CN115614034A - Geochemical identification method for coal bed bottom plate limestone - Google Patents
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Abstract
The invention relates to the technical field of coal mine water disaster prevention and control, in particular to a geochemical identification method for coal bed bottom plate limestone, which comprises the following steps: step A: the invention acquires the samples from the top of the sublevel lithocarpus system to the bottom of the lithocarpus system, and divides the acquired samples into layers, so that the method is convenient and fast to acquire while providing more visual understanding for workers during data processing, extraction or monitoring, the time consumed by acquisition is saved, the chemical identification of the coal bed bottom plate limestone is carried out according to the acquired data, the required corresponding numerical value is quickly identified, the manual and economic investment in the past is optimized, the working efficiency is improved, the treatment effect of the bottom plate area can be more scientifically reflected, the method is further used for guiding the real-time construction of the bedding hole in the thin-layer limestone, and the more scientific evaluation method for the bottom plate effect is provided.
Description
Technical Field
The invention relates to the technical field of coal mine water damage prevention and control, in particular to a geochemical identification method for coal bed bottom plate limestone.
Background
The coal fields mainly adopting the coal seams of the stone-coal system and the stone-coal system are exposed to the outstanding risk of water damage of karst on the bottom plate; at present, the coal field production mines are generally reinforced by a ground directional drilling high-pressure grouting technology before the production of a working face.
The implementation effect of regional management engineering is directly determined by the height of the bedding-following rate, the detection means of the grouting effect of the existing bottom plate can be divided into three types, one is geophysical exploration means, mainly comprising a transient electromagnetic method, a geological radar method, a CT method and the like, wherein the micro earthquake while drilling is also used for evaluating the space spread range of the seam bottom plate cracks of the coal seam and the spread range of slurry at the bottom of a working face; and the second is a drilling inspection method, which is to drill an inspection hole to a grouting position to inspect the diffusion of slurry through underground design, and the third is a multi-index scoring method, and 4-5 parameters such as logging change rate during drilling, drilling fluid consumption, water permeability of a pressurized water test, hole-dividing/sectional grouting single-meter grouting amount and the like are selected to perform index score and weight score calculation, so that an evaluation result is obtained.
In the prior art, geophysical methods are often required to be developed underground and still depend on a roadway which is already excavated. In addition, the noise in the well can also affect the geophysical exploration result; the inspection hole needs to be drilled to L3 underground, on one hand, a plurality of drilling holes need to be designed, time and labor are wasted, and on the other hand, the drilling hole for exposing limestone on the working surface has a certain water inrush risk; the limitation of the multi-index scoring method is represented by incomplete parameter selection, and the determination of the weight scoring of each index is also lack of scientificity. Therefore, the method cannot complete the detection and evaluation of the grouting effect of the coal bed bottom plate in the limestone water damage area treatment. The invention relates to a geochemical identification method for thin limestone, which realizes scientific evaluation of the effect after implementation of directional drilling and is further applied to drill bit direction guidance in the bedding process of directional drilling.
Disclosure of Invention
The invention aims to provide a geochemical identification method for limestone of a coal bed floor, which is characterized in that samples of a charcoal system are taken by sections, the collected samples are hierarchically divided, so that workers can know more intuitively when processing, extracting or monitoring data, the collection method is convenient and fast, the time consumed by collection is saved, chemical identification of the limestone of the coal bed floor is carried out according to the collected data, the required corresponding numerical value is quickly identified, the investment of labor and economy is reduced, the working efficiency is improved, the treatment effect of the floor area can be more scientifically reflected, the thin-layer limestone identification method for guiding real-time construction along a stratum hole is further provided, and a more scientific evaluation method for the treatment effect of the floor is provided.
