average values ​​of the amplitude changes in the incident angle intervals of 1°-X, XY, and YZ, respectively.

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

[0027] Furthermore, in step S2, the well bypass amplitude ratio relationship 1 includes:

[0028] The ratio of data volume B to data volume A is M1;

[0029] The ratio of data volume A to data volume A is 1;

[0030] The ratio of data volume C to data volume A is N1;

[0031] The second target layer segment amplitude ratio relationship under different incident angles in step S304 includes:

[0032] 1° - the ratio of the average amplitude change under the X incident angle to the average amplitude change under the XY incident angle, M2;

[0033] The ratio of the average value of the amplitude change under XY incident angle to the average value of the amplitude change under XY incident angle is 1;

[0034] The ratio N2 of the average amplitude change under the YZ incident angle to the average amplitude change under the XY incident angle.

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

[0036]

[0037]

[0038] Furthermore: in step S5, the correction factor m is used to correct the data volume B to obtain the data volume B1, the correction factor 1 is used to correct the data volume A to obtain the data volume A1, and the correction factor n is used to correct the data volume C to obtain the data volume C1.

[0039] Furthermore: in step S5, before merging the three data bodies B1, A1, and C1, the step further includes: changing the headers of the data bodies B1, A1, and C1 to X, Y, and Z respectively.

[0040] Compared with the original technology, the present invention has the following beneficial effects:

[0041] 1. This method provides a pre-stack angle gather modeling method based on well forward simulation, using full-offset stacked pure wave data as model trace data instead of partial stacked data as model trace data, and the output offset pre-stack gather signal-to-noise ratio is significantly 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 by stacking, so the result data is more accurate than the basic data, and high-quality pre-stack angle gather data is obtained. This bypasses the problem of uneven coverage and low signal-to-noise ratio of onshore seismic data, improves the well-seismic correlation coefficient, and reduces the inversion error of elastic parameters, laying the foundation for obtaining highly deterministic pre-stack inversion elastic parameters and meeting the needs of pre-stack inversion.

[0042] 2. This method uses pre-stack migration data with high imaging accuracy and high seismic reflection retrieval accuracy. After the resolution is improved, the near-angle vertical resolution is high and the frequency band is widened, which is more conducive to describing and predicting the thickness change trend of the target rock mass. The pre-stack inversion algorithm can be used to predict reservoirs in areas with low pre-stack signal-to-noise ratio, such as desert areas and piedmont tectonic belts, thus expanding the application field of the technology.

[0043] 3. At present, the number of seismic acquisitions covers hundreds to thousands of times, and the pre-stack data is huge. This method is based on post-stack seismic data to carry out modeling, and the generated three-angle pre-stack gathers have a small amount of data. The pre-stack inversion occupies less storage resources and computing resources, which is conducive to the promotion of production applications.

[0044] Fourth, this method does not require analysts to manually set the cut-off range and perform quality control on the fitted superposition data, thereby eliminating subjective errors. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0046] Figure 2 It is a flow chart of performing resolution enhancement processing on the offset pure wave data volume A to obtain the data volume B in one embodiment;

[0047] Figure 3This is the profile of the pure wave passing through the well when the data is not sampled and interpolated in time;

[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 To migrate the pure wave through the well profile after time sampling and interpolation of the data;

[0050] Figure 6 The well cross-section diagram after time sampling interpolation and zero-phase statistical deconvolution processing of the data;

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

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

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

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

[0055] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood 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 processing on the offset pure wave data volume A to obtain data volume B, and performing bandpass filtering processing on the offset pure wave data A to obtain data volume C;

[0059] In some embodiments: applying wavelet deconvolution, predictive filtering or Q compensation techniques to perform broadband and resolution-enhancing processing on post-stack seismic data;

[0060] In this embodiment, the resolution of the shifted pure wave data volume A is improved by wavelet deconvolution method to obtain data volume B. The specific steps include:

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

[0062] Step S102: Perform statistical zero-phase wavelet deconvolution on the interpolated data. The zero-phase wavelet is an ideal symmetrical wavelet with no component before time zero. The classic seismic data processing principle shows the time domain and amplitude spectra of typical Ricker, Ormsby and other wavelets with different frequencies. According to the data processing specification, the pure wave data of the post-stack result of seismic data processing is usually assumed to be zero-phase.

