Multi-parameter based reservoir permeability prediction method

Abstract

The present invention provides a method for predicting reservoir permeability based on multiple parameters. The method for predicting reservoir permeability based on multiple parameters includes: Step 1, there is a strong correlation between the pore-structure coupling parameter obtained by multiplying porosity and pore structure and coupling them together and the shear wave velocity and density parameters. Calculate the pore-structure coupling parameter by establishing a fitting relationship between them; Step 2, establish an approximate relationship between the porosity parameter and the pore-structure coupling parameter based on rock physics theory, and invert the porosity based on the pore-structure coupling parameter; Step 3, on the basis of using the P-wave to S-wave velocity ratio to divide different reservoirs, carry out the prediction of permeability with different pore-permeability relationships. The method for predicting reservoir permeability based on multiple parameters fully considers the influence of pore structure characteristics, ensures that the calculated porosity parameter is more reasonable, and on this basis, fully considers the sensitive elastic parameter characteristics of different types of reservoirs, classifies and constructs a more reasonable pore-permeability relationship, and improves the accuracy of permeability prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data interpretation in petroleum geophysical exploration, and particularly to a method for predicting reservoir permeability based on multiple parameters. Background Art

[0002] Permeability represents the connectivity of the pore space in a rock and is a measure of the ease with which a fluid of a certain viscosity can pass through it. It is an important parameter for characterizing the fluid flow in the subsurface and for finely describing reservoir characteristics, and it plays a guiding role in oil and gas exploration and development. Many scholars have conducted extensive research on reservoir permeability prediction. Nie Jianxin et al. (2004) applied a niche genetic algorithm to carry out joint inversion of reservoir physical properties parameters such as porosity and permeability; Sun (2004) introduced a rock physics model based on the theory of porous media dynamics, and by defining pore structure parameters, quantified the influence of pore structure on the acoustic velocity and permeability of reservoir rocks; Weger et al. (2009) used two thin section parameters, including the perimeter divided by the area value (POA) and the main pore size, to describe the complexity of the pore network and the pore size respectively, and studied the influence of these parameters on the pore-permeability relationship of carbonate rocks; El-Wazeer et al. (2010) further established a static model for reservoir simulation using rock physics-based permeability estimation; Fang Zhilong (2012) based on the fluid-saturated unsaturated porous medium BISQ model, used a hybrid genetic algorithm to invert reservoir physical properties parameters such as porosity, water saturation, and permeability; Ling et al. (2014) qualitatively classified porosity-permeability data according to the main pore types and concluded that macro intergranular pores are the most important contributors to fluid flow (in the absence of fractures); Kaydani et al. (2014) used the multi-gene genetic programming (MGGP) method to predict the permeability values of heterogeneous oil reservoirs in Iran; Valentin et al. (2018) evaluated the rock physics types, fractures, specific surface area, flow units, permeability, and effective porosity of carbonate rocks; Wei et al. (2019) estimated the permeability of rocks based on rock samples using various empirical relationships and divided the rock samples into small digital rocks for visualization. In practical applications, laboratory core analysis and rock physics model-based methods are usually used to obtain reservoir permeability information, but their budget costs are high and their applicable conditions are limited, making it difficult to characterize the distribution law of reservoir permeability on a larger scale. Permeability is closely related to porosity and pore structure characteristics, and different types of reservoirs often have different pore-permeability relationships. However, obtaining permeability information based on seismic inversion has strong uncertainty and instability. How to carry out reservoir type division based on the results of seismic multi-parameter inversion and on this basis achieve stable calculation of permeability is of great significance for improving the prediction accuracy of reservoir permeability.

[0003] In the Chinese patent application with the application number: CN201610888949.9, it involves a multi-parameter prediction method for the permeability of tight sandstone reservoirs. It includes: (1) determining the geological main control factors of permeability in tight sandstone reservoirs, where the geological main control factors include porosity, grain size, and fracture development degree; (2) establishing logging prediction models and seismic prediction models for porosity and grain size; (3) determining the geological main control factors of fracture development degree; (4) establishing a fracture development index model based on the geological main control factors obtained in step (3); (5) establishing a multi-parameter permeability comprehensive prediction model with seismic-geological constraints. The method of this invention can accurately predict the permeability of tight sandstone reservoirs for single wells and in the plane under seismic-geological multi-parameter constraints.

