Method for identifying lithology and macroscopic coal rock type of coal-bearing target layer based on element logging

Abstract

The invention discloses a coal-bearing target stratum lithology and macroscopic coal rock type identification method based on element logging, and relates to the technical field of unconventional natural gas exploration and development, and the method comprises the following steps: s1, constructing a coal rock boundary, and dividing a coal rock and a non-coal rock; s2, calculating the content of each component in the coal rock and the non-coal rock; and s3, constructing a non-coal rock lithology identification method and a macroscopic coal rock type identification method in the coal rock. According to the method, division of the coal rock and the non-coal rock can be realized only based on element logging data and a small amount of experimental data, calculation of the content of each component in the rock of the coal-containing target layer is realized, and efficient and accurate identification of the lithology of the non-coal rock and the macroscopic coal rock type can be completed by further innovatively combining with the sulfur element; important technical reference can be provided for geological evaluation and target layer selection of a coal-bearing target layer.

Description

Technical Field

[0001] The present invention relates to the technical field of unconventional natural gas exploration and development, and in particular to a method for identifying the lithology of a target coal-bearing layer and macroscopic coal and rock types based on element logging. Background Art

[0002] Deep coalbed methane exploitation urgently needs to carry out many adaptive technical research and development. Among them, accurate inversion of rock components of coal-bearing target layers, lithology identification, and macro-coal and rock type classification and identification are the basis of geological-engineering sweet spot evaluation. If you want to obtain continuous and more accurate lithology data, you need to conduct research on conventional logging or element logging data. Lithology and macro-coal and rock type identification is based on conventional logging curves, but when there are no conventional logging curves or conventional logging curves cannot effectively distinguish lithology or macro-coal and rock type classification, other effective methods need to be explored. In the case of element logging data, it is generally believed that the lithology or macro-coal and rock type identified by element logging is more reliable, because elements are a direct reflection of the proportion of each component of the rock itself, and the lithology can be determined from the essence of the rock component; however, there is currently little research on how to construct a method for identifying the lithology and macro-coal and rock types of coal-bearing target layers based on element logging.

[0003] Therefore, it is urgent to study a method for identifying the lithology and macroscopic coal rock types of coal-bearing target layers based on element logging. Summary of the invention

[0004] In order to solve the current technical problem of inaccurate identification of non-coal rock lithology and macroscopic coal rock types in coal rocks based on conventional logging curves, the present invention discloses a method for identifying lithology and macroscopic coal rock types of coal-bearing target layers based on element logging. The method first constructs a non-coal rock and coal rock boundary based on element logging and a small amount of experimental analysis data and realizes the division of non-coal rock and coal rock. Different calculation methods are used for the mineral components and industrial components in non-coal rock and coal rock to achieve accurate calculation. Based on the above calculation results and the innovative combination of sulfur element, accurate identification of non-coal rock lithology and macroscopic coal rock types in coal rocks is achieved, which can effectively make up for the problem of large errors in identification based on conventional curves.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The method for identifying the lithology and macroscopic coal rock type of the target coal-bearing layer based on element logging includes the following steps:

[0007] s1. Construct the coal-rock boundary and divide coal-rock and non-coal-rock; determine the coal-rock and non-coal-rock boundary based on element logging data, coring experimental data and cuttings logging data, and divide the target coal-bearing layer into coal-rock and non-coal-rock based on the boundary;

[0008] s2. Calculate the content of each component in coal rock and non-coal rock; for non-coal rock, calculate the content of mineral components based on the oxide closure model and matrix solution; for coal rock, build a calculation model for fixed carbon, volatile matter and moisture based on industrial component experimental data and calculated mineral component content, and calculate the fixed carbon, volatile matter and moisture content in turn;

[0009] s3. Construct a method for identifying the lithology of non-coal rocks and the macroscopic coal rock type in coal rocks, and construct a classification standard for non-coal rocks and macroscopic coal rock types by combining the calculation results of each component in step s2 and the sulfur element.

