Reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data

Abstract

The invention discloses a reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data, and specifically adopts a well bypass data extraction method, a multi-azimuth attribute evaluation partitioning method and a multi-azimuth multi-attribute fusion method to realize reservoir quantitative prediction of OVT domain multi-azimuth seismic data. A reliable technical basis is provided for efficient and accurate application of reservoir prediction based on pre-stack OVT domain multi-azimuth seismic attributes in reservoir fine description; the problems that a seismic sedimentology reservoir prediction method based on a pre-stack OVT domain multi-azimuth-angle data body can only conduct reservoir qualitative analysis, for a reservoir with high reservoir heterogeneity, the river channel sand body prediction precision is low, and the prediction effect is greatly influenced by the technical level of technicians are effectively solved.

Description

Technical Field

[0001] The present disclosure relates to the field of reservoir geophysics, specifically a reservoir prediction method for developing seismic interpretation technology. Background Art

[0002] The statements in this section only provide background information related to the present disclosure and do not constitute prior art.

[0003] The Changyuan Oilfield has entered the late stage of extra-high water cut, with the remaining oil being highly scattered and difficult to exploit. The prediction accuracy of channel sand bodies is one of the important factors affecting fine adjustment and potential tapping. How to obtain effective information from seismic attributes to accurately guide the fine characterization of channel sand bodies has become an urgent problem to be solved. The pre-stack data volume contains five-dimensional information of conventional three-dimensional, azimuth, and offset, becoming one of the important research directions of geophysical technology. Compared with traditional seismic exploration, the five-dimensional wide-azimuth seismic information reflects the variation of amplitude with shot-receiver distance and azimuth, and the variation of formation velocity with azimuth, thus enhancing the ability to identify formation lithology and fluids.

[0004] Currently, the seismic sedimentology reservoir prediction method based on pre-stack data volume extracts seismic attributes according to the principle of the closest distance to the well and calculates the correlation with well parameters, and optimally selects attribute slices at different positions in one or more azimuths with large correlation coefficients to achieve qualitative reservoir analysis.

[0005] For example, Xu Liheng, Chu Wenjing, Ma Haonan, etc. in "A Prediction Method for Thin Interbedded Reservoirs Based on Five-Dimensional Information of Pre-stack Seismic Data" (Journal of China University of Petroleum (Edition of Natural Science), No. 1, 2022) proposed a prediction method for thin interbeds based on pre-stack seismic, and through optimally selecting attribute slices in different azimuths, carried out fine characterization of sedimentary microfacies to guide development and application; Liu Houyu, Bai Junyu in "A Synchronous Inversion Method of Fluid Factor Based on Pre-stack Seismic Gathers" (Computing Techniques for Geophysical and Geochemical Exploration, No. 3, 2021) established a pre-stack seismic gather inversion method for fluid factors, gave the objective functional of pre-stack fluid factor inversion and the analytical solution of its gradient, and carried out well-seismic combined reservoir prediction; Huang Jiangbo, Zuo Zhonghang, Hou Dongjia, etc. in "Application of Pre-stack Density Inversion Technology in Reservoir Prediction of Middle-Deep Layers in Shanan Sag" (Marine Geology Frontiers, No. 2, 2021) used the processed pre-stack CRP gathers to conduct inversion experiments on elastic parameters sensitive to lithology and predicted the spatial distribution of reservoirs. Although the above-mentioned literature has carried out a large amount of research on reservoir prediction using pre-stack OVT domain multi-azimuth seismic data, these methods are still in the qualitative stage, and the quality of reservoir prediction is greatly affected by the technical level of technicians and the work efficiency is low.

[0006] Therefore, there is still a lack of mature understanding and systematic methods for how to fully exploit the extremely rich geological information in pre-stack OVT domain multi-azimuth seismic data and how to use pre-stack data to improve the accuracy of reservoir prediction.

