A method and device for calculating the permeability of tight oil and gas reservoirs based on fine classification of pores

Abstract

The present application relates to the technical field of tight oil and gas development, and particularly discloses a method and device for calculating the permeability of a tight oil and gas reservoir based on fine classification of pores. The method comprises the following steps: obtaining multiple sets of sample data of multiple samples of the tight oil and gas reservoir, each set of sample data including the characteristic pore size of different types of pores of the corresponding sample and the experimentally measured permeability; based on the multiple sets of sample data, fitting to construct a permeability calculation model representing the correlation between the permeability and the characteristic pore size of each pore type; and based on the permeability calculation model, calculating the permeability of the sample to be evaluated. The present application classifies the pore types finely and determines the characteristic pore size of different types of pores, and constructs a permeability calculation model based on the characteristic pore size of different types of pores by considering the contribution of different types of pores to the permeability, thereby greatly improving the calculation accuracy of the permeability.

Description

Technical Field

[0001] This invention relates to the field of tight oil and gas development technology, specifically to a method and apparatus for calculating the permeability of tight oil and gas reservoirs based on fine pore classification. Background Technology

[0002] Permeability is a key parameter for evaluating tight oil and gas reservoirs. Accurate calculation of permeability is of great guiding significance for evaluating sweet spots in tight oil and gas reservoirs and formulating scientific development policies.

[0003] Traditional methods for calculating the permeability of tight oil and gas reservoirs (especially tight sandstone) mainly rely on high-pressure mercury injection experiments and nuclear magnetic resonance experiments, including the Coates model, the SDR (Schlumberger Doyle Laboratory) model, and the Pittman model. The permeability can be expressed by the following formula (1): (1), Where: K represents permeability, φ represents porosity, and f is a variable; in the Coates model, SDR model, and Pittman model, f represents FFI / BVI (NMR free water to bound water saturation ratio), the geometric mean of NMR T2 spectrum, and r, respectively. i (The pore throat radius corresponding to mercury saturation of i%), A, B, and C are the parameters fitted by the multiple linear regression equation.

[0004] Traditional methods have two main drawbacks: First, poor application results. The pore structure of tight oil and gas reservoirs is extremely complex, and the strong heterogeneity leads to no obvious correlation between porosity ϕ and permeability K, resulting in low accuracy in calculating the permeability of tight oil and gas reservoirs using formula (1). Second, waste of core samples. Traditional methods require a large number of high-pressure mercury intrusion or nuclear magnetic resonance experiments to obtain the pore structure parameters of tight oil and gas reservoir samples, and then establish a permeability calculation model based on these parameters. To ensure the accuracy of the permeability calculation model, the number of analytical tests is usually greater than 10. However, core samples are usually only drilled from exploration wells and appraisal wells, and core samples available for analysis are very scarce. In addition, traditional methods have high requirements for core samples, for example, the core samples need to be made into specific shapes, resulting in a large demand for core samples.

[0005] To overcome the shortcomings of traditional methods and reduce the use of core samples while improving the accuracy of tight oil and gas reservoir permeability calculation, it is necessary to study a new method for calculating tight oil and gas reservoir permeability. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a new method for calculating the permeability of tight oil and gas reservoirs, so as to provide scientific guidance for the efficient development of tight oil and gas.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: According to a first aspect of the present invention, a method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification is provided, comprising the following steps: Multiple sets of sample data were obtained from several samples of tight oil and gas reservoirs. Each set of sample data included the characteristic pore size of different types of pores in the corresponding sample and the experimentally measured permeability. Based on the aforementioned multiple sets of sample data, a permeability calculation model is constructed by fitting to characterize the correlation between permeability and the characteristic pore size of each pore type; Based on the permeability calculation model, the permeability of the sample to be evaluated is calculated.

[0008] According to one embodiment of the present invention, obtaining multiple sets of sample data from multiple samples of tight oil and gas reservoirs includes: Backscatter two-dimensional large-area scanning electron microscopy imaging experiments were carried out on the multiple samples to identify different types of pores in each sample and obtain the pore size distribution curves of each pore type. Based on the pore size distribution curves of each pore type, the characteristic pore size of each pore type is obtained.

