Geochemical identification, bedding ratio determination, and bedding ratio control methods for coal seam floor limestone

Abstract

This invention relates to the field of coal mine water hazard prevention technology, specifically to a method for geochemical identification, bedding ratio determination, and bedding control of coal seam floor limestone. The specific identification method includes the following steps: Step A: Segmented core samples are collected from the bottom to the top of the Carboniferous system. This invention collects samples segmentally from the top to the bottom of the Carboniferous system and divides the collected samples into layers. This facilitates data processing, extraction, and monitoring, providing workers with a more intuitive understanding. The collection method is convenient and saves time. Based on the collected data, the chemical identification of the coal seam floor limestone is performed, quickly identifying the required values. This optimizes the previous labor and economic inputs, improves work efficiency, and can more scientifically reflect the treatment effect of the floor area. Furthermore, it can be used to guide the real-time construction of bedding boreholes in thin-layered limestone, providing a more scientific method for evaluating the treatment effect of the floor.

Description

Technical Field

[0001] This invention relates to the field of coal mine water hazard prevention technology, specifically to methods for geochemical identification, determination of bedding ratio, and control of bedding ratio of limestone at the bottom of coal seams. Background Technology

[0002] Coalfields that primarily mine Carboniferous and Carboniferous coal seams are at significant risk of karst water hazards in the floor. Currently, most coalfields employ surface directional drilling and high-pressure grouting technology to reinforce the working face before production.

[0003] The level of the grouting layer directly determines the effectiveness of regional governance projects. Existing detection methods for the grouting effect can be divided into three categories: First, geophysical exploration methods, mainly including transient electromagnetic methods, ground-penetrating radar, and CT methods. Among them, microseismic drilling is also used to assess the spatial distribution range of coal seam floor cracks and the diffusion range of grout at the bottom of the working face. Second, borehole inspection method, which refers to drilling to the grouting position through a designed inspection hole in the well to check the grout diffusion. Third, multi-index scoring method, which selects 4-5 parameters such as drilling time logging change rate, drilling fluid consumption, water pressure test permeability, and grouting volume per meter per hole / segment grouting to calculate index scores and weight scores, thereby obtaining the evaluation results.

[0004] In existing technologies, geophysical methods often need to be carried out underground, still relying on already excavated roadways. Furthermore, underground noise can affect the results of geophysical exploration; inspection holes need to be drilled underground to L3, requiring the design of multiple boreholes, which is time-consuming and labor-intensive, and drilling to expose limestone on the working face carries a certain risk of water inrush; the limitations of multi-index scoring methods lie in the incomplete selection of parameters and the lack of scientific rigor in determining the weights of each index. Therefore, none of the above methods can complete the detection and evaluation of the grouting effect of coal seam floor in limestone water hazard control. This invention relates to a geochemical identification method for thin-layer limestone, enabling the scientific evaluation of the effects after directional drilling and further application in drill bit direction guidance during directional drilling along the bedding plane. Summary of the Invention

[0005] The purpose of this invention is to provide a method for geochemical identification, bedding ratio determination, and bedding control of coal seam floor limestone. By collecting Carboniferous samples in segments and dividing the collected samples into layers, the method facilitates data processing, extraction, and monitoring, providing workers with a more intuitive understanding. The collection method is convenient and saves time. Based on the collected data, the chemical identification of the coal seam floor limestone is performed, quickly identifying the required values, reducing labor and economic input, improving work efficiency, and more scientifically reflecting the treatment effect of the floor area. Furthermore, it can be used to guide the identification of thin-layer limestone in real-time bedding borehole construction, providing a more scientific method for evaluating the treatment effect of the floor.

[0006] The objective of this invention can be achieved through the following technical solution: a method for geochemical identification, bedding ratio determination, and bedding ratio control of coal seam floor limestone. The specific identification method includes the following steps:

[0007] Step A: Collect core samples from the bottom to the top of the Carboniferous system in segments. Label the core samples from the bottom to the top of the Carboniferous system as J1, L1, J2, L2, J3, L3, J4 and L4 in sequence. Select core test indicators. Based on the occurrence law of element abundance in sedimentary rocks, determine the major elements with high content. After crushing and pulverizing pretreatment, prepare the samples and test their content to obtain geochemical background data of L1-L4 segment.

