Method for determining superposition scheme through AVO forward modeling to carry out thin cloud rock reservoir seismic recognition
Through AVO forward determination of the superposition scheme, a seismic geological model of the thin dolomite reservoir was established, and forward simulation and superposition scheme were determined. The problem of low seismic recognition accuracy of deep thin dolomite reservoirs was solved, and the fidelity of seismic data and effective identification of the favorable distribution range of the thin dolomite reservoir was achieved.
Patent Information
- Application Number
- CN202311618282.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-29
- Publication Date
- 2025-05-30
AI Technical Summary
The prior art is difficult to effectively identify the seismic identification of deep thin dolomite reservoirs, resulting in a decrease in the accuracy of seismic attribute analysis and seismic inversion results, and the prediction results of thin dolomite reservoirs are quite different from those of real drilling.
The AVO forward performance determination superposition scheme was adopted. By collecting basic data from the research area, a seismic geological model of the Thin Domestic reservoir was established, and forward performance simulation of large offset distances was carried out. The seismic response characteristics of the reservoir and the AVO characteristics of the forward performance set were obtained. The amplitude characteristic change law of the CRP channel set before the different offset distances was analyzed, the optimal offset superposition scheme was determined, the best post-stack seismic data was obtained, and the seismic sensitive attributes were selected and extracted to identify the favorable distribution range of the Thin Domestic reservoir.
The fidelity of seismic data is achieved, the seismic identification accuracy of Thin Dolomites reservoirs is improved, the favorable distribution range of Thin Dolomites reservoirs can be effectively identified, and the exploration risks are reduced. The prediction results of Thin Dolomites reservoir development parameters are more in line with well data.
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Figure CN120065325A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of oil exploration and development, and particularly relates to a method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine a stacking scheme. Background Art
[0002] At present, seismic identification methods for thin dolomite reservoirs at home and abroad usually use seismic attribute analysis methods to qualitatively describe favorable areas for reservoir development, and use seismic inversion methods to quantitatively predict reservoir development parameters.
[0003] In seismic attribute analysis methods, the extraction of seismic attributes is achieved by using a variety of mathematical methods (such as Fourier transform, power spectrum analysis, complex trace analysis, autocorrelation function, and autoregressive analysis, etc.). In the mid-1990s, with the emergence and development of statistical attributes, a large number of geostatistical methods have been widely used in attribute extraction, such as covariance, linear regression, wavelet transform, simulated annealing, etc. Such technologies play an important role in extracting seismic attributes such as coherence volume, identifying and qualitatively describing faults, channel sand bodies, and even fracture and cave development zones in carbonate reservoirs.
[0004] Seismic inversion method is a process of imaging (solving) the spatial structure and physical properties of underground rock formations by using surface-observed seismic data and constrained by known geological laws and drilling and logging data. Seismic inversion based on geophysical theory can be divided into post-stack inversion and pre-stack inversion according to the differences in the data used. When there is an impedance difference between the interfaces of two different lithologies underground, seismic waves will be reflected. The process of solving the impedance attributes of underground lithology interfaces by using the acoustic impedance curve calculated from the continuous curve measured in the wellbore and two-dimensional or three-dimensional post-stack seismic data is called impedance inversion, or post-stack inversion. And there are mainly two different types of pre-stack seismic inversion: one is the inversion method based on the wave equation theory; the other is the inversion method based on the Zoeppritz equation and its series of approximate expressions. With the maturity of the "two-wide and one-high" seismic technology, the acquisition, processing, and interpretation technologies based on OVT domain seismic data have all developed vigorously, and the azimuth AVO attributes and AVAZ inversion technologies based on OVT domain seismic data have also emerged, achieving good results in the fields of fracture prediction, fluid detection, and in-situ stress prediction, greatly expanding the application fields of seismic inversion, especially in the prediction of special lithologies and unconventional reservoir sweet spots.
[0005] In the above method, reservoir seismic identification mainly relies on seismic data with high fidelity. However, after the conventional NMO processing of the actually collected large-offset seismic data, low-frequency phenomena will occur in the pre-stack seismic data, resulting in abnormal increase in the amplitude values of far-offset seismic data. After stacking the entire trace gather, abnormal strong amplitude artifacts caused by non-geological bodies may appear, interfering with reservoir seismic identification. Therefore, the pre-stack CRP gather containing abnormal amplitudes of far offsets and the post-stack seismic data of the full offset will reduce the accuracy of seismic attribute analysis and seismic inversion results, making the prediction results of thin dolomite reservoirs quite different from the actual drilled wells. Summary of the Invention
[0006] The present invention aims at the problem in the prior art that it is difficult to effectively identify deep thin dolomite reservoirs in seismic exploration, and provides a method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme realizes the fidelity of seismic data, improves the accuracy of seismic identification of thin dolomite reservoirs, and can effectively identify the favorable distribution range of thin dolomite reservoirs.
