Seismic geology combined prediction method for sedimentary diagenesis facies

Abstract

The invention provides a sedimentary diagenesis facies seismic geology combined prediction method. The prediction method comprises the steps that S1, reservoir parameters and reservoir diagenesis facies types corresponding to reservoir core slices are determined; s2, determining pore structure types of multiple types of lithogenous phases; s3, classifying the sedimentary diagenetic facies, and dividing the sedimentary diagenetic facies into favorable sedimentary diagenetic facies and secondary favorable sedimentary diagenetic facies; s4, establishing a linear multivariate discriminant function of the sedimentary lithogenous phase; s5, determining the distribution characteristics of the sedimentary diagenesis facies on the single well; s6, rock physical elastic parameters of various wells are calculated; step S7, screening sensitive elastic parameters capable of distinguishing favorable sedimentary diagenesis phases and secondary favorable sedimentary diagenesis phases; and S8, determining the spatial distribution range of the favorable sedimentary lithogenous phase and the secondary favorable sedimentary lithogenous phase. And a foundation is laid for application of sedimentary diagenesis phase distribution characteristics in oil-gas exploration.

Description

Technical Field

[0001] This invention relates to the fields of geophysics and petroleum geology, and in particular to a method for joint prediction of sedimentary diagenetic facies seismic geology. Background Technology

[0002] Sedimentary diagenetic facies are defined as the material manifestations reflecting the diagenetic environment, that is, the sum of petrological, geochemical, and petrophysical characteristics reflecting the diagenetic environment. With the deepening of reservoir research, the concept of sedimentary diagenetic facies is increasingly widely accepted and used by geological researchers both domestically and internationally. Although there is currently no complete consensus on the expression of the term "sedimentary diagenetic facies," most definitions involve diagenesis and diagenetic minerals. However, the formation mechanisms, controlling factors, and distribution characteristics of various sedimentary diagenetic facies are complex and diverse, and there is still no unified and clear understanding. Due to different focuses, research areas, and purposes, domestic and international scholars do not have a unified standard for classifying sedimentary diagenetic facies, but most classifications and names are based on diagenetic minerals, diagenetic environments, types of diagenesis, intensity of diagenesis, and porosity-permeability characteristics. Currently, the geophysical characterization of sedimentary diagenetic facies mainly relies on well logging data with high vertical resolution and good continuity. Vertical sedimentary diagenetic facies characterization in a single well primarily involves systematically analyzing the sedimentary facies, diagenesis and intensity, and diagenetic evolution sequences of key wells to classify sedimentary diagenetic facies types and establish standard sedimentary diagenetic facies profiles. Core data offers advantages such as comprehensiveness, directness, objectivity, and high resolution, enabling fine-grained classification of sedimentary diagenetic facies. However, it also has significant limitations, such as high cost and low spatial coverage, posing challenges to the study and subsequent application of the vertical and horizontal distribution patterns of sedimentary diagenetic facies. In contrast, well logging and seismic data offer advantages such as high spatial coverage, widespread availability, and relatively low cost. Therefore, core analysis data can be used to calibrate well logging and seismic data for joint seismic-geological prediction of the spatial distribution of sedimentary diagenetic facies. This approach can compensate for the insufficient spatial resolution of geophysical methods, expand the spatial coverage of sedimentary diagenetic facies research, and ultimately be more effectively applied to reservoir geological evaluation. Therefore, considering the distribution characteristics of sedimentary lithologic facies, a combined seismic and geological evaluation is used as a prerequisite for the above research methods, in order to provide scientific guidance for oil and gas exploration based on the distribution characteristics of sedimentary lithologic facies in the study area. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a combined sedimentary lithologic facies seismic-geological prediction method to overcome or at least partially solve the above problems.

[0004] According to one aspect of the present invention, a combined sedimentary diagenetic facies seismic-geological prediction method is provided, the prediction method comprising:

[0005] Step S1: Determine the reservoir parameters and diagenetic facies type corresponding to the reservoir core thin section;

[0006] Step S2: Determine the pore structure type of various diagenetic facies;

[0007] Step S3: Classify the sedimentary diagenetic facies into favorable sedimentary diagenetic facies and less favorable sedimentary diagenetic facies;

[0008] Step S4: Establish a linear multivariate discriminant function for sedimentary diagenetic facies;

[0009] Step S5: Determine the distribution characteristics of sedimentary diagenetic facies in a single well;

[0010] Step S6: Calculate the rock physical elastic parameters for various wells;

[0011] Step S7: Screen sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies;

[0012] Step S8: Determine the spatial distribution range of favorable and less favorable sedimentary diagenetic facies.