[0007] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art. Summary of the Invention

[0008] In view of this, the present disclosure provides a reservoir quantitative prediction method based on prestack OVT domain multi-azimuth seismic data, which solves the problems that the current reservoir prediction by combining well and seismic data using prestack OVT domain multi-azimuth seismic data is in a qualitative stage and cannot perform quantitative prediction, and the current prediction work efficiency is low and the accuracy is insufficient.

[0009] To achieve the above invention object, the reservoir quantitative prediction method based on prestack OVT domain multi-azimuth seismic data includes: Making a set of seismic sedimentology stratigraphic slices from the prestack OVT domain multi-azimuth seismic data volume in the study area; Determining the optimal slice for each azimuth angle of the target layer from the set of stratigraphic slices; Based on the same area of the slices, comparing the reflection degree of effective information on each of the optimal slices, selecting the optimal slice with the best reflection as the characteristic slice, and regarding the area as the dominant area of the characteristic slice; selecting the characteristic slices corresponding to other areas of the slices in the same way; Extracting multiple seismic attributes from the dominant area of the characteristic slice, sorting the seismic attributes using the correlation coefficient, and completing the attribute sorting of the characteristic slice; Fusing the seismic attributes at the same order position in each attribute sorting into one attribute slice to obtain multiple fused attribute slices; Based on the multiple fused attribute slices, using the method of multiple regression to perform the reservoir quantitative prediction.

[0010] In the present disclosure and possible embodiments, the number of the characteristic slices is 3 - 5. In the present disclosure and possible embodiments, the method for making the set of seismic sedimentology stratigraphic slices includes: Determining one or two seismic marker beds near the target layer according to the fine seismic geological horizon calibration result in the study area; Under the control of the seismic marker bed, determining the seismic reflection time range of the stratigraphic slice, and making the set of seismic sedimentology stratigraphic slices from the prestack OVT domain multi-azimuth seismic data volume using the equal-proportion dissection method within the range.

[0011] In the present disclosure and possible embodiments, the method for determining the optimal slice for each azimuth angle of the target layer includes: Using the correlation coefficient and crossplot analysis method, establish a linear correlation between reservoir parameters and the amplitude attributes of the wellbore side trace, obtain the correlation coefficient between the reservoir parameters and the amplitude attributes of the wellbore side trace, and select the optimal slice for each azimuth angle from the formation slice set according to the highest correlation coefficient criterion.

[0012] In the present disclosure and possible embodiments, the method for obtaining the amplitude attributes of the wellbore side trace includes: Extract the amplitude attribute data of four traces along two directions, perpendicular and along the provenance, according to the nearest distance principle, and use the inverse distance weighting method to obtain the amplitude attributes of the wellbore side trace for each formation slice in the formation slice set.

[0013] In the present disclosure and possible embodiments, it is characterized in that the reflection degree of the effective information on the optimal slice is determined according to the area of the high-confidence value region.

[0014] In the present disclosure and possible embodiments, the method for determining the reflection degree of the effective information on each optimal slice includes: Extract the amplitude attributes of the wellbore side trace of the optimal slice, determine a circular region with a preset value as the radius centered on the seismic sampling point, obtain the wellbore area corresponding to each sampling point, and determine the correlation coefficient corresponding to each sampling point based on the amplitude attribute value of the wellbore side trace of each well within the wellbore area corresponding to each sampling point and the sandstone thickness of the target sedimentary sand body layer of each well in the well logging data; determine the correlation coefficient corresponding to each sampling point as the credibility of the seismic attribute of each sampling point at the slice of the target layer.

[0015] In the present disclosure and possible embodiments, the method for sorting attributes using the correlation coefficient includes: Extract the amplitude attribute data of four traces along two directions, perpendicular and along the provenance, according to the nearest distance principle, and use the inverse distance weighting method to obtain the amplitude attributes of the wellbore side trace of the characteristic slice; Using the correlation coefficient and crossplot analysis method, establish a linear correlation between reservoir parameters and the amplitude attributes of the wellbore side trace, and obtain the correlation coefficient between the reservoir parameters and the amplitude attributes of the wellbore side trace; Sort the attributes in descending order according to the correlation coefficient.