The purpose of the invention can be realized by the following technical scheme: a geochemical identification method for a coal bed bottom limestone comprises the following steps:
step A: the method comprises the steps of taking rock core samples from the bottom of a carboniferous system to the top of the carboniferous system in a segmented manner, sequentially marking the rock core samples from the bottom of the carboniferous system to the top of the carboniferous system as J1, L1, J2, L2, J3, L3, J4 and L4, selecting rock core test indexes, determining main element types with higher content according to abundance occurrence rules of sedimentary rock elements, carrying out crushing and crushing pretreatment, preparing samples, and then loading the samples on a machine to detect the content to obtain geochemical background data of L1-L4 segments;
and B: the method comprises the following steps of establishing a standard geochemistry recognition mode of a target area by utilizing the geochemistry abundance characteristics of formation principal component elements, specifically:
step B1: establishing a principal component element abundance profile recognition mode, determining dominant principal component elements in each layer of limestone and limestone interlayer clastic rocks, and selecting a principal component element abundance range and a threshold value;
and step B2: establishing a principal component element geochemical factor score recognition model, removing element indexes with weak correlation through person correlation analysis, determining the types of the principal component elements participating in the factor analysis, setting operation parameters and conditions in the SPSS, rotating by adopting a maximum variance method to obtain a factor analysis result, extracting a common factor with the highest contribution rate, calculating the score of a core sample on the common factor, and establishing a factor score recognition model between limestone and clastic rock and between L1-L4 limestone;
and C: taking directional drilling holes L1-L3 rock debris, wherein the sampling rule is that rock debris samples are continuously taken every 0.5m from the bedding hole drilling to 10m above the top of the L1, and the sampling is finished after the rock debris samples enter the L3 extension, and according to the flow of the step B, sample preparation and testing are carried out to obtain data;
step D: analyzing the stratum source of the rock debris, calculating the factor score of the rock debris by using L1-L3 limestone rock debris data, projecting in the factor score identification model established in the step B2, and verifying the effect of the identification model in the step B;
and E, step E: extracting known rock core sample data and rock debris sample data to be judged, selecting Fisher judgment in a judgment analysis method, calculating a judgment function with high fitting degree to complete the classification of the known sample and the rock debris sample to be judged, and obtaining the bedding rate of the directional drill in a target area according to the classification accuracy of the rock debris sample;
step F: according to the CaO range and the threshold value in the L1-L3 limestone calibrated in the step B1; after the directional drill enters three ashes, the deviation of the heel layer has the following two conditions:
(1) The angle of the drill bit is inclined upwards, the drilling direction is the top of L3 and is close to an interlayer J3 between L2 and L3;
(2) The angle of the drill bit is deviated downwards, the drilling direction is L3 bottom and is close to an interlayer J4 between L3 and L4;
and (3) respectively adjusting the angle of the drill bit according to the change of the main element component CaO in the rock debris under the conditions of (1) and (2), so that the track of the directional drill is kept in the L3, checking whether the track abnormality of the bedding is eliminated according to the geochemical data of the rock debris continuously brought out by the directional drill, and increasing the angle adjustment of the drill bit until the normal bedding is recovered if the track abnormality of the bedding is not eliminated.
Further, the specific process of selecting the abundance range and the threshold value of the main element comprises the following steps:
collecting all elements in each layer of limestone and limestone interlayer clastic rock, calibrating the elements into profile data, identifying the elements in the profile data, identifying a plurality of different elements, calibrating the different elements into the element data, marking the element data as YSi, i =1,2,3 \8230, \8230n1, n1 as positive integers, when i is equal to 1, YSi represents a first element, calibrating the content of the different elements into element content data, and marking the element content data as HLi, i =1,2,3 \823030, n1, when i is equal to 1, HLi represents the element content of the first element, and YSi and HLi correspond to each other one by one;
selecting element data YSi and element content data HLi, sorting the element content data HLi from large to small to obtain element content ranking data, summing the total content of all the element data in the element content ranking data, and calculating the total content of elements;
and calculating the proportion of the element ranking data to the total amount of the elements, calculating the proportion of each element in the element content ranking data, calibrating the proportion to be abundance data, ranking the abundance data of each element from large to small to obtain the abundance ranking data, calculating the range value of the abundance data of each element in the abundance ranking data, and calculating the abundance range of the main element and the threshold value of the main element.