[0007] The present invention can achieve the following technical solutions to solve its problems: The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme includes the following steps: S1. Collect basic data of the study area; S2. Comprehensively utilize seismic data and logging data in the study area to establish a seismic-geological model of the thin dolomite reservoir in the target area; conduct forward modeling of the thin dolomite reservoir with large offsets to obtain the seismic response characteristics of the reservoir and the AVO characteristics of the forward gather; S3. Analyze the variation law of the amplitude characteristics of pre-stack CRP gathers with different offsets in the study area, compare them with the AVO characteristics of the forward gather, and select the effective signal interval that can accurately reflect the seismic response characteristics of the reservoir; determine the optimal offset stacking scheme to obtain the best post-stack seismic data; S4. Based on the best post-stack seismic data, select and extract seismic sensitive attributes to identify the favorable distribution range of thin dolomite reservoirs.
[0008] Further, the basic data of the study area in step S1 includes seismic data and logging data.
[0009] Further, the seismic data includes: pre-stack CRP gather seismic data, post-stack seismic data and seismic interpretation horizons; The logging data includes: conventional logging curves, drilling and logging data, well deviation data, core data, layer data, logging interpretation results, single-well test production data and regional geological overview.
[0010] Further, the method for obtaining the seismic response characteristics of the reservoir and the AVO characteristics of the forward gather in step S2 includes: Based on the seismic interpretation horizons and combined with the well logging curves, a seismic-geological model of the thin dolomite reservoir in the target area is constructed; the parameters of a large-offset seismic observation system close to actual field acquisition are given; forward modeling is carried out to obtain shot gather data; after migration and stacking processing of the shot gather data, CRP gather records and post-stack seismic profiles are obtained; AVO analysis is performed on the reservoir section of the CRP gather to obtain the law of the change of the amplitude of the reservoir section with the offset in the forward modeling result, and the AVO characteristics of the forward gather are obtained; the post-stack seismic profile is analyzed to obtain the seismic response characteristics of the thin dolomite reservoir under different development conditions.
[0011] Further, the law of the change of the amplitude of the reservoir section with the offset in the forward modeling result is that: as the offset increases, the amplitude value of the reservoir section gradually decreases.
[0012] Further, the seismic response characteristics of the thin dolomite reservoir under different development conditions are that: when the reservoir is developed, bright spot reflections appear inside; the thicker the reservoir is developed, the more significant the bright spot characteristics inside are, and the amplitude energy has a positive correlation with the reservoir thickness.
[0013] Further, the method for determining the optimal offset stacking scheme in step S3 and obtaining the optimal post-stack seismic data includes: According to the well logging curves and interpretation results, pre-stack forward modeling of actual drilling or geological models is carried out to obtain forward gathers, and the amplitude characteristics of the near and far offsets of the CRP gathers of the thin dolomite reservoir in the study area under different development conditions are analyzed; Combined with the actual pre-stack CRP gather seismic data, the profile differences between the actual seismic gather and the forward gather are compared and analyzed to clarify the similarities and differences between the actual gather and the forward gather, and the effective signal interval in the actual seismic gather with similar AVO characteristics to the forward gather is selected as the optimal stacking data range that can accurately reflect the seismic response characteristics of the reservoir, and the actual seismic gathers within the optimal data range are stacked to obtain the optimal seismic data; The optimal seismic data is calibrated with a synthetic seismogram, and the correlation between the synthetic seismogram and the seismic data is analyzed. If the correlation reaches 80% or above, the data is used as the final seismic data for the next step; if the correlation is lower than 80%, the profile differences between the actual seismic gather and the forward gather are repeatedly compared, and the effective signal interval is further optimized until the correlation with the synthetic seismogram reaches 80% or above.