[0013] Optionally, step S1: determining the reservoir parameters and diagenetic facies type corresponding to the reservoir core section specifically includes:

[0014] Core sections of various sedimentary facies zones and various lithofacies reservoirs were analyzed using polarized light microscopy.

[0015] Observe the reservoir diagenesis type in the thin section and determine the reservoir parameters corresponding to the thin section;

[0016] Determine the reservoir diagenetic facies type corresponding to the thin section.

[0017] Optionally, the reservoir parameters include: apparent compaction rate, apparent cementation rate, and apparent dissolution rate.

[0018] Optionally, step S2: determining the pore structure type of multiple diagenetic facies specifically includes:

[0019] Mercury intrusion porosimetry and nuclear magnetic resonance spectroscopy were performed on samples of various diagenetic facies to clarify the pore structure types of multiple diagenetic facies.

[0020] Optionally, step S3: classifying sedimentary diagenetic facies into favorable and less favorable sedimentary diagenetic facies specifically includes:

[0021] Studies on sedimentary characteristics and diagenetic characteristics;

[0022] Based on core observations, well logging data, and well logging data, the sedimentary diagenetic facies were named SDF according to the characteristics of sedimentary facies, lithofacies, pore structure, and diagenetic facies.

[0023] Based on the analysis of physical properties, pore throat and nuclear magnetic resonance characteristics, sedimentary diagenetic facies are classified into favorable sedimentary diagenetic facies and secondary favorable sedimentary diagenetic facies.

[0024] Optionally, step S4: establishing a linear multivariate discriminant function for sedimentary diagenetic facies specifically includes:

[0025] Based on the principles of core calibration logging, establish the relationship between logging response and sedimentary diagenetic facies;

[0026] Fisher discriminant analysis was performed on sedimentary diagenetic facies types to establish linear multivariate discriminant functions for sedimentary diagenetic facies.

[0027] Optionally, step S5: clarifying the distribution characteristics of sedimentary diagenetic facies in a single well specifically includes:

[0028] Based on the discriminant function, a program for quantitative classification of sedimentary diagenetic facies was designed and written, forming a well logging identification program for sedimentary diagenetic facies.

[0029] Single-well batch prediction of sedimentary diagenetic facies;

[0030] The distribution characteristics of sedimentary diagenetic facies in a single well were clarified.

[0031] Optionally, step S6: calculating the rock physical elastic parameters of various wells specifically includes:

[0032] For wells with measured shear wave velocities VS, the Xu-White method was used to predict the shear wave velocity VS', and the predicted shear wave velocity was quality controlled.

[0033] The rock physical elastic parameters of each well were calculated.

[0034] Optionally, the rock physical elastic parameters specifically include: elastic parameter curves for longitudinal wave impedance AI, transverse wave impedance SI, longitudinal / transverse wave velocity ratio VP / VS, Lambe constant Lambda, shear modulus K, bulk modulus Mu, and Young's modulus E.

[0035] Optionally, step S7: screening sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies specifically includes:

[0036] Histogram and cross-plot analysis were performed on various parameters and sedimentary diagenetic facies.

[0037] Screening for sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies.

[0038] Optionally, step S8: determining the spatial distribution range of favorable and less favorable sedimentary diagenetic facies specifically includes:

[0039] The sensitive elastic parameters are inverted using pre-stack waveform indication inversion.

[0040] Determine the spatial distribution range of favorable and less favorable sedimentary diagenetic facies.

[0041] This invention provides a combined seismic and geological prediction method for sedimentary diagenetic facies. The prediction method includes: Step S1: Determining reservoir parameters and diagenetic facies types corresponding to thin sections of reservoir cores; Step S2: Determining the pore structure types of various diagenetic facies; Step S3: Classifying sedimentary diagenetic facies into favorable and less favorable sedimentary diagenetic facies; Step S4: Establishing a linear multivariate discriminant function for sedimentary diagenetic facies; Step S5: Determining the distribution characteristics of sedimentary diagenetic facies in a single well; Step S6: Calculating the rock physical elastic parameters of various wells; Step S7: Screening sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies; Step S8: Determining the spatial distribution range of favorable and less favorable sedimentary diagenetic facies. This provides an effective approach for evaluating the distribution characteristics of sedimentary diagenetic facies in rift basin reservoirs and lays the foundation for the application of sedimentary diagenetic facies distribution characteristics in oil and gas exploration.

