Shale micro-pore structure characterization method considering complex pore morphology and application

Abstract

The invention belongs to the technical field of unconventional oil-gas exploration, and particularly relates to a shale micro-pore structure characterization method considering complex pore morphology and application, the characterization method comprises the following steps: S1, obtaining pore structure characteristics of a real reservoir core, the pore structure characteristics comprising a maximum pore width lambda max, m and a minimum pore width lambda min, m, the total porosity is phi, dimensionless, the total pore frequency is m, and the pore shape distribution parameters are determined; and S2, on the basis of the characteristic parameters of the real reservoir core obtained in the step S1, constructing a porous medium model in a complex pore form to represent a shale micro-pore structure. The porous medium model constructed by the method not only can be used for quantitative prediction of occurrence and flow characteristics of fluid in a pore structure, but also can guide fluid migration law research in exploration and development of shale gas reservoirs, and provides powerful support for optimization of an exploitation scheme and improvement of the recovery ratio.

Description

Technical Field

[0001] The present invention belongs to the technical field of unconventional oil and gas exploration, and specifically relates to a shale microscopic pore structure characterization method and application taking into account complex pore morphology. Background Art

[0002] Traditional pore structure characterization methods have been widely used in the characterization and evaluation of conventional oil and gas reservoirs. However, due to the extremely complex pore structure of shale reservoirs, it is difficult to accurately and quantitatively describe the pore structure of shale using conventional methods. The method of constructing a digital core or pore network model based on SEM images or nano-CT images can realize statistical analysis of multi-scale pore space and quantitatively describe porosity, fractal dimension, tortuosity and pore connectivity. However, the digital three-dimensional model constructed by this method is very computationally intensive when used for quantitative characterization of fluid occurrence and flow simulation, and has limitations in use. At the same time, if the pore network model is used for calculation, the pore morphology is simplified to a certain extent, resulting in the deviation of the calculation results from the true value.

[0003] The fractal characterization method of pore structure has been applied to unconventional reservoirs such as shale. The fractal theory of pore structure can characterize the spatial distribution of pore structure, reflect the multi-scale characteristics of pore structure, and construct the relationship between fluid motion state and pore structure. At present, the analytical method based on fractal theory mainly simplifies the shale pore system into a capillary bundle model in the process of pore structure characterization. The geometric shapes of all pores in the study are approximated by circular tubes, ignoring the influence of the real pore shape. However, the pore geometry in shale reservoirs includes various pore shapes from circular pores to slit-shaped pores. This approximate treatment method causes the calculation results to deviate seriously from the true value. At the same time, the flow of fluid in shale bedrock is greatly affected by the pore shape.

[0004] Therefore, the characterization of complex pore morphology cannot be ignored in the description of shale pore structure, and a quantitative characterization method for shale reservoir pore structure that takes into account pore morphology characteristics is urgently needed. Summary of the invention

[0005] The present invention is based on the Monte Carlo characterization of pore morphology and the fractal Monte Carlo characterization of shale pore size. By superimposing fractal units, a porous medium model considering complex pore morphology is constructed, thereby achieving an upgrade from the microscopic scale of pores to the mesoscopic scale of porous media. A fractal Monte Carlo pore structure quantitative characterization method considering pore morphology is established, thereby achieving quantitative characterization of characteristic parameters of the pore structure of shale reservoirs, and laying a foundation for the quantitative characterization of fluids in shale gas reservoirs and the study of migration laws.

[0006] A method for characterizing shale microscopic pore structure based on complex pore morphology, characterized by comprising the following steps:

[0007] S1. Obtaining the pore structure characteristics of the real reservoir core, the pore structure characteristics include the maximum pore width λ max , m, minimum pore width λ min , m, total porosity φ, dimensionless, total pore frequency m, pore shape distribution parameters, the pore shape distribution parameters include pore aspect ratio R hv The distribution interval [a, b], the normal distribution mean μ and the normal distribution standard deviation σ;

[0008] S2. Based on the characteristic parameters of the real reservoir core obtained in step S1, a porous medium model with complex pore morphology is constructed to more accurately characterize the microscopic pore structure of shale, which specifically includes the following sub-steps:

