Method, system and equipment for determining particle size median of clastic rock and medium

Abstract

The invention provides a method, a system, equipment and a medium for determining the granularity median of clastic rock, and the method comprises the following steps: building a granularity median histogram based on the experimental analysis result of the granularity of the clastic rock; based on the granularity median histogram, establishing a thermodynamic diagram of the debris granularity median data and multiple groups of logging data, and determining a logging curve of density, natural gamma and neutron-density porosity difference with relatively high correlation with the granularity median; performing Z-value standardization on the logging curves of the density, the natural gamma and the neutron-density porosity difference with relatively high correlation with the particle size median; extracting standardized logging curve characteristics by adopting a principal component analysis method, and calculating a comprehensive evaluation value F by selecting a principal component variable of which the cumulative variance contribution is greater than a preset value; according to the obtained comprehensive evaluation value F, fitting by using a multiple linear regression method to obtain a particle size median of the clastic rock; the purpose of accurately calculating the particle size median of the clastic rock can be achieved, and reliable parameters are provided for geological exploration.

Description

Technical Field

[0001] The invention relates to the technical field of geological exploration, and in particular to a method, system, equipment and medium for determining the median particle size of clastic rocks. Background Art

[0002] Grain size analysis of clastic rocks is the core of quantitative evaluation of oil and gas reservoirs and one of the important factors that determine the effectiveness of reservoirs. Grain size is the most basic structural feature of clastic rock particles, reflecting information such as source supply, hydrodynamic conditions and sedimentary environment. The median grain size refers to the value corresponding to the cumulative curve where the cumulative percentage of the deposited grain size is 50%, which can be used to evaluate the coarse and fine grain characteristics of clastic rocks.

[0003] At present, the existing technology often uses experimental methods such as core mechanical crushing and screening, direct measurement and sandstone thin section image analysis to obtain the median particle size of clastic rocks. However, the experimental cost is expensive and the coring process is discontinuous during drilling, which cannot continuously reflect the reservoir properties. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method, system, equipment and medium for determining the median particle size of clastic rocks, which can accurately determine the median particle size of clastic rocks and provide reliable parameters for geological exploration.

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

[0006] Compared with the prior art, the present invention has the following beneficial technical effects:

[0007] The present invention provides a method, system, device and medium for determining the median grain size of clastic rocks, comprising the following steps: establishing a median grain size histogram based on experimental analysis results of clastic rock grain size; establishing a thermal map of clastic grain size median data and multiple groups of well logging data based on the median grain size histogram, and determining well logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median grain size; performing Z value standardization on the well logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median grain size; and extracting the characteristics of the standardized well logging curves using a principal component analysis method. The principal component variables whose cumulative variance contribution is greater than a preset value are selected to calculate a comprehensive evaluation value F; the median grain size of clastic rock is fitted using a multivariate linear regression method based on the obtained comprehensive evaluation value F; the present application proposes a method for extracting standardized logging curve characteristics using a principal component analysis method, accurately obtaining the median grain size of clastic rock based on the calculated comprehensive evaluation value F, eliminating redundant information on the basis of retaining the characteristics of the original data, and effectively reducing the interference of redundant information in the logging data on the calculation of the median grain size, thereby achieving the purpose of accurately calculating the median grain size of clastic rock and providing reliable parameters for geological exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a method for determining the median particle size of clastic rocks in an embodiment of the present invention;

[0009] Figure 2 is a histogram of median grain size data of clastic rock in an embodiment of the present invention;

[0010] Figure 3 The thermal diagram of the median grain size data of clastic rocks and the well logging data in the embodiment of the present invention is shown in FIG.

[0011] Figure 4 A result diagram of the characteristic values ​​and contribution rates calculated by the principal component analysis method in an embodiment of the present invention;

[0012] Figure 5 This is a comparison chart of the median particle size of clastic rocks obtained by multivariate linear regression fitting in an embodiment of the present invention and the median particle size obtained in the laboratory;

[0013] Figure 6 This is a graph showing the particle size evaluation results of a core profile of a well in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0015] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] The present invention provides a method for determining the median particle size of clastic rocks, such as Figure 1As shown, the following steps are included:

[0018] Based on the experimental analysis results of clastic rock grain size, a grain size median histogram is established;

[0019] Based on the median grain size histogram, a thermal map of the median grain size data and multiple sets of logging data is established to determine the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median grain size;

[0020] Performing Z-value standardization on the density, natural gamma and neutron-density porosity difference logging curves with high correlation with the median grain size;

[0021] The principal component analysis method is used to extract the characteristics of the standardized logging curve, and the principal component variables whose cumulative variance contribution is greater than the preset value are selected to calculate the comprehensive evaluation value F;

[0022] According to the comprehensive evaluation value F, the median grain size of clastic rock was obtained by fitting using the multivariate linear regression method.