Further, the specific process of calculating the range value of the abundance data of each element in the abundance ranking data is as follows:
summing the abundance of each element in the abundance ranking data, calculating a total abundance value, calculating a mean value according to the total abundance value and the abundance number, calculating an average abundance value, setting an abundance fluctuation threshold value, calculating a difference value between the average abundance value and the abundance fluctuation threshold value, and calculating an abundance weighing value;
matching the abundance measurement value with the abundance ranking data, matching a value matched with the abundance measurement value in the abundance ranking data, calibrating the value as a selected definition value, calibrating the ranking value corresponding to the selected definition value as a main element threshold value, and calibrating the element data of which the ranking value is greater than the main element threshold value in the abundance ranking data as the main element data;
the method comprises the steps of carrying out average calculation on abundance data corresponding to each element data in different acquisition times, calculating an abundance data average value, carrying out difference calculation on the abundance data average value and corresponding abundance data respectively, calculating a plurality of abundance difference values, carrying out average calculation on the abundance difference values, calculating an abundance average difference value, subtracting the abundance average difference value from the abundance data average value, calculating an abundance minimum value, adding the abundance average difference value to the abundance data average value, calculating an abundance maximum value, and calibrating a value between the abundance minimum value and the abundance maximum value to be a main quantity element abundance range.
Further, the specific treatment process of the bedding rate of the directional drilling in the target area comprises the following steps:
calculating a discrimination function with high fitting degree according to a Fisher discriminant analysis method to complete the classification of the known sample and the sample to be discriminated, and removing Ca and Si elements before discriminant analysis;
selecting Fe from main elements of the upper-section stratum at the top of the carboniferous system 2 O 3 、MgO、MnO、K 2 O、SO 3 、TiO 2 Carrying out discriminant analysis on the equal components to obtain a discriminantThe function and the distribution condition, the characteristic value and the variance contribution rate of the coefficient of the function on each principal component element are characterized in that a discriminant function corresponding to the variance contribution rate in the first column is marked as F1;
in addition, the known sample is divided into 8 components, wherein 1-8 groups are respectively a first ash top J1, a first ash L1, an L1-L2 interlayer J2, a second ash L2, an L2-L3 interlayer J3, a third ash L3, an L3-L4 interlayer J4 and a fourth ash L4; the classification of each sample can be obtained by utilizing the discrimination function F1 and the component mass center value, the calculation process is that the functional value is obtained by utilizing the substitution of the test data into the discrimination function, and then the functional value is added with the mass center value of each group of the training data, and the minimum numerical value, namely the minimum distance, is the classification of the test data;
in the discrimination analysis result of the Fisher function, the identification levels of the limestone rock fragment samples T7-18-T7-20 are all L3, the level of the limestone rock fragment sample is successfully traced, and according to the calculation formula: bedding rate of directional drilling in the target zone = L3 number of rock chip samples/total number of rock chip samples taken.
Further, the specific process of increasing the drill angle adjustment in the step F is as follows:
after the drill bit enters the marine shale, testing the main quantity elements of the sample and monitoring the change of the Ca content of the sample; when the drill bit enters the bottom of the L2 and enters the interlayer, the abundance of the Ca element in the rock debris is reduced to a preset value of a low-calcium element; and keeping drilling until the abundance of Ca element is increased to a preset value of Ca element, and determining the position for performing bedding grouting.