[0009] S21. The porous medium model is subdivided into M fractal units according to different pore shapes, wherein the pore shape is characterized by the pore aspect ratio, and each fractal unit randomly generates pores using the Monte Carlo method, that is, M randomly generated pore aspect ratios are obtained;

[0010] S22. The pore aspect ratio of the jth fractal unit is j takes the value of 1.2.3…M. The random generation is controlled by the expectation of a truncated normal distribution:

[0011] Assumptions Originally it obeys the normal distribution, and at the same time limits the value range of X to (a, b), the probability density function of X is:

[0012]

[0013] In formula (I), a and b are the range of values ​​of normal distribution X, that is, the pore aspect ratio R in S1 hv The distribution interval is [a, b]; μ is the mean of normal distribution, σ is the standard deviation of normal distribution, both obtained from S1; is the probability density function of the standard normal distribution, and Φ(x) is the cumulative distribution function of the standard normal distribution.

[0014] For the two-sided truncated normal distribution, the mathematical expectation and variance are:

[0015]

[0016] In formula (II), E(X) is the expectation of the truncated normal distribution; Var(X) is the variance of the truncated normal distribution;

[0017] S23. The pore frequency of the jth fractal unit is mj , with a porosity of φ j , dimensionless, the pore frequency and porosity of the jth fractal unit satisfy the following formula:

[0018]

[0019] In formula (III), φ and m are both obtained from S1.

[0020] S24. The maximum pore width of the jth fractal unit is λ max , the minimum pore width is λ min .

[0021] Each fractal unit cell has the same pore shape (R hv is a constant), different numbers of pores, and different units have the same maximum and minimum pore openings. The distribution of pore size on each fractal unit conforms to the fractal principle.

[0022] That is, the maximum pore width of the jth fractal unit is λ max , the minimum pore width is λ min , the porosity is φ j The pore shape is The number of pores is ω j .

[0023] S25. Pore fractal dimension of the jth fractal unit for:

[0024]

[0025] In formula (IV), d is the Euclidean dimension in fractal theory, and in three-dimensional space d = 3;

[0026] S26. The probability density function f(λ) of the aperture distribution of the jth fractal unit cell is:

[0027]

[0028] S27. The inverse function of the probability cumulative distribution function of the aperture distribution of the jth fractal unit cell is:

[0029]

[0030] In formula (VI), F -1 (v) is a random number of a specified distribution, i.e., a randomly generated pore width λ = F -1 (υ);υ~υ(0,1) obeys uniform distribution;

[0031] S28. Based on the probability cumulative distribution function, the cutoff interval of υ can be obtained:

[0032]

[0033] In formula (VII), rand(1, ω j ) is a random number between 0 and 1, the number of pores ω j It can be obtained through formula (3) in fractal theory.

[0034] S29. Average tortuosity of the non-circular cross-section capillary of the jth fractal unit for:

[0035]

[0036] S210. Average opening of the capillary tube with non-circular cross section of the jth fractal unit for:

[0037]

[0038] S211. Fractal dimension of capillary tortuosity of the jth fractal unit for:

[0039]

[0040] S212. The apparent length of the jth fractal unit for:

[0041]

[0042] S213. Pore cross-sectional area of ​​the jth fractal unit for:

[0043]

[0044] In formula (XIII), n is the number of Monte Carlo simulations. The average value of multiple simulations is used to determine the pore structure parameters. Calculations show that when the cumulative simulation is 1000 times, the Monte Carlo simulation results are very close to the exact solution of fractal theory. Generally, the value of n is 1000.

[0045] S214. Combined with the Monte Carlo method of pore morphology, M fractal units are constructed, and a porous medium model with complex pore morphology is constructed by superimposing the M fractal units to achieve pore scale upgrade. The pore cross-sectional area A of the porous medium model is p and the pore volume V p They are:

[0046]

[0047] Furthermore,

[0048] Since there are unknown parameters in both formula (XI) and formula (XII) and Therefore, in the calculation process, we can give Setting initial values Then, based on formula (XII), Then, according to formula (XI), the new Defined as By judging when the conditions are met It is believed that the calculated and If both of the above formulas are satisfied, the calculation ends. Repeat the cycle calculation until the conditions are met and obtain and The value of .

