A method for establishing a pore-permeability model considering microscopic pore throat distribution and a readable storage medium

Abstract

This invention discloses a method for establishing a porosity-permeability model considering the distribution of microscopic pore throats. The method includes: statistically analyzing the results of high-pressure mercury intrusion porosimetry or nuclear magnetic resonance experiments on a first core sample to plot a first relationship chart between the average pore throat radius and atmospheric pressure gas permeability; statistically analyzing the results of overburden pressure porosity-permeability experiments on a second core sample to plot a second relationship chart between atmospheric pressure gas permeability and overburden pressure permeability, and a third relationship chart between overburden pressure permeability and overburden pressure porosity; using the first and second relationship charts, converting the pore throat radius distribution relationship of the target area into an overburden pressure permeability distribution relationship; based on the geological model of the target area, establishing an intermediate random attribute model that follows an average distribution from 0 to 1; using the overburden pressure permeability distribution relationship to obtain the cumulative distribution of overburden pressure permeability, establishing a permeability attribute model; using the third relationship chart, establishing a porosity attribute model; and based on the permeability and porosity attribute models, constructing a mathematical model for numerical simulation studies to quantify the differences in development effects among different types of reservoirs.

Description

Technical Field

[0001] This invention relates to the field of oilfield geological modeling technology, and more specifically, to a method for establishing a pore-permeability model that considers the distribution of microscopic pore throats and a readable storage medium. Background Technology

[0002] Geological modeling is a crucial step in offshore oilfield development planning and forms the basis for subsequent reservoir numerical simulation studies and the prediction of reasonable development indicators. Different types of reservoirs exhibit significant differences in microscopic pore-throat distribution. Even when reservoir properties (porosity, permeability, average pore-throat radius, etc.) are similar, the distribution of these microscopic pores and throats varies, leading to substantial differences in fluid permeability and oil-water / oil-gas two-phase flow characteristics. This severely impacts the development of reasonable reservoir development plans and the prediction of development indicators. Furthermore, in the early stages of offshore oilfield development, there are relatively few exploration wells. For example, in fault-block reservoirs, each block typically has only one exploration well. Conventional methods for establishing porosity-permeability models include homogeneous modeling based on exploration well data and interpolation modeling combining data from neighboring blocks. These methods cannot effectively characterize the differences in pore-throat distribution among different reservoir types, resulting in porosity-permeability models that fail to adequately account for the influence of variations in reservoir property distribution. A Chinese patent application discloses a method for determining reservoir permeability, which operates according to the following steps: Step (1) performing NMR T2 experiments and mercury intrusion porosimetry (MIP) experiments on the core sample and obtaining NMR T2 spectrum and MIP data; Step (2) establishing pore throat delineation boundaries using MIP data; Step (3) establishing regional NMR T2 spectrum pore component delineation boundaries based on pore throat delineation criteria; Step (4) calculating the ratio of the envelope area of ​​the relaxation time interval corresponding to different pore throats to the total area, i.e., the pore component value, based on the delineation boundaries in Step (3); Step (5) calculating the reservoir permeability based on the pore component values ​​obtained in Step (4). However, this technical solution still cannot effectively characterize the differences in pore throat distribution among different types of reservoirs, resulting in the established pore-permeability model being unable to effectively consider the influence of differences in reservoir physical property distribution. Summary of the Invention

[0003] To overcome the problem that the existing porosity-permeability model establishment methods cannot effectively characterize the differences in pore throat distribution among different types of reservoirs, resulting in the established porosity-permeability models failing to effectively consider the differences in reservoir physical property distribution, this invention provides a porosity-permeability model establishment method that considers the microscopic pore throat distribution.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for establishing a pore-permeability model considering the distribution of microscopic pore throats, comprising the following steps:

[0005] S1: Statistically analyze the high-pressure mercury intrusion or nuclear magnetic resonance (NMR) test results of a large number of first core samples in the study area, and plot the first relationship chart between the average pore throat radius and atmospheric pressure gas permeability based on the high-pressure mercury intrusion or NMR test results; statistically analyze the overburden porosity and permeability test results of second core samples in the same well section in the same area as the first core samples, and plot the second relationship chart between atmospheric pressure gas permeability and overburden permeability, and the third relationship chart between overburden permeability and overburden porosity based on the overburden porosity and permeability test results;

