WO2022011893A1 - Reservoir-based modeling method and device for pore network model - Google Patents

Reservoir-based modeling method and device for pore network model Download PDF

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WO2022011893A1
WO2022011893A1 PCT/CN2020/126212 CN2020126212W WO2022011893A1 WO 2022011893 A1 WO2022011893 A1 WO 2022011893A1 CN 2020126212 W CN2020126212 W CN 2020126212W WO 2022011893 A1 WO2022011893 A1 WO 2022011893A1
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reservoir
data volume
pore
dimensional tensor
tensor data
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PCT/CN2020/126212
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French (fr)
Chinese (zh)
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王猛
杨玉卿
唐雁冰
张志强
高永德
杨鑫
刘海波
刘志杰
李闽
何玉春
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中海油田服务股份有限公司
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Publication of WO2022011893A1 publication Critical patent/WO2022011893A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02A90/30—Assessment of water resources

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  • the embodiments of the present disclosure relate to, but are not limited to, the field of petroleum logging, and in particular, relate to a modeling method and device based on a pore network model of a reservoir.
  • Embodiments of the present disclosure provide a modeling method based on a pore network model of a reservoir, including:
  • the correlation length is obtained according to the reservoir image, and a three-dimensional tensor convolution kernel is calculated according to the correlation length; wherein, the correlation length represents the average radius of the preselected black circles in the reservoir image;
  • a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
  • a pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
  • the calculating a three-dimensional tensor convolution kernel according to the correlation length includes:
  • h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center, L c represents the correlation length.
  • the acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and acquiring the pore throat radius distribution according to the T2 spectrum includes:
  • n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
  • T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
  • r m is the m-th pore throat radius
  • T 2m is the m-th amplitude value of the T2 spectrum
  • c is a preset conversion coefficient
  • m is a positive integer.
  • the above method also has the following characteristics:
  • the forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
  • a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume
  • the random function is the following log-normal distribution random function:
  • the mathematical expectation ⁇ and the standard deviation ⁇ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
  • volume data bodies including:
  • the construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
  • the disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
  • the moving each node coordinate according to a preset rule includes:
  • i is the node serial number in the x direction
  • j is the node serial number in the y direction
  • k is the node serial number in the z direction
  • i, j and k are integers greater than
  • rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
  • establishing a pore network model according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure including:
  • a pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
  • Embodiments of the present disclosure also provide a modeling device based on a pore network model of a reservoir, including: a memory and a processor;
  • the memory configured to hold a program for modeling a pore network model of the reservoir
  • the processor configured to read a program that executes the modeling of the pore network model for the reservoir, executes the following modeling method:
  • the correlation length is obtained according to the reservoir image, and a three-dimensional tensor convolution kernel is calculated according to the correlation length; wherein, the correlation length represents the average radius of the preselected black circles in the reservoir image;
  • a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
  • a pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
  • the calculating a three-dimensional tensor convolution kernel according to the correlation length includes:
  • h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center, L c represents the correlation length.
  • the acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and acquiring the pore throat radius distribution according to the T2 spectrum includes:
  • n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
  • T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
  • rm is the mth pore throat radius
  • T2m is the mth amplitude value of the T2 spectrum
  • c is a preset conversion coefficient
  • m is a positive integer.
  • the forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
  • a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume
  • the random function is the following log-normal distribution random function:
  • the mathematical expectation ⁇ and the standard deviation ⁇ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
  • volume data bodies including:
  • the construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
  • the disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
  • the moving each node coordinate according to a preset rule includes:
  • i is the node serial number in the x direction
  • j is the node serial number in the y direction
  • k is the node serial number in the z direction
  • i, j and k are integers greater than
  • rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
  • establishing a pore network model according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure including:
  • a pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
  • FIG. 1 is a schematic diagram of a modeling method based on a pore network model of a reservoir according to an embodiment of the present disclosure.
  • FIG. 2 is an example of a sandstone formation reservoir image obtained by electrical imaging logging according to an embodiment of the present disclosure.
  • FIG. 3 is an example of an image of a conglomerate formation reservoir obtained by electrical imaging logging according to an embodiment of the present disclosure.
  • FIG. 4 is an example of a carbonate rock formation reservoir image obtained by electrical imaging logging according to an embodiment of the present disclosure.
  • Figure 5 is an example of a convolution kernel.
  • FIG. 6 is an example of a T2 spectrum obtained by nuclear magnetic resonance logging according to an embodiment of the present disclosure.
  • FIG. 7 is an example of a frequency distribution curve of a pore throat radius according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic diagram of an example of generating a three-dimensional tensor data volume conforming to the frequency distribution of pore throat radius according to an embodiment of the present disclosure.
  • FIG. 10a is an example of an ordered spatial structure diagram of an embodiment of the present disclosure.
  • FIG. 10b is an example of a disordered space structure diagram according to an embodiment of the present disclosure.
  • FIG. 11 is an example of a pore network model of an embodiment of the present disclosure.
  • FIG. 12 is a schematic diagram of a modeling apparatus based on a pore network model of a reservoir according to an embodiment of the present disclosure.
  • FIG. 1 is a schematic diagram of a modeling method for a reservoir-based pore network model according to an embodiment of the present disclosure. As shown in FIG. 1 , the modeling method in this embodiment includes steps S11-S16:
  • a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir.
  • S13 may be executed before or after S11 and S12, and may be executed in parallel with S11 and S12; the sequence of S14 and S15 is not limited, and may also be executed in parallel.
  • the three-dimensional tensor convolution kernel E(h) can be calculated according to the following formula:
  • h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center, L c represents the correlation length.
  • n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology may be acquired; wherein, n is a positive integer; and the n T2 spectra are obtained. And, the amplitude value of the T2 spectrum obtained after the summation is converted into the frequency distribution of the pore throat radius through a preset quantitative relationship.
  • r m is the m-th pore throat radius
  • T 2m is the m-th amplitude value of the T2 spectrum
  • c is a preset conversion coefficient
  • m is a positive integer.
  • a three-dimensional stable random field can be established by a random function to form an initial three-dimensional tensor data volume
  • the random function is the following log-normal distribution random function:
  • the mathematical expectation ⁇ and the standard deviation ⁇ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
  • the 3D tensor convolution kernel and the initial 3D tensor data volume may be sequentially multiplied by tensor points, and the result of the point multiplication may be calculated according to the values in the initial 3D tensor data volume. They are sequentially stacked to generate a 3D tensor data volume that conforms to the frequency distribution of the pore throat radius of the reservoir.
  • the number of nodes of the disordered space structure may be determined according to the data quantity of the initial three-dimensional tensor data volume
  • the disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
  • each node coordinate (x, y, z) can be moved according to the following formula:
  • i is the node serial number in the x direction
  • j is the node serial number in the y direction
  • k is the node serial number in the z direction
  • i, j and k are integers greater than
  • rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
  • a pore network model can be established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
  • FIG. 2 is a sandstone formation reservoir image obtained by electrical imaging logging according to an embodiment of the present disclosure.
  • FIG. 3 is an image of a conglomerate formation reservoir obtained by electrical imaging logging according to an embodiment of the present disclosure
  • FIG. 4 is an image of a carbonate formation reservoir obtained by electrical imaging logging according to an embodiment of the present disclosure.
  • the thickness of the above-mentioned three strata (that is, the reservoir) is all 0.5m. Some of the black blobs in the picture are circled.
  • the average of the radii of the one or more black masses is the correlation length L c .
  • the value of L c can be identified and measured by ImageJ, it is not necessary to identify each black group, only one or more samples need to be identified and averaged.
  • the correlation length of different reservoirs is different, generally sandstone ⁇ conglomerate ⁇ carbonate rock.
  • the 3D tensor convolution kernel can be calculated by the correlation length according to the covariance model in geostatistics.
  • the three-dimensional tensor convolution kernel E(h) exp(-2h/L c ), L c is the correlation length, which is related to the lithology; it is obtained according to the electro-imaging analysis; h represents the three-dimensional coordinate system with (L x , L y , L z ) is the center of the sphere, the distance from the sphere whose radius is less than or equal to Lc to the center of the sphere,
  • a three-dimensional convolution kernel (as shown in Figure 5) can be implemented by the following procedure.
  • FIG. 6 is an example of a T2 spectrum obtained by nuclear magnetic resonance logging according to an embodiment of the present disclosure.
  • n T2 spectra are obtained according to the following method: the formation (ie, the reservoir) with a thickness of 0.5 m is divided into n sub-formations (ie, sub-reservoirs), and one T2 spectrum is obtained for each sub-formation through nuclear magnetic resonance logging.
  • n is set by the user as required.
  • FIG. 7 is an example of a frequency distribution curve of a pore throat radius according to an embodiment of the present disclosure.