[0023] Preferably, in the process of establishing the median particle size histogram based on the experimental analysis results of the clastic rock particle size, it is necessary to exclude data points below 5% and above 95% of the cumulative curve of the histogram;

[0024] It should be noted that in this embodiment, data points below 5% and above 95% of the histogram cumulative curve need to be eliminated. The eliminated data points are irrelevant data points. Eliminating irrelevant data points is a commonly used method for analyzing data. It is mainly achieved by establishing a median particle size histogram to intuitively display the area in the median particle size data set and eliminate the interference of irrelevant median particle size data points. Furthermore, those skilled in the art can set the percentage of eliminated data points according to actual needs.

[0025] Preferably, the process of establishing a thermal map of the median particle size data and multiple sets of logging data based on the median particle size histogram and determining the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median particle size is as follows:

[0026]

[0027] Where ρ represents the correlation coefficient, X1 represents the well logging curve, X2 represents the median particle size, Cov represents the covariance, and E represents the mathematical expectation.

[0028] Preferably, the process of performing Z value standardization on the well logging curves of density, natural gamma and neutron-density porosity difference which have a high correlation with the median particle size is as follows:

[0029]

[0030] Among them, x is the original logging data, is the average value of the original logging data, and σ is the standard deviation of the original logging data.

[0031] Preferably, the process of extracting the standardized logging curve characteristics by using the principal component analysis method and selecting the principal component variables whose cumulative variance contribution is greater than a preset value to calculate the comprehensive evaluation value F is as follows:

[0032] Based on the covariance matrix of the data of the well logging curve, the eigenvalues ​​and eigenvectors of the covariance matrix are obtained;

[0033] Based on the eigenvalues ​​and eigenvectors, the first m principal component variables whose cumulative variance contributions are greater than a preset value are selected, and the comprehensive evaluation value F is calculated.

[0034] Preferably, the preset value is 85%.

[0035] Preferably, the process of fitting the median particle size of clastic rock using a multivariate linear regression method according to the obtained comprehensive evaluation value F is as follows:

[0036] Determine the independent variables and dependent variables that need to be subjected to regression analysis from the data, with F as the independent variable and the median particle size as the dependent variable;

[0037] Using the least squares fitting method, a multiple linear regression model was established based on the relationship between independent variables and dependent variables;

[0038] The model is evaluated and optimized using evaluation indicators to obtain the optimized model, which is then used to predict the median grain size of clastic rocks to obtain the median grain size of clastic rocks.

[0039] The present invention provides an embodiment as follows:

[0040] Based on the experimental analysis results of clastic rock grain size, a grain size median histogram is established;

[0041] Based on the median grain size histogram, a thermal map of the median grain size data and multiple sets of logging data is established to determine the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median grain size;

[0042] Performing Z-value standardization on the density, natural gamma and neutron-density porosity difference logging curves with high correlation with the median grain size;

[0043] The principal component analysis method is used to extract the characteristics of the standardized logging curve, and the principal component variables whose cumulative variance contribution is greater than the preset value are selected to calculate the comprehensive evaluation value F;

[0044] According to the comprehensive evaluation value F, the median grain size of clastic rock was obtained by fitting using the multivariate linear regression method.

[0045] Preferably, Figure 2 As shown, in the process of establishing the median particle size histogram based on the experimental analysis results of the clastic rock particle size, it is necessary to remove the data points below 5% and above 95% of the histogram cumulative curve;

[0046] It should be noted that in this embodiment, data points below 5% and above 95% of the histogram cumulative curve need to be eliminated. The eliminated data points are irrelevant data points. Eliminating irrelevant data points is a commonly used method for analyzing data. It is mainly achieved by establishing a median particle size histogram to intuitively display the area in the median particle size data set and eliminate the interference of irrelevant median particle size data points. Furthermore, those skilled in the art can set the percentage of eliminated data points according to actual needs.

[0047] Preferably, the heat map of the debris median particle size data and multiple sets of logging data is established based on the particle size median histogram, such as Figure 3 As shown in Figure 1, the process of determining the density, natural gamma and neutron-density porosity difference logs with high correlation with the median grain size is:

[0048]

[0049] Where ρ represents the correlation coefficient, X1 represents the well logging curve, X2 represents the median particle size, Cov represents the covariance, and E represents the mathematical expectation.