[0006] S2: Using the first and second relationship maps from step S1, the pore throat radius distribution relationship obtained in the target area is transformed into the overburden pressure permeability distribution relationship;

[0007] S3: Based on the geological model of the target area, establish an intermediate random attribute model that follows an average distribution from 0 to 1, and calculate the cumulative distribution of overburden permeability using the overburden permeability distribution relationship in step S2. Match the values ​​of each grid in the intermediate random attribute model with the cumulative distribution results of overburden permeability to establish a permeability attribute model; at the same time, use the third relationship chart between overburden permeability and overburden porosity in step S1 to establish a porosity attribute model.

[0008] S4: Based on the permeability and porosity attribute models in step S3, construct corresponding mathematical models for numerical simulation studies to quantify the differences in development effects of different types of reservoirs.

[0009] Preferably, in step S1, the relationship between the average pore throat radius and the atmospheric pressure gas permeability is obtained by fitting the first relationship chart as follows: In the formula, k is the average throat radius. g denoted as ρ_permeability measured at normal pressure, and a and b as fitting coefficients.

[0010] Preferably, in step S1, the relationship between atmospheric pressure gas permeability and overlying pressure permeability is obtained by fitting the second relationship chart as follows: In the formula, k op denoted as , where is the pressure permeability, and c and d are fitting coefficients.

[0011] Preferably, in step S1, the relationship between overburden permeability and overburden porosity is obtained by fitting the third relationship chart as follows: In the formula, φ op denoted as , where is the porosity of the overburden, and e and f are fitting coefficients.

[0012] Preferably, in step S2, the pore throat radius distribution relationship obtained in the target area is transformed into the overburden permeability distribution relationship based on the relationship between the average pore throat radius and atmospheric pressure gas permeability and the relationship between atmospheric pressure gas permeability and overburden pressure permeability in S1.

[0013] Preferably, step S2 specifically involves: converting the pore throat radius distribution relationship into the atmospheric pressure gas permeability distribution relationship using the relationship between the average pore throat radius and atmospheric pressure gas permeability, and then converting the atmospheric pressure gas permeability distribution relationship into the overburden permeability distribution relationship using the relationship between atmospheric pressure gas permeability and overburden pressure permeability.

[0014] Preferably, in step S3, the value of each grid in the intermediate random attribute model is matched with the cumulative distribution result of the overburden permeability. If the value in a certain grid does not exceed the cumulative probability value corresponding to a certain overburden permeability, the overburden permeability value is assigned to that grid. The method is used to match the values ​​of all grids in the intermediate random attribute model one by one to obtain the overburden permeability values ​​of all grids in the model and establish a permeability attribute model.

[0015] Preferably, in step S3, the overburden porosity values ​​of all grids in the model are calculated using the relationship between overburden permeability and overburden porosity in step S1, and a porosity attribute model is established.

[0016] Preferably, in step S2, the pore throat radius distribution relationship is obtained using high-pressure mercury intrusion or nuclear magnetic resonance experiments.

[0017] In another aspect, the present invention provides a readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to perform the pore permeation model establishment method considering the microscopic pore throat distribution as described above.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention addresses the problem that conventional porosity-permeability modeling methods cannot effectively characterize the differences in microscopic pore-throat distribution among different types of reservoirs. Based on extensive regional high-pressure mercury intrusion porosimetry, nuclear magnetic resonance (NMR), and overburden porosity-permeability experiments, it establishes a method for converting the pore-throat radius distribution relationship into the overburden porosity-permeability distribution relationship. Then, the reservoir microscopic pore-throat distribution results obtained from high-pressure mercury intrusion porosimetry or NMR experiments in the target area are introduced into the porosity-permeability model to establish a geological model that considers the reservoir microscopic pore-throat distribution. On this basis, a mathematical model is constructed for numerical simulation studies to quantify the impact of differences in microscopic pore-throat distribution among different types of reservoirs on reservoir development effectiveness. This method expands the geological modeling methods for early-stage offshore oilfield development, solves the problem that existing geological modeling methods struggle to consider the impact of differences in reservoir microscopic pore-throat distribution on development plans and indicators, and provides a porosity-permeability modeling method that is reasonable, reliable, and highly operable for numerical simulation studies and development plan formulation for different types of reservoirs. Attached Figure Description