[0016] In the present disclosure and possible embodiments, normalize the multiple seismic attributes extracted from the dominant region of the characteristic slice.

[0017] In the present disclosure and possible embodiments, it is characterized in that: When fusing, the attributes in the overlapping region adopt two data averaging methods to perform multi-azimuth attribute fusion in different regions.

[0018] The present disclosure has the following beneficial effects: The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data of the present disclosure innovatively adopts the well-side trace data extraction method, multi-azimuth attribute evaluation zoning method and multi-azimuth multi-attribute fusion method to realize the reservoir quantitative prediction of multi-azimuth seismic data in the OVT domain, providing a reliable technical basis for the efficient and accurate application of reservoir prediction based on pre-stack OVT domain multi-azimuth seismic attributes in reservoir fine description. Thus, it effectively solves the problem that the seismic sedimentology reservoir prediction method based on pre-stack OVT domain multi-azimuth data volume extracts seismic attributes according to the principle of the closest distance to the well and calculates the correlation with well parameters, selects one azimuth or attribute slices at different positions of multiple azimuths with large correlation coefficients to realize reservoir qualitative analysis, and for reservoirs with strong reservoir heterogeneity, the prediction accuracy of channel sand bodies is low and the prediction effect is greatly affected by the technical level of technicians. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Through the description of the embodiments of the present disclosure with reference to the following drawings, the above and other objects, features and advantages of the present disclosure will become clearer. In the drawings: Figure 1 is the well-side trace seismic attribute extraction method of the embodiment of the present disclosure; Figure 2 are the preferred 9 formation slices of the embodiment of the present disclosure; Figure 3 is the schematic diagram of the characteristic slice and the advantageous area of the embodiment of the present disclosure; Figure 4 is the zoning map of the embodiment of the present disclosure; Figure 5 is the multi-attribute splicing of different regions with the sorting of 1 of the embodiment of the present disclosure; Figure 6 is the prediction effect analysis diagram of the embodiment of the present disclosure. EMBODIMENTS

[0020] The following describes the present disclosure based on embodiments. However, it should be noted that the present disclosure is not limited to these embodiments. In the following detailed description of the present disclosure, some specific details are described in detail. However, for the parts that are not described in detail, those skilled in the art can also fully understand the present disclosure.

[0021] In addition, those of ordinary skill in the art should understand that the provided drawings are only for illustrating the purpose, features and advantages of the present disclosure, and the drawings are not actually drawn to scale. At the same time, unless the context clearly requires, the words such as "including", "comprising" and the like in the whole specification and claims should be interpreted as the meaning of including rather than exclusive or exhaustive meaning; that is, the meaning of "including but not limited to".

[0022] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the following further describes the present invention in detail according to the accompanying drawings, and lists embodiments and application examples. Embodiment

[0023] For comparison, in the reservoir prediction method based on pre-stack OVT domain multi-azimuth seismic data, the traditional method is as follows: according to the fine seismic geological horizon calibration results of the study area, determine one or two seismic marker horizons near the target layer; under the control of the determined one or two seismic marker horizons, determine the seismic reflection time range of the formation slice, and make a set of seismic sedimentology formation slices within this range; by means of seismic sedimentology, analyze the amplitude attributes reflecting the reservoir sand body characteristics of the formation slices of the target layer in different azimuth data volumes, perform attribute slice optimization, and based on the azimuth 140° - 160° attribute slices selected by optimization, combined with well data, qualitatively describe the channel sand bodies in Unit B of Block A.

[0024] The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to the embodiments of the present disclosure is specifically as follows: 1. According to the fine seismic geological horizon calibration results of the study area, determine one or two seismic marker horizons near the target layer.

[0025] 2. Under the control of the seismic marker horizons determined in step 1, determine the seismic reflection time range of the formation slice, and within this range, use the equal-proportion dissection method to make a set of seismic sedimentology formation slices for the pre-stack OVT domain multi-azimuth seismic data volume.