[0014] Further, the method for preferentially selecting and extracting seismic sensitive attributes and identifying the favorable distribution range of the thin dolomite reservoir in step S4 includes: Based on the post-stack seismic data and logging data, well-seismic calibration is carried out; the variation law of seismic reflection characteristics under different reservoir development conditions is analyzed, combined with the seismic response characteristics of the reservoir in the forward modeling section, and through waveform comparison and time slice browsing, sensitive attributes of the post-stack seismic data are optimized and extracted to obtain the reservoir prediction plan map of the amplitude attribute of the target interval, and the favorable distribution range of the thin dolomite reservoir is identified.
[0015] Further, based on the pre-stack CRP gather seismic data within the effective signal interval in step S2, the prediction results of the development parameters of the thin dolomite reservoir are obtained by using the pre-stack inversion method.
[0016] Further, the method for obtaining the prediction results of the development parameters of the thin dolomite reservoir by using the pre-stack inversion method includes: Based on the pre-stack CRP gather seismic data and logging data, elastic parameter calculation and crossplot analysis are carried out on different logging curves to determine the sensitive parameters that can distinguish the reservoir; the AVO characteristics under different development conditions of the reservoir are compared and analyzed, and it is clear that there are differences in the pre-stack AVO characteristics of high-quality reservoirs, better reservoirs and general reservoirs; pre-stack inversion is carried out by using the sensitive parameter curve and the CRP gather data within the effective signal interval to obtain the prediction results of different development parameters of the reservoir, so as to realize the quantitative prediction of the distribution scale and development parameters of the thin dolomite reservoir.
[0017] (1) In this method, the forward modeling gather is compared with the actual seismic gather to determine the optimal offset stacking scheme, eliminating the interference of the amplitude anomaly of the far trace in the pre-stack CRP gather in the seismic identification process of the thin dolomite reservoir, realizing the authenticity of the seismic data, and the extracted sensitive attributes have higher conformity to the reservoir, reducing the exploration risk.
[0018] (2) The effect of pre-stack inversion using the pre-stack CRP gather seismic data within the optimal offset is much better than that of the full offset gather data, and the prediction results of the development parameters of the thin dolomite reservoir are more consistent with the well data, improving the seismic identification accuracy of the thin dolomite reservoir. Description of the Drawings
[0019] Att Figure 1 is the flow chart of the method of the present invention; Att Figure 2 is the forward modeling section of the embodiment of the present invention; Att Figure 3 is the AVO characteristic comparison chart of the forward modeling gather and the actual seismic gather of the embodiment of the present invention; Att Figure 4 is the maximum amplitude attribute plan map of the target interval of the embodiment of the present invention; Att Figure 5 is the dolomite reservoir thickness prediction plan map of the embodiment of the present invention. Detailed Embodiment
[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
[0021] As Figure 1 shown, a method for seismic identification of thin dolomite reservoirs using AVO forward modeling to determine the stacking scheme includes the following steps: S1. Collect basic data of the study area; The basic data of the study area includes seismic data and logging data. The seismic data includes: pre-stack CRP gather seismic data, post-stack seismic data, and seismic interpretation horizons; the logging data includes: conventional logging curves, drilling and logging data, well deviation data, core data, stratification data, logging interpretation results, single-well test production capacity data, and regional geological profiles.
[0022] S2. Comprehensively utilize the seismic data and logging data in the study area to establish a seismic-geological model of the thin dolomite reservoir in the target area; conduct forward modeling of the thin dolomite reservoir with large offsets to obtain the seismic response characteristics of the reservoir and the AVO characteristics of the forward gather; the specific method includes: Based on the seismic interpretation horizons, combine with the logging curves to construct a seismic-geological model of the thin dolomite reservoir in the target area; given the large-offset seismic acquisition system parameters close to the actual field acquisition; conduct forward modeling to obtain shot gather data; after performing migration and stacking processing on the shot gather data, obtain CRP gather records and post-stack seismic profiles; conduct AVO analysis on the reservoir section of the CRP gather to obtain the law of the amplitude change of the reservoir section with the offset in the forward result, and obtain the AVO characteristics of the forward gather; analyze the post-stack seismic profile to obtain the seismic response characteristics of the thin dolomite reservoir under different development conditions.
[0023] The law of the amplitude change of the reservoir section with the offset in the forward result is: as the offset increases, the amplitude value of the reservoir section gradually decreases.
[0024] The seismic response characteristics of the thin dolomite reservoir under different development conditions are: when the reservoir is developed, bright spot reflections appear inside; the thicker the reservoir is developed, the more significant the bright spot characteristics inside are, and the amplitude energy has a positive correlation with the reservoir thickness.