[0042] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart of a combined sedimentary diagenetic facies seismic-geological prediction method provided in an embodiment of the present invention. Detailed Implementation

[0045] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0046] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0048] The present invention provides a method for joint prediction of sedimentary and diagenetic facies seismic geology, comprising the following steps:

[0049] Core sections from different sedimentary facies zones and reservoirs with different lithofacies were analyzed using polarized light microscopy. The diagenetic types of the reservoirs were observed in the sections, and the apparent compaction rate, apparent cementation rate, and apparent dissolution rate corresponding to each section were determined, thus identifying the diagenetic facies type. Mercury intrusion porosimetry and nuclear magnetic resonance (NMR) tests were performed on samples of different diagenetic facies to clarify the pore structure types of different diagenetic facies. Based on the study of sedimentary and diagenetic characteristics, combined with core observations and well logging data, sedimentary diagenetic facies were named SDF according to the characteristics of sedimentary facies + lithofacies + pore structure + diagenetic facies. Combining physical properties, pore throat, and NMR characteristics, sedimentary diagenetic facies were classified into favorable and less favorable sedimentary diagenetic facies. According to the principle of "core-calibrated logging," the relationship between logging response and sedimentary diagenetic facies was established. Fisher discriminant analysis was performed on sedimentary diagenetic facies types to establish linear multivariate discriminant functions for sedimentary diagenetic facies. The established discriminant function was programmed and designed to quantitatively classify sedimentary diagenetic facies, forming a well logging identification program for sedimentary diagenetic facies. Single-well batch predictions of sedimentary diagenetic facies were performed to clarify their distribution characteristics in individual wells. Wells with measured shear wave velocities (VS) were selected, and the Xu-White method was applied to predict VS'. Quality control was performed on the predicted VS velocities. Then, other elastic parameter curves for each well were calculated, including P-wave impedance AI, S-wave impedance SI, P-wave / S-wave velocity ratio VP / VS, Lambda constant, shear modulus K, bulk modulus Mu, and Young's modulus E, among other rock physical elastic parameters. Histograms and cross-plots were performed on each parameter and the sedimentary diagenetic facies to screen sensitive elastic parameters that could distinguish between favorable and less favorable sedimentary diagenetic facies. Pre-stack waveform inversion was applied to invert the sensitive elastic parameters, ultimately determining the spatial distribution range of favorable and less favorable sedimentary diagenetic facies.

[0050] The following are several specific embodiments of the application of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, in a specific embodiment 1 of the present invention, the combined sedimentary lithogenic facies seismic-geological prediction method includes the following steps:

[0053] Step 1: Use a polarizing microscope to analyze core sections of reservoirs with different sedimentary facies and different lithofacies, observe the reservoir diagenesis type in the thin sections, determine the apparent compaction rate, apparent cementation rate and apparent dissolution rate corresponding to the reservoir thin section, and determine the reservoir diagenetic facies type corresponding to the thin section.

[0054] Step 2: Mercury intrusion porosimetry and nuclear magnetic resonance (NMR) tests were performed on samples of different diagenetic facies to clarify the pore structure types of different diagenetic facies.

[0055] Step 3: Through the study of sedimentary characteristics and diagenetic characteristics, combined with core observation and well logging data, the sedimentary diagenetic facies are named SDF according to the characteristics of sedimentary facies + lithofacies + pore structure + diagenetic facies. Combined with physical property, pore throat and nuclear magnetic resonance characteristics analysis, the sedimentary diagenetic facies are classified into favorable sedimentary diagenetic facies and secondary favorable sedimentary diagenetic facies.

[0056] Step 4: Based on the principle of "core calibration logging", establish the relationship between logging response and sedimentary diagenetic facies, perform Fisher discriminant analysis on sedimentary diagenetic facies types, and establish a linear multivariate discriminant function for sedimentary diagenetic facies;

[0057] Step 5: Design the programming for the established discrimination function, write a quantitative classification program for sedimentary diagenetic facies, form a well logging identification program for sedimentary diagenetic facies, and perform batch prediction of sedimentary diagenetic facies in single wells to clarify the distribution characteristics of sedimentary diagenetic facies in single wells.