[0004] In the Chinese patent application with the application number: CN201610330618.3, it involves a method and device for determining reservoir permeability. The method includes: selecting reservoir core samples, determining the permeability and nuclear magnetic resonance echo train of the reservoir core samples; inverting the nuclear magnetic resonance echo train of the reservoir core samples to obtain the nuclear magnetic resonance T2 spectrum of the reservoir core samples, calculating the characteristic parameters of the nuclear magnetic resonance T2 spectrum of the reservoir core samples and the porosity of the reservoir core samples; establishing a multivariate statistical relationship between the permeability of the reservoir core samples, the characteristic parameters of the nuclear magnetic resonance T2 spectrum of the reservoir core samples, and the porosity of the reservoir core samples; and determining the permeability of the reservoir according to the multivariate statistical relationship, the characteristic parameters of the T2 spectrum of the reservoir, and the porosity of the reservoir. This invention has the advantages of simplicity, accuracy, and good reliability, and has obvious practical application effects, providing strong technical support for reservoir classification, production capacity prediction, and reservoir modeling.

[0005] In the Chinese patent application with the application number: CN201811432648.0, it involves a method, device, and storage medium for predicting the permeability of reservoir rocks. The method includes: obtaining nuclear magnetic resonance echo data of multiple water-saturated rock samples in the study area; determining the permeability characterization parameters of the study area according to a preset kernel function and the nuclear magnetic resonance echo data; determining the permeability prediction model of the study area according to the permeability characterization parameters; and predicting the permeability of the target reservoir in the study area according to the permeability prediction model. The implementation mode of this application can accurately predict the permeability of reservoir rocks.

[0006] The above prior arts are all quite different from the present invention and fail to solve the technical problems we want to solve. Therefore, we have invented a new multi-parameter-based method for predicting reservoir permeability. Summary of the Invention

[0007] The object of the present invention is to provide a multi-parameter-based method for predicting reservoir permeability that predicts reservoir permeability through multi-parameter information such as reservoir elastic characteristics.

[0008] The object of the present invention can be achieved by the following technical measures: A reservoir permeability prediction method based on multiple parameters, the reservoir permeability prediction method based on multiple parameters includes:

[0009] Step 1, there is a strong correlation between the pore structure coupling parameter obtained by multiplying porosity and pore structure and coupling them together and the shear wave velocity and density parameters. Calculate the pore structure coupling parameter by establishing a fitting relationship between them;

[0010] Step 2, establish an approximate relationship between the porosity parameter and the pore structure coupling parameter based on rock physics theory, and invert the porosity based on the pore structure coupling parameter;

[0011] Step 3, on the basis of using the P-wave to S-wave velocity ratio to divide different reservoirs, carry out permeability prediction of different pore-permeability relationships.

[0012] The object of the present invention can also be achieved by the following technical measures:

[0013] In Step 1, according to rock physics theory, it is known that the two parameters of porosity and pore structure are always coupled together and jointly affect the reservoir characteristics. It is difficult to directly establish a good fitting relationship between the porosity and pore structure parameters and parameters such as velocity and density. However, the pore structure coupling parameter obtained by multiplying porosity and pore structure and coupling them together has a strong correlation with the shear wave velocity and density parameters. Establishing a fitting relationship between the pore structure coupling parameter and the shear wave velocity and density parameters helps to lay a foundation for the inversion of the porosity parameter.

[0014] In Step 1, the relationship formula between the pore structure coupling parameter constructed by the fitting relationship and the shear wave velocity and density is:

[0015] P = a1*(ρ*V s ) k + a2*(ρ*V s ) k-1 +…+ a n (1)

[0016] In the formula, P represents the pore structure coupling parameter, that is, the parameter obtained by multiplying porosity and pore structure and coupling them together, ρ represents density, V s represents the shear wave velocity, a1, a2, …, a n represent the coefficients of the linear fitting formula, and k represents the exponent of the fitting formula.

[0017] In Step 1, accurately calibrate these coefficients according to the logging data measurement results in the actual work area; in the case of obtaining the fitting relationship between the pore structure coupling parameter and the shear wave velocity and density, substitute the data of the actual work area into formula (1) to obtain the result of the pore structure coupling parameter.

[0018] In Step 2, an approximate relationship between the porosity parameter and the pore structure coupling parameter is established based on petrophysical theory, thus avoiding the instability caused by directly separating the porosity and pore structure parameters, and calculating the porosity directly based on the pore structure coupling parameter and the elastic parameter.