[0026] 3. According to the principle of the closest distance, extract four-channel amplitude attribute data along the two directions of perpendicular and along the provenance, and use the inverse distance weighting method to obtain the amplitude attributes of the well-side traces of each formation slice in the formation slice set made in step 2.

[0027] 4. Select the optimal slice corresponding to each azimuth of the target layer from the formation slice set made in step 2. The specific selection method is as follows: Use the correlation coefficient and cross-plot analysis method to establish a linear correlation relationship between reservoir parameters (such as sandstone thickness, sand-to-shale ratio, etc.) and the amplitude attributes of the well-side traces, obtain the correlation coefficient between the reservoir parameters of the target layer and the amplitude attributes of the well-side traces, and select the slice with the highest correlation coefficient corresponding to each azimuth from the formation slice set in step 2 as the optimal slice.

[0028] 5. Since the reservoir information reflected by pre-stack azimuthal attributes is generally similar, but there are detailed differences. For example, some effective information is better reflected in the upper right corner of the slice, while some is better reflected in the lower left corner. According to such a rule, taking the upper right corner area of the slice as an example, one slice can always be selected from all slices, and the effective information is best reflected in this area of this slice; similarly, for other areas of the slice, one slice can always be selected, and the effective information is best reflected in this area of this slice.

[0029] If, based on the same area of the slice, the reflection degrees of the effective information on each of the optimal slices selected in step 4 are compared, and the optimal slice with the best reflection is selected as the feature slice, and the area is regarded as the dominant area of the feature slice; the feature slices corresponding to other areas of the slice are selected in the same way.

[0030] According to the above method, 3 to 5 optimal slices are selected as feature slices for each area. Since the dominant areas of the 3 to 5 selected feature slices are different from each other, if these dominant areas are equivalent to squares or rectangles and then these equivalent areas are spliced together, they can approximately form the entire slice. Further, if the attribute information is extracted from the dominant areas of these feature slices, it can better reflect the reservoir characteristics, thus meeting the requirements for the efficient and accurate application of the pre-stack OVT domain multi-azimuth seismic data reservoir prediction method in the fine description of the reservoir.

[0031] Among them, regarding the reflection degree of the effective information on the slice, it is confirmed and represented by the following method: Extract the amplitude attribute of the well-side trace of the optimal slice according to the method in step 3. Determine a circular area with the seismic sampling point as the center and a preset value as the radius, so as to obtain the well site area corresponding to each sampling point. Based on the amplitude attribute value of the well-side trace of each well in the well site area corresponding to each sampling point and the sandstone thickness of the target sedimentary sand body layer of each well in the logging data, determine the correlation coefficient corresponding to each sampling point; determine the correlation coefficient corresponding to each sampling point as the credibility of the seismic attribute of each sampling point at the slice of the target layer. On the slice, the larger the area of the high-credibility value region, the better the effective information reflected.

[0032] 6. Select one from the feature slices selected in step 5, extract seismic attributes from the dominant area of this feature slice, and perform attribute sorting using the correlation coefficient; the specific method is as follows: According to the seismic horizon corresponding to this feature slice, extract various seismic attributes of the seismic data volume from the dominant area, extract the amplitude attribute of the well-side trace of the various seismic attributes according to the method in step 3, obtain the correlation coefficient according to the method in step 4, and perform attribute sorting according to the preferred well-seismic correlation coefficient. Select the top 4 to 8 preferred attributes, and at the same time perform normalization processing on each attribute.

[0033] For the remaining characteristic slices, the same method is used for attribute sorting.

[0034] 7. Based on the attribute sorting determined in step 6, the attributes with the same sequence positions in each attribute sorting are fused into an attribute map as an attribute slice. When fusing, the attributes in the overlapping area adopt the method of averaging two kinds of data to achieve multi-directional attribute fusion in different areas, and multiple fused attribute slices are obtained.

[0035] 8. Based on the multiple fused attribute slices obtained in step 7, the method of multiple regression is used to perform reservoir quantitative prediction on pre-stack OVT domain multi-azimuth seismic data.