[0009] According to one embodiment of the present invention, based on the pore size distribution curves of each pore type, the characteristic pore size of each pore type is obtained, including: Based on the pore size distribution curves of each pore type, the cumulative frequency distribution curves of each pore type are obtained; Based on the cumulative frequency distribution curves of each pore type, the characteristic pore size of each pore type is obtained. According to one embodiment of the present invention, the characteristic pore size is the pore size corresponding to the 50% cumulative frequency distribution curve.

[0010] According to one embodiment of the present invention, obtaining multiple sets of sample data of multiple samples of tight oil and gas reservoirs includes: before conducting backscatter two-dimensional large-area scanning electron microscopy imaging experiments on the multiple samples, sequentially performing oil / salt washing treatment, mechanical grinding treatment and argon ion lithography treatment on the multiple samples.

[0011] According to one embodiment of the present invention, the oil / salt washing treatment includes the following steps: The sample was placed in an oil washing instrument, and organic solvents were used to wash away the residual oil and mud from the sample. Place the washed oil sample into a crucible, repeatedly add distilled water and boil to wash away the residual salt in the sample; After washing with salt, the sample is placed in an oven to dry and remove the water from the sample.

[0012] According to one embodiment of the present invention, mechanical grinding process makes the sample flatness less than 1 μm.

[0013] According to one embodiment of the present invention, argon ion polishing results in a maximum sample diameter of 38 mm and a maximum processing area diameter of 25 mm.

[0014] According to one embodiment of the present invention, different types of pores include intergranular pores, dissolution pores, and intergranular pores.

[0015] According to one embodiment of the present invention, based on the multiple sets of sample data, a permeability calculation model is constructed by fitting to characterize the correlation between permeability and the characteristic pore size of each pore type, including: using experimentally measured permeability as the dependent variable, and using the average characteristic pore size of intergranular pores, dissolution pores and intergranular pores as independent variables, and constructing a permeability calculation model by fitting.

[0016] According to an embodiment of the present invention, the constructed permeability calculation model is represented by the following formula:

[0017] Where: K Cal Calculate the permeability (mD) and R for the model. Intra-50% RD iso-50% and R Inter-50% , respectively, represent the characteristic pore sizes of intergranular pores, dissolution pores, and intergranular pores, in nm; A and B are fitting coefficients, and C is a constant, dimensionless.

[0018] According to one embodiment of the present invention, the permeability of the sample to be evaluated is calculated based on the permeability calculation model, including: Obtain the characteristic pore sizes of each pore type in the sample to be evaluated; The permeability of the sample to be evaluated is calculated by substituting the characteristic pore sizes of each pore type in the sample into the permeability calculation model.

[0019] According to one embodiment of the present invention, the tight oil and gas reservoir is tight sandstone.

[0020] According to a second aspect of the present invention, a device for calculating the permeability of tight oil and gas reservoirs based on fine pore classification is provided, comprising: The sample data acquisition module is used to acquire multiple sets of sample data from multiple samples of tight oil and gas reservoirs. Each set of sample data includes the characteristic pore size of different types of pores in the corresponding sample and the experimentally measured permeability. The permeability calculation model construction module is used to construct a permeability calculation model that characterizes the correlation between permeability and the characteristic pore size of each pore type based on the multiple sets of sample data obtained by the sample data acquisition module. The permeability calculation module is used to calculate the permeability of the sample to be evaluated based on the permeability calculation model constructed by the permeability calculation model construction module.

[0021] By adopting the above technical solution, the present invention has at least the following beneficial technical effects: The permeability calculation method for tight oil and gas reservoirs based on fine pore classification provided by this invention improves the accuracy of permeability calculation by finely classifying pore types, determining the characteristic pore size of different types of pores, and considering the contribution of different types of pores to permeability to construct a permeability calculation model based on the characteristic pore size of different types of pores. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating the method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification provided by this invention; Figure 2 A block diagram of the tight oil and gas reservoir permeability calculation device based on fine pore classification provided by the present invention; Figure 3 The curves show the intergranular pore size distribution and cumulative frequency distribution in sample 1. Figure 4 The curves show the pore size distribution and cumulative frequency distribution of the dissolution pores in sample 1. Figure 5 The curves show the intergranular pore size distribution and cumulative frequency distribution in sample 1. Figure 6 This is a cross-plot of permeability versus median pore radius within the grain; Figure 7 This is a cross-plot of permeability and the average median radius of (intergranular pores + dissolution pores); Figure 8 A cross-plot of the permeability calculated by the model and the permeability measured experimentally is generated. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to specific examples and the accompanying drawings.