  • the T2 spectral amplitude values are converted into pore throat radius frequency distributions by a quantitative relationship.
  • FIG. 8 is an example of an initial three-dimensional tensor data volume according to an embodiment of the present disclosure.
  • the initial three-dimensional tensor data volume can be used to establish a three-dimensional stable random field through a random function (logarithmic uniform or logarithmic normal, etc.) according to the frequency distribution curve of the pore throat radius to form an initial three-dimensional tensor data volume.
  • a random function logarithmic uniform or logarithmic normal, etc.
  • the average value (ie mathematical expectation) ⁇ and the standard deviation ⁇ of the pore throat radius frequency distribution can be obtained by fitting the pore throat radius frequency distribution curve through Matlab, and the following log-normal distribution random function can be obtained:
  • FIG. 9 is a schematic diagram of generating a three-dimensional tensor data volume conforming to the frequency distribution of pore throat radius according to an embodiment of the present disclosure.
  • the constructed three-dimensional tensor convolution kernel is used to slide over the three-dimensional tensor data volume to perform tensor point multiplication calculations in turn, and each calculation obtains a point.
  • the result values of all channels are stacked in the original order to form a new three-dimensional tensor, forming a three-dimensional tensor data volume that conforms to the frequency distribution characteristics of the pore throat radius of the reservoir.
  • the generated micropores can be observed through vtk - Image of dissolved pore carbonate rock model.
  • FIG. 10a is an example of an ordered spatial structure diagram of an embodiment of the present disclosure.
  • FIG. 10b is an example of a disordered space structure diagram according to an embodiment of the present disclosure.
  • the construction process of the disordered space structure is as follows:
  • each node represents a pore, and the nodes are connected by throats.
  • each node representing a pore is connected by six throats; similarly, each throat is also connected by six pores.
  • the distance between each node in each direction (that is, the x direction, the y direction and the z direction) is set to l, the number of nodes is set to d, and the side length of the model is (d-1) ⁇ l.
  • a probability function with probability p is set in the program, and a (pseudo) random number generator is used to determine whether there is a tube bundle connection between each adjacent node in the x, y and z directions.
  • a function to generate random numbers, thereby generating random probabilities.
  • the rand() function can be used to generate random numbers, thereby generating random probabilities.
  • the specific C/C++ code is:
  • rand()%100 the computer randomly generates any integer in the range of 0-99.
  • the connectivity of the network model can also be quantitatively described by coordination numbers.
  • the coordination number of conventional sandstone reservoir rocks is in the range of 4-6, and the coordination number of low-permeability sandstone reservoir rocks is 3-4.
  • a coordination number of 6 means that one node is connected to the other 6 node bundles.
  • FIG. 11 is an example of a pore network model of an embodiment of the present disclosure.
  • the data in the 3D tensor data volume that conforms to the frequency distribution characteristics of the pore throat radius of the reservoir are sequentially assigned to the nodes of the disordered spatial structure, and the pore network model that conforms to the real geological characteristics of the reservoir can be constructed.
  • the above modeling process can use multi-GPU computing on a workstation equipped with 8 graphics cards and a memory of 512GB, thereby expanding the scale of the model (the range of about 100 meters, the thickness of about 10 meters, and the total number of pore nodes exceeds 1 billion).
  • the pore network model established in the embodiments of the present disclosure mainly relies on the electrical imaging logging and nuclear magnetic resonance logging methods, taking into account the physical parameters of the reservoir, and to a certain extent solves the problem that the established geological model lacks a certain physical meaning. To a certain extent, it solves the bottleneck problem of difficult formation modeling with strong heterogeneity, and is also applicable to carbonate formations, providing a certain reference for modeling low-permeability tight oil and gas reservoirs.
  • FIG. 12 is a schematic diagram of a modeling apparatus based on a pore network model of a reservoir according to an embodiment of the present disclosure.
  • the modeling device based on the pore network model of the reservoir includes: a memory and a processor;
  • the memory configured to hold a program for modeling a pore network model of the reservoir
  • the processor configured to read and execute the program for modeling the pore network model of the reservoir, executes the following modeling method:
  • the correlation length is obtained according to the reservoir image, and a three-dimensional tensor convolution kernel is calculated according to the correlation length; wherein, the correlation length represents the average radius of the preselected black circles in the reservoir image;
  • a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
  • a pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
  • the calculating a three-dimensional tensor convolution kernel according to the correlation length includes:
  • h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center, L c represents the correlation length.
  • the acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and acquiring the pore throat radius distribution according to the T2 spectrum includes:
  • n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
  • T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
  • r m is the m-th pore throat radius
  • T 2m is the m-th amplitude value of the T2 spectrum
  • c is a preset conversion coefficient
  • m is a positive integer.
  • the forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
  • a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume
  • the random function is the following log-normal distribution random function:
  • the mathematical expectation ⁇ and the standard deviation ⁇ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
  • volume data bodies including:
  • the construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
  • the disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
  • the moving each node coordinate according to a preset rule includes:
  • i is the node serial number in the x direction
  • j is the node serial number in the y direction
  • k is the node serial number in the z direction
  • i, j and k are integers greater than
  • rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
  • establishing a pore network model according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure including:
  • a pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.

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Abstract

A reservoir-based modeling method and device for a pore network model. The method comprises: obtaining a reservoir image obtained by means of electrical imaging well logging; obtaining a correlation length according to the reservoir image, and calculating a three-dimensional tensor convolution kernel according to the correlation length; obtaining a T2 spectrum of the reservoir obtained by nuclear magnetic resonance well logging, and obtaining pore-throat radius frequency distribution according to the T2 spectrum; forming an initial three-dimensional tensor data volume according to the pore-throat radius frequency distribution; generating, according to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume and by means of a convolutional neural network forward propagation algorithm, a three-dimensional tensor data volume conforming to the pore-throat radius frequency distribution of the reservoir; constructing a disordered spatial structure of a pore network model according to the initial three-dimensional tensor data volume; and establishing the pore network model according to the three-dimensional tensor data volume conforming to the pore-throat radius frequency distribution of the reservoir and the disordered spatial structure.

Description

基于储层的孔隙网络模型的建模方法及装置Modeling method and device for reservoir-based pore network model 技术领域technical field
本公开实施例涉及但不限于石油测井领域，尤其涉及一种基于储层的孔隙网络模型的建模方法及装置。The embodiments of the present disclosure relate to, but are not limited to, the field of petroleum logging, and in particular, relate to a modeling method and device based on a pore network model of a reservoir.
背景技术Background technique
随着油气田开发的不断深入，油气流体(尤其是低渗致密油气藏)在地下的分布情况也越来越复杂，油藏地质建模是油藏储层研究中的重要部分。建模方法主要分为两个方面，即确定性建模与随机性建模。确定性建模是对井间未知区给出确定性的预测结果，随机性建模是应用随机模拟的方法对未知区给出多种可能的预测结果。但在某些非均质性强、特低渗致密的储层，尤其是在特征复杂、非均质性极强、溶孔发育的碳酸盐岩储层，受插值精度与方法的限制，导致分析研究方面还存在着一定的不足，某些情况下也缺乏一定的物理意义。With the continuous development of oil and gas fields, the distribution of oil and gas fluids (especially low-permeability tight oil and gas reservoirs) in the ground is becoming more and more complex. Reservoir geological modeling is an important part of reservoir research. The modeling methods are mainly divided into two aspects, namely deterministic modeling and stochastic modeling. Deterministic modeling is to give deterministic prediction results for the unknown area between wells, and stochastic modeling is to use the method of stochastic simulation to provide a variety of possible prediction results for the unknown area. However, in some tight reservoirs with strong heterogeneity and ultra-low permeability, especially in carbonate reservoirs with complex characteristics, strong heterogeneity and developed dissolved pores, the interpolation accuracy and method are limited. As a result, there are still some deficiencies in analysis and research, and in some cases, there is also a lack of certain physical significance.
发明概述SUMMARY OF THE INVENTION
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。The following is an overview of the topics detailed in this article. This summary is not intended to limit the scope of protection of the claims.