[0036] I. Research background and experimental block: In recent years, the reservoir prediction method of seismic sedimentology in the Daqing Placanticline Oilfield has played an important role in the fine description of sand bodies such as the division of single-stage channels, the determination of channel boundaries and trends, improving the prediction and description accuracy of inter-well sand bodies. The research results have effectively guided the development and potential tapping, demonstrating the great role of well-seismic combined reservoir prediction in the fine description of reservoirs.

[0037] At present, the seismic sedimentology reservoir prediction method based on pre-stack data volume extracts seismic attributes and well parameters for correlation calculation according to the principle of the closest distance to the well, and optimizes the attribute slices at different positions in one or more azimuths with large correlation coefficients to achieve reservoir qualitative analysis. For reservoirs with strong reservoir heterogeneity, there is a problem of low prediction accuracy of channel sand bodies, and the prediction effect is greatly affected by the technical level of technicians.

[0038] Therefore, this application example takes Block B of Well-Dense Network A in the Daqing Placanticline Oilfield as an example, and uses the reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data disclosed in this application for research. This research is based on determining the azimuth angle of 20° as the interval in the OVT domain, and a partial stacking scheme of nine-fold azimuth angle gathers is established, namely 0° - 20°, 20° - 40°, 40° - 60°, 60° - 80°, 80° - 100°, 100° - 120°, 120° - 140°, 140° - 160°, 160° - 180°, and a nine-fold pre-stack OVT domain multi-azimuth data volume is output to provide seismic data volume for subsequent reservoir prediction.

[0039] Based on the output nine-fold pre-stack OVT domain multi-azimuth seismic data for reservoir quantitative prediction, the specific steps are as follows: 1. According to the calibration results of fine seismic geological horizons in the study area, determine one or two seismic marker horizons near Unit B.

[0040] 2. Under the control of one or two seismic marker horizons, determine the seismic reflection time range of the stratigraphic slice. Within this range, use the equal-proportion dissection method to create a set of seismic sedimentology stratigraphic slices for the pre-stack OVT domain multi-azimuth data volume.

[0041] 3. According to the principle of the nearest distance, extract four-channel amplitude attribute data along two directions, perpendicular and along the provenance. Specifically, Figure 1 extract the seismic attributes at seismic bins 1, 2, 4, and 6, and use the inverse distance weighting method to obtain the amplitude attributes of the well-side traces for each stratigraphic slice in the slice set.

[0042] 4. Use the correlation coefficient and cross-plot analysis method to establish the linear correlation relationship between the reservoir parameters of Unit B (such as sandstone thickness, sand-to-shale ratio, etc.) and the amplitude attributes of the well-side traces, obtain the correlation coefficient between the reservoir parameters of Unit B and the seismic attributes, and take the slice with the highest correlation coefficient as the optimal slice. Then, based on the stratigraphic slice set in step 2, complete the determination of 9 optimal slices for the nine azimuth seismic data volumes of Unit B. Specifically, Figure 2 as shown.

[0043] 5. According to the well-side trace amplitude attribute extraction method in step 3, extract the nine azimuth well-side trace attributes of Unit B from the 9 optimal slices. Determine a circular area centered on the seismic sampling points with a preset value as the radius, so as to obtain the well field area corresponding to each sampling point in the seismic sampling points. Based on the well-side trace attribute values of each well in the well field area corresponding to each sampling point and the sandstone thickness of the target sedimentary sand body layer of each well in the logging data, determine the correlation coefficient corresponding to each sampling point, and determine the correlation coefficient corresponding to each sampling point as the credibility of the seismic attribute of each sampling point at the target stratigraphic slice; on the slice, the larger the area of the high-credibility value region, the better the effective information reflected.

[0044] As Figure 3 shown, according to the area of the high-credibility value region, select 4 optimal slices with checkmarks from the 9 optimal slices as the characteristic slices, and circle the dominant regions on the characteristic slices. Specifically as follows: For the optimal slice in the azimuth range of 0 - 20°, the area of the high-credibility value region in its lower right corner is larger than that of the other 8 slices in this region. Therefore, take the optimal slice in the azimuth range of 0 - 20° as the characteristic slice, and determine the lower right corner region of this slice as the dominant region.

