Reasonable pore throat segmentation factor selection method for glutenite reservoir pore network model

Abstract

The invention discloses a reasonable pore throat segmentation factor selection method for a glutenite reservoir pore network model, and the method comprises the steps: obtaining a three-dimensional grayscale image of a glutenite reservoir sample, and obtaining a three-dimensional digital core model based on the three-dimensional grayscale image; setting a plurality of pore throat segmentation factors, and obtaining a plurality of pore network models of the glutenite reservoir samples according to the three-dimensional digital core models of the glutenite reservoir samples and the pore throat segmentation factors; calculating the theoretical permeability of the pore network model of each glutenite reservoir sample; determining the actually measured permeability of the glutenite reservoir sample through an indoor test, comparing the actually measured permeability with each theoretical permeability, and selecting the pore throat segmentation factor corresponding to the closest theoretical permeability as the reasonable pore throat segmentation factor of the glutenite reservoir pore network model. The reasonable pore throat segmentation factor of the shrinkage pore sample selected through experimental comparison can be well matched with a real reservoir, and the accuracy of a pore network model is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of establishing a pore network model of a sandy conglomerate reservoir, and in particular relates to a method for selecting a reasonable pore throat segmentation factor of a sandy conglomerate reservoir pore network model. Background Art

[0002] Sandy conglomerate reservoirs are important concealed oil and gas reservoirs in China's continental petroliferous basins. The diagenetic evolution of rich and complex interstitial components is a key factor in the formation of sandy conglomerate reservoirs. As an important diagenetic component in the sandy conglomerate sedimentary body, the tuffaceous interstitial formed by volcanic activity restricts the formation and evolution of sandy conglomerate reservoirs. With the continuous increase in burial depth, the intermediate-acidic tuffaceous interstitial will undergo dehydration and shrinkage to produce a large number of shrinkage cracks. Unlike dissolution pores, the shrinkage cracks are mainly fusiform, with the short axis mainly extending slightly from the middle of the interstitial to both sides, while the long axis extends to a large extent. The shrinkage cracks developed in the tuffaceous interstitial not only become the main oil and gas storage space in the formation, but also serve as an ideal drainage channel for pore fluids. How to accurately describe the characteristics of the shrinkage pore structure is particularly important for the evaluation of sandy conglomerate reservoirs.

[0003] As a platform for studying the microstructure and seepage characteristics of porous media, the pore network model can reproduce complex pore spaces and accurately and quantitatively calculate pore structures and physical properties. According to the topological characteristics of the constructed network model, the three-dimensional pore network model can be divided into two categories: regular topological pore network model and real topological pore network model. The regular topological pore network model refers to a pore network model in which pores and throats are regularly arranged in space. Fatt (1956) initially proposed this network model with a regular topological structure, which played an important role in the early stages of pore-level simulation. Due to the different characterization methods and size assignment methods of pore and throat units, the regular topological pore network model has a variety of expressions. The real topological pore network model refers to a network model that is topologically equivalent to the pore space of the digital core. The methods for establishing this type of model include: Voronoi polyhedron method, pore space center axis method, maximum sphere method, and hybrid method. At present, scholars generally use the maximum sphere algorithm to extract the pore network model, characterize the topological structure of the real core pore space, and realize the accurate extraction of the pore network model in the three-dimensional digital core. In the process of extracting the pore network model, it is particularly important to accurately divide the boundaries of pores and throats, that is, to select a reasonable pore throat segmentation factor. However, there has been little research on how to select a reasonable pore throat segmentation factor in sandstone reservoir samples with developed shrinkage pores. Researchers usually select pore throat segmentation factors based on previous experience. However, the actual conditions of reservoirs in different study areas are different, which may lead to a large difference between the selected pore throat segmentation factor and the actual one, thereby reducing the accuracy of the pore network model. Summary of the invention

[0004] The present invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir comprises the following steps:

[0007] S1. Obtain a three-dimensional grayscale image of a sandy conglomerate reservoir sample in the study area, and obtain a three-dimensional digital core model of the sandy conglomerate reservoir sample based on the three-dimensional grayscale image of the sandy conglomerate reservoir sample;

[0008] S2. setting a plurality of pore-throat segmentation factors of different sizes, and obtaining a pore network model of the conglomerate reservoir sample under the conditions of a plurality of different pore-throat segmentation factors according to the three-dimensional digital core model of the conglomerate reservoir sample and the set pore-throat segmentation factors;

[0009] S3, respectively calculating the theoretical permeability of the pore network model of the sandstone reservoir sample under different pore throat segmentation factors;

[0010] S4. Determine the measured permeability of the conglomerate reservoir sample through indoor experiments, compare the measured permeability with various theoretical permeabilities, and select the pore throat segmentation factor corresponding to the closest theoretical permeability as the reasonable pore throat segmentation factor of the conglomerate reservoir pore network model.