本公开实施例提供了基于储层的孔隙网络模型的建模方法，包括：Embodiments of the present disclosure provide a modeling method based on a pore network model of a reservoir, including:
获取通过电成像测井得到的储层图像；Obtain reservoir images obtained by electrical imaging logging;
根据所述储层图像获取相关长度，根据所述相关长度计算三维张量卷积核；其中，所述相关长度表示所述储层图像中的预选黑团的半径平均值；The correlation length is obtained according to the reservoir image, and a three-dimensional tensor convolution kernel is calculated according to the correlation length; wherein, the correlation length represents the average radius of the preselected black circles in the reservoir image;
获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径频率分布；根据所述孔喉半径频率分布形成初始三维张量数据体；obtaining the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and obtaining the frequency distribution of the pore throat radius according to the T2 spectrum; forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius;
根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体；According to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
根据初始三维张量数据体构建孔隙网络模型的无序空间结构；Construct the disordered spatial structure of the pore network model according to the initial 3D tensor data volume;
根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型。A pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
一种示例性的实施例中，所述根据所述相关长度计算三维张量卷积核，包括：In an exemplary embodiment, the calculating a three-dimensional tensor convolution kernel according to the correlation length includes:
按照如下公式计算三维张量卷积核E(h)：Calculate the three-dimensional tensor convolution kernel E(h) according to the following formula:
E(h)＝exp(-2h/L c) E(h)=exp(-2h/L c )
其中，h表示三维坐标系中以(L x，L y，L z)为球心，半径小于等于Lc的球面到所述球心的距离，
Figure PCTCN2020126212-appb-000001
L c表示所述相关长度。 Among them, h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center,
Figure PCTCN2020126212-appb-000001
L c represents the correlation length.
一种示例性的实施例中，所述获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径分布，包括：In an exemplary embodiment, the acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and acquiring the pore throat radius distribution according to the T2 spectrum, includes:
获取通过核磁共振测井技术得到的所述储层的n个子储层的n个T2谱；其中，n为正整数；acquiring n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
对所述n个T2谱进行求和，通过预设定量关系将求和后得到的T2谱的幅度值转换成孔喉半径频率分布。The n T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
一种示例性的实施例中，所述预设定量关系为r m＝cT 2m； An exemplary embodiment, the predetermined quantitative relationship r m = cT 2m;
其中，r m为第m个孔喉半径，T 2m为T2谱的第m个幅度值，c为预设的转换系数，m为正整数。 Wherein, r m is the m-th pore throat radius, T 2m is the m-th amplitude value of the T2 spectrum, c is a preset conversion coefficient, and m is a positive integer.
一种示例性的实施例中，上述方法还具有下面特点：In an exemplary embodiment, the above method also has the following characteristics:
所述根据所述孔喉半径频率分布形成初始三维张量数据体，包括：The forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
根据所述孔喉半径频率分布，通过随机函数建立三维稳定随机场，形成初始三维张量数据体；According to the frequency distribution of the pore throat radius, a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume;
其中，所述随机函数为如下对数正态分布随机函数：Wherein, the random function is the following log-normal distribution random function:
Figure PCTCN2020126212-appb-000002
Figure PCTCN2020126212-appb-000002
其中，通过拟合所述孔喉半径频率分布得到数学期望μ和标准偏差σ，x表示孔喉半径。The mathematical expectation μ and the standard deviation σ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
一种示例性的实施例中，所述根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体，包括：In an exemplary embodiment, according to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor conforming to the frequency distribution of the pore throat radius of the reservoir. volume data bodies, including:
将所述三维张量卷积核与所述初始三维张量数据体依次进行张量点乘，将点乘结果按照所述初始三维张量数据体中的顺序堆叠起来，生成符合储层孔喉半径频率分布的三维张量数据体。Perform tensor point multiplication on the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume in turn, and stack the results of the point multiplication in the order in the initial three-dimensional tensor data volume to generate a pore-throat conformity to the reservoir. A 3D tensor data volume of radial frequency distributions.
一种示例性的实施例中，所述根据所述初始三维张量数据体构建孔隙网络模型的无序空间结构，包括：In an exemplary embodiment, the construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
根据所述初始三维张量数据体的数据数量确定所述无序空间结构的节点数；Determine the number of nodes of the disordered space structure according to the data quantity of the initial three-dimensional tensor data volume;
根据所述节点数以及每个节点间的间隔距离L构建包含X、Y、Z三个方向的三维立方体网络；Constructing a three-dimensional cube network including three directions of X, Y, and Z according to the number of nodes and the separation distance L between each node;
计算所述三维立方体网络中每个节点的坐标；calculating the coordinates of each node in the three-dimensional cube network;
根据预设第一概率函数确定X方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the X direction according to the preset first probability function, and assign the tube bundle radius;
根据预设第二概率函数确定Y方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the Y direction according to the preset second probability function, and assign the tube bundle radius;
通过预设规则移动每个节点坐标；Move each node coordinate by preset rules;
根据所述三维立方体网络、确定是否有管束连通的结果、分配的管束半径和移动后的节点坐标生成无序空间结构。The disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
一种示例性的实施例中，所述通过预设规则移动每个节点坐标包括：In an exemplary embodiment, the moving each node coordinate according to a preset rule includes:
按照如下公式移动每个节点坐标(x,y,z)：Move each node coordinate (x, y, z) according to the following formula:
(x,y,z)＝[(i-1)L±rand()％(0.5L),(j-1)L±rand()％(0.5L),(k-1)L±rand()％(0.5L)](x,y,z)=[(i-1)L±rand()%(0.5L),(j-1)L±rand()%(0.5L),(k-1)L±rand( )% (0.5L)]
其中，i为x方向的节点序号，j为y方向的节点序号，k为z方向的节点序号，i、j和k为大于0的整数，rand()％(0.5L)表示随机生成0.5L范围内的任意整数。Among them, i is the node serial number in the x direction, j is the node serial number in the y direction, k is the node serial number in the z direction, i, j and k are integers greater than 0, rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
一种示例性的实施例中，根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型，包括：In an exemplary embodiment, establishing a pore network model according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure, including:
将所述符合储层孔喉半径频率分布的三维张量数据体依次赋值到所述无序空间结构的节点上来建立孔隙网络模型。A pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
本公开实施例还提供了一种基于储层的孔隙网络模型的建模装置，包括：存储器和处理器；Embodiments of the present disclosure also provide a modeling device based on a pore network model of a reservoir, including: a memory and a processor;
所述存储器，设置为保存用于进行储层的孔隙网络模型的建模的程序；the memory configured to hold a program for modeling a pore network model of the reservoir;
所述处理器，用于读取执行所述用于储层的孔隙网络模型的建模的程序，执行如下的建模方法：The processor, configured to read a program that executes the modeling of the pore network model for the reservoir, executes the following modeling method:
获取通过电成像测井得到的储层图像；Obtain reservoir images obtained by electrical imaging logging;
根据所述储层图像获取相关长度，根据所述相关长度计算三维张量卷积核；其中，所述相关长度表示所述储层图像中的预选黑团的半径平均值；The correlation length is obtained according to the reservoir image, and a three-dimensional tensor convolution kernel is calculated according to the correlation length; wherein, the correlation length represents the average radius of the preselected black circles in the reservoir image;
获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径频率分布；根据所述孔喉半径频率分布形成初始三维张量数据体；obtaining the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and obtaining the frequency distribution of the pore throat radius according to the T2 spectrum; forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius;
根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体；According to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
根据初始三维张量数据体构建孔隙网络模型的无序空间结构；Construct the disordered spatial structure of the pore network model according to the initial 3D tensor data volume;
根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型。A pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
一种示例性的实施例中，所述根据所述相关长度计算三维张量卷积核，包括：In an exemplary embodiment, the calculating a three-dimensional tensor convolution kernel according to the correlation length includes:
按照如下公式计算三维张量卷积核E(h)：Calculate the three-dimensional tensor convolution kernel E(h) according to the following formula:
E(h)＝exp(-2h/L c) E(h)=exp(-2h/L c )
其中，h表示三维坐标系中以(L x，L y，L z)为球心，半径小于等于Lc的球面到所述球心的距离，
Figure PCTCN2020126212-appb-000003
L c表示所述相关长度。 Among them, h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center,
Figure PCTCN2020126212-appb-000003
L c represents the correlation length.
一种示例性的实施例中，所述获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径分布，包括：In an exemplary embodiment, the acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and acquiring the pore throat radius distribution according to the T2 spectrum, includes:
获取通过核磁共振测井技术得到的所述储层的n个子储层的n个T2谱；其中，n为正整数；acquiring n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
对所述n个T2谱进行求和，通过预设定量关系将求和后得到的T2谱的幅度值转换成孔喉半径频率分布。The n T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
一种示例性的实施例中，所述预设定量关系为rm＝cT2m；In an exemplary embodiment, the preset quantitative relationship is rm=cT2m;
其中，rm为第m个孔喉半径，T2m为T2谱的第m个幅度值，c为预设的转换系数，m为正整数。Wherein, rm is the mth pore throat radius, T2m is the mth amplitude value of the T2 spectrum, c is a preset conversion coefficient, and m is a positive integer.