[0008] Step B: Utilize the geochemical abundance characteristics of major elements in the stratigraphy to establish a standard geochemical identification model for the target area, specifically:

[0009] Step B1: Establish a major element abundance profile identification model, determine the dominant major elements in each layer of limestone and limestone interlayer clastic rocks, and select the abundance range and threshold of major elements.

[0010] Step B2: Establish a major element geochemical factor score identification model. Through PEN correlation analysis, remove element indicators with weak correlation to determine the major elements participating in factor analysis. After setting the running parameters and conditions in SPSS, use the maximum variance method to rotate and obtain the factor analysis results. Extract the common factor with the highest contribution rate, calculate the score of the core sample on the common factor, and establish factor score identification models between limestone and clastic rocks, and between L1-L4 limestone.

[0011] Step C: Take rock cuttings from directional boreholes L1-L3. The sampling rule is to start from the top of L1 and take rock cuttings every 0.5m continuously until entering L3 extension. According to the process in step B, prepare samples and test them to obtain data.

[0012] Step D: Analyze the source of the rock fragment stratigraphy, calculate its factor score using the L1-L3 limestone rock fragment data, project it into the factor score identification model established in step B2, and verify the effectiveness of the identification model in step B.

[0013] Step E: Extract known core sample data and rock cutting sample data to be judged, select Fisher discriminant analysis, calculate a discriminant function with high fitting degree to complete the classification of known samples and samples to be judged, and obtain the bedding rate of directional drilling in the target area based on the classification accuracy of rock cutting samples.

[0014] Step F: Based on the CaO range and threshold in the L1-L3 limestone as specified in Step B1; after directional drilling enters the limestone layer, the following two situations may occur if there is a deviation in the layer:

[0015] (1) The drill bit angle is slightly upward, and the drilling direction is the top of L3, close to the interlayer J3 between L2 and L3;

[0016] (2) The drill bit angle is slightly downward, and the drilling direction is the bottom of L3, close to the interlayer J4 between L3 and L4;

[0017] Based on the changes in the major element CaO component in the rock cuttings as described in (1) and (2) above, the drill bit angle was adjusted to maintain the directional drilling trajectory in L3. Then, based on the geochemical data of the rock cuttings continuously brought out by the directional drilling, it was checked whether the abnormality of the trailing trajectory had been eliminated. If it had not been eliminated, the drill bit angle was increased until the normal trailing trajectory was restored.

[0018] Furthermore, the specific process for selecting the abundance range and threshold of major elements is as follows:

[0019] All elements in each layer of limestone and the clastic rocks interbedded with limestone were collected and labeled as profile data. The elements in the profile data were identified, and several different elements were identified and labeled as element data. The element data were marked as YSi, i = 1, 2, 3...n1, where n1 is a positive integer. When i equals 1, YSi represents the first element. The contents of several different elements were labeled as element content data and marked as HLi, i = 1, 2, 3...n1. When i equals 1, HLi represents the content of the first element, and YSi and HLi are in one-to-one correspondence.

[0020] Select element data YSi and element content data HLi, sort the element content data HLi from largest to smallest to obtain an element content ranking data, and sum the total content of all element data in the element content ranking data to calculate the total element content.

[0021] The proportion of each element in the element ranking data is calculated relative to the total element amount. The proportion of each element in the element content ranking data is calculated and labeled as abundance data. The abundance data of each element is sorted from largest to smallest to obtain the abundance ranking data. The range value of the abundance data of each element in the abundance ranking data is calculated to obtain the abundance range of the principal elements and the threshold of the principal elements.

[0022] Furthermore, the specific process for calculating the range value of the abundance data for each element in the abundance ranking data is as follows:

[0023] The abundance of each element in the abundance ranking data is summed to calculate the total abundance value. The mean abundance value is calculated based on the total abundance value and the number of abundances. An abundance fluctuation threshold is set, and the difference between the abundance mean value and the abundance fluctuation threshold is calculated to obtain the abundance measurement value.

[0024] The abundance measure value is matched with the abundance ranking data. The values ​​that match the abundance measure value in the abundance ranking data are identified and marked as selected definition values. The ranking values ​​corresponding to the selected definition values ​​are marked as principal element thresholds. The element data in the abundance ranking data whose ranking values ​​are greater than the principal element thresholds are marked as principal element data.