[0058] Step 6: Select wells with measured shear wave velocity VS, apply the Xu-White method to predict the shear wave velocity VS', and perform quality control on the predicted shear wave velocity. Then, calculate a series of rock physical elastic parameters for each well, such as P-wave impedance AI, shear wave impedance SI, P-wave / S-wave velocity ratio VP / VS, Lambe constant Lambda, shear modulus K, bulk modulus Mu, and Young's modulus E.

[0059] Step 7: Perform histogram and cross-plot analysis on each parameter and sedimentary diagenetic facies to screen sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies.

[0060] Step 8: Apply pre-stack waveform indicative inversion to invert sensitive elastic parameters, and finally determine the spatial distribution range of favorable and secondary favorable sedimentary diagenetic facies.

[0061] Example 2

[0062] In a specific embodiment 2 of the present invention, the sedimentary diagenetic facies seismic-geological joint prediction method of the present invention includes:

[0063] In step 1, core thin sections of reservoirs from different sedimentary facies zones and lithological reservoirs were analyzed using a polarizing microscope. The diagenetic type of the reservoir was observed in the thin sections, and the apparent compaction rate, apparent cementation rate, and apparent dissolution rate corresponding to the reservoir thin sections were determined, thus identifying the diagenetic facies type corresponding to each thin section. A total of eight sub-categories of diagenetic facies were identified: weakly compacted, weakly dissolved, and weakly cemented diagenetic facies; strongly compacted, strongly dissolved, and weakly cemented diagenetic facies; strongly compacted, weakly dissolved, and weakly cemented diagenetic facies; strongly compacted, weakly dissolved, and strongly cemented diagenetic facies; and compacted and dense diagenetic facies.

[0064] In step 2, mercury intrusion porosimetry (MIP) was performed on eight sub-types of diagenetic facies samples to clarify the MIP curve characteristics of different types of diagenetic facies. Specifically, the pore-throat type of the weakly compacted, weakly dissolved, and weakly cemented diagenetic facies and the strongly compacted, strongly dissolved, and weakly cemented diagenetic facies was identified as large-pore-narrow-throat; the pore-throat type of the strongly compacted, moderately dissolved, and weakly cemented diagenetic facies and the moderately compacted, weakly dissolved, and weakly cemented diagenetic facies was identified as relatively large-pore-narrow-throat; the pore-throat type of the strongly compacted, weakly dissolved, and weakly cemented diagenetic facies and the compacted dense facies was identified as small-pore-narrow-throat; and the pore-throat type of the strongly compacted, weakly dissolved, and strongly cemented diagenetic facies and the compacted dense facies was identified as large-pore-micro-throat. Simultaneously, nuclear magnetic resonance (NMR) tests were performed on the eight sub-types of diagenetic facies samples to clarify the NMR spectral characteristics of different types of diagenetic facies. Among them, the pore throat types of the weakly compacted, weakly dissolved, and weakly cemented lithogenic facies and the strongly compacted, strongly dissolved, and weakly cemented lithogenic facies are single-peak large-pore type; the pore throat types of the strongly compacted, moderately dissolved, and weakly cemented lithogenic facies and the moderately compacted, weakly dissolved, and weakly cemented lithogenic facies are double-peak main large-pore type; the pore throat types of the strongly compacted, weakly dissolved, and weakly cemented lithogenic facies and the strongly compacted, weakly dissolved, and moderately cemented lithogenic facies are double-peak main small-pore type; and the pore throat types of the strongly compacted, weakly dissolved, and strongly cemented lithogenic facies and the compacted dense facies are single-peak small-pore type.

