CN107576772B - Method for quantitatively evaluating coal body structure type by using logging data - Google Patents
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Abstract
The invention discloses a method for quantitatively evaluating coal body structure type by utilizing logging data, which comprises the following steps: firstly, obtaining logging data: selecting a logging curve for evaluating a coal body structure, carrying out numerical value standardization on all logging data, and then extracting logging data of a pure coal section; then, principal component analysis was performed: performing principal component analysis and extracting principal components, and establishing a comprehensive score coefficient formula; and determining an evaluation model of the coal body structure: selecting a reference well, dividing the coal body structure of the coal bed, extracting logging data, and obtaining scoring areas of various coal body structures through a comprehensive scoring coefficient formula, namely an evaluation model; and finally, reversely deducing the coal body structures of other wells by using the evaluation model in the steps, overcoming the defect that a single logging index cannot truly reflect the coal body structure, screening a plurality of main evaluation indexes, and then carrying out curve standardization, assignment superposition and linear combination to simplify and quantitatively divide the logging reaction of the coal body structure.
Description
Technical Field
The invention relates to the technical field of applying a statistical theory to coal body structure logging judgment, in particular to a method for quantitatively evaluating a coal body structure type by using logging data.
Background
After the coal-containing basin is formed in China, the multi-stage tectonic movement is carried out, and tectonic coal with different degrees is developed, so that the problem that the yield of the coal bed gas is not negligible is solved. The coal body structure of the coal bed plays an inherent control role in the occurrence and migration of coal bed gas. Therefore, the coal body structure transverse and longitudinal distribution of the coal bed gas block is of great significance to the delineation of the favorable coal bed gas block and the improvement of production measures.
The tectonic coal is a coal bed or a coal body with different deformation characteristics, wherein the original structure and the structure of the coal bed are transformed to different degrees under the stress action in a certain deformation environment. Due to the different degrees of coal body breakage, the coal body structure is generally classified into primary structure coal, cracked coal, pulverized coal and minced coal 4 types. Currently, there are no three aspects to the identification of coal body structure, namely drill hole coring, underground identification and well log identification. The former two are undoubtedly the most direct methods for dividing the coal body structure, but firstly, the structural coal is soft and fragile in lithology and extremely low in sampling rate, the coal body structure is difficult to describe, and secondly, the unemployed data cannot be obtained by roadway cataloging. Therefore, the method for identifying the coal body structure by using the well logging curve becomes a necessary method.
Lithology curves (GR, CAL, SP): in coal field geological exploration, natural gamma ray logging is mainly used for dividing lithology, explaining coal seam thickness and structure and the like. The coal is brittle, when drilling into the coal bed, the coal bed is easily damaged by a drill bit, and the well diameter curve generally shows the diameter expansion. In coal-series formations, coal beds have obvious density difference from rock strata, and in the form of density curves, the coal beds show sudden density reduction and are in finger-shaped curves (thin layers) or box-shaped curves (thick layers). The sound wave time difference reflects the sound propagation speed of the medium, the sound velocity of the coal bed is low, and the sound velocity is often displayed as a high value on a time difference curve. The compensated neutron logging mainly reflects the content of hydrogen elements in rock, and is expressed as a hydrogen index, the coal is rich in hydrogen elements, and the compensated neutron logging response value is higher. The resistivity of the coal bed is relatively high, but the resistivity of the coal can be greatly changed due to different coal rank, ash content and moisture content in the coal, and the curve is represented as a high-value peak or a tooth boxing curve. The micro-spherical focus is mainly used for detecting the resistivity of a flushing zone and is matched with deep and shallow lateral resistivity logging to evaluate the invasion characteristic of the stratum.
In conclusion, the three-porosity curve, the resistivity curve, the natural gamma curve and the well diameter curve have obvious reaction in the coal bed, so that the coal reservoir can be well identified, and the thickness of the coal bed can be determined. However, at present, the identification of the well logging curve of the coal body structure is only suitable for a certain block or even a certain layer, or the qualitative identification of the well logging curve of the coal body structure is summarized empirically, and a set of universal method is not suitable for the quantitative identification of the coal body structure through the well logging curve. Therefore, the authors first proposed a method for quantitatively partitioning the well log values of the tectonic coal zones by principal component analysis.