[0011] In the above technical solution, the conglomerate reservoir samples in the study area are conglomerate reservoir samples with developed shrinkage pores.

[0012] In the above technical solution, the method for acquiring the three-dimensional grayscale image of the sandstone reservoir sample in the study area is CT scanning, preferably X-ray micron-level CT scanning.

[0013] In the above technical solution, the specific method of obtaining the three-dimensional digital core model of the conglomerate reservoir sample based on the three-dimensional grayscale image of the conglomerate reservoir sample in step S1 includes the following steps:

[0014] S11, performing binary segmentation on the three-dimensional grayscale image of the conglomerate reservoir sample in the study area by using the maximum inter-class variance method to obtain the value assigned to each pixel of the three-dimensional grayscale image of the conglomerate reservoir sample;

[0015] The assigned value is 0 or 1, wherein 0 represents a pore voxel and 1 represents a granular voxel;

[0016] S12. Obtain a three-dimensional digital core model corresponding to the conglomerate reservoir sample according to the value assigned to each pixel of the three-dimensional grayscale image of the conglomerate reservoir sample.

[0017] In the above technical solution, step S2 specifically includes the following steps:

[0018] S21. For each point in the pore of the three-dimensional digital core model, a sphere is expanded outward with the point as the center until the sphere hits the rock skeleton;

[0019] S22, storing all the sphere data obtained in step S21 into a list, and arranging them from large to small according to radius;

[0020] S23, starting from the first sphere in the list, delete the spheres on the right side of the sphere that intersect with it or are contained by it, and finally only the non-intersecting spheres are left in the list, and the non-intersecting spheres fill the entire pore space;

[0021] S24. The non-intersecting spheres obtained in step S23 constitute a maximum sphere set. In the maximum sphere set, the largest maximum sphere in the space between the rock skeletons is defined as a pore by the set pore throat segmentation factor. After the pore unit is determined, the space occupied by the pore unit is continuously expanded until it intersects with other maximum sphere units. The intersecting maximum sphere is defined as the center of the throat in the pore structure of the rock, thereby dividing all positions in the three-dimensional digital core model into one of the pores, throats and skeletons. The data body obtained after all positions are defined is the pore network model.

[0022] In the above technical solution, the value range of the pore throat segmentation factor is 0-1.

[0023] In the above technical solution, the calculation formula of the theoretical permeability of the pore network model in step S3 is as follows:

[0024]

[0025] Where: K is the theoretical permeability, unit is μm 2 ;μ i is the viscosity of phase i fluid, in mPa·s; Q i It is the flow rate under the pressure difference applied to the pore network model when the pore network model is fully saturated with phase i fluid, in cm 3 / s; A is the cross-sectional area of ​​the model, in cm 2 ;P I is the inlet pressure, in units of 10 -1 MPa; P O is the outlet pressure, in units of 10 -1 MPa; L is the model length in cm.

[0026] A method for determining the influence of the division of pore throat partition factors on the structural parameters of a pore network model comprises the following steps:

[0027] (I) Statistically analyze the pore radius distribution of the pore network model with different pore throat partition factors and determine the effect of the pore throat partition factor on the pore radius distribution;

[0028] (II) Statistically analyze the throat volume distribution of the pore network model with different pore throat partition factors and determine the effect of the pore throat partition factor on the throat volume distribution;

[0029] (III) Statistically analyze the throat length distribution of the pore network model with different pore throat partition factors to determine the effect of the pore throat partition factor on the throat length distribution;

[0030] (IV) The shape factor distribution of the pore network model with different pore throat partition factors is statistically analyzed to determine the influence of the pore throat partition factor on the shape factor distribution.