[0050] It should be noted that the data heat map is a visualization tool that can display the size and distribution of data through the depth of color. Its main functions are as follows: visual display of data. The heat map can quickly display the size of data through the depth of color, making the data more intuitive and easy to understand; discovering the patterns of data. The heat map can help people quickly discover the patterns of data, such as the distribution range of data, the density of data, etc.; comparing data. The heat map can compare the differences between different data. For example, the changes in data in different periods can be compared through the heat map; optimizing decisions. The heat map can help people make more informed decisions based on the distribution of data. As a visualization tool, the heat map can provide better understanding, discovery, comparison and optimization of data, thereby better supporting decision-making.

[0051] Preferably, the process of performing Z value standardization on the well logging curves of density, natural gamma and neutron-density porosity difference which have a high correlation with the median particle size is as follows:

[0052]

[0053] Among them, x is the original logging data, is the average value of the original logging data, and σ is the standard deviation of the original logging data.

[0054] Preferably, Figure 3 As shown, the process of using the principal component analysis method to extract the standardized logging curve characteristics and selecting the principal component variables with cumulative variance contribution greater than 85% to calculate the comprehensive evaluation value F is as follows:

[0055] Based on the covariance matrix of the data of the well logging curve, the eigenvalues ​​and eigenvectors of the covariance matrix are obtained;

[0056] Based on the eigenvalues ​​and eigenvectors, the first m principal component variables whose cumulative variance contributions are greater than a preset value are selected, and the comprehensive evaluation value F is calculated.

[0057] The eigenvalues ​​and eigenvectors of the covariance matrix can be used to understand the overall variation of the data and the contribution of each feature to the whole, and can also be used to analyze the relationship between features and the structural characteristics of the data as a whole; it should be noted that the covariance matrix is ​​a matrix that describes the distribution of data, in which each element Cov(i,j) represents the covariance between the i-th feature and the j-th feature, and the eigenvalues ​​and eigenvectors of the covariance matrix can be used to describe some important properties of the data.

[0058] The eigenvalue is the solution to the characteristic equation of a matrix. For a covariance matrix, its eigenvalue can usually be used to measure the overall change of the data and the contribution of each feature to the whole. Specifically, if the absolute value of an eigenvalue is larger, it means that the degree of variation of the feature in the entire data is greater and the contribution to the whole is also greater.

[0059] The eigenvector is a vector corresponding to a certain eigenvalue, which can represent the weight distribution of the feature corresponding to the eigenvalue in each dimension. For the covariance matrix, each eigenvector can represent the correlation degree between a feature and each dimension, that is, the weight of the feature in each dimension.

[0060] Preferably, Figure 5 As shown, the process of fitting the median particle size of clastic rock using the multivariate linear regression method according to the obtained comprehensive evaluation value F is as follows:

[0061] Determine the independent variables and dependent variables: Determine the independent variables and dependent variables that need to be subjected to regression analysis from the data, with F as the independent variable and the median of the granularity as the dependent variable;

[0062] Establish model: Use the least squares fitting method to establish a multiple linear regression model based on the relationship between independent variables and dependent variables;

[0063] Model evaluation: Evaluate the model using appropriate evaluation metrics, such as coefficient of determination, standard error, confidence interval, etc.

[0064] Model optimization: If the model performs poorly, you can consider optimizing the model, such as adjusting model parameters;

[0065] Application of the model: After the model is verified and optimized, it is applied to predict and analyze the median grain size of clastic rocks.

[0066] like Figure 6 This is the result of the core section grain size evaluation of a well. Figure 6 The first track on the left is the geological stratification, the second track is the natural gamma (GR) curve, the third track is the porosity logging curve, including the density (DEN) and neutron (CNL) logging curves, and the fourth track is the median grain size, including the median grain size (MD) of core analysis and the calculated median grain size (FMD). Among them, the FMD calculated in the median grain size track is obtained using the method provided in the above embodiment, and the calculated median grain size (FMD) and the median grain size (MD) of core analysis are in good agreement.

[0067] The present invention provides a system for determining the median grain size of clastic rocks, comprising:

[0068] The acquisition module is used to establish a median grain size histogram based on the experimental analysis results of the clastic rock grain size;

[0069] A preprocessing module is used to establish a thermal map of the median particle size data and multiple sets of logging data based on the median particle size histogram, and determine the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median particle size;

[0070] A standardization processing module is used to perform Z value standardization on the well logging curves of density, natural gamma and neutron-density porosity difference which have a high correlation with the median particle size;

[0071] A selection module is used to extract the characteristics of the standardized logging curve by using the principal component analysis method, and to select the principal component variables whose cumulative variance contribution is greater than a preset value to calculate the comprehensive evaluation value F;

[0072] The output module is used to obtain the median grain size of the clastic rock by fitting the obtained comprehensive evaluation value F using a multivariate linear regression method.