一种示例性的实施例中，所述根据所述孔喉半径频率分布形成初始三维张量数据体，包括：In an exemplary embodiment, the forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
根据所述孔喉半径频率分布，通过随机函数建立三维稳定随机场，形成初始三维张量数据体；According to the frequency distribution of the pore throat radius, a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume;
其中，所述随机函数为如下对数正态分布随机函数：Wherein, the random function is the following log-normal distribution random function:
Figure PCTCN2020126212-appb-000004
Figure PCTCN2020126212-appb-000004
其中，通过拟合所述孔喉半径频率分布得到数学期望μ和标准偏差σ，x表示孔喉半径。The mathematical expectation μ and the standard deviation σ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
一种示例性的实施例中，所述根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体，包括：In an exemplary embodiment, according to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor conforming to the frequency distribution of the pore throat radius of the reservoir. volume data bodies, including:
将所述三维张量卷积核与所述初始三维张量数据体依次进行张量点乘，将点乘结果按照所述初始三维张量数据体中的顺序堆叠起来，生成符合储层孔喉半径频率分布的三维张量数据体。Perform tensor point multiplication on the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume in turn, and stack the results of the point multiplication in the order in the initial three-dimensional tensor data volume to generate a pore-throat conformity to the reservoir. A 3D tensor data volume of radial frequency distributions.
一种示例性的实施例中，所述根据所述初始三维张量数据体构建孔隙网络模型的无序空间结构，包括：In an exemplary embodiment, the construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
根据所述初始三维张量数据体的数据数量确定所述无序空间结构的节点数；Determine the number of nodes of the disordered space structure according to the data quantity of the initial three-dimensional tensor data volume;
根据所述节点数以及每个节点间的间隔距离L构建包含X、Y、Z三个方向的三维立方体网络；Constructing a three-dimensional cube network including three directions of X, Y, and Z according to the number of nodes and the separation distance L between each node;
计算所述三维立方体网络中每个节点的坐标；calculating the coordinates of each node in the three-dimensional cube network;
根据预设第一概率函数确定X方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the X direction according to the preset first probability function, and assign the tube bundle radius;
根据预设第二概率函数确定Y方向每个相邻节点之间是否有管束连通， 并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the Y direction according to a preset second probability function, and assign the tube bundle radius;
通过预设规则移动每个节点坐标；Move each node coordinate by preset rules;
根据所述三维立方体网络、确定是否有管束连通的结果、分配的管束半径和移动后的节点坐标生成无序空间结构。The disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
一种示例性的实施例中，所述通过预设规则移动每个节点坐标包括：In an exemplary embodiment, the moving each node coordinate according to a preset rule includes:
按照如下公式移动每个节点坐标(x,y,z)：Move each node coordinate (x, y, z) according to the following formula:
(x,y,z)＝[(i-1)L±rand()％(0.5L),(j-1)L±rand()％(0.5L),(k-1)L±rand()％(0.5L)](x,y,z)=[(i-1)L±rand()%(0.5L),(j-1)L±rand()%(0.5L),(k-1)L±rand( )% (0.5L)]
其中，i为x方向的节点序号，j为y方向的节点序号，k为z方向的节点序号，i、j和k为大于0的整数，rand()％(0.5L)表示随机生成0.5L范围内的任意整数。Among them, i is the node serial number in the x direction, j is the node serial number in the y direction, k is the node serial number in the z direction, i, j and k are integers greater than 0, rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
一种示例性的实施例中，根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型，包括：In an exemplary embodiment, establishing a pore network model according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure, including:
将所述符合储层孔喉半径频率分布的三维张量数据体依次赋值到所述无序空间结构的节点上来建立孔隙网络模型。A pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
在阅读并理解了附图和详细描述后，可以明白其他方面。Other aspects will become apparent upon reading and understanding of the drawings and detailed description.
附图概述BRIEF DESCRIPTION OF THE DRAWINGS
图1为本公开实施例的基于储层的孔隙网络模型的建模方法的示意图。FIG. 1 is a schematic diagram of a modeling method based on a pore network model of a reservoir according to an embodiment of the present disclosure.
图2为本公开实施例的通过电成像测井得到的砂岩地层储层图像示例。FIG. 2 is an example of a sandstone formation reservoir image obtained by electrical imaging logging according to an embodiment of the present disclosure.
图3为本公开实施例的通过电成像测井得到的砾岩地层储层图像示例。FIG. 3 is an example of an image of a conglomerate formation reservoir obtained by electrical imaging logging according to an embodiment of the present disclosure.
图4为本公开实施例的通过电成像测井得到的碳酸盐岩地层储层图像示例。FIG. 4 is an example of a carbonate rock formation reservoir image obtained by electrical imaging logging according to an embodiment of the present disclosure.
图5为卷积核示例。Figure 5 is an example of a convolution kernel.
图6为本公开实施例的通过核磁共振测井得到的T2谱示例。FIG. 6 is an example of a T2 spectrum obtained by nuclear magnetic resonance logging according to an embodiment of the present disclosure.
图7为本公开实施例的孔喉半径频率分布曲线示例。FIG. 7 is an example of a frequency distribution curve of a pore throat radius according to an embodiment of the present disclosure.
图8为公开实施例的初始三维张量数据体示例。8 is an example of an initial three-dimensional tensor data volume of a disclosed embodiment.
图9为本公开实施例生成符合孔喉半径频率分布的三维张量数据体示例的示意图。FIG. 9 is a schematic diagram of an example of generating a three-dimensional tensor data volume conforming to the frequency distribution of pore throat radius according to an embodiment of the present disclosure.
图10a为本公开实施例的有序空间结构图示例。FIG. 10a is an example of an ordered spatial structure diagram of an embodiment of the present disclosure.
图10b为本公开实施例的无序空间结构图示例。FIG. 10b is an example of a disordered space structure diagram according to an embodiment of the present disclosure.
图11为本公开实施例的孔隙网络模型示例。FIG. 11 is an example of a pore network model of an embodiment of the present disclosure.
图12为本公开实施例的基于储层的孔隙网络模型的建模装置的示意图。FIG. 12 is a schematic diagram of a modeling apparatus based on a pore network model of a reservoir according to an embodiment of the present disclosure.
详述detail
下文中将结合附图对本公开的实施例进行详细说明。需要说明的是，在不冲突的情况下，本公开中的实施例及实施例中的特征可以相互任意组合。Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that, the embodiments of the present disclosure and the features of the embodiments may be arbitrarily combined with each other under the condition of no conflict.
图1为本公开实施例的基于储层的孔隙网络模型的建模方法的示意图，如图1所示，本实施例的建模方法包括步骤S11-S16：FIG. 1 is a schematic diagram of a modeling method for a reservoir-based pore network model according to an embodiment of the present disclosure. As shown in FIG. 1 , the modeling method in this embodiment includes steps S11-S16:
S11、获取通过电成像测井得到的储层图像。S11. Acquire a reservoir image obtained by electrical imaging logging.
S12、根据储层图像获取相关长度，根据相关长度计算三维张量卷积核。S12 , obtaining a correlation length according to the reservoir image, and calculating a three-dimensional tensor convolution kernel according to the correlation length.
S13、获取核磁共振测井得到的储层的T2谱，根据T2谱获取孔喉半径频率分布，根据孔喉半径频率分布形成初始三维张量数据体。S13 , acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, acquiring the frequency distribution of the pore throat radius according to the T2 spectrum, and forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius.
S14、根据三维张量卷积核和初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体。S14. According to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir.
S15、根据初始三维张量数据体构建孔隙网络模型的无序空间结构。S15, constructing the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume.
S16、根据符合储层孔喉半径频率分布的三维张量数据体和无序空间结构建立孔隙网络模型。S16 , establishing a pore network model according to the three-dimensional tensor data volume and the disordered spatial structure conforming to the frequency distribution of the pore throat radius of the reservoir.
上述步骤中，S13可以在S11、S12之前或之后执行，可以和S11、S12并行执行；S14和S15的先后顺序不限，也可以并行执行。In the above steps, S13 may be executed before or after S11 and S12, and may be executed in parallel with S11 and S12; the sequence of S14 and S15 is not limited, and may also be executed in parallel.
一种示例性的实施例中，可以按照如下公式计算三维张量卷积核E(h)：In an exemplary embodiment, the three-dimensional tensor convolution kernel E(h) can be calculated according to the following formula:
E(h)＝exp(-2h/L c) E(h)=exp(-2h/L c )
其中，h表示三维坐标系中以(L x，L y，L z)为球心，半径小于等于Lc的球面到所述球心的距离，
Figure PCTCN2020126212-appb-000005
L c表示所述相关长度。 Among them, h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center,
Figure PCTCN2020126212-appb-000005
L c represents the correlation length.