[0031] In the above technical solution, the expression of the shape factor in step (IV) is as follows:

[0032] G=A / P 2 (8)

[0033] Where: G is the shape factor, A is the cross-sectional area of ​​the pore and throat, in m 2 ; P is the circumference of the cross section of the pore and throat unit, in meters.

[0034] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0035] The beneficial effects of the present invention are:

[0036] The present invention provides a method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir. The reasonable pore throat segmentation factor of a shrinkage pore sample selected by experimental comparison can be well consistent with the real reservoir, thereby improving the accuracy of the pore network model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic flow chart of the method of the present invention;

[0038] Figure 2 The three-dimensional grayscale image of the conglomerate reservoir sample is obtained based on micrometer CT in Example 1 of the present invention;

[0039] Figure 3 It is a schematic diagram of a process of obtaining a three-dimensional digital core model of a conglomerate reservoir sample based on a three-dimensional grayscale image of the conglomerate reservoir sample in the present invention;

[0040] Figure 4 is a three-dimensional digital core image obtained according to the three-dimensional grayscale image in Example 1 of the present invention;

[0041] Figure 5 Schematic diagram of pore throat division in Example 1 of the present invention;

[0042] Figure 6 It is a schematic flow chart of step S2 in the present invention;

[0043] Figure 7 is a pore network model obtained based on the three-dimensional digital core in Example 1 of the present invention;

[0044] Figure 8 is the pore radius distribution of the shrinkage pore network model with different pore throat partitioning factors in Example 2 of the present invention;

[0045] Fig. 9 is the throat radius distribution of the shrinkage pore network model with different pore throat segmentation factors in Example 2 of the present invention;

[0046] Fig.10 is the throat length distribution of the shrinkage pore network model with different pore throat segmentation factors in Example 2 of the present invention;

[0047] Fig.11 It is the shape factor distribution of the shrinkage pore network model with different pore throat partition factors in Example 2 of the present invention.

[0048] For ordinary technicians in this field, other relevant drawings can be obtained based on the above drawings without any creative work. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0050] Example 1

[0051] like Figure 1 As shown in FIG. 1 , a method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir includes the following steps:

[0052] S1. Obtain a three-dimensional grayscale image of a sandy conglomerate reservoir sample in the study area, and obtain a three-dimensional digital core model of the sandy conglomerate reservoir sample based on the three-dimensional grayscale image of the sandy conglomerate reservoir sample;

[0053] The conglomerate reservoir samples in the study area are conglomerate reservoir samples with developed shrinkage pores;

[0054] The method for obtaining the three-dimensional grayscale image of the sandstone reservoir sample in the study area is CT scanning, preferably X-ray micrometer CT scanning. CT scanning has the advantages of true three-dimensional imaging and non-destructive features. X-ray micrometer CT uses conical X-rays to penetrate the object, magnifies the image through objective lenses of different magnifications, and reconstructs a three-dimensional stereo model from a large number of X-ray attenuation images obtained by 360-degree rotation. Using micrometer CT for core scanning can fully display very small feature surfaces through a large amount of image data without destroying the sample.

[0055] like Figure 2 As shown, the three-dimensional grayscale image of the sandstone reservoir sample obtained based on the X-ray micrometer-level CT scanning method has a voxel resolution of 5 μm, a voxel size of 400×400×400, and a physical size of 2 mm×2 mm×2 mm.

[0056] like Figure 3 As shown, the specific method for obtaining a three-dimensional digital core model of a conglomerate reservoir sample based on a three-dimensional grayscale image of the conglomerate reservoir sample comprises the following steps:

[0057] S11, performing binary segmentation on the three-dimensional grayscale image of the conglomerate reservoir sample in the study area to obtain a value assigned to each pixel of the three-dimensional grayscale image of the conglomerate reservoir sample;

[0058] The assigned value is 0 or 1, wherein 0 represents a pore voxel and 1 represents a granular voxel;

[0059] The binary segmentation of the three-dimensional grayscale image is completed by the maximum inter-class variance method (Otsu algorithm). The Otsu algorithm was proposed by Otsu in 1975, also known as the Otsu method or the maximum inter-class variance method, which dynamically determines the threshold of image binarization by maximizing the inter-class variance between the foreground and the background.