[0025] S3. Analyze the variation law of the amplitude characteristics of the pre-stack CRP gathers with different offsets in the study area, and compare with the AVO characteristics of the forward gather to optimize the effective signal interval that can accurately reflect the seismic response characteristics of the reservoir; determine the optimal offset stacking scheme to obtain the best post-stack seismic data, and the specific method includes: According to the logging curves and interpretation results, conduct pre-stack forward modeling of the actual well drilling or geological model to obtain the forward gather, and analyze the amplitude characteristics of the near and far traces of the CRP gather of the thin dolomite reservoir in the study area under different development conditions; Combined with the actual pre-stack CRP gather seismic data, compare and analyze the profile differences between the actual seismic gather and the forward modeling gather, clarify the similarities and differences between the actual gather and the forward modeling gather, select the effective signal interval in the actual seismic gather with similar AVO characteristics to the forward modeling gather as the best stacking data range that can accurately reflect the seismic response characteristics of the reservoir, and stack the actual seismic gathers within the best data range to obtain the best seismic data; Calibrate the synthetic seismogram for the best seismic data, and analyze the correlation between the synthetic seismogram and the seismic data. If the correlation reaches 80% or above, use this data as the final seismic data for the next step; if the correlation is lower than 80%, repeat the comparison of the profile differences between the actual seismic gather and the forward modeling gather, and further optimize the effective signal interval until the correlation with the synthetic seismogram reaches 80% or above.
[0026] S4. Based on the best post-stack seismic data, optimize and extract seismic sensitive attributes to identify the favorable distribution range of thin dolomite reservoirs. The specific methods include: According to the post-stack seismic data and well logging data, conduct well-seismic calibration; analyze the variation law of seismic reflection characteristics under different reservoir development conditions, combine the seismic response characteristics of the reservoir in the forward modeling profile, and through waveform comparison and time slice browsing, optimize and extract the sensitive attributes of the post-stack seismic data to obtain the reservoir prediction plan map of the amplitude attribute of the target interval, and identify the favorable distribution range of thin dolomite reservoirs.
[0027] S5. Based on the pre-stack CRP gather seismic data within the effective signal interval in step S2, use the pre-stack inversion method to obtain the prediction results of the development parameters of thin dolomite reservoirs. The specific methods include: According to the pre-stack CRP gather seismic data and well logging data, calculate the elastic parameters and conduct crossplot analysis for different well logging curves to determine the sensitive parameters that can distinguish the reservoir; compare and analyze the AVO characteristics under different reservoir development conditions, and clarify the differences in pre-stack AVO characteristics between high-quality reservoirs, better reservoirs and general reservoirs; use the sensitive parameter curves and the CRP gather data within the effective signal interval for pre-stack inversion to obtain the prediction results of different reservoir development parameters, so as to realize the quantitative prediction of the distribution scale and development parameters of thin dolomite reservoirs. Embodiment
[0028] Through the method for seismic identification of thin dolomite reservoirs by forward modeling and optimizing seismic data of the present invention, seismic identification is carried out on the dolomite reservoir in the second member of the Ma Formation in Block 125 of Hechuan in the Sichuan Basin, including the following steps: S1. Collect the basic data of the study area; including pre-stack CRP gather seismic data, post-stack seismic data, conventional well logging curves, drilling and logging data, well deviation data, core data, stratification data, well logging interpretation results, seismic interpretation horizons, single well test production capacity data and regional geological overview.
[0029] S2. Based on the seismic interpretation horizons and logging curves, construct the thin dolomite reservoir geological models with different development thicknesses in the target area and the seismic geological models beside the wells; given the large-offset seismic acquisition system parameters close to the actual field acquisition, conduct forward modeling to obtain the shot gather data; use the velocities designed in the geological models to perform migration and stacking processing on the shot gather data to obtain the CRP gather records and the post-stack seismic profiles; conduct AVO analysis on the reservoir sections of the CRP gathers, and the obtained law of the amplitude change of the reservoir sections with the offset in the forward modeling results is: as the offset increases, the amplitude values of the reservoir sections gradually increase; analyze the post-stack seismic profiles, and the obtained seismic response characteristics of the dolomite reservoirs under different development conditions are: when the reservoir develops, bright spot reflections appear inside; the thicker the reservoir thickness, the more prominent the bright spot characteristics inside, and the amplitude energy has a positive correlation with the reservoir thickness.
[0030] As Figure 2 shown, use forward modeling to obtain the seismic response characteristics of the dolomite reservoirs with different development thicknesses.