[0019] Figure 1 This is a flowchart of the porosity permeability model establishment method of the present invention;

[0020] Figure 2This is the first graph showing the relationship between the average pore throat radius and the atmospheric pressure gas permeability in Embodiment 2 of the present invention;

[0021] Figure 3 This is the second graph showing the relationship between atmospheric pressure gas permeability and overburden pressure permeability in Embodiment 2 of the present invention;

[0022] Figure 4 This is the third graph showing the relationship between overburden permeability and overburden porosity in Embodiment 2 of the present invention;

[0023] Figure 5 This is a distribution diagram of the average pore throat radius of the fine sandstone in Embodiment 2 of the present invention;

[0024] Figure 6 This is a distribution diagram of the average pore throat radius of the sandstone in Embodiment 2 of the present invention;

[0025] Figure 7 This is a porosity distribution diagram of fine sandstone overburden in Embodiment 2 of the present invention;

[0026] Figure 8 This is a permeability distribution diagram of fine sandstone overburden in Example 2 of the present invention;

[0027] Figure 9 This is a porosity distribution diagram of sandstone and conglomerate overburden in Embodiment 2 of the present invention;

[0028] Figure 10 This is a permeability distribution diagram of sandstone and conglomerate overburden in Example 2 of the present invention;

[0029] Figure 11 This is a permeability property model diagram of fine sandstone in Embodiment 2 of the present invention;

[0030] Figure 12 This is a permeability property model diagram of sandstone and conglomerate in Embodiment 2 of the present invention;

[0031] Figure 13 This is a model diagram of the porosity properties of fine sandstone in Embodiment 2 of the present invention;

[0032] Figure 14 This is a model diagram of the porosity properties of sandstone and conglomerate in Embodiment 2 of the present invention;

[0033] Figure 15 This is a diagram illustrating the water-driven oil recovery effect in fine sandstone in Embodiment 2 of the present invention.

[0034] Figure 16 This is a diagram illustrating the water-driven oil recovery effect in sandstone and conglomerate in Embodiment 2 of the present invention.

[0035] Figure 17 This is a microscopic visualization of the fluid distribution in the fine sandstone before water-driven oil recovery in Example 2 of this invention.

[0036] Figure 18This is a microscopic visualization of the fluid distribution after water-driven oil recovery in fine sandstone, as shown in Example 2 of this invention.

[0037] Figure 19 This is a microscopic visualization of the fluid distribution in sandstone before water-driven oil recovery in Example 2 of this invention;

[0038] Figure 20 This is a microscopic visualization of the fluid distribution after water-driven oil recovery in sandstone and conglomerate in Example 2 of this invention. Detailed Implementation

[0039] The accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. To better illustrate this embodiment, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings. The positional relationships described in the drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0040] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "long," and "short" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0041] The technical solution of the present invention will be further described in detail below through specific embodiments and in conjunction with the accompanying drawings:

[0042] Example 1

[0043] like Figure 1 As shown, a method for establishing a pore-permeability model considering the distribution of microscopic pore throats includes the following steps:

[0044] S1: Statistically analyze the high-pressure mercury intrusion or nuclear magnetic resonance (NMR) test results of a large number of first core samples in the study area, and plot the first relationship chart between the average pore throat radius and atmospheric pressure gas permeability based on the high-pressure mercury intrusion or NMR test results; statistically analyze the overburden porosity and permeability test results of second core samples in the same well section in the same area as the first core samples, and plot the second relationship chart between atmospheric pressure gas permeability and overburden permeability, and the third relationship chart between overburden permeability and overburden porosity based on the overburden porosity and permeability test results;