[0045] Equivalent the lower right corner region of the optimal slice in the azimuth range of 0 - 20° to a square or rectangle (the shape of the region is preferably a square or rectangle, and the boundary is parallel to the seismic data line trace number), and then correspond Figure 4Sector ④; Complete the azimuth angle sectors of 60 - 80°, 80 - 100°, and 160 - 180° in the same way, corresponding to sectors ②, ③, and ① respectively; at the same time, overlap one trace of data along the main line connecting survey lines for each adjacent area, so that the attributes at the boundary do not mutate when merging the attributes of different areas.

[0046] 6. Area ① corresponds to the seismic attributes of the azimuth angle of 160 - 180°. According to the seismic horizons corresponding to the optimal slices of the azimuth angle of 160 - 180°, extract 68 seismic attributes of the 160 - 180° seismic data volume in area ①. Extract the attributes of the traces beside the wells of the 68 seismic attributes of the azimuth angle of 160 - 180° in area ① according to the method of extracting traces beside the wells described in step 3. Obtain the correlation coefficients between each seismic attribute and well parameters according to the well-seismic correlation coefficient method described in step 4, and then sort the seismic attributes according to the correlation coefficients.

[0047] The 4 seismic attributes with the highest correlation coefficients used in this application example are the original amplitude, maximum amplitude, peak amplitude, and absolute amplitude sum, and the correlation coefficients are 0.42, 0.41, 0.4, and 0.39 respectively, and the rankings are 1, 2, 3, and 4 respectively. At the same time, perform normalization processing on these 4 attributes; complete the sorting of seismic attributes corresponding to areas ④, ②, and ③ for the azimuth angles of 0 - 20°, 60 - 80°, and 80 - 100° according to the above method.

[0048] 7. For the sorting results of the seismic attributes in step 6, splice the attributes with the rank of 1 in each seismic attribute sorting into an attribute map. When splicing, the attributes in the overlapping area are spliced by taking the arithmetic mean of the two overlapping attribute data to achieve the multi-azimuth attribute splicing of different areas. Specifically, as Figure 5 shown. Complete the multi-attribute splicing of different areas with rankings of 2, 3, and 4 according to this method.

[0049] 8. Use the four different seismic attribute slices with rankings of 1, 2, 3, and 4 and the method of multiple regression to achieve the quantitative prediction of reservoirs for multi-azimuth seismic data in the OVT domain. The prediction results are as Figure 6 shown.

[0050] 9. Experimental results and discussions The traditional reservoir prediction method based on pre-stack multi-azimuth seismic data in the OVT domain is in the qualitative stage, and the quality of reservoir prediction is affected by the technical level of technicians, with low work efficiency. However, the quantitative reservoir prediction method based on pre-stack multi-azimuth seismic data volume in this disclosure can perform quantitative prediction of reservoirs, with little influence of the reservoir prediction quality on the technical level of technicians, high work efficiency, and a 15% improvement in accuracy.

[0051] The above application examples have specifically illustrated the whole process of the reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data of the present invention. Its experimental results are accurate and reliable, and can be used for the seismic reservoir prediction work in the Daqing Placanticline Oilfield development, and also for the reservoir prediction work based on seismic attributes in reservoir evaluation and development blocks with similar seismic and geological characteristics.

[0052] Applying the method of the present invention to the target layer B unit in Block A of the Daqing Placanticline Oilfield, compared with the traditional method, this method can realize the reservoir quantitative prediction based on pre-stack OVT domain multi-azimuth seismic data. The result is less affected by the technical level of technicians and has high work efficiency, providing a reliable technical basis for the efficient application of the well-seismic combined reservoir prediction method in the fine description of reservoirs.

[0053] The above-described embodiments are only for expressing the implementation modes of the present disclosure. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present disclosure, several deformations, equivalent substitutions, improvements, etc. can be made, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the patent of the present disclosure should be subject to the appended claims.