[0010] Optionally, in step s1, constructing the coal-rock boundary and dividing coal-rock and non-coal-rock specifically includes:

[0011] s11. Based on core sampling and experimental analysis, the lithology, mineral type and characteristic elements of the target coal-bearing strata are clarified; the lithology of the target coal-bearing strata includes coal rock, sandstone, limestone, ordinary mudstone and carbonaceous mudstone. Non-coal rocks are mainly ash (ash is mainly minerals), while coal rocks are mainly fixed carbon. The minerals corresponding to ash include kaolinite, montmorillonite, illite, quartz, calcite, dolomite, pyrite, siderite and anhydrite. The characteristic elements are Al, K, S, Si, Ca, Mg and Fe.

[0012] s12. Based on the coring data and cuttings logging data, the sum of the lithology distribution and the weight percentage of the characteristic elements Compare and determine the boundary between coal rock and non-coal rock The division threshold f is used to achieve the preliminary division of coal rock and non-coal rock; the details are as follows:

[0013] ;

[0014] ;

[0015] In the formula, , , , , , , are the weight percentages of Al, K, S, Si, Ca, Mg and Fe elements respectively, also known as element dry weight, %; f is the threshold for dividing coal rock and non-coal rock, %.

[0016] Optionally, in step s2, calculating the content of each component in the coal rock and the non-coal rock specifically includes:

[0017] s21. For non-coal rocks, the sum of the weight percentages of oxides of all elements per unit volume in the formation is set to 100, and the weight percentage of each element is determined by the oxide closure model, specifically:

[0018] ;

[0019] ;

[0020] ;

[0021] In the formula, Indicates the oxide percentage corresponding to the i-th element, %; represents the normalization factor; represents the oxide index of the i-th element; Indicates the weight percentage of the i-th element, %; represents the sensitivity factor of the i-th element; Indicates the weight percentage of an element (dry weight of the element), %;

[0022] s22. The contents of various mineral components are calculated by solving the Herron matrix and expressed as:

[0023] ;

[0024] In the formula, is a matrix composed of element weight percentages; A matrix of mineral weight percentages, including , , , , , , , and , + + + + + + + + =100; is the conversion coefficient, which is a square matrix, coefficient C ij represents the conversion coefficient of the ith element in the jth mineral;

[0025] s23. For coal rock, industrial components include fixed carbon ash, ash, volatile matter and moisture, elements include Al, K, S, Si, Ca, Mg, Fe and C, and the sum of Al, K, S, Si, Ca, Mg, Fe represents the ash content, and the C element is considered as the non-ash content. Therefore, the present invention uses the weight percentage of ash V HF Equivalent to , as shown in formula (7); when determining V HF Afterwards, based on the experimental data of industrial components, regression model formulas of fixed carbon, volatile matter and moisture based on ash were constructed, as shown in equations (8), (9) and (10), and the fixed carbon, volatile matter and moisture contents were calculated in turn;

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] In the formula, is the ash content, %; is the fixed carbon content, %; is the volatile content, %; is the moisture content, %; a, A, b, B, c, and C are the slope and intercept of the model when the fixed carbon is calculated based on the ash content, the slope and intercept of the model when the volatile matter is calculated based on the fixed carbon, and the slope and intercept of the model when the moisture is calculated based on the ash content + fixed carbon + volatile matter, respectively;

[0031] s24. Normalize all mineral and industrial components to 100%. The total weight percentage of industrial components per unit volume is 100%, as shown in formula (11). Normalize the industrial components to 100%, as shown in formulas (12), (13), (14), and (15). The normalized ash content According to formula (6), , , , , , , , and The ratio between them is allocated, as shown in formula (16):

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] In the formula, is the normalized ash content, %; is the normalized fixed carbon content, %; is the normalized volatile content, %; is the normalized moisture content, %; is the mineral content calculated by formula (6), n=1, 2, 3, 4, 5, 6, 7, 8, 9 represent , , , , , , , and ; According to the normalized ash content The percentage of various mineral contents distributed, %.