The invention has the beneficial effects that:
according to the invention, through the step-by-step collection of the samples of the coal-bed system and the step division of the collected samples, workers can know the samples more intuitively during data processing, extraction or monitoring, the collection method is convenient and fast, the time consumed by collection is saved, the chemical identification of the limestone of the coal bed bottom plate is carried out according to the collected data, the required corresponding numerical value is quickly identified, the investment of manpower and economy in the past is optimized, the working efficiency is improved, the treatment effect of the bottom plate area can be reflected more scientifically, the method is further used for guiding the real-time construction of the bedding hole in the thin-layer limestone, and a more scientific evaluation method for the treatment effect of the bottom plate is provided.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a cross-sectional view showing the abundance of major elements in the upper section of the top of a carboniferous system according to the present invention;
FIG. 2 is a principal component element factor analysis and identification chart of a first group of samples of the lime system of the present invention;
FIG. 3 is a diagram of the analytical identification of the principal element factors of the first group of L4-L1 stratums of the lime system of the present invention;
FIG. 4 is a diagram of the principal trace factor analysis recognition pattern of the present invention;
FIG. 5 is a track diagram of the directional drilling "heel-to-floor rate" control scheme 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.
Referring to fig. 1-5, the present invention relates to a geochemical identification method for coal bed bottom limestone, which comprises the following steps:
taking a pre-selected mine as an example, taking a rock core sample (8 layers from top to bottom) from the bottom of a carboniferous system to the top of the carboniferous system; the limestone horizon is sequentially L1, L2, L3 and L4 from top to bottom, clastic rock interlayers among the limestone are marked as J1, J2, J3 and J4, and the abundance of main element elements such as Ca, si, fe, al and Mn is tested to be used as a regional background value; see tables 1 and 2.
TABLE 1 Pre-selected mine field Directional drilling cuttings (L1-L4) principal component results
TABLE 2 Pre-selected mine field rock-char system top lamella limestone interlayer clastic rock (J1-J4) principal elemental results
Step B1, taking a pre-selected mine as an example, establishing a principal component element abundance profile recognition mode to obtain the element abundance range of each layer of limestone, and depositing a set of siltstone (-3.5 m) -mudstone (-0.8 m) on a J3 interlayer on a target layer (L3); the average values of the Ca element abundances of the thin-layer limestone L1-L4 are 42.43%,51.36%,53.85% and 55.04% respectively, and the average values of the Si element abundances are 6.73%,2.21%,1.27% and 0.5% respectively. The trend line of the Tai grey layer (d) -element abundance (t) is known, the Ca element abundance is increased and the Si element abundance is reduced along with the increase of the limestone layer (figure 1), and the fitting line equation is shown in the formula (1) and the formula (2), wherein the fitting part only accounts for limestone sections (L1-L4), ca element low-value abnormal points frequently appear in a transition section from limestone to sandstone and a dolostomitic limestone section, the Si and Al element content rising burning vector (LOI) of the limestone in the transition section is reduced, and the Mg element content of the dolostomitic limestone is increased, so the abnormal points can be removed through the element abundance.
tCa＝0.0132d+0.4257 (1)
tSi＝-0.0069d+0.0704 (2)
Step B2, taking a pre-selected mine as an example, establishing a principal component element geochemical factor score recognition model, and displaying a weak correlation index SO as the correlation analysis result of the principal component element through the person correlation analysis 3 Selectively removing SO 3 All other principal component components are used as variables for factor analysis, after setting operation parameters and conditions in the SPSS, a maximum variance method is adopted for rotation to obtain a factor analysis result, and a public factor with the highest contribution rate is extracted (Table 3); the factor analysis extracts three factors of a factor 1 (C1), a factor 2 (C2) and a factor 3 (C3), the cumulative variance contribution rate reaches 86.38%, and the characteristic values are 6.07, 1.81 and 1.62 respectively. Wherein CaO and SiO in the C1 component 2 、Al 2 O 3 、K 2 O、TiO 2 LOI is a high load variable (absolute value), and MgO and TiO in the C2 component 2 Is a high load variable, and MnO in the C3 component is a high load variable.