[0060] The method for determining the threshold value of the three-dimensional grayscale image binarization specifically comprises the following steps:

[0061] (1) Define the between-class variance:

[0062]

[0063] Where: is the inter-class variance, dimensionless; w0 is the ratio of the number of foreground points to the image, dimensionless; w1 is the ratio of the number of background points to the image, dimensionless; u0, u1, u T They are the average grayscale of the foreground point image, the average grayscale of the background point image, and the total average grayscale of the image, dimensionless;

[0064] (2) Define the population variance:

[0065]

[0066] Where: They are the overall variance, between-class variance, and within-class variance, dimensionless;

[0067] (3) Select the optimal threshold t* to satisfy the maximum decision criterion;

[0068] The maximum judgment criterion is expressed as follows:

[0069]

[0070] Where: t* is the optimal threshold; L is the gray level of the image, dimensionless; is the between-class variance, dimensionless; is the population variance, dimensionless;

[0071] S12, according to the assignment of the three-dimensional grayscale image of the conglomerate reservoir sample at each pixel, a three-dimensional digital core model corresponding to the conglomerate reservoir sample is obtained, such as Figure 4 shown.

[0072] S2. setting a plurality of pore-throat segmentation factors of different sizes, and obtaining a pore network model of the conglomerate reservoir sample under the conditions of a plurality of different pore-throat segmentation factors according to the three-dimensional digital core model of the conglomerate reservoir sample and the set pore-throat segmentation factors;

[0073] Since the computational cost of seepage simulation based on digital cores is very high, it is necessary to simplify the digital cores, that is, to extract a pore network model composed of pore throats of simple shapes that can reflect the characteristics of actual rock samples as a research platform.

[0074] The concept of the largest sphere was first proposed by Silin et al. and used to analyze the pore space of rock samples. It was later used by Blunt et al. to study the pore space of sandstone and limestone. The "largest sphere" is defined as a sphere contained in the pore space and not contained by other spheres. The local largest sphere is defined as a "pore". Hu Dong et al. improved this method. The main steps of searching for the largest sphere are as follows: ① For each point in the pore of the three-dimensional digital core model, expand outward to generate a sphere with the point as the center until the sphere hits the rock skeleton; ② Store all these sphere data in a list and arrange them from large to small according to radius; ③ Starting from the first sphere in the list, delete the spheres on the right side of the sphere in the list that intersect with it or are contained by it; ④ Finally, the remaining spheres in the list are non-intersecting spheres, which fill the entire pore space. These non-intersecting spheres constitute a set of maximum spheres, and the locally largest "maximum sphere" in some space is defined as the "pore". After the "pore" is determined, the space occupied by the pore body is continuously expanded until it intersects with other "maximum sphere" units. The intersecting maximum sphere is defined as the center of the "throat" in the pore structure of the rock.

[0075] The pore network model defines the pore space by the maximum inscribed sphere area, and the pore space in the pore network model is precisely divided into the space occupied by the pore and throat units.

[0076] In the process of extracting the pore network model, the center of the pore and the throat can be obtained by the maximum sphere method, and then the corresponding pore radius can be calculated. That is, the radius of the largest inscribed sphere in the pore is the pore radius. The corresponding throat radius can also be obtained by the maximum sphere method.

[0077] In order to calculate the true volume of pores and throats, it is necessary to determine the interface between pores and throats. Here, the pore-throat partition factor is introduced to define it. Figure 5 As shown, the throat length l t It is defined as the Euclidean distance between the centers of two adjacent pores minus the distance from the centers of the two pores to the throat interface:

[0078] l t = l ij -l i -l j (4)

[0079] Pore ​​length l i and l j The definition is as follows:

[0080]

[0081] In formulas 4 to 6: r i is the radius of pore i, in m; r jis the radius of pore j, in m; r t is the radius of the connecting throat between pore i and pore j, in m; l ij is the Euclidean distance between the centers of two adjacent pores, in m; is the distance between the center of pore i and throat t, in m; is the distance between the center points of pore j and throat t, in m; α is the pore-throat segmentation factor, which is mainly used to determine the connection surface between pores and throats, and its distribution range is 0 to 1.