[0025] The abundance data for each element at different collection times are averaged to obtain the mean abundance data. The mean abundance data is then compared with the corresponding abundance data to obtain several abundance differences. These several abundance differences are averaged to obtain the mean abundance difference. The mean abundance data is subtracted from the mean abundance data to obtain the minimum abundance value. The mean abundance data is added to the mean abundance data to obtain the maximum abundance value. The value between the minimum abundance value and the maximum abundance value is defined as the abundance range of the major element.

[0026] Furthermore, the specific process for handling the bedding plane ratio in directional drilling within the target area is as follows:

[0027] A high-fit discriminant function is calculated based on the Fisher discriminant analysis method to classify known samples and samples to be judged. Ca and Si elements are removed before discriminant analysis.

[0028] Discriminant analysis was performed on the major elements of Fe2O3, MgO, MnO, K2O, SO3, and TiO2 in the upper part of the top Carboniferous strata to obtain the discriminant function and the distribution of the coefficients of the function on each major element, as well as the eigenvalues ​​and variance contribution rates. The discriminant function corresponding to the variance contribution rate in the first column was labeled as F1.

[0029] Furthermore, the known samples are divided into 8 groups, from group 1 to group 8, namely: gray top J1, gray L1, L1-L2 interlayer J2, gray L2, L2-L3 interlayer J3, gray L3, L3-L4 interlayer J4, and gray L4. The classification of each sample can be obtained by using the discriminant function F1 and the centroid values ​​of the groups. The calculation process is as follows: after substituting the test data into the discriminant function to obtain the function value, the function value is added to the centroid values ​​of each group of the training data. The smallest value, i.e., the smallest distance, is the classification of the test data.

[0030] According to the discriminant analysis results of the Fisher function, the stratigraphic position of limestone cutting samples T7-18 to T7-20 is L3. The stratigraphic position of the limestone cutting samples has been successfully traced. According to the calculation formula: the stratigraphic position of the directional drill in the target area = number of L3 cutting samples / total number of cutting samples taken.

[0031] Furthermore, the specific process of increasing the drill bit angle adjustment in step F is as follows:

[0032] After the drill bit enters the marine mudstone, it begins to test the major elements of the sample and monitor the changes in its Ca content. When the drill bit enters the interlayer at the bottom of L2, the Ca abundance of the rock cuttings drops to the preset value for low-calcium elements. Drilling continues until the Ca abundance rises to the preset value for high-calcium elements, at which point the location for follow-up grouting is determined.

[0033] The beneficial effects of this invention are:

[0034] This invention involves segmented sampling of Carboniferous sediments and hierarchical division of the collected samples. This facilitates data processing, extraction, and monitoring, providing workers with a more intuitive understanding. The sampling method is convenient and saves time. Based on the collected data, chemical identification of the coal seam floor limestone is performed, quickly identifying the required values. This optimizes previous labor and economic inputs, improves work efficiency, and more scientifically reflects the treatment effect of the floor area. Furthermore, it can be used to guide the real-time construction of bedding boreholes in thin limestone layers, providing a more scientific method for evaluating the treatment effect of the floor. Attached Figure Description

[0035] The invention will now be further described with reference to the accompanying drawings.

[0036] Figure 1 This is a cross-sectional view of the abundance of major elements in the upper part of the Carboniferous system according to the present invention;

[0037] Figure 2 This is the major element factor analysis identification diagram of the first group of lime-based samples of the present invention;

[0038] Figure 3 This is the major element factor analysis and identification diagram of the first group of limestone formations L4 to L1 of the present invention;

[0039] Figure 4 This is a diagram of the main and trace factor analysis and identification mode of the present invention;

[0040] Figure 5 This is a trajectory diagram of the directional drilling "layer-following rate" control technology solution of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1-5 As shown, this invention provides a method for geochemical identification, bedding ratio determination, and bedding ratio control of limestone at the bottom of coal seams, comprising the following steps:

[0043] Step A: Taking the pre-selected mine as an example, core samples were taken from the bottom to the top of the Carboniferous system (a total of 8 layers from top to bottom); among them, the limestone layers from top to bottom are L1, L2, L3, L4, and the clastic rock interlayers between the limestone are marked as J1, J2, J3, J4. The abundance of major elements such as Ca, Si, Fe, Al, and Mn was tested as regional background values; see Table 1 and Table 2.