[0049] Furthermore, step S1 includes the following sub-steps:

[0050] S11. Based on real reservoir cores, multiple SEM images are obtained through scanning electron microscopy;

[0051] S12. Use the image processing software Image J to extract pores from the SEM image obtained in step S11, and perform ellipse fitting on the pores to obtain the pore structure parameters (cross-sectional pore width λ of each pore, pore aspect ratio R hv ) and conduct statistical analysis, draw the pore frequency histogram under different pore shapes (pore aspect ratio) and the corresponding dimensionless pore area fitting diagram under different pore shapes (pore aspect ratio), and determine the maximum pore width λ max , minimum pore width λ min , total porosity φ, pore shape distribution parameter (R hv The distribution interval is [a,b], the normal distribution mean is μ, and the normal distribution standard deviation is σ).

[0052] Furthermore, step S2 is implemented by MATLAB programming.

[0053] The present invention also discloses the application of any of the above-mentioned shale microscopic pore structure characterization methods, which is used in the field of unconventional oil and gas exploration technology.

[0054] Furthermore, it is used for characterizing the microscopic pore structure of shale oil and gas reservoirs. The most direct application of the present invention is in the field of shale gas exploration and development. By accurately characterizing the pore structure of shale reservoirs, we can deeply understand the occurrence state and migration law of shale gas, and provide a scientific basis for key links such as drilling site selection, fracturing design, and gas reservoir evaluation, so as to optimize the mining plan and improve the recovery rate. The present invention is also applicable to the characterization of the pore structure of other unconventional oil and gas resources (such as tight oil, coalbed methane, etc.). These resources also have complex pore systems, and accurate characterization of their pore structure is the key to understanding their storage and migration mechanisms.

[0055] Furthermore, it is used to quantitatively predict the distribution and flow characteristics of fluids in pore structures.

[0056] As unconventional oil and gas resources such as shale gas become increasingly important in the global energy structure, accurate characterization of their pore structure has become the key to promoting technological advancement and industrial upgrading. The application of the present invention will promote the advancement and standardization of related technologies and promote the efficient development of resources such as shale gas.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] By combining scanning electron microscope observation with image processing technology, the present invention can accurately extract and quantify the pore structure characteristics of real shale cores, including the morphology and size distribution of pores, providing a scientific basis for the detailed characterization of the pore structure of shale reservoirs.

[0059] The present invention realizes the discretization and randomization characterization of pore size and morphology by introducing fractal principle and Monte Carlo method, constructs a porous medium model that can reflect the complex pore morphology characteristics, and significantly improves the accuracy and authenticity of pore structure characterization.

[0060] The porous media model constructed in the present invention can not only be used for quantitative prediction of the occurrence and flow characteristics of fluids in pore structures, but also guide the study of fluid migration laws in the exploration and development of shale gas reservoirs, providing strong support for optimizing mining plans and improving recovery rates.

[0061] The multi-scale characterization method of the present invention achieves a seamless connection from the pore microscale to the porous medium mesoscale, opens up a new way for the comprehensive understanding and characterization of the pore structure of shale reservoirs, and has important theoretical significance and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1a This is a nanoscale SEM image of a real shale core sample in Example 1; Figure 1b Based on Figure 1a Extracted pore images; Figure 1c Based on Figure 1bAn ellipse-fitted pore image is obtained by performing ellipse fitting;

[0063] Figure 2a 1 is a pore frequency distribution histogram and a pore area curve diagram of different pore shapes in Example 1; Figure 2b This is a fitting diagram of pore number frequency and dimensionless pore area under different pore shapes in Example 1;

[0064] Figure 3a The pore shape frequency distribution and truncated normal density curve are reconstructed based on multiple SEM image processing and characteristic parameter analysis; Figure 3b It is the cumulative curve of pore shape distribution reconstructed based on multiple SEM image processing and characteristic parameter analysis;

[0065] Figure 4a Schematic diagram of the constructed m fractal units; Figure 4b Schematic diagram of the cross section of the constructed porous media model;

[0066] Figure 5 The pore volume distribution and the distribution curve of the raw water volume under different pore shapes in the porous medium model in Example 1 are shown. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings.