[0082] like Figure 6 As shown in FIG, based on the three-dimensional digital core of the shrinkage pore, by selecting different pore throat segmentation factors α, a shrinkage pore network model with different pore throat space divisions can be constructed. The specific method includes the following steps:

[0083] S21. For each point in the pore of the three-dimensional digital core model, a sphere is expanded outward with the point as the center until the sphere hits the rock skeleton;

[0084] S22, storing all the sphere data into a list, and arranging them from large to small according to radius;

[0085] S23, starting from the first sphere in the list, delete the spheres on the right side of the sphere that intersect with it or are contained by it, and finally the remaining spheres in the list are non-intersecting spheres, which fill the entire pore space;

[0086] S24. These non-intersecting spheres constitute a maximum sphere set. In the maximum sphere set, the largest "maximum sphere" in the space between the rock skeletons is defined as the "pore" by setting the pore throat segmentation factor. After the "pore" is determined, the space occupied by the pore unit is continuously expanded until it intersects with other "maximum sphere" units. The intersecting maximum sphere is defined as the center of the "throat" in the pore structure of the rock, thereby dividing all positions in the three-dimensional digital core model into one of the three categories of "pore", "throat" and "skeleton". The data body obtained after all positions are defined is the pore network model.

[0087] In this embodiment, Figure 7 The figure shows a pore network model of a conglomerate reservoir sample obtained based on the three-dimensional digital core model of the conglomerate reservoir sample and a set pore throat segmentation factor. It should be understood that when the set pore throat segmentation factor is different, the pore network model of the conglomerate reservoir sample will also be different.

[0088] S3, respectively calculating the theoretical permeability of the pore network model of the sandstone reservoir sample under different pore throat segmentation factors;

[0089] In the present invention, the pore network model is considered as a quasi-steady-state model, that is, the flow is completely controlled by capillary force, and the pressure drop caused by viscous force in the model can be ignored compared with the capillary force. According to the invasion-percolation theory, the flow of fluid from one pore to another is instantaneous, and the flow process in the pore throat is not considered. The fluid is an incompressible Newtonian fluid.

[0090] The model is saturated with a fluid and a driving pressure (P I -P O ), the fluid flow is statistically calculated, and the theoretical permeability of the model is solved by Darcy's formula, which is as follows (7):

[0091]

[0092] Where: K is the theoretical permeability, unit is μm 2 ;μ i is the viscosity of phase i fluid, in mPa·s; Q i The flow rate under the pressure difference applied to the model when the model is completely saturated with phase i fluid, in cm 3 / s; A is the cross-sectional area of ​​the model, in cm 2 ;P I is the inlet pressure, in units of 10 -1 MPa; P O is the outlet pressure, in units of 10 -1 MPa; L is the model length, in cm;

[0093] The theoretical permeability of the pore network model of the conglomerate reservoir sample under different pore throat segmentation factors can be calculated by the above formula;

[0094] As shown in Table 1, the corresponding physical property parameters are calculated based on the shrinkage pore network model with different pore throat partition factors. It can be found that as the pore throat partition factor increases, the number of pore throats and porosity remain unchanged, and the permeability decreases.

[0095] Table 1 Changes of porosity and permeability under different pore throat segmentation factors

[0096] α Number of pores Number of throats Porosity Permeability, mD 0 5293 9786 8.66％ 67.2 0.25 5293 9786 8.66％ 40.5 0.5 5293 9786 8.66％ 28.1 0.75 5293 9786 8.66％ 27.4 1 5293 9786 8.66％ 24.3

[0097] S4. Determine the measured permeability of the conglomerate reservoir sample through indoor experiments, compare the measured permeability with various theoretical permeabilities, and select the pore throat segmentation factor corresponding to the closest theoretical permeability as the reasonable pore throat segmentation factor of the conglomerate reservoir pore network model.

[0098] In this embodiment, the gas permeability of the shrinkage pore sample in the indoor experiment is 38mD. Therefore, in the digital core analysis of the shrinkage pore sample, it is more reasonable to select a pore throat segmentation factor of 0.2 to 0.4.

[0099] Example 2

[0100] The technical solution provided by the present invention can not only determine the reasonable pore throat segmentation factor of the pore network model of the sandstone reservoir, but also be used to study the influence of the division of the pore throat segmentation factor on the structural parameters (pore throat radius, throat length, shape factor, etc.) of the pore network model.