[0019] In Step 2, under the guidance of Gassmann theory, the calculation formula for porosity is derived based on the KT model and the DEM model as follows:

[0020]

[0021] In the formula, represents the porosity, P represents the pore structure coupling parameter, μ m represents the bulk modulus of the rock matrix, μ s represents the bulk modulus of the rock in the case of saturated fluid, represents the high-order error term related to the porosity.

[0022] In Step 2, μ m is obtained by statistically analyzing the mineral modulus and composition of the target layer in the work area, and μ s is calculated through the shear wave velocity and density parameters obtained from actual logging data or inverted from seismic data. The specific calculation formula is as follows:

[0023] μ s = ρ * V s 2 (3)

[0024] In the formula, V s and ρ represent the shear wave velocity and density in the logging data respectively.

[0025] In Step 2, represents the high-order error term in the porosity calculation process. When the accuracy of the calculation result cannot meet the requirements, the influence of the high-order term needs to be fully considered. This high-order term has a good fitting relationship with the pore structure coupling parameter. Therefore, the pore structure coupling parameter calculated above is brought into the fitting relationship between and the pore structure coupling parameter for calculation.

[0026] In Step 3, the reservoir type is divided by taking the P-wave to S-wave velocity ratio as the reservoir-sensitive elastic characteristic parameter, and the P-wave to S-wave velocity ratio is used as the bridge to construct the pore-permeability relationship to realize the reasonable construction of the pore-permeability relationship.

[0027] In Step 3, the formula for the constructed pore-permeability relationship is as follows:

[0028]

[0029] In the formula, Perm represents the reservoir permeability, represents porosity, A1, A2, …, A n and B1, B1, …, B n are respectively the pore-permeability relationship coefficients obtained by fitting different types of reservoirs. N represents the fitting exponent of porosity and permeability when the P-wave to S-wave velocity ratio is in the range of (m1, n1), M represents the fitting exponent of porosity and permeability when the P-wave to S-wave velocity ratio is in the range of (m2, n2), and V p / V s represents the P-wave to S-wave velocity ratio. m1, m2, n1, and n2 respectively represent the value ranges of the P-wave to S-wave velocity ratios of different types of reservoirs.

[0030] In step 3, formula (4) elaborates on the process of constructing the pore-permeability relationship classification based on the P-wave to S-wave velocity ratio. In the actual work area, these coefficients and value ranges are accurately calibrated according to the logging data measurement results, or divided into more types according to actual needs.

[0031] The reservoir permeability prediction method based on multiple parameters in the present invention fully considers the influence of pore structure characteristics, ensures that the calculated porosity parameters are more reasonable, and on this basis, fully considers the sensitive elastic parameter characteristics of different types of reservoirs, divides the reservoirs into different types, and constructs the pore-permeability relationship by classification, making the pore-permeability relationship more reasonable, thereby improving the accuracy of permeability prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the actual logging parameter curve graph in a specific embodiment of the present invention;

[0033] Figure 2 is the schematic diagram of pore structure coupling parameters in a specific embodiment of the present invention;

[0034] Figure 3 is the schematic diagram of the high-order error term related to porosity in a specific embodiment of the present invention;

[0035] Figure 4 is the schematic diagram of the porosity inversion result in a specific embodiment of the present invention;

[0036] Figure 5 is the schematic diagram of the P-wave to S-wave velocity ratio in a specific embodiment of the present invention;

[0037] Figure 6 is the schematic diagram of the permeability prediction result in a specific embodiment of the present invention;

[0038] Figure 7 is the flowchart of a specific embodiment of the reservoir permeability prediction method based on multiple parameters of the present invention;

[0039] Figure 8Crossplot of pore structure coupling parameter with shear wave velocity and density in a specific embodiment of the present invention;

[0040] Figure 9 Comparison chart of porosity inversion result and measured result in a specific embodiment of the present invention;

[0041] Figure 10 Seismic profile for predicting permeability in a specific embodiment of the present invention. Detailed implementation manners

[0042] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, and / or combinations thereof.

[0044] The reservoir permeability prediction method based on multi-parameters of the present invention fully considers the coupling effect of porosity and pore structure parameters, constructs different types of pore-permeability relationships using reservoir elastic multi-parameters information, and predicts the reservoir permeability using the corresponding elastic parameters and physical property parameters. Based on the fitting relationship between shear wave impedance and pore structure coupling parameter, porosity is inverted from reservoir elastic parameters, the reservoir types are divided using the shear wave velocity ratio, and corresponding pore-permeability relationships are constructed for different reservoir types, so as to obtain a more reasonable permeability prediction result.