[0045] S2: Using the first and second relationship maps from step S1, the pore throat radius distribution relationship obtained in the target area is transformed into the overburden pressure permeability distribution relationship;

[0046] S3: Based on the geological model of the target area, establish an intermediate random attribute model that follows an average distribution from 0 to 1, and calculate the cumulative distribution of overburden permeability using the overburden permeability distribution relationship in step S2. Match the values ​​of each grid in the intermediate random attribute model with the cumulative distribution results of overburden permeability to establish a permeability attribute model; at the same time, use the third relationship chart between overburden permeability and overburden porosity in step S1 to establish a porosity attribute model.

[0047] S4: Based on the permeability and porosity attribute models in step S3, construct corresponding mathematical models for numerical simulation studies to quantify the differences in development effects of different types of reservoirs.

[0048] In this embodiment, the present invention addresses the problem that conventional porosity-permeability modeling methods cannot effectively characterize the differences in microscopic pore-throat distribution among different types of reservoirs. Based on extensive high-pressure mercury intrusion porosimetry, nuclear magnetic resonance (NMR), and overburden porosity-permeability experiments in the region, a method is established to transform the pore-throat radius distribution relationship into the overburden porosity-permeability distribution relationship. Then, the reservoir microscopic pore-throat distribution results obtained from high-pressure mercury intrusion porosimetry or NMR experiments in the target area are introduced into the porosity-permeability model to establish a geological model that considers the reservoir microscopic pore-throat distribution. On this basis, a mathematical model is constructed for numerical simulation studies to quantify the impact of differences in microscopic pore-throat distribution among different types of reservoirs on reservoir development effectiveness. This method expands the geological modeling methods for early-stage offshore oilfield development, solves the problem that existing geological modeling methods struggle to consider the impact of differences in reservoir microscopic pore-throat distribution on development plans and indicators, and provides a porosity-permeability modeling method that is reasonable, reliable, and highly operable for numerical simulation studies and development plan formulation for different types of reservoirs.

[0049] In step S1, the relationship between the average pore throat radius and the atmospheric pressure gas permeability is obtained by fitting the first relationship chart. In the formula, k is the average throat radius. g denoted as ρ_permeability measured at normal pressure, and a and b as fitting coefficients.

[0050] In step S1, the relationship between atmospheric pressure gas permeability and overlying pressure permeability is obtained by fitting the second relationship chart. In the formula, k op denoted as , where is the pressure permeability, and c and d are fitting coefficients.

[0051] In step S1, the relationship between overburden permeability and overburden porosity is obtained by fitting the third relationship diagram. In the formula, φ opdenoted as , where is the porosity of the overburden, and e and f are fitting coefficients.

[0052] In step S2, based on the relationship between the average pore throat radius and atmospheric pressure gas permeability and the relationship between atmospheric pressure gas permeability and overburden pressure permeability in S1, the pore throat radius distribution relationship obtained in the target area is transformed into the overburden pressure permeability distribution relationship.

[0053] Specifically, step S2 involves: converting the pore throat radius distribution relationship into the atmospheric pressure gas permeability distribution relationship using the relationship between the average pore throat radius and atmospheric pressure gas permeability; and then converting the atmospheric pressure gas permeability distribution relationship into the overburden permeability distribution relationship using the relationship between atmospheric pressure gas permeability and overburden pressure permeability.

[0054] In step S3, the value of each grid in the intermediate random attribute model is matched with the cumulative distribution result of the overburden permeability. If the value in a certain grid does not exceed the cumulative probability value corresponding to a certain overburden permeability, the overburden permeability value is assigned to that grid. The value of all grids in the intermediate random attribute model is matched one by one using this method to obtain the overburden permeability value of all grids in the model and establish the permeability attribute model.

[0055] In step S3, the overburden porosity values ​​of all grids in the model are calculated using the relationship between overburden permeability and overburden porosity from step S1, and a porosity attribute model is established.