[0044] Table 1. Major element results of directional drilling cuttings (L1-L4) from the pre-selected mining area.

[0045]

[0046] Table 2. Major element results of thin-layered limestone interbedded with clastic rocks (J1-J4) at the top of the Carboniferous system in the pre-selected mining field.

[0047]

[0048] Step B1, taking the pre-selected mine as an example, establish a major element abundance profile identification model to obtain the element abundance range of each layer of limestone. The target layer (L3) is overlain by the J3 interlayer, which contains a set of siltstone (~3.5m)-mudstone (~0.8m). The average Ca abundance values ​​of the thin-layered limestone L1-L4 are 42.43%, 51.36%, 53.85%, and 55.04%, respectively, and the average Si abundance values ​​are 6.73%, 2.21%, 1.27%, and 0.5%, respectively. The trend line of limestone layer (d)-element abundance (t) shows that with the increase of limestone layer, the Ca abundance increases and the Si abundance decreases. Figure 1 The fitted line equations are shown in equations (1) and (2). The fitted part only considers the limestone section (L1-L4). Low Ca anomalies often appear in the transition section from limestone to mudstone and the dolomitized limestone section. The Si and Al content of the transition section limestone increases and the calcination vector (LOI) decreases. The Mg content of the dolomitized limestone increases. Therefore, anomalies can be removed by the above element abundance.

[0049] tCa=0.0132d+0.4257 (1)

[0050] tSi=-0.0069d+0.0704 (2)

[0051] Step B2, taking the pre-selected mine as an example, establishes a major element geochemical factor score identification model. Through PEN correlation analysis, the correlation analysis results of the major components show that SO3 is the weakly correlated index. All major components except SO3 are selected as variables for factor analysis. After setting the running parameters and conditions in SPSS, the factor analysis results are obtained by rotation using the maximum variance method, and the common factor with the highest contribution rate is extracted (Table 3). This factor analysis extracted three factors: factor 1 (C1), factor 2 (C2), and factor 3 (C3), with a cumulative variance contribution rate of 86.38% and eigenvalues ​​of 6.07, 1.81, and 1.62, respectively. Among them, CaO, SiO2, Al2O3, K2O, TiO2, and LOI are high loading variables (absolute values) in component C1, MgO and TiO2 are high loading variables in component C2, and MnO is a high loading variable in component C3.

[0052] Table 3. Statistical analysis results of major component factors of thin-layered limestone interbedded clastic rocks in the first group of limestone formations in the pre-selected mining field.

[0053]

[0054] The factor scores of all core samples in the first group were calculated on factors 1, 2, and 3 to establish an identification pattern. The scores of limestone and interlayer samples differed significantly on C1, with limestone samples showing negative scores and interlayer samples showing positive scores. Figure 2 On C2, the scores of limestone samples range from -1.5 to 1.5, with T2-22 being an anomaly. Clastic rock samples are concentrated between -1.2 and 1. On C3, the scores of limestone samples are concentrated between -1.2 and 1.8. Figure 2 As can be seen, with the origin of the C1 axis as the boundary, the limestone samples and interlayer samples are located to the left and right of the origin, respectively. For example, the samples in each layer, such as J1 (T2-1~3), J2 (T2-7~9), J3 (T2-14~15), and J4 (T2-19~21), are concentrated and have a clear identification effect. Figure 2 In sample b, the limestone and interlayer samples are also distributed on both sides of the C1 axis, but the range of vertical variation is increased. Therefore, it can be determined that the factor analysis identification model based on principal elements can be further applied. To confirm the identification effect of factor analysis between limestones (L1-L4) and between interlayers (J1-J4), the factor scores of the first group of limestones (L1-L4) and interlayers (J1-J4) were calculated. Figure 3Factor analysis results showed that the scores for samples L1 in C1 and C3 ranged from 1.52 to 1.73 and from 0.28 to 0.35, respectively; for samples L2, the range was -0.31 to -0.08 and -0.34 to 0.05, respectively; for samples L3, the range was -0.81 to -0.73, respectively; and for samples L4, the range was -0.8 to -0.91 and -0.2 to -0.4, respectively. Therefore, different colored ellipses were used to define scatter points to complete the identification of limestone and interlayers. Figure 3 a); Among them, interbedded clastic rocks usually include multiple layers of sandstone or mudstone, so the identification pattern generally shows a state of multiple points concentrated and one point far away. The interbedded factor analysis in this area extracted three common factors. After comparing the identification effects, the C1 and C2 common factors, which have a more obvious effect on stratigraphic differentiation, were selected and delineated using the same method to establish an identification pattern. Figure 3 b).