一种示例性的实施例中，可以获取通过核磁共振测井技术得到的所述储层的n个子储层的n个T2谱；其中，n为正整数；对所述n个T2谱进行求 和，通过预设定量关系将求和后得到的T2谱的幅度值转换成孔喉半径频率分布。In an exemplary embodiment, n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology may be acquired; wherein, n is a positive integer; and the n T2 spectra are obtained. And, the amplitude value of the T2 spectrum obtained after the summation is converted into the frequency distribution of the pore throat radius through a preset quantitative relationship.
一种示例性的实施例中，所述预设定量关系可以为r m＝cT 2m； An exemplary embodiment, the predetermined relationship may be quantified r m = cT 2m;
其中，r m为第m个孔喉半径，T 2m为T2谱的第m个幅度值，c为预设的转换系数，m为正整数。 Wherein, r m is the m-th pore throat radius, T 2m is the m-th amplitude value of the T2 spectrum, c is a preset conversion coefficient, and m is a positive integer.
一种示例性的实施例中，可以根据所述孔喉半径频率分布，通过随机函数建立三维稳定随机场，形成初始三维张量数据体；In an exemplary embodiment, according to the frequency distribution of the pore throat radius, a three-dimensional stable random field can be established by a random function to form an initial three-dimensional tensor data volume;
其中，所述随机函数为如下对数正态分布随机函数：Wherein, the random function is the following log-normal distribution random function:
Figure PCTCN2020126212-appb-000006
Figure PCTCN2020126212-appb-000006
其中，通过拟合所述孔喉半径频率分布得到数学期望μ和标准偏差σ，x表示孔喉半径。The mathematical expectation μ and the standard deviation σ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
一种示例性的实施例中，可以将所述三维张量卷积核与所述初始三维张量数据体依次进行张量点乘，将点乘结果按照所述初始三维张量数据体中的顺序堆叠起来，生成符合储层孔喉半径频率分布的三维张量数据体。In an exemplary embodiment, the 3D tensor convolution kernel and the initial 3D tensor data volume may be sequentially multiplied by tensor points, and the result of the point multiplication may be calculated according to the values in the initial 3D tensor data volume. They are sequentially stacked to generate a 3D tensor data volume that conforms to the frequency distribution of the pore throat radius of the reservoir.
一种示例性的实施例中，可以根据所述初始三维张量数据体的数据数量确定所述无序空间结构的节点数；In an exemplary embodiment, the number of nodes of the disordered space structure may be determined according to the data quantity of the initial three-dimensional tensor data volume;
根据所述节点数以及每个节点间的间隔距离L构建包含X、Y、Z三个方向的三维立方体网络；Constructing a three-dimensional cube network including three directions of X, Y, and Z according to the number of nodes and the separation distance L between each node;
计算所述三维立方体网络中每个节点的坐标；calculating the coordinates of each node in the three-dimensional cube network;
根据预设第一概率函数确定X方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the X direction according to the preset first probability function, and assign the tube bundle radius;
根据预设第二概率函数确定Y方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the Y direction according to the preset second probability function, and assign the tube bundle radius;
通过预设规则移动每个节点坐标；Move each node coordinate by preset rules;
根据所述三维立方体网络、确定是否有管束连通的结果、分配的管束半径和移动后的节点坐标生成无序空间结构。The disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
一种示例性的实施例中，可以按照如下公式移动每个节点坐标(x,y,z)：In an exemplary embodiment, each node coordinate (x, y, z) can be moved according to the following formula:
(x,y,z)＝[(i-1)L±rand()％(0.5L),(j-1)L±rand()％(0.5L),(k-1)L±rand()％(0.5L)](x,y,z)=[(i-1)L±rand()%(0.5L),(j-1)L±rand()%(0.5L),(k-1)L±rand( )% (0.5L)]
其中，i为x方向的节点序号，j为y方向的节点序号，k为z方向的节点序号，i、j和k为大于0的整数，rand()％(0.5L)表示随机生成0.5L范围内的任意整数。Among them, i is the node serial number in the x direction, j is the node serial number in the y direction, k is the node serial number in the z direction, i, j and k are integers greater than 0, rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
一种示例性的实施例中，可以将所述符合储层孔喉半径频率分布的三维张量数据体依次赋值到所述无序空间结构的节点上来建立孔隙网络模型。In an exemplary embodiment, a pore network model can be established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
图2为本公开实施例的通过电成像测井得到的砂岩地层储层图像。图3为本公开实施例的通过电成像测井得到的砾岩地层储层图像，图4为本公开实施例的通过电成像测井得到的碳酸盐岩地层储层图像。上述三例地层(即储层)的厚度均为0.5m。图中的某些黑团用圆圈圈出。一个或多个黑团的半径的平均值为相关长度L c。L c的值可通过ImageJ进行识别并测量，不需要识别每个黑团，只需要抽样识别一个或多个个取平均值。由图2到图4可看出，不同储层相关长度不同，一般砂岩<砾岩<碳酸盐岩。三维张量卷积核可通过相关长度根据地质统计学中的协方差模型计算得到。 FIG. 2 is a sandstone formation reservoir image obtained by electrical imaging logging according to an embodiment of the present disclosure. FIG. 3 is an image of a conglomerate formation reservoir obtained by electrical imaging logging according to an embodiment of the present disclosure, and FIG. 4 is an image of a carbonate formation reservoir obtained by electrical imaging logging according to an embodiment of the present disclosure. The thickness of the above-mentioned three strata (that is, the reservoir) is all 0.5m. Some of the black blobs in the picture are circled. The average of the radii of the one or more black masses is the correlation length L c . The value of L c can be identified and measured by ImageJ, it is not necessary to identify each black group, only one or more samples need to be identified and averaged. It can be seen from Figure 2 to Figure 4 that the correlation length of different reservoirs is different, generally sandstone < conglomerate < carbonate rock. The 3D tensor convolution kernel can be calculated by the correlation length according to the covariance model in geostatistics.
其中，三维张量卷积核E(h)＝exp(-2h/L c)，L c为相关长度，与岩性有关；是根据电成像分析得到；h表示三维坐标系中以(L x，L y，L z)为球心，半径小于等于Lc的球面到所述球心的距离，
Figure PCTCN2020126212-appb-000007
比如，三维卷积核(如图5所示)可以通过如下程序实现。 Among them, the three-dimensional tensor convolution kernel E(h)=exp(-2h/L c ), L c is the correlation length, which is related to the lithology; it is obtained according to the electro-imaging analysis; h represents the three-dimensional coordinate system with (L x , L y , L z ) is the center of the sphere, the distance from the sphere whose radius is less than or equal to Lc to the center of the sphere,
Figure PCTCN2020126212-appb-000007
For example, a three-dimensional convolution kernel (as shown in Figure 5) can be implemented by the following procedure.
Figure PCTCN2020126212-appb-000008
Figure PCTCN2020126212-appb-000008
Figure PCTCN2020126212-appb-000009
Figure PCTCN2020126212-appb-000009
图6为本公开实施例的通过核磁共振测井得到的T2谱示例。图中共有n个T2谱。这n个T2谱是按照如下方法获取的：将厚度为0.5m地层(即储层)分成n个子地层(即子储层)，每个子地层通过核磁共振测井得到1个T2谱。其中，n由用户根据需要设定。FIG. 6 is an example of a T2 spectrum obtained by nuclear magnetic resonance logging according to an embodiment of the present disclosure. There are a total of n T2 spectra in the figure. The n T2 spectra are obtained according to the following method: the formation (ie, the reservoir) with a thickness of 0.5 m is divided into n sub-formations (ie, sub-reservoirs), and one T2 spectrum is obtained for each sub-formation through nuclear magnetic resonance logging. Among them, n is set by the user as required.
图7为本公开实施例的孔喉半径频率分布曲线示例。通过将n个T2谱累加，得到总的T2谱。通过定量关系将T2谱幅度值转换成孔喉半径频率分布。其中，定量关系可以为r m＝cT 2m，r m为第m个孔喉半径，T 2m为T2谱的第m个幅度值，c为预设的转换系数，m为正整数。国内外大量统计表明，孔喉长度分布一般在50-200微米，且渗透率越低，孔喉长度越长。 FIG. 7 is an example of a frequency distribution curve of a pore throat radius according to an embodiment of the present disclosure. By accumulating n T2 spectra, the total T2 spectrum is obtained. The T2 spectral amplitude values are converted into pore throat radius frequency distributions by a quantitative relationship. Wherein the quantitative relationship can be r m = cT 2m, r m is the m-th pore throat radius, T 2m T2 spectrum is the m-th amplitude value, c is a predetermined conversion coefficient, m is a positive integer. A large number of statistics at home and abroad show that the distribution of pore throat length is generally 50-200 microns, and the lower the permeability, the longer the pore throat length.