[0045] The following are several specific embodiments of applying the present invention.

[0046] Embodiment 1

[0047] In a specific Embodiment 1 of applying the present invention, as Figure 7 shown, it is a flow chart of the reservoir permeability prediction method based on multi-parameters of the present invention, and the method specifically includes the following steps:

[0048] Step 101, there is a strong correlation between the pore structure coupling parameter obtained by multiplying porosity and pore structure and coupling them together and the shear wave velocity and density parameters. Calculate the pore structure coupling parameter by establishing a fitting relationship between them

[0049] According to rock physics theory, the two parameters of porosity and pore structure are always coupled together and jointly affect reservoir characteristics. It is difficult to directly establish a good fitting relationship between porosity and pore structure parameters and parameters such as velocity and density. However, the pore structure coupling parameter obtained by multiplying porosity and pore structure together has a strong correlation with shear wave velocity and density parameters. Establishing the fitting relationship between the pore structure coupling parameter and shear wave velocity and density parameters helps to lay a foundation for the inversion of porosity parameters. The present invention constructs the relationship between the two through the fitting relationship:

[0050] P = a1*(ρ*V s ) k + a2*(ρ*V s ) k-1 +...+ a n (1)

[0051] In the formula, P represents the pore structure coupling parameter, that is, the parameter obtained by multiplying porosity and pore structure together, ρ represents density, V s represents shear wave velocity, a1, a2,..., a n represent the coefficients of the linear fitting formula, k represents the exponent of the fitting formula, and these coefficients can be accurately calibrated according to the logging data measurement results in the actual work area. In the case of obtaining the fitting relationship between the pore structure coupling parameter and shear wave velocity and density, substituting the data of the actual work area into formula (1) can obtain the result of the pore structure parameter.

[0052] Step 102: Establish an approximate relationship between the porosity parameter and the pore structure coupling parameter based on rock physics theory, and invert the porosity based on the pore structure coupling parameter

[0053] Since the porosity and pore structure parameters are coupled together and jointly affect the elastic characteristics of the reservoir, the two items in the pore structure coupling parameter cannot be directly separated. In order to further study the pore permeability relationship of the reservoir, an approximate relationship between the porosity parameter and the pore structure coupling parameter can be established based on rock physics theory, thereby avoiding the instability caused by directly separating the porosity and pore structure parameters, and directly calculating the porosity based on the pore structure coupling parameter and elastic parameters. The present invention derives the following formula to calculate the porosity under the guidance of the Gas smann theory based on the KT model and the DEM model:

[0054]

[0055] In the formula, represents porosity, P represents the pore structure coupling parameter, μ m represents the bulk modulus of the rock matrix, μ s represents the bulk modulus of the rock in the case of saturated fluid, represents the high-order error term related to porosity.

[0056] μ in the present invention m is obtained by statistically analyzing the mineral modulus and components of the target layer in the work area. μ s is then calculated using the shear wave velocity and density parameters obtained from actual observations or inverted from seismic data. The specific calculation formula is as follows:

[0057] μ s =ρ*V s 2 (3)

[0058] In the formula, V s and ρ respectively represent the shear wave velocity and density in the logging data.

[0059] represents the high-order error term in the porosity calculation process. When the accuracy of the calculation result cannot meet the requirements, the influence of the high-order term needs to be fully considered. This high-order term has a good fitting relationship with the pore structure coupling parameter. Therefore, the pore structure coupling parameter calculated above can be substituted into the fitting relationship between and the pore structure coupling parameter for calculation.

[0060] Step 103: Based on dividing different reservoirs using the shear wave velocity ratio, carry out permeability prediction for different pore-permeability relationships

[0061] Permeability is closely related to porosity, and the pore-permeability relationships of different types of reservoirs (such as mudstone and sandstone) often have certain differences. Making full use of the information of elastic multi-parameter inversion results and constructing pore-permeability relationships according to different reservoir types helps to improve the accuracy of permeability prediction. In the present invention, the shear wave velocity ratio is used as the sensitive elastic characteristic parameter of the reservoir to divide the reservoir types, and the shear wave velocity ratio is used as the bridge for classifying and constructing pore-permeability relationships to achieve the reasonable construction of pore-permeability relationships. The specific formula adopted in the implementation of the present invention is as follows:

[0062]