[0055] Step C: Taking the above-mentioned pre-selected mine as an example, take rock cuttings samples from the treatment of bedding boreholes in the coal mining face area; start from 10m above L1 and go up to the bedding target area L3; select major elements such as Ca, Si, Fe, Al, and Mn as identification indicators; the test results are shown in Table 3.

[0056] Table 4. Major element test results of the first group of limestone cuttings samples from the bedding-line pores of the pre-selected mining field.

[0057]

[0058] Step D, based on the background identification pattern of the thin-layer limestone established by the major element factor analysis in the core sample of Step B2. Figure 3 Background values ​​L1 to L3 are represented by solid circles of different colors; then, factor analysis of limestone cuttings was performed, and factor scores of directional drilling cuttings samples were calculated. The factor scores were marked with "×" lines of different colors and plotted onto the principal element factor analysis background recognition mode. Figure 4 It can be seen that, except for one point in the L1 rock cutting sample which deviated slightly, all other samples were projected to the vicinity of the L1 to L3 background points, and the uniform distribution feature was that the projection position was to the left of the background point. The reason for this phenomenon may be the small distance error caused by the independent calculation of rock core and rock cutting during the factor analysis process, but the overall trend of the two is basically the same and does not affect the discrimination. This verifies the effect of the identification model in step B.

[0059] Step E: Extract known core sample data and rock cutting sample data to be classified, select Fisher discriminant analysis, and calculate a discriminant function with high fitting degree to complete the classification of known samples and samples to be classified.

[0060] The purpose of Fisher discriminant analysis is to calculate a discriminant function with a high degree of fit to classify known samples and samples to be classified. As mentioned earlier, the abundance of Ca and Si elements in rock fragments can be contaminated by the influence of adjacent strata. Before performing discriminant analysis, Ca and Si elements that are more easily affected by contamination are removed.

[0061] Taking the upper section of the Carboniferous strata as an example, the major elements Fe2O3, MgO, MnO, K2O, SO3, TiO2 and other components were selected for discriminant analysis; the results of the discriminant analysis are shown in Table 5.

[0062] In Table 5, F1 to F7 represent the seven discriminant functions obtained from the discriminant analysis and the distribution of their coefficients across the principal elements. Regarding eigenvalues ​​and variance contribution rates, F1 to F3 account for 99.7% of the variance contribution rate in all samples, with F1 reaching 95%. Furthermore, its eigenvalues ​​are significantly higher than the other discriminant functions. Therefore, discriminant function F1 carries most of the sample information and can be used as the discriminant function in this discriminant analysis, as shown in Equation 3.

[0063] F1=-25812×Fe2O3-163.875×Al2O3+58.713×MgO+1910.420×MnO+313.557×K2O+177.830×SO3-722.387×TiO2+86.848×LOI-18.169 (3)

[0064] In addition, the known samples were divided into 8 groups, which are: top of gray 1 (J1), gray 1 (L1), L1-L2 interlayer (J2), gray 2 (L2), L2-L3 interlayer (J3), gray 3 (L3), L3-L4 interlayer (J4), and gray 4 (L4). The classification of each sample can be obtained by using the discriminant function F1 and the centroid value of the group. The calculation process is as follows: after substituting the test data into the discriminant function, the function value is obtained. Then, the function value is added to the centroid value of each group of the training data (i.e., "core background value"). The smallest value, i.e. the smallest distance, is the classification of the test data. Table 5 shows the centroid values ​​of each group.

[0065] The discriminant analysis results of the Fisher function are shown in Table 6. The identified stratigraphic positions of limestone cutting samples T7-18 to T7-20 are all L3, indicating that the directional drilling trajectory remained within L3. Overall, the stratigraphic accuracy based on the Fisher discriminant analysis source resolution of the cutting samples was 75%, with a 100% accuracy rate for limestone stratigraphic positions, effectively verifying the correctness of the directional drilling direction. The stratigraphic alignment rate within the target area is calculated based on the classification accuracy of the cutting samples (number of L3 cutting samples / total number of cutting samples taken).