TABLE 3 statistic table of principal component factor analysis results of first group of thin-layer limestone interlayer clastic rocks of preselected mine field limestone series
Calculating factor scores of all samples of the first group of core samples on a factor 1, a factor 2 and a factor 3 to establish an identification mode, wherein the score difference of the limestone sample and the interlayer sample on the C1 is obvious, the score of the limestone sample is a negative value, and the score of the interlayer sample is a positive value (figure 2); on C2, the limestone sample is divided into a range of-1.5 to 1.5, wherein T2-22 is an abnormal point, and the clastic rock sample is concentrated between-1.2 and 1; on C3, the limestone sample score is centered between-1.2 and 1.8; FIG. 2a shows that, with the origin of the C1 axis as a boundary, the limestone sample and the interlayer sample are respectively distributed on the left side and the right side of the origin, such as the samples of J1 (T2-1-3), J2 (T2-7-9), J3 (T2-14-15) and J4 (T2-19-21) in layers, and the recognition effect is clear; in FIG. 2b, the limestone and the interlayer sample are distributed on two sides of the C1 axis in the same way, but the longitudinal variation range is increased, so that the factor analysis recognition mode established on the basis of the principal quantity elements can be further applied; in order to confirm the recognition effect of the factor analysis between the limestone (L1 to L4) and the interlayer (J1 to J4), the factor scores of the first group of limestone (L1 to L4) and interlayer (J1 to J4) were calculated in the distribution (fig. 3); factor analysis results showed that the L1 samples scored a range of C1 and C3: 1.52-1.73, 0.28-0.35; the range of L2 samples is: -0.31 to-0.08, -0.34 to 0.05; the range of L3 samples is: -0.81 to-0.73; the range of L4 samples is: -0.8 to-0.91, -0.2 to-0.4; therefore, the dispersed points are defined by elliptical circles with different colors to finish the limestone discrimination and the interlayer identification (figure 3 a); the interlayer clastic rock usually comprises a plurality of layers of sandstone or mudstone, so that the recognition mode generally has a state of multiple concentrated points and one far away point, three common factors are extracted by interlayer factor analysis in the local region, after the recognition effect is compared, the common factors of C1 and C2 which are more obvious in layer position distinction are selected, and the recognition mode is established by the same method (figure 3 b).
Step C, taking the pre-selected mine as an example, and taking a rock debris sample of a coal face area control bedding hole; taking the target area L3 from 10m above the L1; selecting main elements such as Ca, si, fe, al, mn and the like as identification indexes; the test results are shown in Table 3.
TABLE 4 preselected mine stratosphere hole lime series first group of rock debris sample principal element test results
Step D, analyzing and establishing a background recognition mode (figure 3) of the thin limestone according to the main element factors in the core sample of the step B2, wherein the background values of L1-L3 are represented by solid circles with different colors; then, conducting factor analysis on the limestone debris, calculating the factor score of the directional drilling debris sample, and pointing the factor score to a background recognition mode of the principal element factor analysis by using the line type of X with different colors (figure 4); it can be seen that all samples except the L1 rock debris sample with a point slightly deviated are projected to the vicinity of the L1-L3 background points, and the uniform distribution characteristic is that the projection position is on the left side of the background points; the reason for the phenomenon is probably that the small distance error is caused by independently calculating the rock core and the rock debris in the factor analysis process, but the general trends of the two are basically consistent without influencing the judgment; the effect of identifying the model in the step B is verified.
And E, extracting the data of the known core sample and the data of the rock debris sample to be judged, selecting fisher judgment in a judgment analysis method, and calculating a judgment function with high fitting degree to finish the classification of the known sample and the sample to be judged.
The Fisher discriminant analysis method aims to calculate a discriminant function with high fitting degree to complete the classification of a known sample and a sample to be discriminated; as mentioned above, the abundance of Ca and Si elements in the rock debris can be affected by adjacent strata to cause mixed dyeing, and the Ca and Si elements which are easily affected by the mixed dyeing are removed before discriminant analysis is carried out.