[0063] In the formula, Perm represents the reservoir permeability, represents the porosity, A1, A2,..., A n and B1, B1,..., B n are respectively the pore-permeability relationship coefficients fitted for different types of reservoirs. N represents the porosity-permeability fitting index when the shear wave velocity ratio is within the range of (m1, n1), M represents the porosity-permeability fitting index when the shear wave velocity ratio is within the range of (m2, n2), V p / V sDenote the P-wave to S-wave velocity ratio. m1, m2, n1, and n2 respectively represent the value ranges of the P-wave to S-wave velocity ratios of different types of reservoirs. The above formula elaborates on the process of classifying and constructing the porosity-permeability relationship based on the P-wave to S-wave velocity ratio. In an actual work area, these coefficients and value ranges can be accurately calibrated according to the measurement results of well logging data, or can be divided into more types according to actual needs.

[0064] Example 2

[0065] In a specific Example 2 of applying the present invention, actual data from a certain area is used to test the experimental effect of the present invention. The longitudinal wave velocity V of the actual well logging data P , the transverse wave velocity V s and the density ρ (such as Figure 1 ).

[0066] By constructing a fitting relationship between the transverse wave velocity and density and the pore-structure coupling parameter, the pore-structure coupling parameter P (such as Figure 2 ) is calculated using the fitting relationship.

[0067] Substitute the pore-structure coupling parameter P, the saturated rock shear modulus μ s calculated based on the P-wave and S-wave velocities and density, the rock matrix shear modulus μ m obtained based on the statistical analysis of the mineral components of the target layer, and the higher-order term (such as Figure 3 ) calculated from the pore-structure coupling parameter P into the porosity calculation formula respectively, and a relatively reasonable porosity (such as Figure 4 ) can be obtained.

[0068] Use the P-wave to S-wave velocity ratio (such as Figure 5 ) as the basis to construct different porosity-permeability relationships, so as to achieve reasonable prediction of permeability (such as Figure 6 ).

[0069] Example 3

[0070] In a specific Example 3 of applying the present invention, actual data from a certain area is used to test the experimental effect of the present invention. Conduct crossplot analysis (such as Figure 8 ) on the pore-structure coupling parameter in the well logging data with the transverse wave velocity and density, and obtain the fitting relationship between the pore-structure coupling parameter and the transverse wave velocity and density:

[0071] P = 0.0422 * (ρ * V s ) 2 - 0.7475 * (ρ * V s ) + 3.2607 (5)

[0072] In the formula, P represents the pore-structure coupling parameter, ρ represents the density, and V sIt represents the shear wave velocity. Different coefficients will be obtained using different data. The coefficients of the present invention are only applicable to the test work area. For other work areas, fitting based on actual data is required to obtain them.

[0073] Using the pore structure coupling parameters obtained in the previous step, combined with the calculated μ m and the inverted μ s , substituting them into formula (2) can further invert to obtain the corresponding porosity (such as Figure 9 ).

[0074] Based on the measured core porosity and permeability data, the pore-permeability relationship of the reservoir in the work area is divided into two categories, and different pore-permeability relationships are obtained through classification fitting:

[0075] Type I pore-permeability relationship:

[0076]

[0077] Type II pore-permeability relationship:

[0078]

[0079] After dividing the reservoir into two categories using the shear wave velocity ratio, after obtaining the porosity data, the corresponding permeability can be obtained using the corresponding pore-permeability relationship (such as Figure 10 ).

[0080] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0081] Except for the technical features described in the specification, they are all known technologies to those skilled in the art.

Claims

1. A reservoir permeability prediction method based on multiple parameters, characterized in that, The reservoir permeability prediction method based on multiple parameters includes: Step 1, there is a strong correlation between the pore structure coupling parameter obtained by multiplying porosity and pore structure and coupled together, and the shear wave velocity and density parameters. Calculate the pore structure coupling parameter by establishing the fitting relationship between them. The relationship formula between the pore structure coupling parameter constructed by the fitting relationship and the shear wave velocity and density is: P = a1 * (ρ * V s ) k + a2 * (ρ * V s ) k-1 + … + a n (1) In the formula, P represents the pore structure coupling parameter, that is, the parameter obtained by coupling the porosity and the pore structure after multiplication, ρ represents the density, and V s represents the shear wave velocity, a1, a2, …, a n represent the coefficients of the linear fitting formula, and k represents the exponent of the fitting formula; Step 2, establish an approximate relationship between the porosity parameter and the pore structure coupling parameter based on rock physics theory, and invert the porosity based on the pore structure coupling parameter. Step 3, on the basis of using the P-wave to S-wave velocity ratio to divide different reservoirs, carry out the permeability prediction of different pore-permeability relationships.