Claims

1. A reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data, characterized in that, Including: Making a set of seismic sedimentology stratigraphic slices from the prestack OVT domain multi-azimuth seismic data volume in the study area; Determining the optimal slice for each azimuth of the target layer from the set of stratigraphic slices; Based on the same area of the slices, comparing the reflection degree of effective information on each of the optimal slices, selecting the optimal slice with the best reflection as the characteristic slice, and regarding the area as the dominant area of the characteristic slice; selecting the characteristic slices corresponding to other areas of the slices in the same way; Extracting multiple seismic attributes from the dominant area of the characteristic slice, sorting the seismic attributes using the correlation coefficient, and completing the attribute sorting of the characteristic slice; Fusing the seismic attributes at the same order position in each attribute sorting into one attribute slice to obtain multiple fused attribute slices; Based on the multiple fused attribute slices, using the method of multiple regression to perform quantitative prediction of the reservoir.

2. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 1, characterized in that: The number of the characteristic slices is 3 - 5.

3. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 1 or 2, characterized in that, The method for making the set of seismic sedimentology stratigraphic slices includes: According to the fine seismic geological horizon calibration result in the study area, determining one or two seismic marker horizons near the target layer; Under the control of the seismic marker horizons, determining the seismic reflection time range of the stratigraphic slices, and making the set of seismic sedimentology stratigraphic slices from the prestack OVT domain multi-azimuth seismic data volume using the equal-proportion dissection method within the range.

4. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 3, characterized in that, The method for determining the optimal slice for each azimuth of the target layer includes: Using the correlation coefficient and crossplot analysis method to establish a linear correlation relationship between reservoir parameters and the amplitude attribute of the well-side trace, obtaining the correlation coefficient between the reservoir parameters and the amplitude attribute of the well-side trace, and selecting the optimal slice for each azimuth from the set of stratigraphic slices according to the highest correlation coefficient criterion.

5. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 4, characterized in that, The method for obtaining the amplitude attribute of the well-side trace includes: According to the principle of the nearest distance, extracting four-channel amplitude attribute data along two directions perpendicular and along the provenance, and obtaining the amplitude attribute of the well-side trace of each stratigraphic slice in the set of stratigraphic slices using the inverse distance weighted method.

6. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 1, 2, 4 or 5, characterized in that, The method for determining the reflection degree of the effective information on each of the optimal slices includes: Determining the reflection degree of the effective information on the optimal slice according to the area of the high-confidence value region.

7. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 6, characterized in that, The method for determining the confidence includes: Extracting the amplitude attribute of the well-side trace of the optimal slice, determining a circular region with a preset value as the radius centered on the seismic sampling point to obtain the well-site region corresponding to each sampling point, and determining the correlation coefficient corresponding to each sampling point based on the amplitude attribute value of the well-side trace of each well within the well-site region corresponding to each sampling point and the sandstone thickness of the target sedimentary sand body layer of each well in the logging data; determining the correlation coefficient corresponding to each sampling point as the confidence of the seismic attribute of each sampling point at the target layer slice.

8. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 1, 2, 4, 5 or 7, characterized in that, The method for sorting attributes using the correlation coefficient includes: According to the principle of the nearest distance, extracting four-channel amplitude attribute data along two directions perpendicular and along the provenance, and obtaining the amplitude attribute of the well-side trace of the characteristic slice using the inverse distance weighted method; Using the correlation coefficient and crossplot analysis method, establish a linear correlation relationship between the reservoir parameters and the amplitude attributes of the wellbore side trace, and obtain the correlation coefficient between the reservoir parameters and the amplitude attributes of the wellbore side trace; Sort the attributes in descending order according to the correlation coefficient.

9. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 8, characterized in that: Normalize a variety of seismic attributes extracted from the dominant region of the feature slice.

10. The reservoir quantitative prediction method based on pre-stack OVT domain multi-azimuth seismic data according to claim 1, 2, 4, 5, 7 or 9, characterized in that: When fusing, the attributes in the overlapping region adopt two ways of data averaging to perform multi-azimuth attribute fusion in different regions.

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