图8为本公开实施例的初始三维张量数据体示例。该初始三维张量数据体可以根据孔喉半径频率分布曲线通过(对数均匀或对数正态等)随机函数建立三维稳定随机场，形成初始三维张量数据体。FIG. 8 is an example of an initial three-dimensional tensor data volume according to an embodiment of the present disclosure. The initial three-dimensional tensor data volume can be used to establish a three-dimensional stable random field through a random function (logarithmic uniform or logarithmic normal, etc.) according to the frequency distribution curve of the pore throat radius to form an initial three-dimensional tensor data volume.
可以通过Matlab拟合孔喉半径频率分布曲线得到孔喉半径频率分布的平均值(即数学期望)μ和标准偏差σ，从而得到如下对数正态分布随机函数：The average value (ie mathematical expectation) μ and the standard deviation σ of the pore throat radius frequency distribution can be obtained by fitting the pore throat radius frequency distribution curve through Matlab, and the following log-normal distribution random function can be obtained:
Figure PCTCN2020126212-appb-000010
Figure PCTCN2020126212-appb-000010
式中，x表示孔喉半径。where x is the pore throat radius.
图9为本公开实施例的生成符合孔喉半径频率分布的三维张量数据体的示意图。利用构造的三维张量卷积核滑过该三维张量数据体依次进行张量点乘计算，每次计算得到一个点。最后将所有通道的结果值按原来顺序堆叠起来形成一个新的三维张量，形成符合储层孔喉半径频率分布特征的三维张量数据体，这些数据输入python后通过vtk可观察生成的微孔隙-溶孔碳酸盐岩模型图像。FIG. 9 is a schematic diagram of generating a three-dimensional tensor data volume conforming to the frequency distribution of pore throat radius according to an embodiment of the present disclosure. The constructed three-dimensional tensor convolution kernel is used to slide over the three-dimensional tensor data volume to perform tensor point multiplication calculations in turn, and each calculation obtains a point. Finally, the result values of all channels are stacked in the original order to form a new three-dimensional tensor, forming a three-dimensional tensor data volume that conforms to the frequency distribution characteristics of the pore throat radius of the reservoir. After these data are input into python, the generated micropores can be observed through vtk - Image of dissolved pore carbonate rock model.
图10a为本公开实施例的有序空间结构图示例。图10b为本公开实施例的无序空间结构图示例。无序空间结构的构建过程如下：FIG. 10a is an example of an ordered spatial structure diagram of an embodiment of the present disclosure. FIG. 10b is an example of a disordered space structure diagram according to an embodiment of the present disclosure. The construction process of the disordered space structure is as follows:
(1)指定模型的节点数(根据模型大小和精细化程度确定节点数，其值与张量数据体对应)，构建一个X×Y×Z的三维简单立方体网格。每个节点 代表一个孔隙，节点与节点之间由喉道相连。由此建立起来的网络中每个代表孔隙的节点周围都有六个喉道相连；同理，每个喉道的周围也有六个孔隙相连。其每个方向(即x方向、y方向和z方向)每个节点之间的间隔距离设置为l，节点数设置为d，模型的边长为(d-1)×l。(1) Specify the number of nodes of the model (determine the number of nodes according to the size and refinement of the model, and its value corresponds to the tensor data volume), and construct a three-dimensional simple cubic grid of X×Y×Z. Each node represents a pore, and the nodes are connected by throats. In the network thus established, each node representing a pore is connected by six throats; similarly, each throat is also connected by six pores. The distance between each node in each direction (that is, the x direction, the y direction and the z direction) is set to l, the number of nodes is set to d, and the side length of the model is (d-1)×l.
(2)计算出网络模型中每个节点的坐标。计算式为：(x，y，z)＝[(i-1)l，(j-1)l，(k-1)l]，式中i、j、k分别为x方向、y方向和z方向的节点序号，取值分别为1，2，3…。(2) Calculate the coordinates of each node in the network model. The calculation formula is: (x, y, z)=[(i-1)l, (j-1)l, (k-1)l], where i, j, k are the x direction, y direction and The node number in the z direction, the values are 1, 2, 3... respectively.
(3)在程序中设定概率为p的概率函数，通过(伪)随机数发生器确定x、y及z方向每个相邻节点之间是否有管束连通。采用函数产生随机数，从而产生随机概率。例如，在C/C++程序语言中，可采用rand()函数产生随机数，从而产生随机概率。具体的C/C++代码为：(3) A probability function with probability p is set in the program, and a (pseudo) random number generator is used to determine whether there is a tube bundle connection between each adjacent node in the x, y and z directions. Use a function to generate random numbers, thereby generating random probabilities. For example, in the C/C++ programming language, the rand() function can be used to generate random numbers, thereby generating random probabilities. The specific C/C++ code is:
if(rand()％100＜p×100)if(rand()%100＜p×100)
式中，rand()％100—计算机随机生成0-99范围内的任意整数。In the formula, rand()%100—the computer randomly generates any integer in the range of 0-99.
当连通概率p为50％时，rand()函数随机生成的整数中有50％的可能性(概率)小于50，有另外50％的可能性(概率)大于50。因此，该表达式可实现概率p＝50％的管束连接，即当产生了一个小于50的数时，表达式为真，执行管束半径的分配任务(分配管束半径为r)；当产生一个不小于50的数时，表达式为假，不执行任何操作。然后，根据三维立方体网络、每个节点、确认管束连通结果以及管束半径生成有序空间结构(如图10a所示)。When the connectivity probability p is 50%, 50% of the integers randomly generated by the rand() function (probability) are less than 50, and the other 50% are more likely (probability) than 50. Therefore, this expression can realize the pipe bundle connection with probability p=50%, that is, when a number less than 50 is generated, the expression is true, and the assignment task of the pipe bundle radius is performed (the assignment pipe bundle radius is r); For numbers less than 50, the expression is false and no action is performed. Then, an ordered spatial structure is generated based on the three-dimensional cube network, each node, the confirmed tube bundle connectivity results, and the tube bundle radius (as shown in Figure 10a).
网络模型的连通性还可以采用配位数进行定量描述。常规砂岩储层岩石的配位数取值范围为4-6，低渗砂岩储层岩石的配位数为3-4。配位数为6就是指一个节点与其他6个节点管束连通。The connectivity of the network model can also be quantitatively described by coordination numbers. The coordination number of conventional sandstone reservoir rocks is in the range of 4-6, and the coordination number of low-permeability sandstone reservoir rocks is 3-4. A coordination number of 6 means that one node is connected to the other 6 node bundles.
(4)通过移动节点坐标(在中心点左边后加上一个不超过喉道长度的随机数)生成随机网络(即无序空间结构)(如图10b所示)，移动后的节点坐标为(x,y,z)＝[(i-1)l+(rand()％(0.5l),(j-1)l+(rand()％(0.5l),(k-1)l+(rand()％(0.5l)]。(4) Generate a random network (ie, a disordered space structure) by moving the node coordinates (add a random number not exceeding the length of the throat to the left of the center point) (as shown in Figure 10b), and the moved node coordinates are ( x,y,z)=[(i-1)l+(rand()%(0.5l),(j-1)l+(rand()%(0.5l),(k-1)l+(rand() % (0.5l)].
图11为本公开实施例的孔隙网络模型示例。将符合储层孔喉半径频率分布特征的三维张量数据体中的数据依次赋值到无序空间结构的节点上，即可构造出符合储层真实地质特征的孔隙网络模型。FIG. 11 is an example of a pore network model of an embodiment of the present disclosure. The data in the 3D tensor data volume that conforms to the frequency distribution characteristics of the pore throat radius of the reservoir are sequentially assigned to the nodes of the disordered spatial structure, and the pore network model that conforms to the real geological characteristics of the reservoir can be constructed.
以上建模过程可借助配有8张显卡，内存为512GB的工作站运用多GPU 计算，从而扩大了模型尺度(100米左右范围、10米左右厚度，总计超过10亿孔隙节点数)，建立起符合储层图像和孔喉特征的单井油气藏储层孔隙网络模型。The above modeling process can use multi-GPU computing on a workstation equipped with 8 graphics cards and a memory of 512GB, thereby expanding the scale of the model (the range of about 100 meters, the thickness of about 10 meters, and the total number of pore nodes exceeds 1 billion). Reservoir pore network model for single-well oil and gas reservoirs with reservoir images and pore throat characteristics.
本公开实施例建立的孔隙网络模型主要借助于电成像测井与核磁共振测井方法，考虑了储层物性参数，在一定程度上解决了建立的地质模型缺乏一定的物理意义的难题。并在一定程度上解决了非均质性强的地层建模难的瓶颈问题，也适用于碳酸盐岩地层，为低渗致密油气藏建模也提供了一定的参考依据。The pore network model established in the embodiments of the present disclosure mainly relies on the electrical imaging logging and nuclear magnetic resonance logging methods, taking into account the physical parameters of the reservoir, and to a certain extent solves the problem that the established geological model lacks a certain physical meaning. To a certain extent, it solves the bottleneck problem of difficult formation modeling with strong heterogeneity, and is also applicable to carbonate formations, providing a certain reference for modeling low-permeability tight oil and gas reservoirs.