[0068] Example 1

[0069] (1) Based on the scanning electron microscope technology, nanoscale SEM images of real shale core samples were obtained, and the image processing software Image J was used to extract pores from the obtained SEM images. That is, Image J was used to perform pore edge recognition and threshold segmentation on the SEM images. The pore structure parameters and pore distribution images were quantitatively extracted according to the pore threshold segmentation results. The pore distribution image is shown in Figure 1. Figure 1a is a nanoscale SEM image of a real shale core sample in this embodiment; Figure 1b Based on Figure 1a Extracted pore images; Figure 1c Based on Figure 1b An ellipse-fitted pore image is obtained by performing ellipse fitting;

[0070] (2) The pore structure parameters obtained by threshold segmentation image processing were statistically analyzed and the pore frequency histograms under different pore aspect ratios were drawn, and the pore structure parameter λ was obtained. min =2×10 -9 m,λ max =2×10 -7 m, μ = 1 (truncated normal distribution [1,6]), σ = 2. Figure 2a-2b As shown, Figure 2a 1 is a pore frequency distribution histogram and a pore area curve diagram of different pore shapes in this embodiment; Figure 2b : is a fitting diagram of pore number frequency and dimensionless pore area under different pore shapes in this embodiment;

[0071] (3) Divide into 60 fractal units (M=60), use the S2 step in the manual to calculate the parameters of each fractal unit and reconstruct the porous medium model. In this embodiment, this step is implemented by MATLAB programming. Figure 3a-Figure 3b , Figure 4a-4b As shown, Figure 3a The pore shape frequency distribution and truncated normal density curve are reconstructed based on multiple SEM image processing and characteristic parameter analysis; Figure 3b It is the cumulative curve of pore shape distribution reconstructed based on multiple SEM image processing and characteristic parameter analysis; Figure 4a Schematic diagram of the constructed m fractal units; Figure 4b Schematic diagram of the cross-section of the constructed porous media model.

[0072] (4) Based on MATLAB programming, the pore volume of each fractal unit is randomly calculated. At the same time, based on the calculation method of native water saturation in non-circular pores (the theoretical calculation method in reference Fu J, Su Y, Chen Z, et al. Distribution of a water film confined in inorganic nanopores in real shale gas reservoirs [J]. Journal of Petroleum Science and Engineering, 2021, 209(01): 109831.), the native water content in pores of different shapes is calculated. The pore volume distribution and the distribution of the original water volume under different pore shapes in the porous media model can be obtained, as shown in Fig. Figure 5 As shown;

[0073] (5) Based on the above calculation method, the quantitative characterization of porous media based on the real shale pore characteristics can be achieved. At the same time, the analysis of the calculation results shows that the circular pores are mainly single-peak distribution type (μ = 1, σ = 2). This type of pore structure is mainly composed of circular or quasi-circular pores, which presents a "convex" shape on the pore shape cumulative frequency curve, and the normal distribution curve is a monoclinic peak. For porous media with circular pores as the main component (μ = 1, σ = 2), the dimensionless pore volume is uniformly distributed, and the peak value appears at R hv = about 3.5. As the pore aspect ratio R hv As the volume of the dimensionless primary water phase increases, the primary water in this type of porous media is mainly distributed in the nearly circular pores.