2. The reservoir permeability prediction method based on multiple parameters according to claim 1, characterized in that In Step 1, according to rock physics theory, it is known that the two parameters of porosity and pore structure are always coupled together and jointly affect the reservoir characteristics. It is difficult to directly establish a good fitting relationship between the porosity and pore structure parameters and the parameters such as velocity and density. However, the pore structure coupling parameter obtained by multiplying porosity and pore structure and coupled together has a strong correlation with the shear wave velocity and density parameters. Establishing the fitting relationship between the pore structure coupling parameter and the shear wave velocity and density parameters helps to lay a foundation for the inversion of the porosity parameter.

3. The reservoir permeability prediction method based on multiple parameters according to claim 1, wherein In Step 1, accurately calibrate these coefficients according to the logging data measurement results in the actual work area; in the case of obtaining the fitting relationship between the pore structure coupling parameter and the shear wave velocity and density, substitute the data of the actual work area into formula (1) to obtain the result of the pore structure parameter.

4. The reservoir permeability prediction method based on multiple parameters according to claim 1, wherein In Step 2, establish an approximate relationship between the porosity parameter and the pore structure coupling parameter based on rock physics theory, thereby avoiding the instability caused by directly separating the porosity and pore structure parameters, and directly calculate the porosity based on the pore structure coupling parameter and elastic parameters.

5. The reservoir permeability prediction method based on multiple parameters according to claim 4, characterized in that, In Step 2, the formula for calculating porosity under the guidance of Gas smann theory based on the KT model and DEM model is: In the formula, represents porosity, P represents the pore structure coupling parameter, μ m represents the bulk modulus of the rock matrix, μ s represents the bulk modulus of the rock in the case of saturated fluid, represents the high-order error term related to porosity.

6. The reservoir permeability prediction method based on multiple parameters according to claim 5, wherein In step 2, μ m is obtained by statistically analyzing the mineral moduli and components of the target layer in the work area, μ s is then calculated using the shear wave velocity and density parameters obtained from actual observations or inversion from seismic data. The specific calculation formula is as follows: μ s = ρ * V s 2 (3) Where, V s and ρ represent the shear wave velocity and density in the logging data, respectively.

7. The reservoir permeability prediction method based on multiple parameters according to claim 5, wherein In step 2, represents the high-order term of the error in the porosity calculation process. When the accuracy of the calculation result cannot meet the requirements, the influence of the high-order term needs to be fully considered. This high-order term has a good fitting relationship with the pore structure coupling parameter. Therefore, substituting the pore structure coupling parameter calculated above into the fitting relationship between ο(φ n ) and the pore structure coupling parameter for calculation.

8. The reservoir permeability prediction method based on multiple parameters according to claim 1, characterized in that In Step 3, use the P-wave to S-wave velocity ratio as the reservoir-sensitive elastic characteristic parameter to divide the reservoir types, and use the P-wave to S-wave velocity ratio as the bridge for classifying and constructing the pore-permeability relationship to realize the reasonable construction of the pore-permeability relationship.

9. The reservoir permeability prediction method based on multiple parameters according to claim 8, characterized in that In Step 3, the formula for the constructed pore-permeability relationship is as follows: In the formula, Perm represents the reservoir permeability, represents the porosity, A1, A2, …, A n and B1, B1, …, B n are the pore-permeability relationship coefficients obtained by fitting different types of reservoirs respectively. N represents the fitting exponent of porosity and permeability when the P-wave to S-wave velocity ratio is in the range of (m1, n1), M represents the fitting exponent of porosity and permeability when the P-wave to S-wave velocity ratio is in the range of (m2, n2), V p / V s represents the P-wave to S-wave velocity ratio, and m1, m2, n1, n2 represent the value ranges of the P-wave to S-wave velocity ratios of different types of reservoirs respectively.

10. The reservoir permeability prediction method based on multiple parameters according to claim 9, wherein In Step 3, formula (4) describes the process of implementing the classification and construction of the pore-permeability relationship based on the P-wave to S-wave velocity ratio. Accurately calibrate these coefficients and value ranges according to the logging data measurement results in the actual work area, or divide them into more types according to actual needs.

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