图12为本公开实施例的基于储层的孔隙网络模型的建模装置的示意图。该基于储层的孔隙网络模型的建模装置，包括：存储器和处理器；FIG. 12 is a schematic diagram of a modeling apparatus based on a pore network model of a reservoir according to an embodiment of the present disclosure. The modeling device based on the pore network model of the reservoir includes: a memory and a processor;
所述存储器，设置为保存用于进行储层的孔隙网络模型的建模的程序；the memory configured to hold a program for modeling a pore network model of the reservoir;
所述处理器，用于读取执行所述用于进行储层的孔隙网络模型的建模的程序，执行如下的建模方法：The processor, configured to read and execute the program for modeling the pore network model of the reservoir, executes the following modeling method:
获取通过电成像测井得到的储层图像；Obtain reservoir images obtained by electrical imaging logging;
根据所述储层图像获取相关长度，根据所述相关长度计算三维张量卷积核；其中，所述相关长度表示所述储层图像中的预选黑团的半径平均值；The correlation length is obtained according to the reservoir image, and a three-dimensional tensor convolution kernel is calculated according to the correlation length; wherein, the correlation length represents the average radius of the preselected black circles in the reservoir image;
获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径频率分布；根据所述孔喉半径频率分布形成初始三维张量数据体；obtaining the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and obtaining the frequency distribution of the pore throat radius according to the T2 spectrum; forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius;
根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体；According to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
根据初始三维张量数据体构建孔隙网络模型的无序空间结构；Construct the disordered spatial structure of the pore network model according to the initial 3D tensor data volume;
根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型。A pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
一种示例性的实施例中，所述根据所述相关长度计算三维张量卷积核，包括：In an exemplary embodiment, the calculating a three-dimensional tensor convolution kernel according to the correlation length includes:
按照如下公式计算三维张量卷积核E(h)：Calculate the three-dimensional tensor convolution kernel E(h) according to the following formula:
E(h)＝exp(-2h/L c) E(h)=exp(-2h/L c )
其中，h表示三维坐标系中以(L x，L y，L z)为球心，半径小于等于Lc 的球面到所述球心的距离，
Figure PCTCN2020126212-appb-000011
L c表示所述相关长度。 Wherein, h represents the distance from the spherical surface of the three-dimensional coordinate system with (L x , Ly , L z ) as the center of the sphere, and the radius is less than or equal to Lc to the spherical center,
Figure PCTCN2020126212-appb-000011
L c represents the correlation length.
一种示例性的实施例中，所述获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径分布，包括：In an exemplary embodiment, the acquiring the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and acquiring the pore throat radius distribution according to the T2 spectrum, includes:
获取通过核磁共振测井技术得到的所述储层的n个子储层的n个T2谱；其中，n为正整数；acquiring n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
对所述n个T2谱进行求和，通过预设定量关系将求和后得到的T2谱的幅度值转换成孔喉半径频率分布。The n T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
一种示例性的实施例中，所述预设定量关系为r m＝cT 2m； An exemplary embodiment, the predetermined quantitative relationship r m = cT 2m;
其中，r m为第m个孔喉半径，T 2m为T2谱的第m个幅度值，c为预设的转换系数，m为正整数。 Wherein, r m is the m-th pore throat radius, T 2m is the m-th amplitude value of the T2 spectrum, c is a preset conversion coefficient, and m is a positive integer.
一种示例性的实施例中，所述根据所述孔喉半径频率分布形成初始三维张量数据体，包括：In an exemplary embodiment, the forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
根据所述孔喉半径频率分布，通过随机函数建立三维稳定随机场，形成初始三维张量数据体；According to the frequency distribution of the pore throat radius, a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume;
其中，所述随机函数为如下对数正态分布随机函数：Wherein, the random function is the following log-normal distribution random function:
Figure PCTCN2020126212-appb-000012
Figure PCTCN2020126212-appb-000012
其中，通过拟合所述孔喉半径频率分布得到数学期望μ和标准偏差σ，x表示孔喉半径。The mathematical expectation μ and the standard deviation σ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
一种示例性的实施例中，所述根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体，包括：In an exemplary embodiment, according to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor conforming to the frequency distribution of the pore throat radius of the reservoir. volume data bodies, including:
将所述三维张量卷积核与所述初始三维张量数据体依次进行张量点乘，将点乘结果按照所述初始三维张量数据体中的顺序堆叠起来，生成符合储层孔喉半径频率分布的三维张量数据体。Perform tensor point multiplication on the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume in turn, and stack the results of the point multiplication in the order in the initial three-dimensional tensor data volume to generate a pore-throat conformity to the reservoir. A 3D tensor data volume of radial frequency distributions.
一种示例性的实施例中，所述根据所述初始三维张量数据体构建孔隙网络模型的无序空间结构，包括：In an exemplary embodiment, the construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
根据所述初始三维张量数据体的数据数量确定所述无序空间结构的节点数；Determine the number of nodes of the disordered space structure according to the data quantity of the initial three-dimensional tensor data volume;
根据所述节点数以及每个节点间的间隔距离L构建包含X、Y、Z三个方向的三维立方体网络；Constructing a three-dimensional cube network including three directions of X, Y, and Z according to the number of nodes and the separation distance L between each node;
计算所述三维立方体网络中每个节点的坐标；calculating the coordinates of each node in the three-dimensional cube network;
根据预设第一概率函数确定X方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the X direction according to the preset first probability function, and assign the tube bundle radius;
根据预设第二概率函数确定Y方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the Y direction according to the preset second probability function, and assign the tube bundle radius;
通过预设规则移动每个节点坐标；Move each node coordinate by preset rules;
根据所述三维立方体网络、确定是否有管束连通的结果、分配的管束半径和移动后的节点坐标生成无序空间结构。The disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
一种示例性的实施例中，所述通过预设规则移动每个节点坐标包括：In an exemplary embodiment, the moving each node coordinate according to a preset rule includes:
按照如下公式移动每个节点坐标(x,y,z)：Move each node coordinate (x, y, z) according to the following formula:
(x,y,z)＝[(i-1)L±rand()％(0.5L),(j-1)L±rand()％(0.5L),(k-1)L±rand()％(0.5L)](x,y,z)=[(i-1)L±rand()%(0.5L),(j-1)L±rand()%(0.5L),(k-1)L±rand( )% (0.5L)]
其中，i为x方向的节点序号，j为y方向的节点序号，k为z方向的节点序号，i、j和k为大于0的整数，rand()％(0.5L)表示随机生成0.5L范围内的任意整数。Among them, i is the node serial number in the x direction, j is the node serial number in the y direction, k is the node serial number in the z direction, i, j and k are integers greater than 0, rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
一种示例性的实施例中，根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型，包括：In an exemplary embodiment, establishing a pore network model according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure, including:
将所述符合储层孔喉半径频率分布的三维张量数据体依次赋值到所述无序空间结构的节点上来建立孔隙网络模型。A pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
本领域普通技术人员可以理解上述方法中的全部或部分步骤可通过程序来指令相关硬件完成，所述程序可以存储于计算机可读存储介质中，如只读存储器、磁盘或光盘等。可选地，上述实施例的全部或部分步骤也可以使用一个或多个集成电路来实现。相应地，上述实施例中的模块/单元可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。本公开不限制于任何特定形式的硬件和软件的结合。Those skilled in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. Optionally, all or part of the steps in the above embodiments may also be implemented using one or more integrated circuits. Correspondingly, the modules/units in the above embodiments may be implemented in the form of hardware, and may also be implemented in the form of software functional modules. The present disclosure is not limited to any particular form of combination of hardware and software.
本公开还可有其他多种实施例，在不背离本公开精神及其实质的情况下，熟悉本领域的技术人员当可根据本公开作出多种相应的改变和变形，但这些相应的改变和变形都应属于本公开所附的权利要求的保护范围。The present disclosure may also have other various embodiments. Without departing from the spirit and essence of the present disclosure, those skilled in the art can make various corresponding changes and modifications according to the present disclosure, but these corresponding changes and All modifications should fall within the protection scope of the appended claims of the present disclosure.