Claims

1. A method for characterizing shale microscopic pore structure taking into account complex pore morphology, characterized in that: The steps include: S1. Obtaining the pore structure characteristics of the real reservoir core, the pore structure characteristics include the maximum pore width λ max , m, minimum pore width λ min , m, total porosity φ, dimensionless, total pore frequency m, and pore shape distribution parameters, the pore shape distribution parameters include pore aspect ratio R hv The distribution interval [a, b], the normal distribution mean μ and the normal distribution standard deviation σ; S2. Based on the characteristic parameters of the real reservoir core obtained in step S1, a porous medium model with complex pore morphology is constructed to characterize the microscopic pore structure of shale, which specifically includes the following sub-steps: S21. subdividing the porous medium model into M fractal units according to different pore shapes, wherein the pore shapes are characterized by the pore aspect ratio, and each fractal unit randomly generates pores using the Monte Carlo method; S22. The pore aspect ratio of the jth fractal unit is j takes the value of 1.2.3…M. The random generation is controlled by the expectation of a truncated normal distribution: Assumptions Originally it obeys the normal distribution, and at the same time limits the value range of X to (a, b), the probability density function of X is: In formula (I), is the probability density function of the standard normal distribution, Φ(x) is the cumulative distribution function of the standard normal distribution; For the two-sided truncated normal distribution, the mathematical expectation and variance are: In formula (II), E(X) is the expectation of the truncated normal distribution; Var(X) is the variance of the truncated normal distribution; S23. The pore frequency of the jth fractal unit is m j , the porosity is φ j , the pore frequency and porosity of the jth fractal unit body satisfy the following formula: S24. The maximum pore width of the jth fractal unit is λ max , the minimum pore width is λ min ; S25. Pore fractal dimension of the jth fractal unit for: In formula (IV), d is the Euclidean dimension in fractal theory, and in three-dimensional space d = 3; S26. The probability density function f(λ) of the aperture distribution of the jth fractal unit cell is: S27. The inverse function of the probability cumulative distribution function of the aperture distribution of the jth fractal unit cell is: In formula (VI), F -1 (υ) is a random number of a specified distribution, i.e., the randomly generated pore width λ = F -1 (υ);υ~υ(0,1) obeys uniform distribution; S28. Based on the probability cumulative distribution function, the cutoff interval of υ can be obtained: In formula (VII), rand(1, ω j ) is a random number between 0 and 1, the number of pores ω j It can be obtained through fractal theory; S29. Average tortuosity of the non-circular cross-section capillary of the jth fractal unit for: S210. Average opening of the capillary tube with non-circular cross section of the jth fractal unit for: S211. Fractal dimension of capillary tortuosity of the jth fractal unit for: S212. The apparent length of the jth fractal unit cell for: S213. The pore cross-sectional area of ​​the jth fractal unit is: In formula (XIII), n is the number of Monte Carlo simulations; S214. A porous medium model with complex pore morphology is constructed by superimposing the M fractal units, wherein the pore cross-sectional area A of the porous medium model is p and the pore volume V p They are:

2. The method for characterizing shale microscopic pore structure according to claim 1, characterized in that: Since there are unknown parameters in both formula (XI) and formula (XII) and Therefore, in the calculation process, we can give Setting initial values Then, based on formula (XII), Then, according to formula (XI), the new Defined as By judging when the conditions are met It is believed that the calculated and If the above two formulas can be satisfied at the same time, the calculation ends; if the conditions are not satisfied, Repeat the cycle calculation until the conditions are met and obtain and The value of .

3. The method for characterizing shale microscopic pore structure according to claim 1, characterized in that: Step S1 includes the following sub-steps: S11. Based on real reservoir cores, multiple SEM images are obtained through scanning electron microscopy; S12. Use the image processing software Image J to extract pores from the SEM image obtained in step S11, perform ellipse fitting on the pores to obtain pore structure parameters and perform statistical analysis, draw pore frequency histograms under different pore shapes and the corresponding dimensionless pore area fitting diagrams under different pore shapes, and determine the maximum pore width λ max , minimum pore width λ min , total porosity φ, pore shape distribution parameters.

4. The method for characterizing shale microscopic pore structure according to claim 1, characterized in that: The step S2 is implemented by MATLAB programming.

5. Application of the shale microscopic pore structure characterization method according to any one of claims 1 to 4, characterized in that: Used in the field of unconventional oil and gas exploration technology.

6. The use of the shale microscopic pore structure characterization method according to claim 5, characterized in that: Used for characterization of microscopic pore structure of shale oil and gas reservoirs.

7. The use of the shale microscopic pore structure characterization method according to claim 5, characterized in that: Used for quantitative prediction of the storage and flow characteristics of fluids in pore structures.

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