[0101] In the process of constructing the shrinkage pore network model, the real pore space is divided into pore and throat units in a general sense, and is characterized by cylindrical units with circular, square and arbitrary triangular cross-sectional shapes. The structural parameters of the regular units are statistically analyzed to evaluate the pore network structure in detail. The main structural parameters include pore throat radius, throat length, shape factor, etc.

[0102] The specific methods for studying the influence of the division of pore throat partition factors on the structural parameters of the pore network model include:

[0103] (I) Statistically analyze the pore radius distribution of the pore network model with different pore throat partition factors and determine the effect of the pore throat partition factor on the pore radius distribution;

[0104] like Figure 8 As shown in the figure, with the continuous increase of the pore throat segmentation factor, the pore radius distribution changes little.

[0105] (II) Statistically analyze the throat volume distribution of the pore network model with different pore throat partition factors and determine the effect of the pore throat partition factor on the throat volume distribution;

[0106] like Fig. 9 As shown in Figure 2, with the continuous increase of the pore-throat segmentation factor, the throat radius distribution does not change much.

[0107] (III) Statistically analyze the throat length distribution of the pore network model with different pore throat partition factors to determine the effect of the pore throat partition factor on the throat length distribution;

[0108] like Fig.10 As shown in the figure, with the continuous increase of the pore-throat segmentation factor, the throat length distribution curve continues to shift to the right, and the aspect ratio distribution continues to shift to the right.

[0109] (IV) The shape factor distribution of the pore network model with different pore throat partition factors is statistically analyzed to determine the influence of the pore throat partition factor on the shape factor distribution.

[0110] The shape factor is used to quantitatively characterize the shape characteristics of the pore and throat unit in the pore space, and is defined as follows:

[0111] G=A / P 2 (8)

[0112] Where G is the shape factor, A is the cross-sectional area of ​​the pore and throat, m 2 ; P is the circumference of the cross section of the pore and throat unit, m.

[0113] like Fig.11 As shown in Figure 2, as the pore-throat segmentation factor increases, the shape factor shifts to the right.

[0114] In summary, the selection of the pore throat segmentation factor does not affect the number of pore throats, but has a significant impact on the pore throat volume, throat length and permeability calculation. This is because the pore throat segmentation factor is used to allocate the interface position of the pore and throat, and does not affect the pore throat radius, the number of pore throats and porosity. The larger the pore throat segmentation factor, the more the interface between the pore and the throat is biased towards the pore side, the pore volume decreases, and the throat volume increases.

[0115] The present invention obtains a three-dimensional grayscale image of a shrinkage pore development sample based on micrometer CT, performs binary segmentation through a maximum class spacing algorithm to obtain a corresponding three-dimensional digital core of shrinkage pores, and extracts a corresponding shrinkage pore network model through a maximum sphere algorithm. Based on the three-dimensional digital core of the shrinkage pores, by selecting different pore throat segmentation factors, a shrinkage pore network model with different pore throat space divisions can be constructed, and the theoretical permeabilities of the pore network models of the conglomerate reservoir samples under the conditions of different pore throat segmentation factors are calculated respectively. Then, the measured permeabilities of the conglomerate reservoir samples are determined through indoor tests, and the measured permeabilities are compared with the various theoretical permeabilities. The pore throat segmentation factor corresponding to the closest theoretical permeability is selected as the reasonable pore throat segmentation factor of the conglomerate reservoir pore network model.

[0116] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for selecting reasonable pore throat segmentation factors for a pore network model of a sandstone reservoir, characterized by: The following steps are involved: S1. Obtain a three-dimensional grayscale image of a conglomerate reservoir sample in the study area, and obtain a three-dimensional digital core model of the conglomerate reservoir sample based on the three-dimensional grayscale image of the conglomerate reservoir sample; S2. setting a plurality of pore-throat segmentation factors of different sizes, and obtaining a pore network model of the conglomerate reservoir sample under the conditions of a plurality of different pore-throat segmentation factors according to the three-dimensional digital core model of the conglomerate reservoir sample and the set pore-throat segmentation factors; S3, respectively calculating the theoretical permeability of the pore network model of the sandstone reservoir sample under different pore throat segmentation factors; S4. Determine the measured permeability of the conglomerate reservoir sample through indoor experiments, compare the measured permeability with various theoretical permeabilities, and select the pore throat segmentation factor corresponding to the closest theoretical permeability as the reasonable pore throat segmentation factor of the conglomerate reservoir pore network model.