[0073] In another embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for determining the median grain size of clastic rocks.

[0074] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the computer device and the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for determining the median grain size of clastic rocks in the above embodiment.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the median particle size of clastic rocks, characterized in that: The following steps are involved: Based on the experimental analysis results of clastic rock grain size, a grain size median histogram is established; Based on the median grain size histogram, a thermal map of the median grain size data and multiple sets of logging data is established to determine the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median grain size; Performing Z-value standardization on the density, natural gamma and neutron-density porosity difference logging curves with high correlation with the median grain size; The principal component analysis method is used to extract the characteristics of the standardized logging curve, and the principal component variables whose cumulative variance contribution is greater than the preset value are selected to calculate the comprehensive evaluation value F; According to the comprehensive evaluation value F, the median grain size of clastic rock was obtained by fitting using the multivariate linear regression method.

2. A method for determining the median particle size of clastic rocks according to claim 1, characterized in that: In the process of establishing the median particle size histogram based on the experimental analysis results of the clastic rock particle size, it is necessary to eliminate data points below 5% and above 95% of the cumulative curve of the histogram.

3. A method for determining the median particle size of clastic rocks according to claim 1, characterized in that: The process of establishing a thermal map of the debris grain median data and multiple sets of logging data based on the grain median histogram and determining the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the grain median is as follows: Where ρ represents the correlation coefficient, X1 represents the well logging curve, X2 represents the median particle size, Cov represents the covariance, and E represents the mathematical expectation.

4. A method for determining the median particle size of clastic rocks according to claim 1, characterized in that: The process of Z-value standardization of the well logging curves of density, natural gamma and neutron-density porosity difference which have high correlation with the median particle size is as follows: Among them, x is the original logging data, is the average value of the original logging data, and σ is the standard deviation of the original logging data.

5. A method for determining the median particle size of clastic rocks according to claim 1, characterized in that: The process of using the principal component analysis method to extract the standardized logging curve characteristics and selecting the principal component variables whose cumulative variance contribution is greater than the preset value to calculate the comprehensive evaluation value F is as follows: Based on the covariance matrix of the data of the well logging curve, the eigenvalues ​​and eigenvectors of the covariance matrix are obtained; Based on the eigenvalues ​​and eigenvectors, the first m principal component variables whose cumulative variance contributions are greater than a preset value are selected, and the comprehensive evaluation value F is calculated.

6. A method for determining the median particle size of clastic rocks according to claim 1 or 5, characterized in that: The preset value is 85%.

7. A method for determining the median particle size of clastic rocks according to claim 1, characterized in that: The process of fitting the median particle size of clastic rock using the multivariate linear regression method according to the obtained comprehensive evaluation value F is as follows: Determine the independent variables and dependent variables that need to be subjected to regression analysis from the data, with F as the independent variable and the median particle size as the dependent variable; Using the least squares fitting method, a multiple linear regression model was established based on the relationship between independent variables and dependent variables; The model is evaluated and optimized using evaluation indicators to obtain the optimized model, which is then used to predict the median grain size of clastic rocks to obtain the median grain size of clastic rocks.

8. A system for determining the median grain size of clastic rocks, characterized in that: A method for determining the median grain size of clastic rocks according to any one of claims 1 to 7, comprising: The acquisition module is used to establish a median grain size histogram based on the experimental analysis results of the clastic rock grain size; A preprocessing module is used to establish a thermal map of the median particle size data and multiple sets of logging data based on the median particle size histogram, and determine the logging curves of density, natural gamma and neutron-density porosity difference with high correlation with the median particle size; A standardization processing module is used to perform Z value standardization on the well logging curves of density, natural gamma and neutron-density porosity difference which have a high correlation with the median particle size; A selection module is used to extract the characteristics of the standardized logging curve by using the principal component analysis method, and to select the principal component variables whose cumulative variance contribution is greater than a preset value to calculate the comprehensive evaluation value F; The output module is used to obtain the median grain size of the clastic rock by fitting the obtained comprehensive evaluation value F using a multivariate linear regression method.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for determining the median grain size of clastic rock as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for determining the median grain size of clastic rock as claimed in any one of claims 1 to 7 are implemented.

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