Taking the upper stratum at the top of the carboniferous system as an example, fe in the main element is selected 2 O 3 、MgO、MnO、K 2 O、SO 3 TiO2 and the likePerforming line discrimination analysis; the results of discriminant analysis are shown in table 5.
In table 5, F1 to F7 respectively represent the discriminant function in 7 obtained by discriminant analysis and the distribution of coefficients of the function on each principal component element; in the eigenvalue and variance contribution rate, F1 to F3 account for 99.7% of the variance contribution rate in all samples, wherein the variance contribution rate of F1 reaches 95%, and the eigenvalue is much higher than the remaining discriminant functions, so that the discriminant function F1 carries most of the information of the sample, and can be used as the discriminant function of the discriminant analysis method, specifically, see formula 3:
F1＝-25812×Fe 2 O 3 -163.875×Al 2 O 3 +58.713×MgO+1910.420×MnO+313.557
×K 2 O+177.830×SO 3 -722.387×TiO 2 +86.848×LOI-18.169(3)
in addition, the known sample was divided into 8 groups, 1 to 8 groups were, respectively, first ash top (J1), first ash (L1), L1 to L2 interlayer (J2), second ash (L2), L2 to L3 interlayer (J3), third ash (L3), L3 to L4 interlayer (J4), and fourth ash (L4); the classification of each sample can be obtained by utilizing the discrimination function F1 and the component mass center value, the calculation process is that the functional value is obtained by utilizing the substitution of the test data into the discrimination function, and then the functional value is added with the mass center value of each group of the training data (namely the rock core background value), and the minimum value, namely the minimum distance, is the classification of the test data; table 5 shows the centroid values for each packet.
The discriminant analysis result of the Fisher function is shown in Table 6, the identification levels of the limestone-rock fragment samples T7-18-T7-20 are all L3, and the level of the limestone-rock fragment samples is successfully traced, which indicates that the directional drilling track is kept in L3; on the whole, the layer judgment accuracy based on Fisher discriminant analysis source analysis of the rock debris sample is 75%, wherein the discrimination accuracy of the limestone layer reaches 100%, and the accuracy of the directional drilling direction is effectively verified. And (4) obtaining the bedding rate of the directional drill in the target area according to the classification accuracy (L3 rock debris sample number/total taken rock debris sample number) of the rock debris samples.
TABLE 5 statistical table of Fisher discriminant analysis results of principal component
TABLE 6 principal component element Fisher function recognition results
Step F, according to the CaO range and the threshold value in the L1-L3 limestone calibrated in the step B1; an embodiment of controlling the bedding-in hole 'bedding ratio' is provided by taking the geochemical abundance identification of CaO as an example (figure 5), and the specific process is as follows: after the drill bit enters the marine shale, testing the main elements of the sample, and observing the change of the Ca content of the sample; when the drill bit enters the bottom of the L2 and enters the interlayer, the abundance of the Ca element in the rock debris is reduced to below 10 percent; keeping drilling until the abundance of Ca element is increased by 40%, and determining the position for performing bedding layer grouting; at the moment, the stratum can be ensured only by observing the change of the Ca element, and three expected states are provided, wherein firstly, the abundance of the Ca element maintains a stable state, which shows that the directional drilling track always stays at the middle upper part of L3 and mostly appears when the dip angle of the stratum is flat; secondly, the abundance of the Ca element in the rock debris is rapidly reduced, which implies that the drilling track deviates to the stratum with the L2-L3 interlayer, and at the moment, the drilling direction needs to be adjusted downwards to keep the accurate bedding; thirdly, the abundance of the elements rises first and then falls, the corresponding drilling direction is lower than L3, the continuous drilling possibly passes through L3, and the drilling angle needs to be adjusted upwards at the moment; finally finishing the grouting engineering of the bottom plate of the working face; the method is successfully applied to the pre-selection coal field, and the effect is good.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (5)
1. A geochemical identification method for coal bed bottom limestone is characterized by comprising the following steps:
step A: taking core samples from the bottom of a carboniferous system to the top of the carboniferous system in sections, sequentially marking the core samples from the bottom of the carboniferous system to the top of the carboniferous system as J1, L1, J2, L2, J3, L3, J4 and L4, selecting core test indexes, determining main element types with higher content according to abundance occurrence rules of sedimentary rock elements, carrying out crushing and crushing pretreatment, preparing samples, and then testing the content on a computer to obtain L1-L4 sections of geochemical background data;