[0039] Optionally, in step s3, constructing a method for identifying non-coal rock lithology and macroscopic coal rock type in coal rock specifically includes:

[0040] s31, based on mineral content , sulfur content and clastic rock classification triangle map to identify non-coal rock lithology and integrate minerals into clay minerals , felsic minerals and carbonate minerals It should be noted that siderite is a carbonate mineral and is classified as , but pyrite is a sulfide, not , or , so it does not participate in lithology identification. , and Re-normalize and get , , ;based on , , The classification of mudstone, sandstone and limestone is realized by using the triangular plate of clastic rock classification. Then, based on the sulfur content, mudstone is divided into ordinary mudstone and carbonaceous mudstone, specifically:

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] s32. Based on the sulfur content, the macroscopic coal and rock types are identified. The conventional classification method is based on the conventional three-porosity logging curve (AC acoustic curve, DEN density curve, CNL neutron curve). However, there is a complex overlapping relationship between the macroscopic coal and rock types and the conventional curves, so it is difficult to identify the macroscopic coal and rock types based on the conventional curves. Through statistical analysis, it is found that sulfur can better distinguish the macroscopic coal and rock types, specifically:

[0050] ;

[0051] Wherein, S is the sulfur content, %; s1, s2, and s3 are the threshold values ​​for dividing different macroscopic coal rock types.

[0052] The beneficial effect of the present invention is that the non-coal rock lithology identification and macro-coal rock type identification method constructed by the present invention can firstly realize the division of coal rock and non-coal rock and realize the calculation of the content of each component in the rock of the coal-bearing target layer based only on element logging data and a small amount of experimental data. Further, the innovative combination of sulfur element can complete the efficient and accurate identification of non-coal rock lithology and macro-coal rock type, which can provide important technical reference for the geological evaluation of coal-bearing target layers and the selection of target layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for identifying the lithology and macroscopic coal and rock types of a target coal-bearing layer based on element logging according to the present invention;

[0054] Figure 2 A lithology characteristic map determined by core sampling and experimental analysis of a target layer in a research area according to an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of the coal-rock boundary construction shown in one embodiment of the present invention;

[0056] Figure 4 A schematic diagram of the division of coal rock and non-coal rock is shown in an embodiment of the present invention;

[0057] Figure 5 This is a schematic diagram of a fixed carbon calculation model based on ash content according to an embodiment of the present invention;

[0058] Figure 6 This is a schematic diagram of a volatile matter calculation model based on fixed carbon according to an embodiment of the present invention;

[0059] Figure 7 This is a schematic diagram of a moisture calculation model based on ash + fixed carbon + volatile matter according to an embodiment of the present invention;

[0060] Figure 8 This is a diagram showing the calculation results of industrial components of Well W1 in the study area according to an embodiment of the present invention;

[0061] Fig. 9 A schematic diagram of non-coal rock lithology identification according to an embodiment of the present invention;

[0062] Fig. 10A Schematic diagram of macroscopic coal and rock type identification based on DEN-CNL intersection curve;

[0063] Fig. 10B Schematic diagram for identifying macroscopic coal and rock types based on DEN-AC cross-curves;

[0064] Fig.11 This is a schematic diagram of macroscopic coal and rock type identification based on sulfur element according to an embodiment of the present invention;

[0065] Fig.12 The lithology identification result of Well W1 in the study area is shown in one embodiment of the present invention;

[0066] Fig.13 The lithology identification result of Well W2 in the study area is shown as an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0068] The identification method of lithology and macroscopic coal rock type of coal-bearing target layer based on element logging, such as Figure 1 As shown, the following steps are included:

[0069] s1. Construct the coal-rock boundary line and divide coal-rock and non-coal rock; determine the coal-rock and non-coal rock boundary line based on element logging data, coring experiment data and cuttings logging data, and divide the target coal-bearing layer into coal-rock and non-coal rock based on the boundary line.

[0070] Since the target coal-bearing layer often develops not only coal rock but also a variety of non-coal rocks, in order to facilitate the selection of subsequent calculation methods, it is first necessary to distinguish between coal rock and non-coal rock. The embodiment of the present invention takes a coal-bearing target layer in the eastern part of the Ordos Basin in China as an example. Through basic core sampling and experimental analysis, it is clarified that the main lithologies developed in the target layer include coal rock, sandstone, limestone, mudstone and carbonaceous mudstone. Non-coal rocks are mainly ash, ash is mainly minerals, and coal rocks are mainly fixed carbon. The minerals corresponding to the ash are mainly kaolinite, montmorillonite, illite, quartz, calcite, dolomite, pyrite, siderite and anhydrite. The main elements or characteristic elements corresponding to the minerals are Al, K, S, Si, Ca, Mg, and Fe, accounting for about 98% of the total elements measured by element logging (the effective measurement range of element logging is usually from Na to U in the periodic table, excluding C). Figure 2 shown.