The Principal Component Analysis (PCA) algorithm, as a classical feature extraction method, is developed irrespectively of the development of the pattern recognition discipline. The pattern recognition is carried out on any system, all factors influencing the system are considered for comprehensively analyzing the pattern recognition, the system is described in detail, namely, the variable parameters are selected more, and therefore the more accurate and deeper the system is recognized. Research shows that information which is related to each other is contained in the variables, in other words, the variables have information overlapping to a certain extent, and the direct use of the variables for system analysis makes it difficult to master the main part of the system and influences the complexity of problem solving. In addition, too much data not only occupies a large amount of storage space and consumes more resources, but also increases the time (time consuming) for information processing. Therefore, how to effectively extract features of a system to be analyzed becomes the most central step of pattern recognition. The PCA method enables redundancy analysis (noise reduction) and feature extraction of the obtained system data.
The PCA converts the multivariable problem in the high-dimensional space into the low-dimensional space to form a new few variables (comprehensive variables), the new variables are used for replacing the original variables to carry out subsequent processing, the problem in the high-dimensional space is converted into the low-dimensional space to be processed, the conversion is not random, the newly obtained variables are required to be a linear combination of the original variables, the method can reduce the dimensionality of a multivariable data system, and the statistical digital characteristics of the system variables can be simplified. The processing process of the PCA can be regarded as being composed of two parts, wherein one part is an application basis of feature extraction and data compression; the other part is the basis for data and image reconstruction. This chapter mainly introduces the basic theory and implementation process of the PCA algorithm. PCA has its own advantages and disadvantages as a classical mode-classification-oriented feature extraction method. The method has the advantages that the method is not influenced by subjective factors, the internal structural relation among all dimensional indexes is obtained by deeply analyzing data, the obtained main components are not related to each other, the cross information is less, namely the data redundancy is less, and therefore the method is very beneficial to the feature extraction of the data.
Disclosure of Invention
In order to achieve the purpose, the invention provides the following technical scheme:
a method for quantitatively evaluating the structural type of a coal body by utilizing logging data comprises the following steps:
s100, obtaining logging data: selecting a logging curve for evaluating a coal body structure, carrying out numerical value standardization on all logging data, and then extracting logging data of a pure coal section;
s200, main component analysis: performing principal component analysis and extracting principal components, and establishing a comprehensive score coefficient formula;
s3OO, determining an evaluation model of a coal body structure: selecting a reference well, dividing the coal body structure of the coal bed, extracting logging data, and obtaining scoring areas of various coal body structures through a comprehensive scoring coefficient formula, namely an evaluation model;
and S400, reversely deducing the coal body structure of each other well by using the evaluation model in the step.
As a preferred embodiment of the present invention, the required types of the coal body structure in step S100 include: natural Gamma Ray (GR), compensation density logging (DEN), well diameter (CAL), apparent resistivity (Rt), depth lateral resistivity (LLS/D), microsphere focusing resistivity (MSFL), Compensation Neutron Logging (CNL), acoustic time difference logging (AC) and microelectrode logging (MML), and the steps are to obtain various logging curve values of a certain coal seam by taking a well as a unit and store the values in a data column form by screening logging curves meeting the requirement types.
As a preferred embodiment of the present invention, the step S100 further includes: setting the extraction interval of the coal bed logging data to be 0.1m or 0.125 m.
As a preferred technical solution of the present invention, in step S100, the step of extracting the logging data of the pure coal section includes: according to the existing lithological interpretation data, the logging data are preferably summarized, and data rows of a waste rock clamping section, two small-layer logging at the top and the bottom and two small-layer logging near the top and the bottom plate are removed.