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

[0064] Zero-phase wavelet is an ideal symmetrical wavelet with no component before time zero. The time domain and amplitude spectra of typical Ricker, Ormsby and other wavelets of different frequencies are shown in the classic seismic data processing principles. According to the data processing specifications, the pure wave data of the post-stack results of seismic data processing are 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 used in seismic acquisition is closest to the minimum phase wavelet. High-frequency minimum phase wavelets (for example, above 80 Hz) have no components before time zero and have high resolution, but poor identification of reflection layer groups.

[0066] For actual pre-stack gathers, seismic data processing usually uses minimum phase wavelet for predictive deconvolution; for post-stack seismic pure wave data, which is usually zero phase, zero phase wavelet is used for deconvolution. The study of using zero phase wavelet deconvolution on post-stack pure wave data to improve seismic data resolution meets the seismic data processing specifications.

[0067] Step S104: Perform point, line, surface, and volume quality control analysis on the denoised data. If the processed data does not meet the standards, return to the previous step S103 to further optimize the processing parameters. Performing quality inspection and analysis of two- and three-dimensional data after wavelet deconvolution to improve resolution is part of the processing specification requirements. This workflow may find that the original seismic data is truncated, and abnormal processing results appear in high-steep reflection areas.

[0068] Step S105: Output the final resolution-enhanced processing result data volume B.

[0069] The archived data of the pure wave migration results are usually sampled at 2ms or 4ms, and the corresponding highest frequencies of seismic data are 500Hz and 250Hz, but the seismic spectrum has a certain degree of symmetry, and the highest seismic frequency corresponding to 4ms sampling is only 125Hz. Considering that strong reflections of coal seams and steep-angle seismic reflections are prone to produce false reflections when the resolution is improved, reducing the sampling interval of seismic data by interpolation can reduce the occurrence of abnormal reflections.

[0070] Figure 3 and Figure 4 The figure shows the migration pure wave through the well profile when the data is not sampled and interpolated in time. Figure 3 ) and the well profile after zero-phase statistical deconvolution ( Figure 4 ) shows that the post-stack zero-phase statistical wavelet deconvolution to improve resolution will give rise to the false impression of increased coal seam seismic reflection anomalies; Figure 5 and Figure 6 The pure wave migration through the well section after steps S101, S102 and S103 is shown ( Figure 5 ) and the well profile after zero-phase statistical deconvolution ( Figure 6 ), it can be clearly seen that the effect of improving the resolution by the through-well quality control line is obvious. In this example, the spectrum of the target layer is broadened by 10 Hz, so that the seismic data can reflect the thickness changes of the target sand body.

[0071] Step S2: taking data volume B as the near angle, data volume A as the middle angle, and data volume C as the far angle to calculate the well side channel amplitude ratio relationship 1;

[0072] In this embodiment: in step S2, the well bypass amplitude ratio relationship 1 includes:

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

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

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

[0076] Step S3: through well forward simulation, the amplitude ratio relationship of the near, middle and far angle target intervals is calculated using simulation data;

[0077] In some embodiments, in step S3, the specific steps of using simulation data to calculate the amplitude ratio relationship of near, medium and far angle target layer segments through well forward simulation include:

[0078] Step S301: using well data to carry out forward simulation of the wave equation Aki-Richards approximate equation, generating wave equation simulation gather data under different incident angles X, Y, and Z, where X is the near angle, Y is the middle angle, and Z is the far angle, and 1°<X<Y<Z<45°;

[0079] Step S302: selecting the reflection peak or trough of the target layer segment from the simulated gather data of different incident angles;

[0080] Step S303: Counting the reflection waveform amplitude of the target layer at different incident angles;

[0081] Step S304: according to the statistical result of step S303, the second amplitude ratio relationship of the target layer segment under different incident angles is obtained.