and B: the method comprises the following steps of establishing a standard geochemistry identification mode of a target area by utilizing geochemical abundance characteristics of formation principal element, specifically:
step B1: establishing a principal component element abundance profile recognition mode, determining dominant principal component elements in each layer of limestone and limestone interlayer clastic rocks, and selecting a principal component element abundance range and a threshold value;
and step B2: establishing a principal component element geochemical factor score recognition model, removing element indexes with weak correlation through person correlation analysis, determining the types of the principal component elements participating in the factor analysis, setting operation parameters and conditions in the SPSS, rotating by adopting a maximum variance method to obtain a factor analysis result, extracting a common factor with the highest contribution rate, calculating the score of a core sample on the common factor, and establishing a factor score recognition model between limestone and clastic rock and between L1-L4 limestone;
and C: b, directional drilling holes L1-L3 of rock debris are taken, the sampling rule is that rock debris samples are continuously taken every 0.5m from the bedding hole drilling to 10m above the top of the L1, sampling is finished after the rock debris samples enter L3 extension, and sample preparation and testing are carried out according to the flow of the step B to obtain data;
step D: analyzing the stratum source of the rock debris, calculating the factor score of the rock debris by using L1-L3 limestone rock debris data, projecting in the factor score identification model established in the step B2, and verifying the effect of the identification model in the step B;
step E: extracting known core sample data and to-be-judged rock fragment sample data, selecting fisher judgment in a judgment analysis method, calculating a judgment function with high fitting degree to finish classification of the known sample and the to-be-judged sample, and obtaining bedding rate of the directional drill in a target area according to the classification accuracy of the rock fragment sample;
step F: according to the CaO range and the threshold value in the L1-L3 limestone calibrated in the step B1; after the directional drill enters three ashes, the following two situations exist when the deviation occurs to the heel layer:
(1) The angle of the drill bit is inclined upwards, the drilling direction is the top of L3 and is close to an interlayer J3 between L2 and L3;
(2) The angle of the drill bit is deviated downwards, the drilling direction is L3 bottom and is close to an interlayer J4 between L3 and L4;
and (3) respectively adjusting the angle of the drill bit according to the change of the main element component CaO in the rock debris according to the conditions in the steps (1) and (2), so that the track of the directional drill is maintained to stay in the L3, checking whether the track abnormity of the bedding is eliminated according to the geochemical data of the rock debris continuously brought out by the directional drill, and increasing the angle adjustment of the drill bit until the normal bedding is recovered if the track abnormity is not eliminated.
2. The geochemical identification method for the limestone of a coal seam floor according to claim 1, wherein the specific process of selecting the abundance range and the threshold value of the principal component elements comprises the following steps:
collecting all elements in each layer of limestone and limestone interlayer clastic rock, calibrating the elements into profile data, identifying the elements in the profile data, identifying a plurality of different elements, calibrating the different elements into the element data, marking the element data as YSi, i =1,2,3 \8230, \8230n1, n1 as positive integers, when i is equal to 1, YSi represents a first element, calibrating the content of the different elements into element content data, and marking the element content data as HLi, i =1,2,3 \823030, n1, when i is equal to 1, HLi represents the element content of the first element, and YSi and HLi correspond to each other one by one;
selecting element data YSi and element content data HLi, sorting the element content data HLi from large to small to obtain element content ranking data, summing the total content of all the element data in the element content ranking data, and calculating the total content of elements;
and calculating the proportion of the element ranking data to the total amount of the elements, calculating the proportion of each element in the element content ranking data, calibrating the proportion to be abundance data, ranking the abundance data of each element from large to small to obtain the abundance ranking data, calculating the range value of the abundance data of each element in the abundance ranking data, and calculating the abundance range of the main element and the threshold value of the main element.