As a preferred technical solution of the present invention, the step S200 specifically includes the following steps:
s201, standardizing the original logging data:
wherein: x represents a log data column, such as well diameter (CAL); i represents a value in the column; xmaxRepresents the maximum value of the column, XminRepresents the minimum value of the row; x is the number ofiIs a normalized value
S202, calculating a correlation coefficient matrix R among the normalized logging values:
wherein r isij(i, j ═ 1,2, …, p) as the original variable xiAnd xjOf correlation coefficient rii＝rjiThe calculation formula is as follows:
wherein:
is the variable average value of the ith row;
is the average value of the j row variable;
s203, calculating the characteristic value and the characteristic vector, wherein the process is a necessary step for obtaining the principal component score coefficient:
first, the eigen equation | λ I-R | ═ 0 is solved, the eigenvalues are found by the jacobian method, and are arranged according to the magnitude: lambda [ alpha ]1≥λ2≥Λ≥λpNot less than 0; then, the corresponding characteristic values lambda are respectively obtainediCharacteristic vector e ofi(i ═ 1,2, …, p), and requires | | | ei＝1||；
S204, calculating the contribution rate and the accumulated contribution rate of the principal component:
the contribution rate and the accumulated contribution rate of the principal component measure the information of the converted principal component extracted from the original data X;
the contribution rate refers to the proportion of the eigenvalue corresponding to the ith principal component in the sum of all eigenvalues of the covariance matrix, the larger the ratio is, the stronger the capability of synthesizing the original index information of the ith principal component is, and the eigenvalue corresponding to the ith principal component is calculated by the following formula:
the accumulated contribution rate refers to the proportion of the sum of the characteristic values of the first k principal components in the sum of all the characteristic values, the larger the ratio is, the more comprehensively the first k principal components represent the information of the original data, and the calculation formula is as follows:
s205, extracting principal components and establishing a comprehensive score formula
Selecting principal components with characteristic values larger than 1 to enable the cumulative variance contribution rate to meet more than 60%, and replacing the original p variables with the selected first d principal components for analysis to achieve the purpose of original multi-column data dimension reduction, and synthesizing a scoring formula:
as a preferred technical solution of the present invention, the principle of selecting the reference well in step S300 includes:
(1) the total thickness of the reference well coal seam and the adjacent well coal seam is generally larger;
(2) according to the manual test or observation judgment, the referenced well coal core covers all coal body structure types from primary structure coal to crushed coal and even minced coal, and the drilling depth corresponding to each type of coal body structure is accurately positioned.
(3) The ratio of the gangue is lower than 15%.
As a preferred technical solution of the present invention, the step of extracting the scoring areas of the coal body structure in step S300 specifically includes: and accurately comparing the structural types of various coal bodies in the reference well with the scores of the coal bodies with equal depths, and positioning the boundary values corresponding to the corresponding coal body structures, wherein the interval values are the quantitative coal body structure.
As a preferred technical solution of the present invention, the method for reversely deducing the coal structure type in step S300 specifically comprises: the method comprises the steps of firstly calculating the scoring coefficient of coal body structures of all longitudinal small layers of other well coal seams in a work area, and then reducing the coal body structure types among scoring areas of a reference well.
The invention has the beneficial effects that: the method adopted by the invention can overcome the defect that a single logging index can not truly reflect the coal body structure, and simultaneously can screen a plurality of main evaluation indexes, and then carry out curve standardization, assignment superposition and linear combination, so that the logging reaction of the coal body structure can be simplified and quantitatively divided.
Drawings
FIG. 1 is a flow chart of a method for quantitatively evaluating a structural type of a coal body by using well log data according to an embodiment of the present invention;
FIG. 2 is a plot of a coal seam log in accordance with an embodiment of the present invention;
FIG. 3 is a diagram illustrating the scoring of the structure of the reference coal seam according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a technical solution: a method for quantitatively evaluating the structural type of a coal body by utilizing logging data comprises the following steps:
s100, acquiring logging data: selecting a logging curve for evaluating a coal body structure, carrying out numerical value standardization on all logging data, and then extracting logging data of a pure coal section; setting the extraction interval of coal bed logging data to be 0.1m or 0.125 m;
the required types of the coal body structure in the steps comprise: natural Gamma Ray (GR), compensation density logging (DEN), well diameter (CAL), apparent resistivity (Rt), depth lateral resistivity (LLS/D), microsphere focusing resistivity (MSFL), Compensation Neutron Logging (CNL), acoustic time difference logging (AC) and microelectrode logging (MML), and the steps are to obtain various logging curve values of a certain coal seam by taking a well as a unit and store the values in a data column form by screening logging curves meeting the requirement types.
In the above steps, the step of extracting the logging data of the pure coal section is as follows: according to the existing lithological interpretation data, the logging data are preferably summarized, and data rows of a waste rock clamping section, two small-layer logging at the top and the bottom and two small-layer logging near the top and the bottom plate are removed.