[0025] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0026] According to a first aspect of the present invention, a method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification is provided, such as... Figure 1 As shown, the method includes the following steps: S1: Obtain multiple sets of sample data from multiple samples of tight oil and gas reservoirs. Each set of sample data includes the characteristic pore size of different types of pores in the corresponding sample and the experimentally measured permeability. S2: Based on multiple sets of sample data, a permeability calculation model is constructed by fitting to characterize the correlation between permeability and the characteristic pore size of each pore type; S3: Calculate the permeability of the sample to be evaluated based on the permeability calculation model.

[0027] The permeability calculation method for tight oil and gas reservoirs based on fine pore classification provided by this invention improves the accuracy of permeability calculation by finely classifying pore types, determining the characteristic pore size of different types of pores, and considering the contribution of different types of pores to permeability to construct a permeability calculation model based on the characteristic pore size of different types of pores.

[0028] In some embodiments, step S1, acquiring multiple sets of sample data from multiple samples of tight oil and gas reservoirs, includes: conducting backscattered two-dimensional large-area scanning electron microscopy (SEM) imaging experiments on multiple samples, identifying different types of pores for each sample and obtaining pore size distribution curves for each pore type; and obtaining characteristic pore sizes for each pore type based on the pore size distribution curves. For example, conducting backscattered two-dimensional large-area scanning electron microscopy imaging experiments on samples, using ImageJ software, identifying different types of pores, and calculating and obtaining pore size distribution curves for different types of pores. The "pore size distribution curve" mentioned here is also called the "frequency distribution curve," which shows the distribution of pore sizes within each pore size range by mapping the number of pore sizes in different pore size ranges to the pore size range. The horizontal axis of the pore size distribution curve represents the pore size range, and the vertical axis represents the number of pore sizes within that pore size range. The "characteristic pore size" mentioned here refers to a representative statistical characteristic value of pore size used to accurately describe the size distribution of the corresponding pore type. In this invention, by conducting backscattered two-dimensional large-area scanning electron microscopy imaging experiments on samples, the pore size distribution range of different types of pores can be quantitatively characterized. Compared to traditional methods, the scanning electron microscopy imaging method has lower requirements for core samples, requiring only a small amount of core samples to carry out the experiment, thus reducing the use of core samples.

[0029] In some embodiments, the characteristic pore size of each pore type is obtained based on the pore size distribution curves of each pore type, including: obtaining the cumulative frequency distribution curve of each pore type based on the pore size distribution curves of each pore type; and obtaining the characteristic pore size of each pore type based on the cumulative frequency distribution curves of each pore type. The "cumulative frequency distribution curve" mentioned here, also called the "cumulative distribution curve," is a curve plotting the cumulative percentage of pore size as a function of pore size, arranged in ascending order. The horizontal axis of the cumulative frequency distribution curve represents the pore size, and the vertical axis represents the cumulative percentage of pore size. In this invention, by obtaining the cumulative frequency distribution curve, the characteristic pore size of the corresponding pore type can be conveniently and quickly determined. In some embodiments, the characteristic pore size is the pore size corresponding to the 50% cumulative frequency distribution curve.

[0030] In some embodiments, different types of pores include intergranular pores, dissolution pores, and intergranular pores. Intergranular pores, also known as intragranular pores, mainly form between carbonate crystals and are formed due to recrystallization; therefore, the pores are usually relatively regular. Dissolution pores are pores formed by the dissolution of matrix, cement, etc., between particles, and mainly include intergranular dissolution pores, rock fragment dissolution pores, and casting pores. Intergranular pores refer to the spaces between particles that are not filled with mud or cement when the particle content in the rock is predominant (greater than 50-60%).