[0065] In step 3, through the study of sedimentary characteristics and diagenetic characteristics, combined with core observations and well logging data, the sedimentary diagenetic facies are named according to the characteristics of sedimentary facies + lithofacies + pore structure + diagenetic facies. The sedimentary diagenetic facies are first classified into 8 sub-types: ① tributary river / shallow water delta—gravelly sandstone / medium-fine sandstone—Type I macroporous fine-throat type—weakly compacted, weakly dissolved, weakly cemented diagenetic facies; ② tributary river / shallow water delta—gravelly sandstone / medium-fine sandstone / siltstone—Type I macroporous fine-throat type—strongly compacted, strongly dissolved, weakly cemented diagenetic facies; ③ tributary river / shallow water delta / shoal bar—medium-fine sandstone / siltstone—Type II medium-pore-fine-throat type. ④ Main diagenetic facies: strong compaction, medium dissolution, and weak cementation; ⑤ Floodplain / Floodplain / Floodplain / Fluebar—Middle to fine sandstone / siltstone—Type II medium-pore-narrow throat type—strong compaction, weak dissolution, and weak cementation; ⑥ Floodplain / Floodplain / Fluebar—Fine sandstone / siltstone—Type III small-pore-narrow throat type—strong compaction, weak dissolution, and weak cementation; ⑦ Floodplain / Floodplain / Fluebar—Fine sandstone / siltstone—Type IV small-pore-micro-throat type—strong compaction, weak dissolution, and strong cementation; ⑧ Floodplain / Floodplain / Fluebar—Fine sandstone / siltstone—Type IV small-pore-micro-throat type—compacted and dense diagenetic facies. Based on the eight subcategories of sedimentary lithogenic facies, and according to physical properties, pore throat, and nuclear magnetic resonance characteristics, the eight subcategories of sedimentary lithogenic facies are clustered and summarized into four major categories: SDF1-SDF4. SDF1 includes ①②, SDF2 includes ③④, SDF3 includes ⑤⑥, and SDF4 includes ⑦⑧. These four major categories of sedimentary lithogenic facies are further classified into favorable sedimentary lithogenic facies and less favorable sedimentary lithogenic facies. SDF1 and SDF2 are favorable sedimentary lithogenic facies, while SDF3 and SDF4 are less favorable sedimentary lithogenic facies.

[0066] In step 4, based on the principle of "core calibration logging," the relationship between logging response and sedimentary diagenetic facies is established. Fisher discriminant analysis is performed on the sedimentary diagenetic facies types, and linear multivariate discriminant functions are established for each sedimentary diagenetic facies. The discriminant function expression is as follows:

[0067] SDF1=-1.035SP-0.063GR+9.386AC-0.72CNL+646.785DEN+6.498RT-1184.02

[0068] SDF2=-1.023SP-0.124GR+8.9AC-0.758CNL+646.735DEN+4.808RT-1115.758

[0069] SDF3=-0.921SP-0.083GR+8.819AC-0.587CNL+645.013DEN+4.21RT-1110.12

[0070] SDF4=-1.024SP+0.153GR+8.892AC-0.382CNL+653.768DEN+4.261RT-1154.787

[0071] In step 5, the established discrimination function is programmed and a quantitative classification program for sedimentary diagenetic facies is written to form a well logging identification program for sedimentary diagenetic facies. The sedimentary diagenetic facies are then batch predicted in single wells to clarify the distribution characteristics of sedimentary diagenetic facies in single wells.

[0072] In step 6, wells with measured shear wave velocities VS were selected, and the Xu-White method was applied to predict the shear wave velocity VS'. The predicted shear wave velocity was then quality-controlled, showing a similarity coefficient of 0.89 with the measured velocity, indicating high quality of the predicted shear wave curve. Subsequently, other elastic parameter curves for each well were calculated, including P-wave impedance AI, shear wave impedance SI, P-wave / S-wave velocity ratio VP / VS, Lambe constant Lambda, shear modulus K, bulk modulus Mu, Young's modulus E, and other rock physical elastic parameters.

[0073] In step 7, histograms and cross-plots were performed on each parameter and sedimentary diagenetic facies to screen sensitive elastic parameters that could distinguish between favorable and less favorable sedimentary diagenetic facies. Cross-plot analysis was performed on elastic parameters such as shear modulus, bulk modulus, and shear wave impedance in the study area, along with Poisson's ratio. Poisson's ratio showed good ability to distinguish between favorable and less favorable sedimentary diagenetic facies. Therefore, based on comprehensive analysis, Poisson's ratio was selected as the target parameter for pre-stack elastic inversion.

[0074] In step 8, the Poisson's ratio parameter is inverted using pre-stack waveform indicative inversion to ultimately determine the spatial distribution range of favorable and less favorable sedimentary diagenetic facies. Favorable sedimentary diagenetic facies information is displayed as bright to light colors (red to yellow), while less favorable sedimentary diagenetic facies information is displayed as lighter colors (green).

[0075] Beneficial effects: The combined seismic and geological prediction method for sedimentary diagenetic facies provides an effective approach for evaluating the distribution characteristics of sedimentary diagenetic facies in rift basin reservoirs and lays the foundation for the application of sedimentary diagenetic facies distribution characteristics in oil and gas exploration.