[0082] The linear modeling method usually only simulates and models the angle gathers within 39°. If the angle exceeds 39°, the error of linear modeling will be large. In some preferred embodiments, X, Y, and Z are 10°, 20°, 30°, 12°, 24°, 36°, or 13°, 26°, 39°, respectively.

[0083] In this embodiment: in step S301, the incident angle X is 12°, Y is 24°, and Z is 36°. The wave equation simulation gather data at 12°, 24°, and 36° refer to the average amplitude variation at the incident angle intervals of 1°-12°, 12°-24°, and 24°-36°, respectively.

[0084] The second target layer segment amplitude ratio relationship under different incident angles in step S304 includes:

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

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

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

[0088] The well data is forward modeled with angle gathers and the amplitude of the middle layer is statistically analyzed with the angle. Figure 7 and Figure 8 As shown, the pre-stack gathers from 1° to 45° of the actual well data forward simulation ( 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 according to the amplitude ratio relationship 1 in step S2 and the amplitude ratio 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 angle gathers.

[0094] In this embodiment: the correction factor m is used to correct the data volume B to obtain the data volume B1, the correction factor 1 is used to correct the data volume A to obtain the data volume A1, and the correction factor n is used to correct the data volume C to obtain the 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. In step S104, various noises are removed to highlight the effective wave signals of underground strata. The signal-to-noise ratio of the stacking result data of pre-stack migration is higher than that of pre-stack gathers. It is the superposition of each sub-offset trace within the effective offset, and contains the effective signals of underground strata at various angles. Therefore, starting from the stacking result of pre-stack migration, reverse modeling analysis is performed on pre-stack sub-angle gathers, avoiding the problem of uneven coverage of each offset distance caused by complex factors such as the surface of the land.

[0097] like Fig. 9 As shown: the prestack angle gather modeling method in the above embodiment generates basic data that can meet the shallow, medium and deep prestack seismic reservoir prediction of the target area. The modeling method is applied to carry out prestack inversion, which improves the well-seismic correlation coefficient (the well-seismic correlation coefficient reaches above 0.9) and reduces the inversion error of elastic parameters, laying the foundation for obtaining prestack inversion elastic parameters with high certainty.

[0098] like Fig.10 As shown in the figure: The cross-plot of pre-stack inversion elastic parameters identifies thin gas layers and potential target layers. Compared with post-stack seismic inversion, pre-stack inversion adds five elastic parameters, including shear wave impedance, density, P-wave velocity, S-wave velocity, and P-S wave velocity ratio. These parameters can not only be used for reservoir description, but also provide basic data for rock mechanics modeling, thus expanding the application of seismic inversion technology in exploration, development, evaluation and geological engineering.

[0099] The following are explanations of some terms used in this manual:

[0100] Migration pure wave data: It is the official result data of seismic data processing and is used for seismic interpretation, reservoir prediction and archiving.

[0101] Post-stack resolution-enhancing data processing: The data obtained by processing the formal results of seismic data is processed using methods such as zero-phase wavelet deconvolution, spectrum whitening, and Q compensation to broaden the data spectrum.

[0102] Bandpass filtering data processing: Bandpass filtering is performed on the official results of seismic data processing to improve the signal-to-noise ratio of the results.

[0103] Prestack time migration outputs gather data: After the prestack time migration regression processing of seismic data processing, part of the gather is usually cut off to obtain the best stack imaging results. This data is the basic data for seismic prestack reservoir prediction research. Prestack gather optimization and angle processing are usually carried out based on this data.

[0104] Angle-based prestack gathers: gather data arranged in near, middle and far angles while maintaining the AVO / AVA amplitude relationship, which can be directly used for prestack seismic inversion and reservoir prediction.

[0105] Wave equation forward simulation pre-stack gathers: The P- and S-wave velocities and densities obtained from well logging are used to generate pre-stack seismic response gathers of formations by numerical simulation using the Kirchhoff wave equation under the conditions of selected wavelets, maximum observation offset (including trace spacing) or maximum incident angle. This data is mainly used for fidelity quality control analysis of seismic data wellside traces and AVO analysis of pre-stack seismic reservoir prediction.

[0106] Aki-Richards approximate equation: It is a commonly used approximate formula to characterize the seismic reflection amplitude at different incident angles.