[0066] Table 5. Statistical table of Fisher discriminant analysis results for major components.

[0067]

[0068]

[0069] Table 6. Fisher Function Identification Results for Principal Elements

[0070]

[0071] Step F, based on the CaO range and threshold in the L1-L3 limestone precipitates identified in Step B1, proposes an implementation scheme for controlling the "bedding ratio" of bedding pores, taking CaO geochemical abundance identification as an example. Figure 5 The specific process is as follows: After the drill bit enters the marine mudstone, it begins to test the major elements of the sample and observe the changes in its Ca content. When the drill bit enters the interlayer at the bottom of L2, the Ca abundance in the rock cuttings drops below 10%. Drilling continues until the Ca abundance rises to 40%, at which point the location for follow-up grouting is determined. At this point, only the change in Ca content needs to be observed to ensure follow-up. There are three expected states: First, the Ca abundance remains stable, indicating that the directional drilling trajectory always stays in the upper part of L3, which often occurs when the stratum dip is gentle. Second, the Ca abundance in the rock cuttings decreases rapidly, suggesting that the drilling trajectory is biased towards the L2-L3 interlayer. In this case, the drilling direction needs to be adjusted downwards to maintain accurate follow-up. Third, the element abundance first rises and then falls, corresponding to a drilling direction biased below L3. Continuing to drill may pass through L3, in which case the drilling angle needs to be adjusted upwards. Finally, the grouting project of the working face bottom plate is completed. This method has been successfully applied in pre-selected coalfields with good results.