[0031] In some embodiments, step S1, acquiring multiple sets of sample data from multiple samples of tight oil and gas reservoirs, further includes: sequentially performing oil / salt washing, mechanical grinding, and argon ion polishing on the multiple samples before conducting scanning electron microscopy (SEM) imaging experiments. Performing these sample processing steps before SEM imaging helps improve the accuracy of particle size measurement and prevents interference.

[0032] In some embodiments, the oil / salt washing process includes the following steps: placing the sample in an oil washing instrument and using an organic solvent to wash away residual oil and mud from the sample; placing the oil-washed sample in a crucible and repeatedly adding distilled water to boil, washing away residual salt from the sample; and placing the salt-washed sample in an oven to dry, removing water from the sample. For example, first, the sample is placed in a high-temperature, high-pressure oil washing instrument and using an organic solvent to wash away residual oil and mud from the core; then, the oil-washed sample is placed in a crucible and repeatedly adding distilled water to boil, washing away residual salt from the sample; finally, the salt-washed sample is placed in an oven and the temperature is raised to 100°C to remove water from the sample.

[0033] In some embodiments, mechanical grinding is used to make the sample flatness less than 1 μm.

[0034] In some embodiments, argon ion polishing results in a maximum sample diameter of 38 mm and a maximum processing area diameter of 25 mm.

[0035] In some embodiments, step S1 involves acquiring multiple sets of sample data from multiple samples of a tight oil and gas reservoir, including: measuring the permeability of multiple samples experimentally. For example, the permeability of multiple samples can be measured using high-pressure mercury intrusion porosimetry and nuclear magnetic resonance (NMR) experiments.

[0036] In some embodiments, in step S2, based on multiple sets of sample data, a permeability calculation model is constructed by fitting to characterize the correlation between permeability and the characteristic pore size of each pore type, including: using experimentally measured permeability as the dependent variable, and using the characteristic pore size of intergranular pores and the average characteristic pore size of dissolution pores and intergranular pores as independent variables, and constructing a permeability calculation model by fitting.

[0037] In some embodiments, the constructed permeability calculation model is represented by the following equation (2): (2), Where: K Cal Calculate the permeability (mD) and R for the model. Intra-50% RD iso-50% and R Inter-50% , respectively, represent the characteristic pore sizes of intergranular pores, dissolution pores, and intergranular pores, in nm; A and B are fitting coefficients, and C is a constant, dimensionless.

[0038] In some embodiments, step S3, based on a permeability calculation model, calculates the permeability of the sample to be evaluated, including: obtaining the characteristic pore sizes of each pore type in the sample to be evaluated; substituting the characteristic pore sizes of each pore type in the sample to be evaluated into the permeability calculation model to calculate the permeability of the sample to be evaluated. The characteristic pore sizes of each pore type in the sample to be evaluated can be obtained through backscattered two-dimensional large-area scanning electron microscopy imaging experiments.

[0039] The method for calculating the permeability of tight oil and gas reservoirs provided by this invention is applicable to tight sandstone. The method is also applicable to other types of tight oil and gas reservoirs.

[0040] According to a second aspect of the present invention, a device for calculating the permeability of tight oil and gas reservoirs based on fine pore classification is provided, such as... Figure 2As shown, the device includes: a sample data acquisition module 10, used to acquire multiple sets of sample data from multiple samples of tight oil and gas reservoirs, each set of sample data including the characteristic pore size of different types of pores in the corresponding sample and the experimentally measured permeability; a permeability calculation model construction module 20, used to construct a permeability calculation model characterizing the correlation between permeability and the characteristic pore size of each pore type based on the multiple sets of sample data acquired by the sample data acquisition module 10; and a permeability calculation module 30, used to calculate the permeability of the sample to be evaluated based on the permeability calculation model constructed by the permeability calculation model construction module 20.

[0041] The tight oil and gas reservoir permeability calculation device based on fine pore classification provided by this invention improves the accuracy of permeability calculation by finely classifying pore types, determining the characteristic pore size of different types of pores, and considering the contribution of different types of pores to permeability to construct a permeability calculation model based on the characteristic pore size of different types of pores.

[0042] To make the technical means and objectives of this invention easier to understand, the invention will be further described below with reference to specific embodiments.