[0056] In step S2, the distribution relationship of pore throat radius is obtained using high-pressure mercury intrusion or nuclear magnetic resonance experiments.

[0057] Example 2

[0058] To make the operation process, significance, and necessity of this invention more easily understood, a specific implementation example is given below, along with accompanying drawings, as follows: Figures 2 to 20 As shown, a detailed explanation is provided below. A method for establishing a pore-permeability model considering the distribution of microscopic pore throats includes the following steps:

[0059] Step 1: The study area is the Beibu Gulf Basin, and the stratigraphic position is the Liushagang Formation. High-pressure mercury intrusion porosimetry, nuclear magnetic resonance (NMR), and overburden pressure porosimetry data from 84 exploration well core samples were statistically analyzed. Based on the high-pressure mercury intrusion porosimetry or NMR results, a first-order graph showing the relationship between the average pore throat radius and atmospheric pressure gas permeability was plotted (see...). Figure 2 Based on the first relationship chart, the relationship between the average pore throat radius and the atmospheric pressure gas permeability is obtained as follows: A second graph depicting the relationship between atmospheric pressure gas permeability and overlying pressure permeability was plotted based on the results of the overlying pressure permeability test (see...). Figure 3 ) and a graph showing the relationship between overlying permeability and overlying porosity (see Figure 4 The relationship between atmospheric pressure gas permeability and overlying pressure permeability was obtained by fitting the second relationship graph. The relationship between overburden permeability and overburden porosity was obtained by fitting the third relationship diagram.

[0060] Step 2: Establish a core geological model with 200×51×51=520200 grids, of which 109600 are invalid grids. The grid step size is 0.5mm, forming an approximately cylindrical core model with a length of 10cm and a diameter of 2.55cm. The aim is to finely depict the microscopic pore throat distribution characteristics of the core reservoir.

[0061] In this example, high-pressure mercury intrusion porosimetry (HIP) results were obtained from two core samples: one from fine sandstone and the other from conglomerate. The distribution relationship of the pore throat radii in the two core samples was plotted (see...). Figure 5 , Figure 6 Using the established regional relationship map, the overburden permeability distribution relationships of fine sandstone and conglomerate were calculated respectively. Specifically, the pore throat radius distribution relationship was transformed into the atmospheric pressure gas permeability distribution relationship using the relationship between the average pore throat radius and atmospheric pressure gas permeability, and then the atmospheric pressure gas permeability distribution relationship was transformed into the overburden permeability distribution relationship using the relationship between atmospheric pressure gas permeability and overburden permeability.

[0062] Step 3: Establish an intermediate stochastic attribute model for the effective grids in the core geological model, following an average distribution from 0 to 1. Calculate the cumulative distribution of overburden permeability using the overburden permeability distribution relationship of the target core. Match the values ​​of each grid in the intermediate stochastic attribute model with the cumulative distribution results of overburden permeability to establish a permeability attribute model. Simultaneously, establish a porosity attribute model using a chart showing the relationship between overburden permeability and overburden porosity. Statistical results of overburden porosity and permeability distribution and attribute model for fine sandstone and conglomerate are shown in [link to relevant documentation]. Figures 7 to 10 Thus, the overburden porosity and overburden permeability values ​​of all effective grids have been obtained.

[0063] Based on the core geological model, grids with a core radius greater than 2.55 cm were filled with invalid grids, and their porosity and permeability were assigned a value of 0. The effective and invalid grid porosity and permeability data from the attribute model were rearranged to establish separate porosity and permeability attribute models for fine sandstone and conglomerate cores (see...). Figures 11 to 14 ).

[0064] Step 4: Set appropriate reservoir conditions and conduct numerical simulation studies on core waterflooding. The simulation results are shown in [link to simulation results]. Figures 15 to 16 The oil displacement effect of fine sandstone cores was far better than that of conglomerate, a result consistent with the microscopic visualization results of fine sandstone and conglomerate before and after water flooding (see...). Figures 17 to 20 This confirms the reliability of the pore-permeability model establishment method that considers the distribution of microscopic pore throats.