Claims (10)

  1. 一种基于储层的孔隙网络模型的建模方法，包括：A modeling method based on a pore network model of a reservoir, comprising:
    获取通过电成像测井得到的储层图像；Obtain reservoir images obtained by electrical imaging logging;
    根据所述储层图像获取相关长度及储层厚度，根据所述相关长度及储层厚度计算三维张量卷积核；其中，所述相关长度表示所述储层图像中的预选黑团的半径平均值；The correlation length and the reservoir thickness are obtained according to the reservoir image, and the three-dimensional tensor convolution kernel is calculated according to the correlation length and the reservoir thickness; wherein, the correlation length represents the radius of the preselected black group in the reservoir image average value;
    获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径频率分布；根据所述孔喉半径频率分布形成初始三维张量数据体；obtaining the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and obtaining the frequency distribution of the pore throat radius according to the T2 spectrum; forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius;
    根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体；According to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, a convolutional neural network forward propagation algorithm is used to generate a three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir;
    根据初始三维张量数据体构建孔隙网络模型的无序空间结构；Construct the disordered spatial structure of the pore network model according to the initial 3D tensor data volume;
    根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型。A pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure.
  2. 如权利要求1所述的方法，其中，The method of claim 1, wherein,
    所述根据所述相关长度及储层厚度计算三维张量卷积核，包括：The calculation of the three-dimensional tensor convolution kernel according to the correlation length and the reservoir thickness includes:
    按照如下公式计算三维张量卷积核E(h)：Calculate the three-dimensional tensor convolution kernel E(h) according to the following formula:
    E(h)＝exp(-2h/L c) E(h)=exp(-2h/L c )
    其中，h表示三维坐标系中以(L x，L y，L z)为球心，半径小于等于Lc的球面到所述球心的距离，
    Figure PCTCN2020126212-appb-100001
    L c表示所述相关长度。     Among them, h represents the distance from the spherical surface with (L x , Ly , L z ) as the center of the sphere in the three-dimensional coordinate system, and the radius is less than or equal to Lc to the spherical center,
    Figure PCTCN2020126212-appb-100001
    L c represents the correlation length.
  3. 如权利要求1所述的方法，其中，The method of claim 1, wherein,
    所述获取核磁共振测井得到的储层的T2谱，根据所述T2谱获取孔喉半径分布，包括：The acquisition of the T2 spectrum of the reservoir obtained by nuclear magnetic resonance logging, and the acquisition of the pore throat radius distribution according to the T2 spectrum, includes:
    获取通过核磁共振测井技术得到的所述储层的n个子储层的n个T2谱；其中，n为正整数；acquiring n T2 spectra of n sub-reservoirs of the reservoir obtained by nuclear magnetic resonance logging technology; wherein, n is a positive integer;
    对所述n个T2谱进行求和，通过预设定量关系将求和后得到的T2谱的幅度值转换成孔喉半径频率分布。The n T2 spectra are summed, and the amplitude value of the T2 spectrum obtained after the summation is converted into a frequency distribution of the pore throat radius through a preset quantitative relationship.
  4. 如权利要求3所述的方法，其中，The method of claim 3, wherein,
    所述预设定量关系为r m＝cT 2m； The predetermined quantitative relationship r m = cT 2m;
    其中，r m为第m个孔喉半径，T 2m为T2谱的第m个幅度值，c为预设的转换系数，m为正整数。 Wherein, r m is the m-th pore throat radius, T 2m is the m-th amplitude value of the T2 spectrum, c is a preset conversion coefficient, and m is a positive integer.
  5. 如权利要求3所述的方法，其中，The method of claim 3, wherein,
    所述根据所述孔喉半径频率分布形成初始三维张量数据体，包括：The forming an initial three-dimensional tensor data volume according to the frequency distribution of the pore throat radius includes:
    根据所述孔喉半径频率分布，通过随机函数建立三维稳定随机场，形成初始三维张量数据体；According to the frequency distribution of the pore throat radius, a three-dimensional stable random field is established through a random function to form an initial three-dimensional tensor data volume;
    其中，所述随机函数为如下对数正态分布随机函数：Wherein, the random function is the following log-normal distribution random function:
    Figure PCTCN2020126212-appb-100002
    Figure PCTCN2020126212-appb-100002
    其中，通过拟合所述孔喉半径频率分布得到数学期望μ和标准偏差σ，x表示孔喉半径。The mathematical expectation μ and the standard deviation σ are obtained by fitting the frequency distribution of the pore throat radius, and x represents the pore throat radius.
  6. 如权利要求3所述的方法，其中，The method of claim 3, wherein,
    所述根据所述三维张量卷积核和所述初始三维张量数据体采用卷积神经网络正向传播算法生成符合储层孔喉半径频率分布的三维张量数据体，包括：The three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir is generated by using the convolutional neural network forward propagation algorithm according to the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume, including:
    将所述三维张量卷积核与所述初始三维张量数据体依次进行张量点乘，将点乘结果按照所述初始三维张量数据体中的顺序堆叠起来，生成符合储层孔喉半径频率分布的三维张量数据体。Perform tensor point multiplication on the three-dimensional tensor convolution kernel and the initial three-dimensional tensor data volume in turn, and stack the results of the point multiplication in the order in the initial three-dimensional tensor data volume to generate a pore-throat conformity to the reservoir. A 3D tensor data volume of radial frequency distributions.
  7. 如权利要求1所述的方法，其中，The method of claim 1, wherein,
    所述根据所述初始三维张量数据体构建孔隙网络模型的无序空间结构，包括：The construction of the disordered spatial structure of the pore network model according to the initial three-dimensional tensor data volume includes:
    根据所述初始三维张量数据体的数据数量确定所述无序空间结构的节点数；Determine the number of nodes of the disordered space structure according to the data quantity of the initial three-dimensional tensor data volume;
    根据所述节点数以及每个节点间的间隔距离L构建包含X、Y、Z三个方向的三维立方体网络；Constructing a three-dimensional cube network including three directions of X, Y, and Z according to the number of nodes and the separation distance L between each node;
    计算所述三维立方体网络中每个节点的坐标；calculating the coordinates of each node in the three-dimensional cube network;
    根据预设第一概率函数确定X方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the X direction according to the preset first probability function, and assign the tube bundle radius;
    根据预设第二概率函数确定Y方向每个相邻节点之间是否有管束连通，并分配管束半径；Determine whether there is a tube bundle connection between each adjacent node in the Y direction according to the preset second probability function, and assign the tube bundle radius;
    通过预设规则移动每个节点坐标；Move each node coordinate by preset rules;
    根据所述三维立方体网络、确定是否有管束连通的结果、分配的管束半径和移动后的节点坐标生成无序空间结构。The disordered space structure is generated according to the three-dimensional cube network, the result of determining whether there is a tube bundle connected, the assigned tube bundle radius and the moved node coordinates.
  8. 如权利要求7所述的方法，其中，The method of claim 7, wherein,
    所述通过预设规则移动每个节点坐标包括：The moving each node coordinate according to the preset rule includes:
    按照如下公式移动每个节点坐标(x,y,z)：Move each node coordinate (x, y, z) according to the following formula:
    (x,y,z)＝[(i-1)L±rand()％(0.5L),(j-1)L±rand()％(0.5L),(k-1)L±rand()％(0.5L)](x,y,z)=[(i-1)L±rand()%(0.5L),(j-1)L±rand()%(0.5L),(k-1)L±rand( )% (0.5L)]
    其中，i为x方向的节点序号，j为y方向的节点序号，k为z方向的节点序号，i、j和k为大于0的整数，rand()％(0.5L)表示随机生成0.5L范围内的任意整数。Among them, i is the node serial number in the x direction, j is the node serial number in the y direction, k is the node serial number in the z direction, i, j and k are integers greater than 0, rand()%(0.5L) means randomly generated 0.5L Any integer in the range.
  9. 如权利要求1所述的方法，其中，The method of claim 1, wherein,
    根据所述符合储层孔喉半径频率分布的三维张量数据体和所述无序空间结构建立孔隙网络模型，包括：A pore network model is established according to the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir and the disordered spatial structure, including:
    将所述符合储层孔喉半径频率分布的三维张量数据体依次赋值到所述无序空间结构的节点上来建立孔隙网络模型。A pore network model is established by sequentially assigning the three-dimensional tensor data volume conforming to the frequency distribution of the pore throat radius of the reservoir to the nodes of the disordered spatial structure.
  10. 一种基于储层的孔隙网络模型的建模装置，包括：存储器和处理器；A modeling device based on a pore network model of a reservoir, comprising: a memory and a processor;
    所述存储器，设置为保存用于进行储层的孔隙网络模型的建模的程序；the memory configured to hold a program for modeling a pore network model of the reservoir;
    所述处理器，用于读取执行所述用于进行储层的孔隙网络模型的建模的程序，执行如权利要求1-9中任一项的建模方法。The processor, configured to read and execute the program for modeling the pore network model of the reservoir, executes the modeling method according to any one of claims 1-9.
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