[0072] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for geochemical identification, bedding ratio determination, and stratigraphic ratio control of limestone at the bottom of a coal seam, characterized in that, The specific identification method includes the following steps: Step A: Collect core samples from the bottom to the top of the Carboniferous system in segments. Label the core samples from the bottom to the top of the Carboniferous system as J1, L1, J2, L2, J3, L3, J4 and L4 in sequence. Select core test indicators. Based on the occurrence law of element abundance in sedimentary rocks, determine the major elements with high content. After crushing and pulverizing pretreatment, prepare the samples and test their content to obtain geochemical background data of L1-L4 segment. Step B: Utilize the geochemical abundance characteristics of major elements in the stratigraphy to establish a standard geochemical identification model for the target area, specifically: Step B1: Establish a major element abundance profile identification model, determine the dominant major elements in each layer of limestone and limestone interlayer clastic rocks, and select the abundance range and threshold of major elements. Step B2: Establish a major element geochemical factor score identification model. Through PEN correlation analysis, remove element indicators with weak correlation to determine the major elements participating in factor analysis. After setting the running parameters and conditions in SPSS, use the maximum variance method to rotate and obtain the factor analysis results. Extract the common factor with the highest contribution rate, calculate the score of the core sample on the common factor, and establish factor score identification models between limestone and clastic rocks, and between L1-L4 limestone. Step C: Take rock cuttings from directional boreholes L1-L3. The sampling rule is to start from the top of L1 and take rock cuttings every 0.5m continuously until entering L3 extension. According to the process in step B, prepare samples and test them to obtain data. Step D: Analyze the source of the rock fragment stratigraphy, calculate its factor score using the L1-L3 limestone rock fragment data, project it into the factor score identification model established in step B2, and verify the effectiveness of the identification model in step B. Step E: Extract known core sample data and rock cutting sample data to be judged, select Fisher discriminant analysis, calculate a discriminant function with high fitting degree to complete the classification of known samples and samples to be judged, and obtain the bedding rate of directional drilling in the target area based on the classification accuracy of rock cutting samples. Step F: Based on the CaO range and threshold in the L1-L3 limestone as specified in Step B1; after directional drilling enters the limestone layer, the following two situations may occur if there is a deviation in the layer: (1) The drill bit angle is slightly upward, and the drilling direction is the top of L3, close to the interlayer J3 between L2 and L3; (2) The drill bit angle is slightly downward, and the drilling direction is the bottom of L3, close to the interlayer J4 between L3 and L4; Based on the changes in the major element CaO component in the rock cuttings according to the above (1) and (2), the drill bit angle is adjusted to keep the directional drilling trajectory in L3. Then, based on the geochemical data of the rock cuttings continuously brought out by the directional drilling, it is checked whether the abnormality of the trailing trajectory has been eliminated. If it has not been eliminated, the drill bit angle is increased until the normal trailing trajectory is restored. The specific process for selecting the abundance range and threshold of major elements is as follows: All elements in each layer of limestone and the clastic rocks interbedded with limestone were collected and labeled as profile data. The elements in the profile data were identified, and several different elements were identified and labeled as element data. The element data were marked as YSi, i = 1, 2, 3...n1, where n1 is a positive integer. When i equals 1, YSi represents the first element. The contents of several different elements were labeled as element content data and marked as HLi, i = 1, 2, 3...n1. When i equals 1, HLi represents the content of the first element, and YSi and HLi are in one-to-one correspondence. Select element data YSi and element content data HLi, sort the element content data HLi from largest to smallest to obtain an element content ranking data, and sum the total content of all element data in the element content ranking data to calculate the total element content. The proportion of each element in the element ranking data is calculated relative to the total number of elements. The proportion of each element in the element content ranking data is calculated and labeled as abundance data. The abundance data of each element is sorted from largest to smallest to obtain the abundance ranking data. The range value of the abundance data of each element in the abundance ranking data is calculated to obtain the abundance range of the principal elements and the threshold of the principal elements. The specific process for calculating the range value of the abundance data for each element in the abundance ranking data is as follows: The abundance of each element in the abundance ranking data is summed to calculate the total abundance value. The mean abundance value is calculated based on the total abundance value and the number of abundances. An abundance fluctuation threshold is set, and the difference between the abundance mean value and the abundance fluctuation threshold is calculated to obtain the abundance measurement value. The abundance measure value is matched with the abundance ranking data. The values ​​that match the abundance measure value in the abundance ranking data are identified and marked as selected definition values. The ranking values ​​corresponding to the selected definition values ​​are marked as principal element thresholds. The element data in the abundance ranking data whose ranking values ​​are greater than the principal element thresholds are marked as principal element data. The abundance data of each element at different collection times are averaged to obtain the mean abundance data. The mean abundance data is then compared with the corresponding abundance data to obtain several abundance differences. These several abundance differences are averaged to obtain the mean abundance difference. The mean abundance data is subtracted from the mean abundance data to obtain the minimum abundance value. The mean abundance data is added to the mean abundance data to obtain the maximum abundance value. The value between the minimum abundance value and the maximum abundance value is defined as the abundance range of the major element. The specific process for handling the bedding plane ratio in directional drilling within the target area is as follows: A high-fit discriminant function is calculated based on the Fisher discriminant analysis method to classify known samples and samples to be judged. Ca and Si elements are removed before discriminant analysis. Discriminant analysis was performed on the major elements of Fe2O3, MgO, MnO, K2O, SO3, and TiO2 in the upper part of the top Carboniferous strata to obtain the discriminant function and the distribution of the coefficients of the function on each major element, as well as the eigenvalues ​​and variance contribution rates. The discriminant function corresponding to the variance contribution rate in the first column was labeled as F1. Furthermore, the known samples are divided into 8 groups, from group 1 to group 8, namely: gray top J1, gray L1, L1-L2 interlayer J2, gray L2, L2-L3 interlayer J3, gray L3, L3-L4 interlayer J4, and gray L4. The classification of each sample can be obtained by using the discriminant function F1 and the centroid values ​​of the groups. The calculation process is as follows: after substituting the test data into the discriminant function to obtain the function value, the function value is added to the centroid values ​​of each group of the training data. The smallest value, i.e., the smallest distance, is the classification of the test data. According to the discriminant analysis results of the Fisher function, the stratigraphic position of limestone cutting samples T7-18 to T7-20 is L3. The stratigraphic position of the limestone cutting samples has been successfully traced. According to the calculation formula: the stratigraphic position of the directional drill in the target area = number of L3 cutting samples / total number of cutting samples taken.

2. The method for geochemical identification, bedding ratio determination, and bedding ratio control of coal seam floor limestone according to claim 1, characterized in that, The specific process of increasing the drill bit angle in step F is as follows: After the drill bit enters the marine mudstone, it begins to test the major elements of the sample and monitor the changes in its Ca content. When the drill bit enters the interlayer at the bottom of L2, the Ca abundance of the rock cuttings drops to the preset value for low-calcium elements. Drilling continues until the Ca abundance rises to the preset value for high-calcium elements, at which point the location for follow-up grouting is determined.

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