[0043] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0044] Reference Figures 3 to 8 This embodiment adopts the following technical solution: a method for calculating the permeability of tight sandstone based on fine pore classification, including the following steps: Step 1: Conduct backscatter two-dimensional large-area scanning electron microscopy imaging experiments on the sample. Use ImageJ software to identify different types of pores and calculate the pore size distribution curves of different types of pores.

[0045] Before scanning electron microscopy experiments, dense sandstone samples need to be subjected to oil / salt washing, mechanical grinding, and argon ion polishing in sequence.

[0046] The oil / salt washing process and method are as follows: First, the sample is placed in a high-temperature and high-pressure oil washing instrument, and organic solvents are used to wash away the residual oil and mud in the core; then, the oil-washed sample is placed in a crucible, and distilled water is repeatedly added and boiled to wash away the residual salt in the sample; finally, the salt-washed sample is placed in an oven, and the temperature is raised to 100℃ to remove the water from the sample.

[0047] The flatness of mechanical grinding should be less than 1µm.

[0048] The maximum diameter of the sample in the argon ion cutting experiment was 38 mm, and the maximum diameter of the processed area was 25 mm.

[0049] Backscatter scanning electron microscopy experimental conditions: temperature 25℃, pressure 0.101MPa.

[0050] Step 2: Determine the median radius of different porosity types based on the cumulative frequency curves. The different porosity types refer to intergranular pores, dissolution pores, and intergranular pores, respectively.

[0051] Taking sample number 1 as an example, such as Figure 3-5 The figures show the pore size distribution and cumulative frequency distribution curves for intergranular pores, dissolution pores, and intergranular pores, respectively. The pore radius corresponding to the 50% cumulative pore volume curve is taken as the boundary value and denoted as the median radius. The median radii of intergranular pores, dissolution pores, and intergranular pores in sample 1 are 31.5 nm, 246.4 nm, and 1412.2 nm, respectively.

[0052] Step 3: Establish a permeability calculation model for tight sandstone based on the median radius of different types of pores.

[0053] like Figure 6 and Figure 7 As shown, the experimentally measured permeability has a good correlation with the average value of the median radius of intragranular pores and the median radius of (dissolution pores and intergranular pores), with correlation coefficients R² of 0.8801 and 0.8399, respectively. Therefore, a permeability calculation model based on these two parameters can be established.

[0054] With experimentally measured permeability as the dependent variable and the average of the median radii of intragranular pores and the median radii of (dissolution pores and intergranular pores) as the independent variables, the permeability calculation model can be expressed by the following formula: , Where: K Cal Calculate the permeability (mD) and R for the model. Intra-50% R Diso-50% and R Inter-50% , respectively, are the median radii of intergranular pores, dissolution pores, and intergranular pores, i.e., the pore radii corresponding to the 50% cumulative pore volume curve, in nm; A and B are the formula fitting coefficients, and C is a constant, dimensionless.

[0055] Table 1 shows the experimental parameters required to construct the permeability calculation model, as well as the permeability calculation results.

[0056] Table 1. Experimental parameters required for constructing the permeability calculation model and permeability calculation results.

[0057] In the table: R Intra-50% R Diso-50% and R Inter-50% K represents the median radius of intergranular pores, dissolution pores, and intergranular pores, respectively, which corresponds to the pore radius corresponding to the 50% cumulative pore volume curve, in nm; Exp To experimentally measure permeability, K Cal Calculate the permeability, mD, for the model.

[0058] Based on the data in Table 1, the fitting coefficients A and B, and the constant C were determined to be -1.3620, 0.4905, and 0.7279, respectively.

[0059] Therefore, the penetration rate calculation model can be expressed as:

[0060] like Figure 8 As shown, the cross curve between the permeability calculated by the model and the permeability measured by experiments is shown. The correlation coefficient R² is as high as 0.9819, indicating a very strong correlation. Moreover, the average absolute error is only 11.7%, which proves the accuracy of the model's calculation.