[0065] Example 3

[0066] A readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to perform the pore permeation model establishment method considering the microscopic pore throat distribution as described in Examples 1 and 2 above.

[0067] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for establishing a pore-permeability model considering the distribution of microscopic pore throats, characterized in that, Includes the following steps: S1: Statistically analyze the high-pressure mercury intrusion or nuclear magnetic resonance (NMR) test results of a large number of first core samples in the study area, and plot the first relationship chart between the average pore throat radius and atmospheric pressure gas permeability based on the high-pressure mercury intrusion or NMR test results; statistically analyze the overburden porosity and permeability test results of second core samples in the same well section in the same area as the first core samples, and plot the second relationship chart between atmospheric pressure gas permeability and overburden permeability, and the third relationship chart between overburden permeability and overburden porosity based on the overburden porosity and permeability test results; S2: Using the first and second relationship maps from step S1, the pore throat radius distribution relationship obtained in the target area is transformed into the overburden pressure permeability distribution relationship; S3: Based on the geological model of the target area, establish an intermediate random attribute model that follows an average distribution from 0 to 1, and calculate the cumulative distribution of overburden permeability using the overburden permeability distribution relationship in step S2. Match the values ​​of each grid in the intermediate random attribute model with the cumulative distribution results of overburden permeability to establish a permeability attribute model; at the same time, use the third relationship chart between overburden permeability and overburden porosity in step S1 to establish a porosity attribute model. S4: Based on the permeability and porosity attribute models in step S3, construct corresponding mathematical models for numerical simulation studies to quantify the differences in development effects of different types of reservoirs.

2. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 1, characterized in that, In step S1, the relationship between the average pore throat radius and the atmospheric pressure gas permeability is obtained by fitting the first relationship chart. In the formula, k is the average throat radius. g denoted as ρ_permeability measured at normal pressure, and α and β as fitting coefficients.

3. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 2, characterized in that, In step S1, the relationship between atmospheric pressure gas permeability and overlying pressure permeability is obtained by fitting the second relationship graph. In the formula, k op denoted as , where is the pressure permeability, and c and d are fitting coefficients.

4. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 3, characterized in that, In step S1, the relationship between overburden permeability and overburden porosity is obtained by fitting the third relationship chart. In the formula, φ op denoted as , where is the porosity of the overburden, and e and f are fitting coefficients.

5. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 4, characterized in that, In step S2, based on the relationship between the average pore throat radius and atmospheric pressure gas permeability and the relationship between atmospheric pressure gas permeability and overburden pressure permeability in S1, the pore throat radius distribution relationship obtained in the target area is transformed into the overburden pressure permeability distribution relationship.

6. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 5, characterized in that, Step S2 specifically involves: using the relationship between the average pore throat radius and atmospheric pressure gas permeability to transform the pore throat radius distribution relationship into an atmospheric pressure gas permeability distribution relationship; and then using the relationship between atmospheric pressure gas permeability and overburden permeability to transform the atmospheric pressure gas permeability distribution relationship into an overburden permeability distribution relationship.

7. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 6, characterized in that, In step S3, the value of each grid in the intermediate random attribute model is matched with the cumulative distribution result of the overburden permeability. If the value in a certain grid does not exceed the cumulative probability value corresponding to a certain overburden permeability, the overburden permeability value is assigned to that grid. The value of all grids in the intermediate random attribute model is matched one by one using this method to obtain the overburden permeability value of all grids in the model and establish the permeability attribute model.

8. The method for establishing a pore-permeability model considering the distribution of micropore throats according to claim 7, characterized in that, In step S3, the overburden porosity values ​​of all grids in the model are calculated using the relationship between overburden permeability and overburden porosity from step S1, and a porosity attribute model is established.

9. The method for establishing a pore-permeability model considering the distribution of micropore throats according to any one of claims 1 to 8, characterized in that, In step S2, the pore throat radius distribution relationship is obtained using high-pressure mercury intrusion or nuclear magnetic resonance experiments.

10. A readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to perform a pore-permeability model establishment method considering microscopic pore throat distribution as described in any one of claims 1-9.

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