[0071] After obtaining the element content, it is necessary to construct the relationship between the element content and coal rock or non-coal rock. First, it is necessary to obtain the lithology label. Methods that can intuitively obtain lithology generally include coring and cuttings logging. Coring can intuitively identify lithology and conduct rock physics experiments; cuttings logging data can also intuitively observe the lithology of the corresponding formation depth by observing the rock fragments extracted on land during the drilling process, which is also of great significance for the identification of lithology. However, cuttings logging usually produces depth deviations, and the accuracy of lithology identification is usually lower than coring. Therefore, the lithology determined by coring is generally used as the main comparison label. However, considering the economic benefits of development, not all wells will coring or cuttings logging, and coring only obtains discrete lithology label points, and the lithology determined by coring or cuttings logging depends greatly on the experience of field engineers and has great uncertainty. Therefore, if you want to obtain continuous and more accurate lithology data, you need to conduct research on element logging and other data. The embodiment of the present invention is based on the W1 well in the study area with coring and cuttings logging data, and the distribution of lithology is compared with the sum of the weight percentage of the main elements. To compare, qualitatively =43% is determined as the coal-rock boundary f, such as Figure 3 As shown, when When the content is ≥43%, the lithology is non-coal rock (including sandstone, limestone, mudstone and carbonaceous mudstone). When the lithology is less than 43%, it is non-coal rock, such as Figure 4 shown.

[0072] ;

[0073] ;

[0074] In the formula, , , , , , , They are the weight percentages of Al, K, S, Si, Ca, Mg and Fe elements respectively, also known as element dry weight, %.

[0075] s2. Calculate the content of each component in coal rock and non-coal rock; for non-coal rock, calculate the content of mineral components based on the oxide closure model and matrix solution; for coal rock, build a calculation model for fixed carbon, volatile matter and moisture based on industrial component experimental data and calculated mineral component content, and calculate the fixed carbon, volatile matter and moisture content in turn; specifically include:

[0076] s21. For non-coal rocks, the sum of the weight percentages of oxides of all elements per unit volume in the formation is set to 100, and the weight percentage of each element is determined by the oxide closure model as follows:

[0077] ;

[0078] ;

[0079] ;

[0080] In the formula, It represents the percentage of oxide corresponding to the i-th element (dry weight of oxide), %; represents the normalization factor (which is a function of depth); represents the oxide index of the ith element (the inverse of the oxide conversion coefficient); Indicates the weight percentage of the i-th element, %; represents the sensitivity factor (also called sensitivity factor) of the i-th element; Indicates the weight percentage of an element (dry weight of the element), %;

[0081] s22. The contents of various mineral components are calculated by solving the Herron matrix, expressed as:

[0082] ;

[0083] In the formula, is a matrix composed of element weight percentages; A matrix of mineral weight percentages, including , , , , , , , and , + + + + + + + + =100; is the conversion coefficient, which is a square matrix, coefficient C ij It represents the conversion coefficient of the i-th element in the j-th mineral, as shown in Table 1;

[0084] Table 1 Conversion coefficient of mineral content in target layer of study area

[0085]

[0086] s23. For coal rock, industrial components include fixed carbon ash, ash, volatile matter and moisture, elements include Al, K, S, Si, Ca, Mg, Fe and C, and the sum of Al, K, S, Si, Ca, Mg, Fe represents the ash content, while the C element can be considered as the non-ash content. Therefore, in the present invention, the weight percentage of ash V HF Equivalent to , as shown in formula (7); when determining V HF Afterwards, based on the experimental data of industrial components, e.g. Figures 5 to 7 As shown, the regression model formula of fixed carbon, volatile matter and moisture based on ash is constructed, as shown in formula (8), (9) and (10), a=-1.1476, A=90.696, b=-0.1455, B=21.5001, c=-1.0101, C=101, and the fixed carbon, volatile matter and moisture content are calculated in turn;