Step S200, principal component analysis: performing principal component analysis and extracting principal components, and establishing a comprehensive score coefficient formula; the method comprises the following steps:
step S201, raw logging data is standardized:
wherein: x represents a log data column, such as well diameter (CAL); i represents a value in the column; xmaxRepresents the maximum value of the column, XminRepresents the minimum value of the row; x is the number ofiIs a normalized value
Step S202, calculating a correlation coefficient matrix R among the normalized logging values:
wherein r isij(i, j ═ 1,2, …, p) as the original variable xiAnd xjOf correlation coefficient rii＝rjiThe calculation formula is as follows:
wherein:
is the variable average value of the ith row;
is the average value of the j column variable
Step S203, calculating the eigenvalues and eigenvectors, which is a necessary step for obtaining the principal component score coefficients:
first, the eigen equation | λ I-R | ═ 0 is solved, the eigenvalues are found by the jacobian method, and are arranged according to the magnitude: lambda [ alpha ]1≥λ2≥Λ≥λpNot less than 0; then, the corresponding characteristic values lambda are respectively obtainediCharacteristic vector e ofi(i ═ 1,2, …, p), and requires | | | ei＝1||；
Step S204, calculating the contribution rate and the accumulated contribution rate of the principal component:
the contribution rate and the accumulated contribution rate of the principal component measure the information of the converted principal component extracted from the original data X;
the contribution rate refers to the proportion of the eigenvalue corresponding to the ith principal component in the sum of all eigenvalues of the covariance matrix, the larger the ratio is, the stronger the capability of synthesizing the original index information of the ith principal component is, and the eigenvalue corresponding to the ith principal component is calculated by the following formula:
the accumulated contribution rate refers to the proportion of the sum of the characteristic values of the first k principal components in the sum of all the characteristic values, the larger the ratio is, the more comprehensively the first k principal components represent the information of the original data, and the calculation formula is as follows:
step S205, extracting principal component and establishing a comprehensive score formula
Selecting principal components with characteristic values larger than 1 to enable the cumulative variance contribution rate to meet more than 60%, and replacing the original p variables with the selected first d principal components for analysis to achieve the purpose of original multi-column data dimension reduction, and synthesizing a scoring formula:
step S3OO, determining an evaluation model of the coal body structure: selecting a reference well, dividing the coal body structure of the coal bed, extracting logging data, and obtaining scoring areas of various coal body structures through a comprehensive scoring coefficient formula, namely an evaluation model;
the step of extracting scoring intervals of the coal body structure in the step is specifically as follows: and accurately comparing the structural types of various coal bodies in the reference well with the scores of the coal bodies with equal depths, and positioning the boundary values corresponding to the corresponding coal body structures, wherein the interval values are the quantitative coal body structure.
The method for reversely deducing the structure type of the coal body in the steps comprises the following specific steps: the method comprises the steps of firstly calculating the scoring coefficient of coal body structures of all longitudinal small layers of other well coal seams in a work area, and then reducing the coal body structure types among scoring areas of a reference well.
The principle of selecting the reference well in the steps comprises the following steps:
(1) the total thickness of the reference well coal seam and the adjacent well coal seam is generally larger;
(2) according to the manual test or observation judgment, the referenced well coal core covers all coal body structure types from primary structure coal to crushed coal and even minced coal, and the drilling depth corresponding to each type of coal body structure is accurately positioned.
(3) The ratio of the gangue is lower than 15%.
And S400, reversely deducing the coal body structure of each other well by using the evaluation model in the step.
In summary, the above method is that firstly, a logging curve for identifying the coal body structure can be provided in a parameter well through screening, and various curve values corresponding to a target coal seam are obtained after well deviation correction; then extracting logging curve data of the pure coal seam section, performing principal component analysis in a data column form, and extracting principal components after operation to obtain a model; and finally, selecting a reference well (selecting wells with more structural coal types through identifying the rock core or selecting wells with single coal bed coal body structures respectively), carrying out scoring coefficient demarcation on the principal component score corresponding to the reference well, and establishing an evaluation model.
The following describes the content of the method for quantitatively evaluating the coal body structure type by using logging data according to the present invention with reference to specific embodiments.
Example (b): a certain parameter well in the Qinbei ancient crossing area is selected as a reference well, the steps of the method are explained, and the specific contents are as follows:
(1) acquiring logging data:
the interval of the values is set to 0.1m, as shown in the following table 1:
table 1:
(2) screening logging data:
and preferably summarizing the logging data according to the existing lithological interpretation data. And (3) removing data rows (figure 2) of the gangue clamping section, the top and bottom two-layer logging and the near-top bottom plate two-layer logging, namely strictly selecting a pure coal section logging numerical value (the row of red marked data in table 1 is a deletion row) for analysis, wherein the reason is that abnormal high values such as GR and DEN appear in a coal bed near the gangue clamping section or a contact area between the top and bottom of the coal bed and mudstone.