[0031] S3. Based on the pre-stack CRP gather seismic data, logging curves and interpretation results, analyze the amplitude characteristics of the near and far offsets of the CRP gathers beside the wells under different reservoir development conditions. For the wells where thinner reservoirs develop, the CRP gathers show the characteristics of "weak near offset and gradually stronger far offset", and the characteristics of "strong near offset and even stronger far offset" of the dolomite reservoirs with high porosity become weaker; by comparing and analyzing the AVO characteristics of the actual seismic gathers and the forward modeling gathers, it is found that the strong amplitude anomaly in the far offset (>4000m) of the local seismic CRP gathers is not the response of geological bodies; the AVO characteristics of the near-middle (<4000m) offset gathers are similar to those of the forward modeling gathers. Therefore, the gathers with an offset greater than 4000m are regarded as noise interference, and the gathers with an offset less than 4000m are used as the best stacking data range that can accurately reflect the seismic response characteristics of the reservoirs.
[0032] As Figure 3 shown, compare and analyze the actual seismic CRP gathers with the forward modeling results to determine the best stacking data range and eliminate the interference of the far offset amplitude anomaly in the pre-stack CRP gathers during the seismic identification of the thin dolomite reservoirs.
[0033] S4. Based on the post-stack seismic data and logging information described above, conduct well-seismic calibration; analyze the change law of the seismic reflection characteristics under different reservoir development conditions; combined with the seismic response characteristics of the reservoirs in the forward modeling profiles, determine the amplitude energy type attributes as sensitive attributes through waveform comparison and time slice browsing; extract the maximum amplitude attributes of the target layer section of the post-stack seismic data after optimization to clarify the favorable distribution range of the dolomite reservoirs.
[0034] As Figure 4 shown, extract the maximum amplitude attributes of the optimized post-stack seismic data to predict the favorable distribution range of the reservoirs, and the well compliance rate is significantly improved compared with the full-offset data.
[0035] S5. Based on the pre-stack CRP gather seismic data and well logging data, elastic parameter calculations and crossplot analyses are performed on different well logging curves to determine that the sensitive parameters capable of distinguishing reservoirs are the P-wave impedance and the P-to-S wave velocity ratio; by comparatively analyzing the AVO characteristics under different reservoir development conditions, it is clarified that there are significant differences in the pre-stack AVO characteristics between high-quality reservoirs, relatively good reservoirs and general reservoirs; pre-stack inversion is carried out using the sensitive parameter curves and the CRP gather data within the optimal offset to obtain a prediction plan view of the dolomite reservoir thickness. The inversion results have a good correlation with the well data and the prediction plan view of the maximum amplitude attribute, and quantitatively predict the development thickness and distribution scale of the thin dolomite reservoir.
[0036] As Figure 5 shown, using the pre-stack CRP gather data within the optimal offset, a prediction plan view of the development thickness of the thin dolomite reservoir is obtained by means of pre-stack inversion.
[0037] Those of ordinary skill in the art will realize that the embodiments described herein are to assist the reader in understanding the implementation methods of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.
Claims
1. A method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme, characterized in that: It includes the following steps: S1. Collect the basic data of the study area; S2. Comprehensively utilize the seismic data and logging data in the study area to establish a seismic-geological model of the thin dolomite reservoir in the target area; conduct forward modeling of the thin dolomite reservoir with large offset distances to obtain the seismic response characteristics of the reservoir and the AVO characteristics of the forward modeling gather; S3. Analyze the variation law of the amplitude characteristics of the pre-stack CRP gathers with different offset distances in the study area, and compare them with the AVO characteristics of the forward modeling gather to optimize the effective signal interval that can accurately reflect the seismic response characteristics of the reservoir; determine the optimal offset distance stacking scheme to obtain the best post-stack seismic data; S4. Based on the best post-stack seismic data, optimize and extract seismic sensitive attributes to identify the favorable distribution range of the thin dolomite reservoir.
2. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme according to claim 1, characterized in that: The basic data of the study area in step S1 includes seismic data and logging data.
3. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme according to claim 1, characterized in that: The seismic data includes: pre-stack CRP gather seismic data, post-stack seismic data and seismic interpretation horizons; the logging data includes: conventional logging curves, drilling and logging data, well deviation data, core data, stratification data, logging interpretation results, single-well test production capacity data and regional geological overview.
4. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme according to claim 3, characterized in that: The method for obtaining the seismic response characteristics of the reservoir and the AVO characteristics of the forward modeling gather in step S2 includes: Based on the seismic interpretation horizons, combined with the logging curves, construct a seismic-geological model of the thin dolomite reservoir in the target area; given the large offset distance seismic observation system parameters close to the actual field acquisition; conduct forward modeling to obtain shot gather data; after offset and stacking processing of the shot gather data, obtain CRP gather records and post-stack seismic profiles; conduct AVO analysis on the reservoir section of the CRP gather to obtain the law of the amplitude change of the reservoir section with the offset distance in the forward modeling result, and obtain the AVO characteristics of the forward modeling gather; analyze the post-stack seismic profile to obtain the seismic response characteristics of the thin dolomite reservoir under different development conditions.
5. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme according to claim 4, characterized in that: The law of the amplitude change of the reservoir section with the offset distance in the forward modeling result is: as the offset distance increases, the corresponding amplitude value of the reservoir section gradually increases.
6. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme according to claim 4, characterized in that: The seismic response characteristics of the thin dolomite reservoir under different development conditions are: when the reservoir is developed, bright spot reflections appear inside; the larger the developed thickness of the reservoir, the more significant the bright spot characteristics inside, and the amplitude energy has a positive correlation with the reservoir thickness.
7. The method for seismic identification of thin dolomite reservoirs by using AVO forward modeling to determine the stacking scheme according to claim 3, characterized in that: The method for determining the optimal offset stacking scheme in step S3 to obtain the optimal post-stack seismic data includes: Based on the well logging curves and interpretation results, perform prestack forward modeling of the actual well drilling or geological model to obtain a forward gather, and analyze the amplitude characteristics of the near and far traces in the CRP gather under different development conditions of the thin dolomite reservoir in the study area; Combined with the actual prestack CRP gather seismic data, compare and analyze the profile differences between the actual seismic gather and the forward gather, clarify the similarities and differences between the actual gather and the forward gather, select the effective signal interval in the actual seismic gather with similar AVO characteristics to the forward gather as the optimal stacking data range that can accurately reflect the seismic response characteristics of the reservoir, and stack the actual seismic gathers within the optimal data range to obtain the optimal seismic data; Perform synthetic seismogram calibration on the optimal seismic data, analyze the correlation between the synthetic seismogram and the seismic data. If the correlation reaches 80% or above, use this data as the final seismic data for the next step; if the correlation is lower than 80%, repeat the comparison of the profile differences between the actual seismic gather and the forward gather, and further optimize the effective signal interval until the correlation with the synthetic seismogram reaches 80% or above.
8. The method for seismic identification of thin dolomite reservoir by determining the stacking scheme through AVO forward modeling according to claim 3, characterized in that: The method for preferentially extracting seismic sensitive attributes in step S4 to identify the favorable distribution range of thin dolomite reservoir includes: Based on the post-stack seismic data and well logging data, perform well-seismic calibration; analyze the variation law of seismic reflection characteristics under different reservoir development conditions, combine with the seismic response characteristics of the reservoir in the forward profile, and through waveform comparison and time slice browsing, preferentially extract the sensitive attributes of the post-stack seismic data to obtain the reservoir prediction plan map of the amplitude attribute in the target interval, and identify the favorable distribution range of the thin dolomite reservoir.
9. The method for seismic identification of thin dolomite reservoir by determining the stacking scheme through AVO forward modeling according to claim 1, characterized in that: Based on the prestack CRP gather seismic data within the effective signal interval in step S2, use the prestack inversion method to obtain the prediction results of the development parameters of the thin dolomite reservoir.
10. The method for seismic identification of thin dolomite reservoir by determining the stacking scheme through AVO forward modeling according to claim 9, characterized in that: The method for obtaining the prediction results of the development parameters of the thin dolomite reservoir by using the prestack inversion method includes: Based on the prestack CRP gather seismic data and well logging data, perform elastic parameter calculation and crossplot analysis on different well logging curves to determine the sensitive parameters that can distinguish the reservoir; compare and analyze the AVO characteristics under different reservoir development conditions, and clarify the differences in the prestack AVO characteristics of high-quality reservoirs, better reservoirs and general reservoirs; use the sensitive parameter curve and the CRP gather data within the effective signal interval for prestack inversion to obtain the prediction results of different development parameters of the reservoir, so as to realize the quantitative prediction of the distribution scale and development parameters of the thin dolomite reservoir.