[0076] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for joint prediction of sedimentary and diagenetic facies seismic geology, characterized in that, The prediction method includes: Step S1: Determine the reservoir parameters and diagenetic facies type corresponding to the reservoir core thin section; Step S2: Determine the pore structure type of various diagenetic facies; Step S3: Classify the sedimentary diagenetic facies into favorable sedimentary diagenetic facies and less favorable sedimentary diagenetic facies; Step S4: Establish a linear multivariate discriminant function for sedimentary diagenetic facies; Step S5: Determine the distribution characteristics of sedimentary diagenetic facies in a single well; Step S6: Calculate the rock physical elastic parameters for various wells; Step S7: Screen sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies; Step S8: Determine the spatial distribution range of favorable and less favorable sedimentary diagenetic facies.

2. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 1, characterized in that, Step S1, determining the reservoir parameters and diagenetic facies type corresponding to the reservoir core section, specifically includes: Core sections of various sedimentary facies zones and various lithofacies reservoirs were analyzed using polarized light microscopy. Observe the reservoir diagenesis type in the thin section and determine the reservoir parameters corresponding to the thin section; Determine the reservoir diagenetic facies type corresponding to the thin section.

3. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 2, characterized in that, The reservoir parameters include: apparent compaction rate, apparent cementation rate, and apparent dissolution rate.

4. The method for joint prediction of sedimentary and diagenetic facies seismic geology according to claim 1, characterized in that, Step S2: Determining the pore structure types of various diagenetic facies specifically includes: Mercury intrusion porosimetry and nuclear magnetic resonance spectroscopy were performed on samples of various diagenetic facies to clarify the pore structure types of multiple diagenetic facies.

5. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 1, characterized in that, Step S3: Classifying sedimentary diagenetic facies into favorable and less favorable sedimentary diagenetic facies specifically includes: Studies on sedimentary characteristics and diagenetic characteristics; Based on core observations, well logging data, and well logging data, the sedimentary diagenetic facies were named SDF according to the characteristics of sedimentary facies, lithofacies, pore structure, and diagenetic facies. Based on the analysis of physical properties, pore throat and nuclear magnetic resonance characteristics, sedimentary diagenetic facies are classified into favorable sedimentary diagenetic facies and secondary favorable sedimentary diagenetic facies.

6. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 1, characterized in that, Step S4: Establishing a linear multivariate discriminant function for sedimentary diagenetic facies specifically includes: Based on the principles of core calibration logging, establish the relationship between logging response and sedimentary diagenetic facies; Fisher discriminant analysis was performed on sedimentary diagenetic facies types to establish linear multivariate discriminant functions for sedimentary diagenetic facies.

7. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 1, characterized in that, Step S5: Determining the distribution characteristics of sedimentary diagenetic facies in a single well specifically includes: Based on the discriminant function, a program for quantitative classification of sedimentary diagenetic facies was designed and written, forming a well logging identification program for sedimentary diagenetic facies. Single-well batch prediction of sedimentary diagenetic facies; The distribution characteristics of sedimentary diagenetic facies in a single well were clarified.

8. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 1, characterized in that, Step S6: Calculating the rock physical elastic parameters of various wells specifically includes: For wells with measured shear wave velocities VS, the Xu-White method was used to predict the shear wave velocity VS', and the predicted shear wave velocity was quality controlled. The rock physical elastic parameters of each well were calculated.

9. The method for joint prediction of sedimentary diagenetic facies and seismic geology according to claim 8, characterized in that, The specific rock physical elastic parameters include: elastic parameter curves for longitudinal wave impedance AI, transverse wave impedance SI, longitudinal / transverse wave velocity ratio VP / VS, Lambe constant Lambda, shear modulus K, bulk modulus Mu, and Young's modulus E.

10. The method for joint prediction of sedimentary and diagenetic facies seismic geology according to claim 1, characterized in that, Step S7: Screening sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies specifically includes: Histogram and cross-plot analysis were performed on various parameters and sedimentary diagenetic facies. Screening for sensitive elastic parameters that can distinguish between favorable and less favorable sedimentary diagenetic facies.

11. The method for joint prediction of sedimentary and diagenetic facies seismic geology according to claim 1, characterized in that, Step S8, determining the spatial distribution range of favorable and less favorable sedimentary diagenetic facies, specifically includes: The sensitive elastic parameters are inverted using pre-stack waveform indication inversion. Determine the spatial distribution range of favorable and less favorable sedimentary diagenetic facies.

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