[0107] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A pre-stack angle gather modeling method based on well forward simulation, characterized by: The following steps are involved: Step S1: performing resolution enhancement processing on the offset pure wave data volume A to obtain data volume B, and performing bandpass filtering processing on the offset pure wave data A to obtain data volume C; Step S2: taking data volume B as near-angle data, data volume A as mid-angle data, and data volume C as far-angle data to calculate the amplitude ratio of the well side channel 1; Step S3: using the well data in the work area where prestack seismic inversion is planned to be carried out to perform well forward simulation, and using the simulation data to statistically fit the amplitude ratio relationship of the near, medium and far angle target intervals; Step S4: deriving an amplitude correction factor according to the amplitude ratio relationship 1 in step S2 and the amplitude ratio relationship 2 in step S3; Step S5: Use the amplitude correction factor in S4 to perform amplitude correction on the data volume B, the data volume A, and the data volume C, obtain and merge the corrected three data volumes B1, A1, and C1, and output the angle-based gathers.

2. A pre-stack angle gather modeling method based on well forward modeling according to claim 1, characterized in that: In step S1, the resolution of the migrated pure wave data volume A is improved by wavelet deconvolution method to obtain data volume B. The specific steps include: Step S101: performing sampling rate reduction interpolation processing on data A; Step S102: performing statistical zero-phase wavelet deconvolution on the interpolated data; Step S103: performing filtering and denoising processing on the data after the zero-phase wavelet deconvolution processing; Step S104: Perform point, line, surface and volume quality control analysis on the denoised data; Step S105: Output the final resolution-enhanced processing result data volume B.

3. The pre-stack angle gather modeling method based on well forward modeling according to claim 1, characterized in that: In step S3, the forward simulation is performed through the well data, and the specific steps of using the simulation data to calculate the amplitude ratio relationship of the near, medium and far angle target layer segments include: Step S301: using well data to carry out wave equation forward simulation, generating wave equation simulation gather data under different incident angles X, Y, and Z, where X is the near angle, Y is the middle angle, and Z is the far angle, and 1°<X<Y<Z<45°; Step S302: selecting the reflection peak or trough of the target layer segment from the simulated gather data of different incident angles; Step S303: Counting the reflection waveform amplitude of the target layer segment at different incident angles to obtain the amplitude value of the target reflection (peak or trough) of the forward modeling seismic gather; Step S304: according to the statistical result of step S303, the second amplitude ratio relationship of the target layer segment under different incident angles is obtained.

4. The pre-stack angle gather modeling method based on well forward modeling according to claim 3, characterized in that: The wave equation simulation gather data at the incident angles X, Y, and Z in step S301 are the average values ​​of the amplitude changes in the incident angle intervals of 1°-X, XY, and YZ, respectively.

5. The pre-stack 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 pre-stack angle gather modeling method based on well forward modeling according to claim 4, characterized in that: The well bypass amplitude ratio relationship 1 in step S2 includes: The ratio of data volume B to data volume A is M1; The ratio of data volume A to data volume A is 1; The ratio of data volume C to data volume A is N1; The second target layer segment amplitude ratio relationship under different incident angles in step S304 includes: 1° - the ratio of the average amplitude change under the X incident angle to the average amplitude change under the XY incident angle, M2; The ratio of the average value of the amplitude change under XY incident angle to the average value of the amplitude change under XY incident angle is 1; The ratio N2 of the average amplitude change under the YZ incident angle to the average amplitude change under the XY incident angle.

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

8. The pre-stack angle gather modeling method based on well forward simulation according to claim 7, characterized in that: In step S5, the correction factor m is used to correct the data volume B to obtain the data volume B1, the correction factor 1 is used to correct the data volume A to obtain the data volume A1, and the correction factor n is used to correct the data volume C to obtain the data volume C1.

9. The pre-stack angle gather modeling method based on well forward modeling according to claim 1, characterized in that: In step S5, before merging the three data bodies B1, A1, and C1, the step further includes: changing the headers of the data bodies B1, A1, and C1 to X, Y, and Z respectively.

Images