(3) Extracting logging data:
log data for all well clean coal sections were extracted as shown in table 2 below:
table 2:
| hole diameter | Natural gamma | Deep lateral direction | Compensating density |
| 255.1774543 | 6.8860683 | 2.42880089 | 1.42467314 |
| 255.8149406 | 7.2535166 | 2.37596618 | 1.43231732 |
| 288.8807839 | 7.56574157 | 2.7990407 | 1.3951614 |
| 284.7094263 | 7.70787534 | 2.87020844 | 1.39185096 |
| 286.5240637 | 8.43025855 | 2.73823699 | 1.40445179 |
| 254.5143768 | 9.2271089 | 2.34856957 | 1.45475901 |
| 305.0595238 | 9.5057352 | 2.85624632 | 1.44701736 |
| 281.7525797 | 9.5342196 | 2.94426891 | 1.40407524 |
| 305.0595238 | 9.62237861 | 2.92684308 | 1.43821548 |
| 273.9137048 | 9.81747634 | 2.68499416 | 1.41929318 |
| 295.7407551 | 9.89905097 | 2.73378269 | 1.36917131 |
| 265.263899 | 9.90966242 | 2.55977726 | 1.43353458 |
| …… | …… | …… | …… |
(3) Principal component analysis-creation of covariance matrix, testing KMO
The results are shown in table 3 below:
table 3:
in the above contents: the KMO (Kaiser-Meyer-Olkin) test statistic is an index used to compare simple and partial correlation coefficients between variables.
The KMO statistic takes on a value between 0 and 1. When the simple sum of the squared correlation coefficients among all variables is much larger than the sum of the squared partial correlation coefficients, the KMO value is close to 1-the closer the KMO value is to 1, the stronger the correlation among the variables is.
Bartlett sphericity test: the statistic of the Butterworth sphericity test is obtained according to the determinant of the correlation coefficient matrix, if the value is larger and the corresponding concomitant probability value is smaller than the significance level in the user's heart, the zero hypothesis should be rejected, and the correlation coefficient matrix is considered to be impossible to be a unit matrix, namely, the correlation exists between the original variables, so that the correlation coefficient matrix is suitable for principal component analysis;
(4) principal component analysis-extraction of principal component
Table 4:
the index of the influence of the principal component generally takes 1 as a standard, and if the characteristic root is less than 1, the influence of the principal factor is not as good as a basic variable. We extract only principal components with characteristic roots larger than 1. As shown in fig. 3, the first principal component is greater than 1, so we can only say that there are 1 principal components. In addition, we see that the first principal component variance accounts for 54.557% of all principal component variances, approaching more than half of the total data information amount.
(5) Principal component analysis-extraction of the score coefficients for each factor of the principal component (as shown in FIG. 3)
Table 5:
the component score is a score coefficient corresponding to each log type.
(6) Constructing a coal body structure score coefficient formula:
since there is only one Principal Component (PC), the corresponding composite score formula is:
wherein Z is the comprehensive scoring coefficient of the coal body structure of the coal seam point; fCALThe value after dimensionless processing is carried out on the log response value, NCALScores are made for the log components.
(7) Selecting a reference well and carrying out coal bed coal body structure and comprehensive score coefficient correspondence:
and (3) corresponding the calculated comprehensive score coefficient with the coal seam point, and obtaining the results as shown in the following table 6:
table 6:
(8) dividing coal body structure type into different areas
And (3) dividing a discrimination interval according to the corresponding relation between the comprehensive score coefficient of each point and the coal body structure type, wherein the result is shown in the following table 7:
table 7:
in summary, the method has the advantages that the method can overcome the defect that a single logging index cannot truly reflect the coal body structure, and simultaneously can screen out a plurality of main evaluation indexes, and then carry out curve standardization, assignment superposition and linear combination, so that the logging reaction of the coal body structure can be simplified and quantitatively divided.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A method for quantitatively evaluating the structure type of a coal body by utilizing logging data is characterized by comprising the following steps: the method comprises the following steps:
s100, obtaining logging data: the method comprises the steps of obtaining various logging curve values of a certain coal seam by taking a well as a unit and storing the logging curve values in a data column form by screening logging curve types conforming to the structure of an evaluation coal body, carrying out numerical value standardization on all logging data, and then extracting logging data of a pure coal section;
s200, main component analysis: performing principal component analysis and extracting principal components, and establishing a comprehensive score coefficient formula, which specifically comprises the following steps:
s201, standardizing the original logging data:
wherein: x represents a certain well logging data column; i represents a value in the column; xmaxRepresents the maximum value of the column, XminRepresents the minimum value of the row; xiIs a normalized value
S202, calculating a correlation coefficient matrix R among the normalized logging values:
wherein r isijIs the original variable xiAnd xjI, j ═ 1,2, …, p; r isij＝rjiThe calculation formula is as follows:
wherein:
is the variable average value of the ith row;
is the average value of the j row variable;
s203, calculating the characteristic value and the characteristic vector, wherein the process is a necessary step for obtaining the principal component score coefficient:
first, the eigen equation | λ I-R | ═ 0 is solved, the eigenvalues are found by the jacobian method, and are arranged according to the magnitude: lambda [ alpha ]1≥λ2≥Λ≥λpNot less than 0; then, the corresponding characteristic values lambda are respectively obtainediCharacteristic vector e ofiI ═ 1,2, …, p; and requires | | | ei＝1||；
S204, calculating the contribution rate and the accumulated contribution rate of the principal component:
the contribution rate and the accumulated contribution rate of the principal component measure the information of the converted principal component extracted from the original data X;
the contribution rate refers to the proportion of the eigenvalue corresponding to the ith principal component in the sum of all eigenvalues of the covariance matrix, the larger the ratio is, the stronger the capability of synthesizing the original index information of the ith principal component is, and the eigenvalue corresponding to the ith principal component is calculated by the following formula:
the accumulated contribution rate refers to the proportion of the sum of the characteristic values of the first k principal components in the sum of all the characteristic values, the larger the ratio is, the more comprehensively the first k principal components represent the information of the original data, and the calculation formula is as follows:
s205, extracting principal components and establishing a comprehensive score coefficient formula:
selecting principal components with characteristic values larger than 1 to enable the cumulative variance contribution rate to meet more than 60%, replacing the original p variables with the selected first d principal components for analysis, achieving the purpose of original multi-column data dimension reduction, and integrating a score coefficient formula:
s300, determining an evaluation model of the coal body structure: selecting a reference well, dividing the coal body structure of the coal bed, extracting logging data, and obtaining scoring areas of various coal body structures through a comprehensive scoring coefficient formula, namely an evaluation model;
the steps of extracting the coal body structure obtaining area specifically comprise: accurately comparing various coal body structure types in the reference well with scores of the coal body structures with equal depths, and positioning a boundary value corresponding to the corresponding coal body structure, wherein the interval value is an interval of scores of quantitative coal body structure division;
and S400, reversely deducing the coal body structure of each other well by using the evaluation model in the step.
2. The method for quantitatively evaluating the structural type of the coal body by utilizing the logging data as claimed in claim 1, wherein: the required types of the coal body structure in the step S100 include: natural gamma GR, compensated density logging DEN, well diameter CAL, apparent resistivity Rt, depth lateral resistivity LLS/D, microsphere focused resistivity MSFL, compensated neutron logging CNL, acoustic time difference logging AC and microelectrode logging MML.
3. The method for quantitatively evaluating the structural type of the coal body by utilizing the logging data as claimed in claim 1, wherein: the step S100 further includes: setting the extraction interval of the coal bed logging data to be 0.1m or 0.125 m.
4. The method for quantitatively evaluating the structural type of the coal body by utilizing the logging data as claimed in claim 1, wherein: in the step S100, the step of extracting the logging data of the pure coal section includes: according to the existing lithological interpretation data, the logging data are preferably summarized, and data rows of a waste rock clamping section, two small-layer logging at the top and the bottom and two small-layer logging near the top and the bottom plate are removed.
5. The method for quantitatively evaluating the structural type of the coal body by utilizing the logging data as claimed in claim 1, wherein:
the principle of selecting the reference well in the step S300 includes:
the total thickness of the reference well coal seam and the adjacent well coal seam is generally larger;
according to the manual test or observation judgment, the referenced well coal core covers all coal body structure types from primary structure coal to crushed coal and even minced coal, and the drilling depth corresponding to each type of coal body structure is accurately positioned;
the ratio of the gangue is lower than 15%.
6. The method for quantitatively evaluating the structural type of the coal body by utilizing the logging data as claimed in claim 1, wherein: the method for reversely deducing the coal body structure of each other well in the step S400 specifically comprises the following steps: the method comprises the steps of firstly calculating the scoring coefficient of coal body structures of all longitudinal small layers of other well coal seams in a work area, and then reducing the coal body structure types among scoring areas of a reference well.
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