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] s24. All mineral and industrial components are normalized to 100%. The total weight percentage of industrial components per unit volume is 100%, as shown in formula (11). Due to the deviation of the regression model calculation, the calculated total of industrial components will have a slight deviation around 100%. For the convenience of comparison, the industrial components are normalized to 100%, as shown in formulas (12), (13), (14), and (15), and the normalized ash content is According to formula (6), , , , , , , , and The ratio between them is allocated, as shown in formula (16):

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, is the normalized ash content, %; is the normalized fixed carbon content, %; is the normalized volatile content, %; is the normalized moisture content, %; is the mineral content calculated by formula (6), n=1, 2, 3, 4, 5, 6, 7, 8, 9 represent , , , , , , , and ; According to the normalized ash content The percentage of various mineral contents distributed, %.

[0099] like Figure 8As shown, the calculation results of Well W1 when a=-1.1476, A=90.696, b=-0.1455, B=21.5001, c=-1.0101, and C=101 have an average relative error of 8.64%, indicating that the calculation results are reliable and can lay a good foundation for subsequent lithology identification.

[0100] S3. Constructing a method for identifying the lithology of non-coal rocks and the macroscopic coal rock type in coal rocks, and constructing a classification standard for non-coal rocks and macroscopic coal rock types in combination with the calculation results of each component in step S2 and sulfur element; specifically including:

[0101] s31, based on mineral content , sulfur content and clastic rock classification triangle map to identify non-coal rock lithology and integrate minerals into clay minerals , felsic minerals and carbonate minerals It should be noted that siderite is a carbonate mineral and is classified as , but pyrite is a sulfide, not , or , do not participate in this step of lithology identification. , and Re-normalize and get , , ;based on , , The classification of mudstone, sandstone and limestone can be realized by using the triangular plate of clastic rock classification. Then, mudstone can be divided into ordinary mudstone and carbonaceous mudstone based on the sulfur content, such as Fig. 9 As shown, specifically:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] s32. Macro coal rock type identification based on sulfur content. The conventional classification method is based on the conventional three-porosity logging curve (AC acoustic curve, DEN density curve, CNL neutron curve). However, through statistics and analysis of the relationship between conventional curves and macro coal rock types, it is found that there is a complex overlapping relationship between macro coal rock types and conventional curves. Therefore, it is difficult to identify macro coal rock types based on conventional curves. Fig. 10A and Fig. 10B As shown. Through statistical analysis, it is found that sulfur can better distinguish macroscopic coal rock types, such as Fig.11 As shown, specifically:

[0111] .

[0112] Based on the method of the present invention for identifying the lithology of the target coal-bearing layer and the macroscopic coal and rock type based on element logging, the components of Well W1 were calculated and the lithology was identified, such as Fig.12 As shown in the figure, compared with the coring data, the lithology identification results correspond well to the coring data. In order to further explore whether the method constructed in the present invention has a good universal effect, the W2 well around the W1 well was processed, and the processing results are shown in the figure. Fig.13 As shown in the figure, the identified lithology corresponds well to the cuttings logging result, with an accuracy of 87%, indicating that the lithology identification method constructed by the present invention has a good effect. It can provide a technical reference for the lithology identification of the target coal-bearing layer and the selection of coal target layers, and further promote the economic development of coalbed methane in unconventional natural gas.

[0113] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for identifying the lithology and macroscopic coal rock types of target coal-bearing layers based on element logging, characterized in that: The steps include: s1. Construct the coal-rock boundary and divide coal-rock and non-coal-rock; s2. Calculate the content of each component in coal rock and non-coal rock; s3. Construct the lithology identification of non-coal rocks and the identification method of macroscopic coal rock types in coal rocks; In step s1, constructing the coal-rock boundary and dividing coal-rock and non-coal-rock specifically includes: s11. Based on core sampling and experimental analysis, the lithology, mineral type and characteristic elements of the target coal-bearing layer are clarified; The lithology of the target coal-bearing strata includes coal rock, sandstone, limestone, common mudstone and carbonaceous mudstone. The non-coal rock minerals include kaolinite, montmorillonite, illite, quartz, calcite, dolomite, pyrite, siderite and anhydrite. The characteristic elements are Al, K, S, Si, Ca, Mg and Fe. s12. Based on the coring data and cuttings logging data, the sum of the lithology distribution and the weight percentage of the characteristic elements Compare and determine the boundary between coal rock and non-coal rock The division threshold f is used to achieve the preliminary division of coal rock and non-coal rock; the details are as follows: ； ； In the formula, , , , , , , are the weight percentages of Al, K, S, Si, Ca, Mg and Fe elements respectively, also known as element dry weight, %; f is the threshold for dividing coal rock and non-coal rock, %.