3. The method for geochemical identification of the limestone of the coal seam floor as claimed in claim 2, wherein the specific process of calculating the range value of the abundance data of each element in the abundance ranking data is as follows:
summing the abundance of each element in the abundance ranking data, calculating an abundance total value, calculating a mean value according to the abundance total value and the abundance number, calculating an abundance mean value, setting an abundance fluctuation threshold value, calculating a difference value between the abundance mean value and the abundance fluctuation threshold value, and calculating an abundance weighing value;
matching the abundance weighing value with the abundance ranking data, matching out a value matched with the abundance weighing value in the abundance ranking data, calibrating the value as a selected definition value, calibrating a ranking value corresponding to the selected definition value as a main element threshold value, and calibrating element data of which the ranking value is greater than the main element threshold value in the abundance ranking data as main element data;
the method comprises the steps of carrying out mean value calculation on abundance data corresponding to each element data in different acquisition times, calculating a mean value of the abundance data, carrying out difference value calculation on the mean value of the abundance data and corresponding abundance data respectively, calculating a plurality of abundance difference values, carrying out mean value calculation on the plurality of abundance difference values, calculating an average abundance difference value, subtracting the average abundance difference value from the mean value of the abundance data, calculating a minimum abundance value, adding the average abundance difference value to the mean value of the abundance data, calculating a maximum abundance value, and calibrating a numerical value between the minimum abundance value and the maximum abundance value to be a main element abundance range.
4. The geochemical identification method for the limestone of the coal seam floor as claimed in claim 1, wherein the specific treatment process of the bedding gradient of the directional drilling in the target area comprises the following steps:
calculating a discrimination function with high fitting degree according to a Fisher discriminant analysis method to complete the classification of the known sample and the sample to be discriminated, and removing Ca and Si elements before discriminant analysis;
selecting components such as Fe2O3, mgO, mnO, K2O, SO3, tiO2 and the like in the main elements of the upper section of the formation at the top of the carboniferous system to perform discriminant analysis to obtain a discriminant function and the distribution condition of coefficients of the discriminant function on each main element, a characteristic value and a variance contribution rate, and marking the discriminant function corresponding to the variance contribution rate in the first row as F1;
in addition, the known sample is divided into 8 components, wherein 1-8 groups are respectively a first ash top J1, a first ash L1, an L1-L2 interlayer J2, a second ash L2, an L2-L3 interlayer J3, a third ash L3, an L3-L4 interlayer J4 and a fourth ash L4; the classification of each sample can be obtained by utilizing the discrimination function F1 and the component mass center value, the calculation process is that the functional value is obtained by utilizing the substitution of the test data into the discrimination function, and then the functional value is added with the mass center value of each group of the training data, and the minimum numerical value, namely the minimum distance, is the classification of the test data;
in the discrimination analysis result of the Fisher function, the identification levels of the limestone rock fragment samples T7-18-T7-20 are all L3, the level of the limestone rock fragment sample is successfully traced, and according to the calculation formula: bedding rate of directional drilling in the target zone = L3 number of rock chip samples/total number of rock chip samples taken.
5. The geochemical identification method for the limestone of a coal seam floor according to claim 1, wherein the specific process of adjusting the angle of the enlarged drill bit in the step F is as follows:
after the drill bit enters the marine shale, testing the main quantity elements of the sample and monitoring the change of the Ca content of the sample; when the drill bit enters the bottom of the L2 and enters the interlayer, the abundance of the Ca element in the rock debris is reduced to a preset value of a low calcium element; and keeping drilling until the abundance of Ca element is increased to a preset value of Ca element, and determining the position for performing bedding grouting.
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