[0061] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. The sequence numbers of the disclosed embodiments of this invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0062] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification, characterized in that, Includes the following steps: Multiple sets of sample data were obtained from several samples of tight oil and gas reservoirs. Each set of sample data included the characteristic pore size of different types of pores in the corresponding sample and the experimentally measured permeability. Based on the aforementioned multiple sets of sample data, a permeability calculation model is constructed by fitting to characterize the correlation between permeability and the characteristic pore size of each pore type; Based on the permeability calculation model, the permeability of the sample to be evaluated is calculated.

2. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 1, characterized in that, Obtain multiple sets of sample data from various samples of tight oil and gas reservoirs, including: Backscatter two-dimensional large-area scanning electron microscopy imaging experiments were carried out on the multiple samples to identify different types of pores in each sample and obtain the pore size distribution curves of each pore type. Based on the pore size distribution curves of each pore type, the characteristic pore size of each pore type is obtained.

3. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 2, characterized in that, Based on the pore size distribution curves of each pore type, the characteristic pore size of each pore type is obtained, including: Based on the pore size distribution curves of each pore type, the cumulative frequency distribution curves of each pore type are obtained; Based on the cumulative frequency distribution curves of each pore type, the characteristic pore size of each pore type is obtained.

4. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 3, characterized in that, The characteristic pore size is the pore size corresponding to the 50% cumulative frequency distribution curve.

5. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 2, characterized in that, Obtain multiple sets of sample data from various samples of tight oil and gas reservoirs, including: Before conducting backscattered two-dimensional large-area scanning electron microscopy imaging experiments on the multiple samples, the multiple samples were sequentially subjected to oil / salt washing treatment, mechanical grinding treatment, and argon ion photolithography treatment.

6. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 5, characterized in that, The oil / salt washing process includes the following steps: The sample was placed in an oil washing instrument, and organic solvents were used to wash away the residual oil and mud from the sample. Place the washed oil sample into a crucible, repeatedly add distilled water and boil to wash away the residual salt in the sample; After washing with salt, the sample is placed in an oven to dry and remove the water from the sample.

7. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 5, characterized in that, Mechanical grinding ensures that the sample flatness is less than 1 μm, and / or argon ion polishing ensures that the maximum sample diameter is 38 mm and the maximum diameter of the processed area is 25 mm.

8. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 4, characterized in that, Different types of pores include intergranular pores, dissolution pores, and intergranular pores.

9. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 8, characterized in that, Based on the aforementioned multiple sets of sample data, a permeability calculation model is constructed by fitting to characterize the correlation between permeability and the characteristic pore size of each pore type, including: Using experimentally measured permeability as the dependent variable, and the characteristic pore size of intergranular pores, the average characteristic pore size of dissolution pores and intergranular pores as independent variables, a permeability calculation model was constructed by fitting.

10. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 9, characterized in that, The constructed penetration rate calculation model is represented by the following formula: ， Where: K Cal Calculate the permeability (mD) and R for the model. Intra-50% RD iso-50% and R Inter-50% , respectively, represent the characteristic pore sizes of intergranular pores, dissolution pores, and intergranular pores, in nm; A and B are fitting coefficients, and C is a constant, dimensionless.

11. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 1, characterized in that, Based on the aforementioned permeability calculation model, the permeability of the sample to be evaluated is calculated, including: Obtain the characteristic pore sizes of each pore type in the sample to be evaluated; The permeability of the sample to be evaluated is calculated by substituting the characteristic pore sizes of each pore type in the sample into the permeability calculation model.

12. The method for calculating the permeability of tight oil and gas reservoirs based on fine pore classification according to claim 1, characterized in that, The tight oil and gas reservoir is composed of tight sandstone.

13. A device for calculating the permeability of tight oil and gas reservoirs based on fine pore classification, characterized in that, include: The sample data acquisition module is used to acquire multiple sets of sample data from multiple samples of tight oil and gas reservoirs. Each set of sample data includes the characteristic pore size of different types of pores in the corresponding sample and the experimentally measured permeability. The permeability calculation model construction module is used to construct a permeability calculation model that characterizes the correlation between permeability and the characteristic pore size of each pore type based on the multiple sets of sample data obtained by the sample data acquisition module. The permeability calculation module is used to calculate the permeability of the sample to be evaluated based on the permeability calculation model constructed by the permeability calculation model construction module.

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