2. The method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir according to claim 1, characterized in that: The conglomerate reservoir samples in the study area are conglomerate reservoir samples with developed shrinkage pores.

3. The method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir according to claim 1, characterized in that: The method for acquiring the three-dimensional grayscale image of the sandstone reservoir samples in the study area is CT scanning.

4. The method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir according to claim 1, characterized in that: The specific method of obtaining the three-dimensional digital core model of the conglomerate reservoir sample based on the three-dimensional grayscale image of the conglomerate reservoir sample in step S1 comprises the following steps: S11, performing binary segmentation on the three-dimensional grayscale image of the conglomerate reservoir sample in the study area by using the maximum inter-class variance method to obtain the value assigned to each pixel of the three-dimensional grayscale image of the conglomerate reservoir sample; The assigned value is 0 or 1, 0 represents a pore voxel, and 1 represents a granular voxel; S12. Obtain a three-dimensional digital core model corresponding to the conglomerate reservoir sample according to the value assigned to each pixel of the three-dimensional grayscale image of the conglomerate reservoir sample.

5. The method for selecting reasonable pore throat segmentation factors of the pore network model of the conglomerate reservoir according to claim 1 is characterized by: The step S2 specifically includes the following steps: S21. For each point in the pore of the three-dimensional digital core model, a sphere is expanded outward with the point as the center until the sphere hits the rock skeleton; S22, storing all the sphere data obtained in step S21 into a list, and arranging them from large to small according to radius; S23, starting from the first sphere in the list, delete the spheres on the right side of the sphere that intersect with it or are contained by it, and finally only the non-intersecting spheres are left in the list, and the non-intersecting spheres fill the entire pore space; S24. The non-intersecting spheres obtained in step S23 constitute a maximum sphere set. In the maximum sphere set, the largest maximum sphere in the space between the rock skeletons is defined as a pore by the set pore throat segmentation factor. After the pore unit is determined, the space occupied by the pore unit is continuously expanded until it intersects with other maximum sphere units. The intersecting maximum sphere is defined as the center of the throat in the pore structure of the rock, thereby dividing all positions in the three-dimensional digital core model into one of the pores, throats and skeletons. The data body obtained after all positions are defined is the pore network model.

6. The method for selecting a reasonable pore throat segmentation factor for a pore network model of a sandstone reservoir according to claim 5, characterized in that: The value range of the pore throat segmentation factor is 0-1.

7. The method for selecting reasonable pore throat segmentation factors of the pore network model of the conglomerate reservoir according to claim 1 is characterized by: The calculation formula of the theoretical permeability of the pore network model in step S3 is as follows: Where: K is the theoretical permeability, unit is μm 2 ;μ i is the viscosity of phase i fluid, in mPa·s; Q i It is the flow rate under the pressure difference applied to the pore network model when the pore network model is fully saturated with phase i fluid, in cm 3 / s; A is the cross-sectional area of ​​the model, in cm 2 ;P I is the inlet pressure, in units of 10 -1 MPa; P O is the outlet pressure, in units of 10 -1 MPa; L is the model length in cm.

8. A method for determining the influence of the division of the pore throat segmentation factor as claimed in claim 5 on the structural parameters of the pore network model, characterized in that: The following steps are involved: (I) Statistically analyze the pore radius distribution of the pore network model with different pore throat partition factors and determine the effect of the pore throat partition factor on the pore radius distribution; (II) Statistically analyze the throat volume distribution of the pore network model with different pore throat partition factors and determine the effect of the pore throat partition factor on the throat volume distribution; (III) Statistically analyze the throat length distribution of the pore network model with different pore throat partition factors to determine the effect of the pore throat partition factor on the throat length distribution; (IV) The shape factor distribution of the pore network model with different pore throat partition factors is statistically analyzed to determine the influence of the pore throat partition factor on the shape factor distribution.

9. The method for determining the influence of the division of pore throat segmentation factors on the structural parameters of the pore network model according to claim 8, characterized in that: The expression of the shape factor in the step (IV) is as follows: G=A / P 2 (8) Where: G is the shape factor, A is the cross-sectional area of ​​the pore and throat, in m 2 ; P is the circumference of the cross section of the pore and throat unit, in meters.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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