2. The method for identifying the lithology and macroscopic coal rock type of the target coal-bearing layer based on element logging according to claim 1 is characterized in that: In step s2, calculating the content of each component in coal rock and non-coal rock specifically includes: s21. For non-coal rocks, the sum of the weight percentages of oxides of all elements per unit volume in the formation is set to 100, and the weight percentage of each element is determined by the oxide closure model, specifically: ； ； ； In the formula, Indicates the oxide percentage corresponding to the i-th element, %; represents the normalization factor; represents the oxide index of the i-th element; Indicates the weight percentage of the i-th element, %; represents the sensitivity factor of the i-th element; Indicates the weight percentage of an element, %; s22. The contents of various mineral components are calculated by solving the Herron matrix, expressed as: ； In the formula, is a matrix composed of element weight percentages; A matrix of mineral weight percentages, including , , , , , , , and , + + + + + + + + =100; is the conversion coefficient, which is a square matrix, coefficient C ij represents the conversion coefficient of the ith element in the jth mineral; s23. For coal rock, industrial components include fixed carbon ash, ash, volatile matter and moisture, elements include Al, K, S, Si, Ca, Mg, Fe and C, and the sum of Al, K, S, Si, Ca, Mg, Fe Represents the ash content. The C element is considered as the non-ash content. The weight percentage of the ash V HF Equivalent to , as shown in formula (7); when determining V HF Afterwards, based on the experimental data of industrial components, regression model formulas of fixed carbon, volatile matter and moisture based on ash were constructed, as shown in equations (8), (9) and (10), and the fixed carbon, volatile matter and moisture contents were calculated in turn; ； ； ； ； In the formula, is the ash content, %; is the fixed carbon content, %; is the volatile content, %; is the moisture content, %; a, A, b, B, c, and C are the slope and intercept of the model when the fixed carbon is calculated based on the ash content, the slope and intercept of the model when the volatile matter is calculated based on the fixed carbon, and the slope and intercept of the model when the moisture is calculated based on the ash content + fixed carbon + volatile matter, respectively; s24. Normalize all mineral and industrial components to 100%. The total weight percentage of industrial components per unit volume is 100%, as shown in formula (11). Normalize the industrial components to 100%, as shown in formulas (12), (13), (14), and (15). The normalized ash content According to formula (6), , , , , , , , and The ratio between them is allocated, as shown in formula (16): ； ； ； ； ； ； In the formula, is the normalized ash content, %; is the normalized fixed carbon content, %; is the normalized volatile content, %; is the normalized moisture content, %; is the mineral content calculated by formula (6), n=1, 2, 3, 4, 5, 6, 7, 8, 9 represent , , , , , , , and ; According to the normalized ash content The percentage of various mineral contents distributed, %.

3. The method for identifying the lithology and macroscopic coal rock type of the target coal-bearing layer based on element logging according to claim 2 is characterized in that: In step s3, constructing a method for identifying non-coal rock lithology and macroscopic coal rock type in coal rock specifically includes: s31, based on mineral content , sulfur content and clastic rock classification triangle map to identify non-coal rock lithology and integrate minerals into clay minerals , felsic minerals and carbonate minerals ,right , and After re-normalization, we get , , ;based on , , The classification of mudstone, sandstone and limestone is realized by using the triangular plate of clastic rock classification. Then, based on the sulfur content, mudstone is divided into ordinary mudstone and carbonaceous mudstone, specifically: ； ； ； ； ； ； ； ； s32. Macroscopic identification of coal and rock types based on sulfur content, specifically: ； Wherein, S is the sulfur content, %; s1, s2, and s3 are the threshold values ​​for dividing different macroscopic coal rock types.

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